In my previous results, I had used double (not float) for the
following variables: result, sq_i and sq_j. In the case of float
instead of double I get "nan" and not 0.000000.
Quoting Νικόλαος Ταμπουρατζής ntampouratzis@ece.auth.gr:
Dear Jason, all,
I am trying to find the accuracy problem with RISCV-FS and I observe
that the problem is created (at least in my dummy example) because
the variables (double) are set to zero in random simulated time (for
this reason I get different results among executions of the same
code). Specifically for the following dummy code:
#include <cmath>
#include <stdio.h>
int main(){
int dim = 10;
float result;
for (int iter = 0; iter < 2; iter++){
result = 0;
for (int i = 0; i < dim; i++){
for (int j = 0; j < dim; j++){
float sq_i = sqrt(i);
float sq_j = sqrt(j);
result += sq_i * sq_j;
printf("ITER: %d | i: %d | j: %d Result(i: %f | j:
%f | i*j: %f): %f\n", iter, i , j, sq_i, sq_j, sq_i * sq_j, result);
}
}
printf("Final Result: %lf\n", result);
}
}
The correct Final Result in both iterations is 372.721656. However,
I get the following results in FS:
ITER: 0 | i: 0 | j: 0 Result(i: 0.000000 | j: 0.000000 | ij:
0.000000): 0.000000
ITER: 0 | i: 0 | j: 1 Result(i: 0.000000 | j: 1.000000 | ij:
0.000000): 0.000000
ITER: 0 | i: 0 | j: 2 Result(i: 0.000000 | j: 1.414214 | ij:
0.000000): 0.000000
ITER: 0 | i: 0 | j: 3 Result(i: 0.000000 | j: 1.732051 | ij:
0.000000): 0.000000
ITER: 0 | i: 0 | j: 4 Result(i: 0.000000 | j: 2.000000 | ij:
0.000000): 0.000000
ITER: 0 | i: 0 | j: 5 Result(i: 0.000000 | j: 2.236068 | ij:
0.000000): 0.000000
ITER: 0 | i: 0 | j: 6 Result(i: 0.000000 | j: 2.449490 | ij:
0.000000): 0.000000
ITER: 0 | i: 0 | j: 7 Result(i: 0.000000 | j: 2.645751 | ij:
0.000000): 0.000000
ITER: 0 | i: 0 | j: 8 Result(i: 0.000000 | j: 2.828427 | ij:
0.000000): 0.000000
ITER: 0 | i: 0 | j: 9 Result(i: 0.000000 | j: 3.000000 | ij:
0.000000): 0.000000
ITER: 0 | i: 1 | j: 0 Result(i: 1.000000 | j: 0.000000 | ij:
0.000000): 0.000000
ITER: 0 | i: 1 | j: 1 Result(i: 1.000000 | j: 1.000000 | ij:
1.000000): 1.000000
ITER: 0 | i: 1 | j: 2 Result(i: 1.000000 | j: 1.414214 | ij:
1.414214): 2.414214
ITER: 0 | i: 1 | j: 3 Result(i: 1.000000 | j: 1.732051 | ij:
1.732051): 4.146264
ITER: 0 | i: 1 | j: 4 Result(i: 0.000000 | j: 2.000000 | ij:
0.000000): 0.000000
ITER: 0 | i: 1 | j: 5 Result(i: 0.000000 | j: 2.236068 | ij:
0.000000): 0.000000
ITER: 0 | i: 1 | j: 6 Result(i: 0.000000 | j: 2.449490 | ij:
0.000000): 0.000000
ITER: 0 | i: 1 | j: 7 Result(i: 0.000000 | j: 2.645751 | ij:
0.000000): 0.000000
ITER: 0 | i: 1 | j: 8 Result(i: 0.000000 | j: 2.828427 | ij:
0.000000): 0.000000
ITER: 0 | i: 1 | j: 9 Result(i: 0.000000 | j: 3.000000 | ij:
0.000000): 0.000000
ITER: 0 | i: 2 | j: 0 Result(i: 1.414214 | j: 0.000000 | ij:
0.000000): 0.000000
ITER: 0 | i: 2 | j: 1 Result(i: 1.414214 | j: 1.000000 | ij:
1.414214): 1.414214
ITER: 0 | i: 2 | j: 2 Result(i: 1.414214 | j: 1.414214 | ij:
2.000000): 3.414214
ITER: 0 | i: 2 | j: 3 Result(i: 1.414214 | j: 1.732051 | ij:
2.449490): 5.863703
ITER: 0 | i: 2 | j: 4 Result(i: 1.414214 | j: 2.000000 | ij:
2.828427): 8.692130
ITER: 0 | i: 2 | j: 5 Result(i: 1.414214 | j: 2.236068 | ij:
3.162278): 11.854408
ITER: 0 | i: 2 | j: 6 Result(i: 1.414214 | j: 2.449490 | ij:
3.464102): 15.318510
ITER: 0 | i: 2 | j: 7 Result(i: 1.414214 | j: 2.645751 | ij:
3.741657): 19.060167
ITER: 0 | i: 2 | j: 8 Result(i: 1.414214 | j: 2.828427 | ij:
4.000000): 23.060167
ITER: 0 | i: 2 | j: 9 Result(i: 1.414214 | j: 3.000000 | ij:
4.242641): 27.302808
ITER: 0 | i: 3 | j: 0 Result(i: 1.732051 | j: 0.000000 | ij:
0.000000): 27.302808
ITER: 0 | i: 3 | j: 1 Result(i: 1.732051 | j: 1.000000 | ij:
1.732051): 29.034859
ITER: 0 | i: 3 | j: 2 Result(i: 1.732051 | j: 1.414214 | ij:
2.449490): 31.484348
ITER: 0 | i: 3 | j: 3 Result(i: 1.732051 | j: 1.732051 | ij:
3.000000): 34.484348
ITER: 0 | i: 3 | j: 4 Result(i: 1.732051 | j: 2.000000 | ij:
3.464102): 37.948450
ITER: 0 | i: 3 | j: 5 Result(i: 1.732051 | j: 2.236068 | ij:
3.872983): 41.821433
ITER: 0 | i: 3 | j: 6 Result(i: 1.732051 | j: 2.449490 | ij:
4.242641): 46.064074
ITER: 0 | i: 3 | j: 7 Result(i: 1.732051 | j: 2.645751 | ij:
4.582576): 50.646650
ITER: 0 | i: 3 | j: 8 Result(i: 1.732051 | j: 2.828427 | ij:
4.898979): 55.545629
ITER: 0 | i: 3 | j: 9 Result(i: 1.732051 | j: 3.000000 | ij:
5.196152): 60.741782
ITER: 0 | i: 4 | j: 0 Result(i: 2.000000 | j: 0.000000 | ij:
0.000000): 60.741782
ITER: 0 | i: 4 | j: 1 Result(i: 2.000000 | j: 1.000000 | ij:
2.000000): 62.741782
ITER: 0 | i: 4 | j: 2 Result(i: 2.000000 | j: 1.414214 | ij:
2.828427): 65.570209
ITER: 0 | i: 4 | j: 3 Result(i: 2.000000 | j: 1.732051 | ij:
3.464102): 69.034310
ITER: 0 | i: 4 | j: 4 Result(i: 2.000000 | j: 2.000000 | ij:
4.000000): 73.034310
ITER: 0 | i: 4 | j: 5 Result(i: 2.000000 | j: 2.236068 | ij:
4.472136): 77.506446
ITER: 0 | i: 4 | j: 6 Result(i: 2.000000 | j: 2.449490 | ij:
4.898979): 82.405426
ITER: 0 | i: 4 | j: 7 Result(i: 2.000000 | j: 2.645751 | ij:
5.291503): 87.696928
ITER: 0 | i: 4 | j: 8 Result(i: 2.000000 | j: 2.828427 | ij:
5.656854): 93.353783
ITER: 0 | i: 4 | j: 9 Result(i: 2.000000 | j: 3.000000 | ij:
6.000000): 99.353783
ITER: 0 | i: 5 | j: 0 Result(i: 2.236068 | j: 0.000000 | ij:
0.000000): 99.353783
ITER: 0 | i: 5 | j: 1 Result(i: 2.236068 | j: 1.000000 | ij:
2.236068): 101.589851
ITER: 0 | i: 5 | j: 2 Result(i: 2.236068 | j: 1.414214 | ij:
3.162278): 104.752128
ITER: 0 | i: 5 | j: 3 Result(i: 2.236068 | j: 1.732051 | ij:
3.872983): 108.625112
ITER: 0 | i: 5 | j: 4 Result(i: 2.236068 | j: 2.000000 | ij:
4.472136): 113.097248
ITER: 0 | i: 5 | j: 5 Result(i: 2.236068 | j: 2.236068 | ij:
5.000000): 118.097248
ITER: 0 | i: 5 | j: 6 Result(i: 2.236068 | j: 2.449490 | ij:
5.477226): 123.574473
ITER: 0 | i: 5 | j: 7 Result(i: 2.236068 | j: 2.645751 | ij:
5.916080): 129.490553
ITER: 0 | i: 5 | j: 8 Result(i: 2.236068 | j: 2.828427 | ij:
6.324555): 135.815108
ITER: 0 | i: 5 | j: 9 Result(i: 2.236068 | j: 3.000000 | ij:
6.708204): 142.523312
ITER: 0 | i: 6 | j: 0 Result(i: 2.449490 | j: 0.000000 | ij:
0.000000): 142.523312
ITER: 0 | i: 6 | j: 1 Result(i: 2.449490 | j: 1.000000 | ij:
2.449490): 144.972802
ITER: 0 | i: 6 | j: 2 Result(i: 2.449490 | j: 1.414214 | ij:
3.464102): 148.436904
ITER: 0 | i: 6 | j: 3 Result(i: 2.449490 | j: 1.732051 | ij:
4.242641): 152.679544
ITER: 0 | i: 6 | j: 4 Result(i: 2.449490 | j: 2.000000 | ij:
4.898979): 157.578524
ITER: 0 | i: 6 | j: 5 Result(i: 2.449490 | j: 2.236068 | ij:
5.477226): 163.055749
ITER: 0 | i: 6 | j: 6 Result(i: 2.449490 | j: 2.449490 | ij:
6.000000): 169.055749
ITER: 0 | i: 6 | j: 7 Result(i: 2.449490 | j: 2.645751 | ij:
6.480741): 175.536490
ITER: 0 | i: 6 | j: 8 Result(i: 2.449490 | j: 2.828427 | ij:
6.928203): 182.464693
ITER: 0 | i: 6 | j: 9 Result(i: 2.449490 | j: 3.000000 | ij:
7.348469): 189.813162
ITER: 0 | i: 7 | j: 0 Result(i: 2.645751 | j: 0.000000 | ij:
0.000000): 189.813162
ITER: 0 | i: 7 | j: 1 Result(i: 2.645751 | j: 1.000000 | ij:
2.645751): 192.458914
ITER: 0 | i: 7 | j: 2 Result(i: 2.645751 | j: 1.414214 | ij:
3.741657): 196.200571
ITER: 0 | i: 7 | j: 3 Result(i: 2.645751 | j: 1.732051 | ij:
4.582576): 200.783147
ITER: 0 | i: 7 | j: 4 Result(i: 2.645751 | j: 2.000000 | ij:
5.291503): 206.074649
ITER: 0 | i: 7 | j: 5 Result(i: 2.645751 | j: 2.236068 | ij:
5.916080): 211.990729
ITER: 0 | i: 7 | j: 6 Result(i: 2.645751 | j: 2.449490 | ij:
6.480741): 218.471470
ITER: 0 | i: 7 | j: 7 Result(i: 2.645751 | j: 2.645751 | ij:
7.000000): 225.471470
ITER: 0 | i: 7 | j: 8 Result(i: 2.645751 | j: 2.828427 | ij:
7.483315): 232.954785
ITER: 0 | i: 7 | j: 9 Result(i: 2.645751 | j: 3.000000 | ij:
7.937254): 240.892039
ITER: 0 | i: 8 | j: 0 Result(i: 2.828427 | j: 0.000000 | ij:
0.000000): 240.892039
ITER: 0 | i: 8 | j: 1 Result(i: 2.828427 | j: 1.000000 | ij:
2.828427): 243.720466
ITER: 0 | i: 8 | j: 2 Result(i: 2.828427 | j: 1.414214 | ij:
4.000000): 247.720466
ITER: 0 | i: 8 | j: 3 Result(i: 2.828427 | j: 1.732051 | ij:
4.898979): 252.619445
ITER: 0 | i: 8 | j: 4 Result(i: 2.828427 | j: 2.000000 | ij:
5.656854): 258.276300
ITER: 0 | i: 8 | j: 5 Result(i: 2.828427 | j: 2.236068 | ij:
6.324555): 264.600855
ITER: 0 | i: 8 | j: 6 Result(i: 2.828427 | j: 2.449490 | ij:
6.928203): 271.529058
ITER: 0 | i: 8 | j: 7 Result(i: 2.828427 | j: 2.645751 | ij:
7.483315): 279.012373
ITER: 0 | i: 8 | j: 8 Result(i: 2.828427 | j: 2.828427 | ij:
8.000000): 287.012373
ITER: 0 | i: 8 | j: 9 Result(i: 2.828427 | j: 3.000000 | ij:
8.485281): 295.497654
ITER: 0 | i: 9 | j: 0 Result(i: 3.000000 | j: 0.000000 | ij:
0.000000): 295.497654
ITER: 0 | i: 9 | j: 1 Result(i: 3.000000 | j: 1.000000 | ij:
3.000000): 298.497654
ITER: 0 | i: 9 | j: 2 Result(i: 3.000000 | j: 1.414214 | ij:
4.242641): 302.740295
ITER: 0 | i: 9 | j: 3 Result(i: 3.000000 | j: 1.732051 | ij:
5.196152): 307.936447
ITER: 0 | i: 9 | j: 4 Result(i: 3.000000 | j: 2.000000 | ij:
6.000000): 313.936447
ITER: 0 | i: 9 | j: 5 Result(i: 3.000000 | j: 2.236068 | ij:
6.708204): 320.644651
ITER: 0 | i: 9 | j: 6 Result(i: 3.000000 | j: 2.449490 | ij:
7.348469): 327.993120
ITER: 0 | i: 9 | j: 7 Result(i: 3.000000 | j: 2.645751 | ij:
7.937254): 335.930374
ITER: 0 | i: 9 | j: 8 Result(i: 3.000000 | j: 2.828427 | ij:
8.485281): 344.415656
ITER: 0 | i: 9 | j: 9 Result(i: 3.000000 | j: 3.000000 | ij:
9.000000): 353.415656
Final Result: 353.415656
ITER: 1 | i: 0 | j: 0 Result(i: 0.000000 | j: 0.000000 | ij:
0.000000): 0.000000
ITER: 1 | i: 0 | j: 1 Result(i: 0.000000 | j: 1.000000 | ij:
0.000000): 0.000000
ITER: 1 | i: 0 | j: 2 Result(i: 0.000000 | j: 1.414214 | ij:
0.000000): 0.000000
ITER: 1 | i: 0 | j: 3 Result(i: 0.000000 | j: 1.732051 | ij:
0.000000): 0.000000
ITER: 1 | i: 0 | j: 4 Result(i: 0.000000 | j: 2.000000 | ij:
0.000000): 0.000000
ITER: 1 | i: 0 | j: 5 Result(i: 0.000000 | j: 2.236068 | ij:
0.000000): 0.000000
ITER: 1 | i: 0 | j: 6 Result(i: 0.000000 | j: 2.449490 | ij:
0.000000): 0.000000
ITER: 1 | i: 0 | j: 7 Result(i: 0.000000 | j: 2.645751 | ij:
0.000000): 0.000000
ITER: 1 | i: 0 | j: 8 Result(i: 0.000000 | j: 2.828427 | ij:
0.000000): 0.000000
ITER: 1 | i: 0 | j: 9 Result(i: 0.000000 | j: 3.000000 | ij:
0.000000): 0.000000
ITER: 1 | i: 1 | j: 0 Result(i: 1.000000 | j: 0.000000 | ij:
0.000000): 0.000000
ITER: 1 | i: 1 | j: 1 Result(i: 1.000000 | j: 1.000000 | ij:
1.000000): 1.000000
ITER: 1 | i: 1 | j: 2 Result(i: 1.000000 | j: 1.414214 | ij:
1.414214): 2.414214
ITER: 1 | i: 1 | j: 3 Result(i: 1.000000 | j: 1.732051 | ij:
1.732051): 4.146264
ITER: 1 | i: 1 | j: 4 Result(i: 1.000000 | j: 2.000000 | ij:
2.000000): 6.146264
ITER: 1 | i: 1 | j: 5 Result(i: 1.000000 | j: 2.236068 | ij:
2.236068): 8.382332
ITER: 1 | i: 1 | j: 6 Result(i: 1.000000 | j: 2.449490 | ij:
2.449490): 10.831822
ITER: 1 | i: 1 | j: 7 Result(i: 1.000000 | j: 2.645751 | ij:
2.645751): 13.477573
ITER: 1 | i: 1 | j: 8 Result(i: 1.000000 | j: 2.828427 | ij:
2.828427): 16.306001
ITER: 1 | i: 1 | j: 9 Result(i: 1.000000 | j: 3.000000 | ij:
3.000000): 19.306001
ITER: 1 | i: 2 | j: 0 Result(i: 1.414214 | j: 0.000000 | ij:
0.000000): 19.306001
ITER: 1 | i: 2 | j: 1 Result(i: 1.414214 | j: 1.000000 | ij:
1.414214): 20.720214
ITER: 1 | i: 2 | j: 2 Result(i: 1.414214 | j: 1.414214 | ij:
2.000000): 22.720214
ITER: 1 | i: 2 | j: 3 Result(i: 1.414214 | j: 1.732051 | ij:
2.449490): 25.169704
ITER: 1 | i: 2 | j: 4 Result(i: 1.414214 | j: 2.000000 | ij:
2.828427): 27.998131
ITER: 1 | i: 2 | j: 5 Result(i: 1.414214 | j: 2.236068 | ij:
3.162278): 31.160409
ITER: 1 | i: 2 | j: 6 Result(i: 1.414214 | j: 2.449490 | ij:
3.464102): 34.624510
ITER: 1 | i: 2 | j: 7 Result(i: 1.414214 | j: 2.645751 | ij:
3.741657): 38.366168
ITER: 1 | i: 2 | j: 8 Result(i: 1.414214 | j: 2.828427 | ij:
4.000000): 42.366168
ITER: 1 | i: 2 | j: 9 Result(i: 1.414214 | j: 3.000000 | ij:
4.242641): 46.608808
ITER: 1 | i: 3 | j: 0 Result(i: 1.732051 | j: 0.000000 | ij:
0.000000): 46.608808
ITER: 1 | i: 3 | j: 1 Result(i: 1.732051 | j: 1.000000 | ij:
1.732051): 48.340859
ITER: 1 | i: 3 | j: 2 Result(i: 1.732051 | j: 1.414214 | ij:
2.449490): 50.790349
ITER: 1 | i: 3 | j: 3 Result(i: 1.732051 | j: 1.732051 | ij:
3.000000): 53.790349
ITER: 1 | i: 3 | j: 4 Result(i: 1.732051 | j: 2.000000 | ij:
3.464102): 57.254450
ITER: 1 | i: 3 | j: 5 Result(i: 1.732051 | j: 2.236068 | ij:
3.872983): 61.127434
ITER: 1 | i: 3 | j: 6 Result(i: 1.732051 | j: 2.449490 | ij:
4.242641): 65.370075
ITER: 1 | i: 3 | j: 7 Result(i: 1.732051 | j: 2.645751 | ij:
4.582576): 69.952650
ITER: 1 | i: 3 | j: 8 Result(i: 1.732051 | j: 2.828427 | ij:
4.898979): 74.851630
ITER: 1 | i: 3 | j: 9 Result(i: 1.732051 | j: 3.000000 | ij:
5.196152): 80.047782
ITER: 1 | i: 4 | j: 0 Result(i: 2.000000 | j: 0.000000 | ij:
0.000000): 80.047782
ITER: 1 | i: 4 | j: 1 Result(i: 2.000000 | j: 1.000000 | ij:
2.000000): 82.047782
ITER: 1 | i: 4 | j: 2 Result(i: 2.000000 | j: 1.414214 | ij:
2.828427): 84.876209
ITER: 1 | i: 4 | j: 3 Result(i: 2.000000 | j: 1.732051 | ij:
3.464102): 88.340311
ITER: 1 | i: 4 | j: 4 Result(i: 2.000000 | j: 2.000000 | ij:
4.000000): 92.340311
ITER: 1 | i: 4 | j: 5 Result(i: 2.000000 | j: 2.236068 | ij:
4.472136): 96.812447
ITER: 1 | i: 4 | j: 6 Result(i: 2.000000 | j: 2.449490 | ij:
4.898979): 101.711426
ITER: 1 | i: 4 | j: 7 Result(i: 2.000000 | j: 2.645751 | ij:
5.291503): 107.002929
ITER: 1 | i: 4 | j: 8 Result(i: 2.000000 | j: 2.828427 | ij:
5.656854): 112.659783
ITER: 1 | i: 4 | j: 9 Result(i: 2.000000 | j: 3.000000 | ij:
6.000000): 118.659783
ITER: 1 | i: 5 | j: 0 Result(i: 2.236068 | j: 0.000000 | ij:
0.000000): 118.659783
ITER: 1 | i: 5 | j: 1 Result(i: 2.236068 | j: 1.000000 | ij:
2.236068): 120.895851
ITER: 1 | i: 5 | j: 2 Result(i: 2.236068 | j: 1.414214 | ij:
3.162278): 124.058129
ITER: 1 | i: 5 | j: 3 Result(i: 2.236068 | j: 1.732051 | ij:
3.872983): 127.931112
ITER: 1 | i: 5 | j: 4 Result(i: 2.236068 | j: 2.000000 | ij:
4.472136): 132.403248
ITER: 1 | i: 5 | j: 5 Result(i: 2.236068 | j: 2.236068 | ij:
5.000000): 137.403248
ITER: 1 | i: 5 | j: 6 Result(i: 2.236068 | j: 2.449490 | ij:
5.477226): 142.880474
ITER: 1 | i: 5 | j: 7 Result(i: 2.236068 | j: 2.645751 | ij:
5.916080): 148.796553
ITER: 1 | i: 5 | j: 8 Result(i: 2.236068 | j: 2.828427 | ij:
6.324555): 155.121109
ITER: 1 | i: 5 | j: 9 Result(i: 2.236068 | j: 3.000000 | ij:
6.708204): 161.829313
ITER: 1 | i: 6 | j: 0 Result(i: 2.449490 | j: 0.000000 | ij:
0.000000): 161.829313
ITER: 1 | i: 6 | j: 1 Result(i: 2.449490 | j: 1.000000 | ij:
2.449490): 164.278802
ITER: 1 | i: 6 | j: 2 Result(i: 2.449490 | j: 1.414214 | ij:
3.464102): 167.742904
ITER: 1 | i: 6 | j: 3 Result(i: 2.449490 | j: 1.732051 | ij:
4.242641): 171.985545
ITER: 1 | i: 6 | j: 4 Result(i: 2.449490 | j: 2.000000 | ij:
4.898979): 176.884524
ITER: 1 | i: 6 | j: 5 Result(i: 2.449490 | j: 2.236068 | ij:
5.477226): 182.361750
ITER: 1 | i: 6 | j: 6 Result(i: 2.449490 | j: 2.449490 | ij:
6.000000): 188.361750
ITER: 1 | i: 6 | j: 7 Result(i: 2.449490 | j: 2.645751 | ij:
6.480741): 194.842491
ITER: 1 | i: 6 | j: 8 Result(i: 2.449490 | j: 2.828427 | ij:
6.928203): 201.770694
ITER: 1 | i: 6 | j: 9 Result(i: 2.449490 | j: 3.000000 | ij:
7.348469): 209.119163
ITER: 1 | i: 7 | j: 0 Result(i: 2.645751 | j: 0.000000 | ij:
0.000000): 209.119163
ITER: 1 | i: 7 | j: 1 Result(i: 2.645751 | j: 1.000000 | ij:
2.645751): 211.764914
ITER: 1 | i: 7 | j: 2 Result(i: 2.645751 | j: 1.414214 | ij:
3.741657): 215.506572
ITER: 1 | i: 7 | j: 3 Result(i: 2.645751 | j: 1.732051 | ij:
4.582576): 220.089147
ITER: 1 | i: 7 | j: 4 Result(i: 2.645751 | j: 2.000000 | ij:
5.291503): 225.380650
ITER: 1 | i: 7 | j: 5 Result(i: 2.645751 | j: 2.236068 | ij:
5.916080): 231.296730
ITER: 1 | i: 7 | j: 6 Result(i: 2.645751 | j: 2.449490 | ij:
6.480741): 237.777470
ITER: 1 | i: 7 | j: 7 Result(i: 2.645751 | j: 2.645751 | ij:
7.000000): 244.777470
ITER: 1 | i: 7 | j: 8 Result(i: 2.645751 | j: 2.828427 | ij:
7.483315): 252.260785
ITER: 1 | i: 7 | j: 9 Result(i: 2.645751 | j: 3.000000 | ij:
7.937254): 260.198039
ITER: 1 | i: 8 | j: 0 Result(i: 2.828427 | j: 0.000000 | ij:
0.000000): 260.198039
ITER: 1 | i: 8 | j: 1 Result(i: 2.828427 | j: 1.000000 | ij:
2.828427): 263.026466
ITER: 1 | i: 8 | j: 2 Result(i: 2.828427 | j: 1.414214 | ij:
4.000000): 267.026466
ITER: 1 | i: 8 | j: 3 Result(i: 2.828427 | j: 1.732051 | ij:
4.898979): 271.925446
ITER: 1 | i: 8 | j: 4 Result(i: 2.828427 | j: 2.000000 | ij:
5.656854): 277.582300
ITER: 1 | i: 8 | j: 5 Result(i: 2.828427 | j: 2.236068 | ij:
6.324555): 283.906855
ITER: 1 | i: 8 | j: 6 Result(i: 2.828427 | j: 2.449490 | ij:
6.928203): 290.835059
ITER: 1 | i: 8 | j: 7 Result(i: 2.828427 | j: 2.645751 | ij:
7.483315): 298.318373
ITER: 1 | i: 8 | j: 8 Result(i: 2.828427 | j: 2.828427 | ij:
8.000000): 306.318373
ITER: 1 | i: 8 | j: 9 Result(i: 2.828427 | j: 3.000000 | ij:
8.485281): 314.803655
ITER: 1 | i: 9 | j: 0 Result(i: 3.000000 | j: 0.000000 | ij:
0.000000): 314.803655
ITER: 1 | i: 9 | j: 1 Result(i: 3.000000 | j: 1.000000 | ij:
3.000000): 317.803655
ITER: 1 | i: 9 | j: 2 Result(i: 3.000000 | j: 1.414214 | ij:
4.242641): 322.046295
ITER: 1 | i: 9 | j: 3 Result(i: 3.000000 | j: 1.732051 | ij:
5.196152): 327.242448
ITER: 1 | i: 9 | j: 4 Result(i: 3.000000 | j: 2.000000 | ij:
6.000000): 333.242448
ITER: 1 | i: 9 | j: 5 Result(i: 3.000000 | j: 2.236068 | ij:
6.708204): 339.950652
ITER: 1 | i: 9 | j: 6 Result(i: 3.000000 | j: 2.449490 | ij:
7.348469): 347.299121
ITER: 1 | i: 9 | j: 7 Result(i: 3.000000 | j: 2.645751 | ij:
7.937254): 355.236375
ITER: 1 | i: 9 | j: 8 Result(i: 3.000000 | j: 2.828427 | ij:
8.485281): 363.721656
ITER: 1 | i: 9 | j: 9 Result(i: 3.000000 | j: 3.000000 | ij:
9.000000): 372.721656
Final Result: 372.721656
As we can see in the following iterations the sqrt(1) as well as the
result is set to zero for some reason.
ITER: 0 | i: 1 | j: 4 Result(i: 0.000000 | j: 2.000000 | ij:
0.000000): 0.000000
ITER: 0 | i: 1 | j: 5 Result(i: 0.000000 | j: 2.236068 | ij:
0.000000): 0.000000
ITER: 0 | i: 1 | j: 6 Result(i: 0.000000 | j: 2.449490 | ij:
0.000000): 0.000000
ITER: 0 | i: 1 | j: 7 Result(i: 0.000000 | j: 2.645751 | ij:
0.000000): 0.000000
ITER: 0 | i: 1 | j: 8 Result(i: 0.000000 | j: 2.828427 | ij:
0.000000): 0.000000
ITER: 0 | i: 1 | j: 9 Result(i: 0.000000 | j: 3.000000 | ij:
0.000000): 0.000000
Please help me to resolve the accuracy issue! I think that it will
be very useful for gem5 community.
To be noticed, I find the correct simulated tick in which the
application started in FS (using m5 dumpstats), and I start the
--debug-start, but the trace file which is generated is 10x larger
than SE mode for the same application. How can I compare them?
Thank you in advance!
Best regards,
Nikos
Quoting Νικόλαος Ταμπουρατζής ntampouratzis@ece.auth.gr:
Dear Jason,
I am trying to use --debug-start but in FS mode it is very
difficult to find the tick on which the application is started!
However, I am writing the following very simple c++ program:
#include <cmath>
#include <stdio.h>
int main(){
int dim = 4096;
double result;
for (int iter = 0; iter < 2; iter++){
result = 0;
for (int i = 0; i < dim; i++){
for (int j = 0; j < dim; j++){
result += sqrt(i) * sqrt(j);
}
}
printf("Result: %lf\n", result); //Result: 30530733453.127449
}
}
I cross-compile it using: riscv64-linux-gnu-g++ -static -O3 -o
test_riscv test_riscv.cpp
While in X86 (without cross-compilation of course), QEMU-RISCV,
GEM5-SE the result is the same (30530733453.127449), in GEM5-FS the
result is different! In addition, the result is also different
between the 2 iterations.
Please reproduce the error if you want in order to verify my result.
Ηow can the issue be resolved?
Thank you in advance!
Best regards,
Nikos
Quoting Jason Lowe-Power jason@lowepower.com:
Hi Nikos,
You can use --debug-start to start the debugging after some number of
ticks. Also, I would expect that the difference should come up quickly, so
no need to run the program to the end.
For the FS mode one, you will want to just start the trace as the
application starts. This could be a bit of a pain.
I'm not really sure what fundamentally could be different. FS and SE mode
use the exact same code for executing instructions, so I don't think that's
the problem. Have you tried running for smaller inputs or just one
iteration?
Jason
On Wed, Sep 21, 2022 at 9:04 AM Νικόλαος Ταμπουρατζής <
ntampouratzis@ece.auth.gr> wrote:
Dear Bobby,
Iam trying to add --debug-flags=Exec (building the gem5 for gem5.opt
not for gem5.fast which I had) but the debug traces exceed the 20GB
(and it is not finished yet) for less than 1 simulated second. How can
I reduce the size of the debug-flags (or set something more specific)?
In contrast I build the HPCG benchmark with DHPCG_DEBUG flag. If you
want, you can compare these two output files
(hpcg20010909T014640_SE_Mode & HPCG-Benchmark_3.1_FS_Mode). As you can
see, something goes wrong with the accuracy of calculations in FS mode
(benchmark uses double precission). You can find the files here:
http://kition.mhl.tuc.gr:8000/d/68d82f3533/
Best regards,
Nikos
Quoting Jason Lowe-Power jason@lowepower.com:
That's quite odd that it works in SE mode but not FS mode!
I would suggest running with --debug-flags=Exec for both and then
diff to see how they differ.
Cheers,
Jason
On Tue, Sep 20, 2022 at 2:45 PM Νικόλαος Ταμπουρατζής <
ntampouratzis@ece.auth.gr> wrote:
Dear Bobby,
In QEMU I get the same (correct) results that I get in SE mode
simulation. I get invalid results in FS simulation (in both
riscv-fs.py and riscv-ubuntu-run.py). I cannot access real RISCV
hardware at this moment, however, if you want you may execute my xhpcg
binary (http://kition.mhl.tuc.gr:8000/f/4ca25fdd3c/) with the
following configuration:
./xhpcg --nx=16 --ny=16 --nz=16 --npx=1 --npy=1 --npz=1 --rt=0.1
Please let me know if you have any updates!
Best regards,
Nikos
Quoting Jason Lowe-Power jason@lowepower.com:
Hi Nikos,
I notice you said the following in your original email:
In addition, I used the RISCV Ubuntu image
I installed the gcc compiler, compile it (through qemu) and I get
wrong results too.
Is this saying you get the wrong results is QEMU? If so, the bug is in
or the HPCG workload, not in gem5. If not, I would test in QEMU to
sure the binary works there. Another way you could test to see if the
problem is your binary or gem5 would be to run it on real hardware. We
access to some RISC-V hardware here at UC Davis, if you don't have
Dear Bobby,
- I use the original riscv-fs.py which is provided in the latest
release.
I run the gem5 once (./build/RISCV/gem5.fast -d ./HPCG_FS_results
./configs/example/gem5_library/riscv-fs.py) in order to download the
riscv-bootloader-vmlinux-5.10 and riscv-disk-img.
After this I mount the riscv-disk-img (sudo mount -o loop
riscv-disk-img /mnt), put the xhpcg executable and I do the following
changes in riscv-fs.py to boot the riscv-disk-img with executable:
image = CustomDiskImageResource(
local_path = "/home/cossim/.cache/gem5/riscv-disk-img",
)
Set the Full System workload.
board.set_kernel_disk_workload(
kernel=Resource("riscv-bootloader-vmlinux-5.10"),
disk_image=image,
)
Finally, in the gem5/src/python/gem5/components/boards/riscv_board.py
I change the last line to "return ["console=ttyS0",
"root={root_value}", "rw"]" in order to allow the write permissions
the image.
- The HPCG benchmark after some iterations calculates if the results
are valid or not valid. In the case of FS it gives invalid results.
I see from the results, one (at least) problem is that produces
different results in each HPCG execution (with the same
I'm going to need a bit more information to help:
- In what way have you modified
./configs/example/gem5_library/riscv-fs.py? Can you attach the
- What error are you getting or in what way are the results
Dear gem5 community,
I have successfully cross-compile the HPCG benchmark for RISCV
version, without MPI and OpenMP). While it working properly in
mode (./build/RISCV/gem5.fast -d ./HPCG_SE_results
./configs/example/se.py -c xhpcg --options '--nx=16 --ny=16
--npx=1 --npy=1 --npz=1 --rt=0.1'), I get invalid results in FS
simulation using "./build/RISCV/gem5.fast -d ./HPCG_FS_results
./configs/example/gem5_library/riscv-fs.py" (I mount the riscv
and put it).
Can you help me please?
In addition, I used the RISCV Ubuntu image
(
In my previous results, I had used double (not float) for the
following variables: result, sq_i and sq_j. In the case of float
instead of double I get "nan" and not 0.000000.
Quoting Νικόλαος Ταμπουρατζής <ntampouratzis@ece.auth.gr>:
> Dear Jason, all,
>
> I am trying to find the accuracy problem with RISCV-FS and I observe
> that the problem is created (at least in my dummy example) because
> the variables (double) are set to zero in random simulated time (for
> this reason I get different results among executions of the same
> code). Specifically for the following dummy code:
>
>
> #include <cmath>
> #include <stdio.h>
>
> int main(){
>
> int dim = 10;
>
> float result;
>
> for (int iter = 0; iter < 2; iter++){
> result = 0;
> for (int i = 0; i < dim; i++){
> for (int j = 0; j < dim; j++){
> float sq_i = sqrt(i);
> float sq_j = sqrt(j);
> result += sq_i * sq_j;
> printf("ITER: %d | i: %d | j: %d Result(i: %f | j:
> %f | i*j: %f): %f\n", iter, i , j, sq_i, sq_j, sq_i * sq_j, result);
> }
> }
> printf("Final Result: %lf\n", result);
> }
> }
>
>
> The correct Final Result in both iterations is 372.721656. However,
> I get the following results in FS:
>
> ITER: 0 | i: 0 | j: 0 Result(i: 0.000000 | j: 0.000000 | i*j:
> 0.000000): 0.000000
> ITER: 0 | i: 0 | j: 1 Result(i: 0.000000 | j: 1.000000 | i*j:
> 0.000000): 0.000000
> ITER: 0 | i: 0 | j: 2 Result(i: 0.000000 | j: 1.414214 | i*j:
> 0.000000): 0.000000
> ITER: 0 | i: 0 | j: 3 Result(i: 0.000000 | j: 1.732051 | i*j:
> 0.000000): 0.000000
> ITER: 0 | i: 0 | j: 4 Result(i: 0.000000 | j: 2.000000 | i*j:
> 0.000000): 0.000000
> ITER: 0 | i: 0 | j: 5 Result(i: 0.000000 | j: 2.236068 | i*j:
> 0.000000): 0.000000
> ITER: 0 | i: 0 | j: 6 Result(i: 0.000000 | j: 2.449490 | i*j:
> 0.000000): 0.000000
> ITER: 0 | i: 0 | j: 7 Result(i: 0.000000 | j: 2.645751 | i*j:
> 0.000000): 0.000000
> ITER: 0 | i: 0 | j: 8 Result(i: 0.000000 | j: 2.828427 | i*j:
> 0.000000): 0.000000
> ITER: 0 | i: 0 | j: 9 Result(i: 0.000000 | j: 3.000000 | i*j:
> 0.000000): 0.000000
> ITER: 0 | i: 1 | j: 0 Result(i: 1.000000 | j: 0.000000 | i*j:
> 0.000000): 0.000000
> ITER: 0 | i: 1 | j: 1 Result(i: 1.000000 | j: 1.000000 | i*j:
> 1.000000): 1.000000
> ITER: 0 | i: 1 | j: 2 Result(i: 1.000000 | j: 1.414214 | i*j:
> 1.414214): 2.414214
> ITER: 0 | i: 1 | j: 3 Result(i: 1.000000 | j: 1.732051 | i*j:
> 1.732051): 4.146264
> ITER: 0 | i: 1 | j: 4 Result(i: 0.000000 | j: 2.000000 | i*j:
> 0.000000): 0.000000
> ITER: 0 | i: 1 | j: 5 Result(i: 0.000000 | j: 2.236068 | i*j:
> 0.000000): 0.000000
> ITER: 0 | i: 1 | j: 6 Result(i: 0.000000 | j: 2.449490 | i*j:
> 0.000000): 0.000000
> ITER: 0 | i: 1 | j: 7 Result(i: 0.000000 | j: 2.645751 | i*j:
> 0.000000): 0.000000
> ITER: 0 | i: 1 | j: 8 Result(i: 0.000000 | j: 2.828427 | i*j:
> 0.000000): 0.000000
> ITER: 0 | i: 1 | j: 9 Result(i: 0.000000 | j: 3.000000 | i*j:
> 0.000000): 0.000000
> ITER: 0 | i: 2 | j: 0 Result(i: 1.414214 | j: 0.000000 | i*j:
> 0.000000): 0.000000
> ITER: 0 | i: 2 | j: 1 Result(i: 1.414214 | j: 1.000000 | i*j:
> 1.414214): 1.414214
> ITER: 0 | i: 2 | j: 2 Result(i: 1.414214 | j: 1.414214 | i*j:
> 2.000000): 3.414214
> ITER: 0 | i: 2 | j: 3 Result(i: 1.414214 | j: 1.732051 | i*j:
> 2.449490): 5.863703
> ITER: 0 | i: 2 | j: 4 Result(i: 1.414214 | j: 2.000000 | i*j:
> 2.828427): 8.692130
> ITER: 0 | i: 2 | j: 5 Result(i: 1.414214 | j: 2.236068 | i*j:
> 3.162278): 11.854408
> ITER: 0 | i: 2 | j: 6 Result(i: 1.414214 | j: 2.449490 | i*j:
> 3.464102): 15.318510
> ITER: 0 | i: 2 | j: 7 Result(i: 1.414214 | j: 2.645751 | i*j:
> 3.741657): 19.060167
> ITER: 0 | i: 2 | j: 8 Result(i: 1.414214 | j: 2.828427 | i*j:
> 4.000000): 23.060167
> ITER: 0 | i: 2 | j: 9 Result(i: 1.414214 | j: 3.000000 | i*j:
> 4.242641): 27.302808
> ITER: 0 | i: 3 | j: 0 Result(i: 1.732051 | j: 0.000000 | i*j:
> 0.000000): 27.302808
> ITER: 0 | i: 3 | j: 1 Result(i: 1.732051 | j: 1.000000 | i*j:
> 1.732051): 29.034859
> ITER: 0 | i: 3 | j: 2 Result(i: 1.732051 | j: 1.414214 | i*j:
> 2.449490): 31.484348
> ITER: 0 | i: 3 | j: 3 Result(i: 1.732051 | j: 1.732051 | i*j:
> 3.000000): 34.484348
> ITER: 0 | i: 3 | j: 4 Result(i: 1.732051 | j: 2.000000 | i*j:
> 3.464102): 37.948450
> ITER: 0 | i: 3 | j: 5 Result(i: 1.732051 | j: 2.236068 | i*j:
> 3.872983): 41.821433
> ITER: 0 | i: 3 | j: 6 Result(i: 1.732051 | j: 2.449490 | i*j:
> 4.242641): 46.064074
> ITER: 0 | i: 3 | j: 7 Result(i: 1.732051 | j: 2.645751 | i*j:
> 4.582576): 50.646650
> ITER: 0 | i: 3 | j: 8 Result(i: 1.732051 | j: 2.828427 | i*j:
> 4.898979): 55.545629
> ITER: 0 | i: 3 | j: 9 Result(i: 1.732051 | j: 3.000000 | i*j:
> 5.196152): 60.741782
> ITER: 0 | i: 4 | j: 0 Result(i: 2.000000 | j: 0.000000 | i*j:
> 0.000000): 60.741782
> ITER: 0 | i: 4 | j: 1 Result(i: 2.000000 | j: 1.000000 | i*j:
> 2.000000): 62.741782
> ITER: 0 | i: 4 | j: 2 Result(i: 2.000000 | j: 1.414214 | i*j:
> 2.828427): 65.570209
> ITER: 0 | i: 4 | j: 3 Result(i: 2.000000 | j: 1.732051 | i*j:
> 3.464102): 69.034310
> ITER: 0 | i: 4 | j: 4 Result(i: 2.000000 | j: 2.000000 | i*j:
> 4.000000): 73.034310
> ITER: 0 | i: 4 | j: 5 Result(i: 2.000000 | j: 2.236068 | i*j:
> 4.472136): 77.506446
> ITER: 0 | i: 4 | j: 6 Result(i: 2.000000 | j: 2.449490 | i*j:
> 4.898979): 82.405426
> ITER: 0 | i: 4 | j: 7 Result(i: 2.000000 | j: 2.645751 | i*j:
> 5.291503): 87.696928
> ITER: 0 | i: 4 | j: 8 Result(i: 2.000000 | j: 2.828427 | i*j:
> 5.656854): 93.353783
> ITER: 0 | i: 4 | j: 9 Result(i: 2.000000 | j: 3.000000 | i*j:
> 6.000000): 99.353783
> ITER: 0 | i: 5 | j: 0 Result(i: 2.236068 | j: 0.000000 | i*j:
> 0.000000): 99.353783
> ITER: 0 | i: 5 | j: 1 Result(i: 2.236068 | j: 1.000000 | i*j:
> 2.236068): 101.589851
> ITER: 0 | i: 5 | j: 2 Result(i: 2.236068 | j: 1.414214 | i*j:
> 3.162278): 104.752128
> ITER: 0 | i: 5 | j: 3 Result(i: 2.236068 | j: 1.732051 | i*j:
> 3.872983): 108.625112
> ITER: 0 | i: 5 | j: 4 Result(i: 2.236068 | j: 2.000000 | i*j:
> 4.472136): 113.097248
> ITER: 0 | i: 5 | j: 5 Result(i: 2.236068 | j: 2.236068 | i*j:
> 5.000000): 118.097248
> ITER: 0 | i: 5 | j: 6 Result(i: 2.236068 | j: 2.449490 | i*j:
> 5.477226): 123.574473
> ITER: 0 | i: 5 | j: 7 Result(i: 2.236068 | j: 2.645751 | i*j:
> 5.916080): 129.490553
> ITER: 0 | i: 5 | j: 8 Result(i: 2.236068 | j: 2.828427 | i*j:
> 6.324555): 135.815108
> ITER: 0 | i: 5 | j: 9 Result(i: 2.236068 | j: 3.000000 | i*j:
> 6.708204): 142.523312
> ITER: 0 | i: 6 | j: 0 Result(i: 2.449490 | j: 0.000000 | i*j:
> 0.000000): 142.523312
> ITER: 0 | i: 6 | j: 1 Result(i: 2.449490 | j: 1.000000 | i*j:
> 2.449490): 144.972802
> ITER: 0 | i: 6 | j: 2 Result(i: 2.449490 | j: 1.414214 | i*j:
> 3.464102): 148.436904
> ITER: 0 | i: 6 | j: 3 Result(i: 2.449490 | j: 1.732051 | i*j:
> 4.242641): 152.679544
> ITER: 0 | i: 6 | j: 4 Result(i: 2.449490 | j: 2.000000 | i*j:
> 4.898979): 157.578524
> ITER: 0 | i: 6 | j: 5 Result(i: 2.449490 | j: 2.236068 | i*j:
> 5.477226): 163.055749
> ITER: 0 | i: 6 | j: 6 Result(i: 2.449490 | j: 2.449490 | i*j:
> 6.000000): 169.055749
> ITER: 0 | i: 6 | j: 7 Result(i: 2.449490 | j: 2.645751 | i*j:
> 6.480741): 175.536490
> ITER: 0 | i: 6 | j: 8 Result(i: 2.449490 | j: 2.828427 | i*j:
> 6.928203): 182.464693
> ITER: 0 | i: 6 | j: 9 Result(i: 2.449490 | j: 3.000000 | i*j:
> 7.348469): 189.813162
> ITER: 0 | i: 7 | j: 0 Result(i: 2.645751 | j: 0.000000 | i*j:
> 0.000000): 189.813162
> ITER: 0 | i: 7 | j: 1 Result(i: 2.645751 | j: 1.000000 | i*j:
> 2.645751): 192.458914
> ITER: 0 | i: 7 | j: 2 Result(i: 2.645751 | j: 1.414214 | i*j:
> 3.741657): 196.200571
> ITER: 0 | i: 7 | j: 3 Result(i: 2.645751 | j: 1.732051 | i*j:
> 4.582576): 200.783147
> ITER: 0 | i: 7 | j: 4 Result(i: 2.645751 | j: 2.000000 | i*j:
> 5.291503): 206.074649
> ITER: 0 | i: 7 | j: 5 Result(i: 2.645751 | j: 2.236068 | i*j:
> 5.916080): 211.990729
> ITER: 0 | i: 7 | j: 6 Result(i: 2.645751 | j: 2.449490 | i*j:
> 6.480741): 218.471470
> ITER: 0 | i: 7 | j: 7 Result(i: 2.645751 | j: 2.645751 | i*j:
> 7.000000): 225.471470
> ITER: 0 | i: 7 | j: 8 Result(i: 2.645751 | j: 2.828427 | i*j:
> 7.483315): 232.954785
> ITER: 0 | i: 7 | j: 9 Result(i: 2.645751 | j: 3.000000 | i*j:
> 7.937254): 240.892039
> ITER: 0 | i: 8 | j: 0 Result(i: 2.828427 | j: 0.000000 | i*j:
> 0.000000): 240.892039
> ITER: 0 | i: 8 | j: 1 Result(i: 2.828427 | j: 1.000000 | i*j:
> 2.828427): 243.720466
> ITER: 0 | i: 8 | j: 2 Result(i: 2.828427 | j: 1.414214 | i*j:
> 4.000000): 247.720466
> ITER: 0 | i: 8 | j: 3 Result(i: 2.828427 | j: 1.732051 | i*j:
> 4.898979): 252.619445
> ITER: 0 | i: 8 | j: 4 Result(i: 2.828427 | j: 2.000000 | i*j:
> 5.656854): 258.276300
> ITER: 0 | i: 8 | j: 5 Result(i: 2.828427 | j: 2.236068 | i*j:
> 6.324555): 264.600855
> ITER: 0 | i: 8 | j: 6 Result(i: 2.828427 | j: 2.449490 | i*j:
> 6.928203): 271.529058
> ITER: 0 | i: 8 | j: 7 Result(i: 2.828427 | j: 2.645751 | i*j:
> 7.483315): 279.012373
> ITER: 0 | i: 8 | j: 8 Result(i: 2.828427 | j: 2.828427 | i*j:
> 8.000000): 287.012373
> ITER: 0 | i: 8 | j: 9 Result(i: 2.828427 | j: 3.000000 | i*j:
> 8.485281): 295.497654
> ITER: 0 | i: 9 | j: 0 Result(i: 3.000000 | j: 0.000000 | i*j:
> 0.000000): 295.497654
> ITER: 0 | i: 9 | j: 1 Result(i: 3.000000 | j: 1.000000 | i*j:
> 3.000000): 298.497654
> ITER: 0 | i: 9 | j: 2 Result(i: 3.000000 | j: 1.414214 | i*j:
> 4.242641): 302.740295
> ITER: 0 | i: 9 | j: 3 Result(i: 3.000000 | j: 1.732051 | i*j:
> 5.196152): 307.936447
> ITER: 0 | i: 9 | j: 4 Result(i: 3.000000 | j: 2.000000 | i*j:
> 6.000000): 313.936447
> ITER: 0 | i: 9 | j: 5 Result(i: 3.000000 | j: 2.236068 | i*j:
> 6.708204): 320.644651
> ITER: 0 | i: 9 | j: 6 Result(i: 3.000000 | j: 2.449490 | i*j:
> 7.348469): 327.993120
> ITER: 0 | i: 9 | j: 7 Result(i: 3.000000 | j: 2.645751 | i*j:
> 7.937254): 335.930374
> ITER: 0 | i: 9 | j: 8 Result(i: 3.000000 | j: 2.828427 | i*j:
> 8.485281): 344.415656
> ITER: 0 | i: 9 | j: 9 Result(i: 3.000000 | j: 3.000000 | i*j:
> 9.000000): 353.415656
> Final Result: 353.415656
> ITER: 1 | i: 0 | j: 0 Result(i: 0.000000 | j: 0.000000 | i*j:
> 0.000000): 0.000000
> ITER: 1 | i: 0 | j: 1 Result(i: 0.000000 | j: 1.000000 | i*j:
> 0.000000): 0.000000
> ITER: 1 | i: 0 | j: 2 Result(i: 0.000000 | j: 1.414214 | i*j:
> 0.000000): 0.000000
> ITER: 1 | i: 0 | j: 3 Result(i: 0.000000 | j: 1.732051 | i*j:
> 0.000000): 0.000000
> ITER: 1 | i: 0 | j: 4 Result(i: 0.000000 | j: 2.000000 | i*j:
> 0.000000): 0.000000
> ITER: 1 | i: 0 | j: 5 Result(i: 0.000000 | j: 2.236068 | i*j:
> 0.000000): 0.000000
> ITER: 1 | i: 0 | j: 6 Result(i: 0.000000 | j: 2.449490 | i*j:
> 0.000000): 0.000000
> ITER: 1 | i: 0 | j: 7 Result(i: 0.000000 | j: 2.645751 | i*j:
> 0.000000): 0.000000
> ITER: 1 | i: 0 | j: 8 Result(i: 0.000000 | j: 2.828427 | i*j:
> 0.000000): 0.000000
> ITER: 1 | i: 0 | j: 9 Result(i: 0.000000 | j: 3.000000 | i*j:
> 0.000000): 0.000000
> ITER: 1 | i: 1 | j: 0 Result(i: 1.000000 | j: 0.000000 | i*j:
> 0.000000): 0.000000
> ITER: 1 | i: 1 | j: 1 Result(i: 1.000000 | j: 1.000000 | i*j:
> 1.000000): 1.000000
> ITER: 1 | i: 1 | j: 2 Result(i: 1.000000 | j: 1.414214 | i*j:
> 1.414214): 2.414214
> ITER: 1 | i: 1 | j: 3 Result(i: 1.000000 | j: 1.732051 | i*j:
> 1.732051): 4.146264
> ITER: 1 | i: 1 | j: 4 Result(i: 1.000000 | j: 2.000000 | i*j:
> 2.000000): 6.146264
> ITER: 1 | i: 1 | j: 5 Result(i: 1.000000 | j: 2.236068 | i*j:
> 2.236068): 8.382332
> ITER: 1 | i: 1 | j: 6 Result(i: 1.000000 | j: 2.449490 | i*j:
> 2.449490): 10.831822
> ITER: 1 | i: 1 | j: 7 Result(i: 1.000000 | j: 2.645751 | i*j:
> 2.645751): 13.477573
> ITER: 1 | i: 1 | j: 8 Result(i: 1.000000 | j: 2.828427 | i*j:
> 2.828427): 16.306001
> ITER: 1 | i: 1 | j: 9 Result(i: 1.000000 | j: 3.000000 | i*j:
> 3.000000): 19.306001
> ITER: 1 | i: 2 | j: 0 Result(i: 1.414214 | j: 0.000000 | i*j:
> 0.000000): 19.306001
> ITER: 1 | i: 2 | j: 1 Result(i: 1.414214 | j: 1.000000 | i*j:
> 1.414214): 20.720214
> ITER: 1 | i: 2 | j: 2 Result(i: 1.414214 | j: 1.414214 | i*j:
> 2.000000): 22.720214
> ITER: 1 | i: 2 | j: 3 Result(i: 1.414214 | j: 1.732051 | i*j:
> 2.449490): 25.169704
> ITER: 1 | i: 2 | j: 4 Result(i: 1.414214 | j: 2.000000 | i*j:
> 2.828427): 27.998131
> ITER: 1 | i: 2 | j: 5 Result(i: 1.414214 | j: 2.236068 | i*j:
> 3.162278): 31.160409
> ITER: 1 | i: 2 | j: 6 Result(i: 1.414214 | j: 2.449490 | i*j:
> 3.464102): 34.624510
> ITER: 1 | i: 2 | j: 7 Result(i: 1.414214 | j: 2.645751 | i*j:
> 3.741657): 38.366168
> ITER: 1 | i: 2 | j: 8 Result(i: 1.414214 | j: 2.828427 | i*j:
> 4.000000): 42.366168
> ITER: 1 | i: 2 | j: 9 Result(i: 1.414214 | j: 3.000000 | i*j:
> 4.242641): 46.608808
> ITER: 1 | i: 3 | j: 0 Result(i: 1.732051 | j: 0.000000 | i*j:
> 0.000000): 46.608808
> ITER: 1 | i: 3 | j: 1 Result(i: 1.732051 | j: 1.000000 | i*j:
> 1.732051): 48.340859
> ITER: 1 | i: 3 | j: 2 Result(i: 1.732051 | j: 1.414214 | i*j:
> 2.449490): 50.790349
> ITER: 1 | i: 3 | j: 3 Result(i: 1.732051 | j: 1.732051 | i*j:
> 3.000000): 53.790349
> ITER: 1 | i: 3 | j: 4 Result(i: 1.732051 | j: 2.000000 | i*j:
> 3.464102): 57.254450
> ITER: 1 | i: 3 | j: 5 Result(i: 1.732051 | j: 2.236068 | i*j:
> 3.872983): 61.127434
> ITER: 1 | i: 3 | j: 6 Result(i: 1.732051 | j: 2.449490 | i*j:
> 4.242641): 65.370075
> ITER: 1 | i: 3 | j: 7 Result(i: 1.732051 | j: 2.645751 | i*j:
> 4.582576): 69.952650
> ITER: 1 | i: 3 | j: 8 Result(i: 1.732051 | j: 2.828427 | i*j:
> 4.898979): 74.851630
> ITER: 1 | i: 3 | j: 9 Result(i: 1.732051 | j: 3.000000 | i*j:
> 5.196152): 80.047782
> ITER: 1 | i: 4 | j: 0 Result(i: 2.000000 | j: 0.000000 | i*j:
> 0.000000): 80.047782
> ITER: 1 | i: 4 | j: 1 Result(i: 2.000000 | j: 1.000000 | i*j:
> 2.000000): 82.047782
> ITER: 1 | i: 4 | j: 2 Result(i: 2.000000 | j: 1.414214 | i*j:
> 2.828427): 84.876209
> ITER: 1 | i: 4 | j: 3 Result(i: 2.000000 | j: 1.732051 | i*j:
> 3.464102): 88.340311
> ITER: 1 | i: 4 | j: 4 Result(i: 2.000000 | j: 2.000000 | i*j:
> 4.000000): 92.340311
> ITER: 1 | i: 4 | j: 5 Result(i: 2.000000 | j: 2.236068 | i*j:
> 4.472136): 96.812447
> ITER: 1 | i: 4 | j: 6 Result(i: 2.000000 | j: 2.449490 | i*j:
> 4.898979): 101.711426
> ITER: 1 | i: 4 | j: 7 Result(i: 2.000000 | j: 2.645751 | i*j:
> 5.291503): 107.002929
> ITER: 1 | i: 4 | j: 8 Result(i: 2.000000 | j: 2.828427 | i*j:
> 5.656854): 112.659783
> ITER: 1 | i: 4 | j: 9 Result(i: 2.000000 | j: 3.000000 | i*j:
> 6.000000): 118.659783
> ITER: 1 | i: 5 | j: 0 Result(i: 2.236068 | j: 0.000000 | i*j:
> 0.000000): 118.659783
> ITER: 1 | i: 5 | j: 1 Result(i: 2.236068 | j: 1.000000 | i*j:
> 2.236068): 120.895851
> ITER: 1 | i: 5 | j: 2 Result(i: 2.236068 | j: 1.414214 | i*j:
> 3.162278): 124.058129
> ITER: 1 | i: 5 | j: 3 Result(i: 2.236068 | j: 1.732051 | i*j:
> 3.872983): 127.931112
> ITER: 1 | i: 5 | j: 4 Result(i: 2.236068 | j: 2.000000 | i*j:
> 4.472136): 132.403248
> ITER: 1 | i: 5 | j: 5 Result(i: 2.236068 | j: 2.236068 | i*j:
> 5.000000): 137.403248
> ITER: 1 | i: 5 | j: 6 Result(i: 2.236068 | j: 2.449490 | i*j:
> 5.477226): 142.880474
> ITER: 1 | i: 5 | j: 7 Result(i: 2.236068 | j: 2.645751 | i*j:
> 5.916080): 148.796553
> ITER: 1 | i: 5 | j: 8 Result(i: 2.236068 | j: 2.828427 | i*j:
> 6.324555): 155.121109
> ITER: 1 | i: 5 | j: 9 Result(i: 2.236068 | j: 3.000000 | i*j:
> 6.708204): 161.829313
> ITER: 1 | i: 6 | j: 0 Result(i: 2.449490 | j: 0.000000 | i*j:
> 0.000000): 161.829313
> ITER: 1 | i: 6 | j: 1 Result(i: 2.449490 | j: 1.000000 | i*j:
> 2.449490): 164.278802
> ITER: 1 | i: 6 | j: 2 Result(i: 2.449490 | j: 1.414214 | i*j:
> 3.464102): 167.742904
> ITER: 1 | i: 6 | j: 3 Result(i: 2.449490 | j: 1.732051 | i*j:
> 4.242641): 171.985545
> ITER: 1 | i: 6 | j: 4 Result(i: 2.449490 | j: 2.000000 | i*j:
> 4.898979): 176.884524
> ITER: 1 | i: 6 | j: 5 Result(i: 2.449490 | j: 2.236068 | i*j:
> 5.477226): 182.361750
> ITER: 1 | i: 6 | j: 6 Result(i: 2.449490 | j: 2.449490 | i*j:
> 6.000000): 188.361750
> ITER: 1 | i: 6 | j: 7 Result(i: 2.449490 | j: 2.645751 | i*j:
> 6.480741): 194.842491
> ITER: 1 | i: 6 | j: 8 Result(i: 2.449490 | j: 2.828427 | i*j:
> 6.928203): 201.770694
> ITER: 1 | i: 6 | j: 9 Result(i: 2.449490 | j: 3.000000 | i*j:
> 7.348469): 209.119163
> ITER: 1 | i: 7 | j: 0 Result(i: 2.645751 | j: 0.000000 | i*j:
> 0.000000): 209.119163
> ITER: 1 | i: 7 | j: 1 Result(i: 2.645751 | j: 1.000000 | i*j:
> 2.645751): 211.764914
> ITER: 1 | i: 7 | j: 2 Result(i: 2.645751 | j: 1.414214 | i*j:
> 3.741657): 215.506572
> ITER: 1 | i: 7 | j: 3 Result(i: 2.645751 | j: 1.732051 | i*j:
> 4.582576): 220.089147
> ITER: 1 | i: 7 | j: 4 Result(i: 2.645751 | j: 2.000000 | i*j:
> 5.291503): 225.380650
> ITER: 1 | i: 7 | j: 5 Result(i: 2.645751 | j: 2.236068 | i*j:
> 5.916080): 231.296730
> ITER: 1 | i: 7 | j: 6 Result(i: 2.645751 | j: 2.449490 | i*j:
> 6.480741): 237.777470
> ITER: 1 | i: 7 | j: 7 Result(i: 2.645751 | j: 2.645751 | i*j:
> 7.000000): 244.777470
> ITER: 1 | i: 7 | j: 8 Result(i: 2.645751 | j: 2.828427 | i*j:
> 7.483315): 252.260785
> ITER: 1 | i: 7 | j: 9 Result(i: 2.645751 | j: 3.000000 | i*j:
> 7.937254): 260.198039
> ITER: 1 | i: 8 | j: 0 Result(i: 2.828427 | j: 0.000000 | i*j:
> 0.000000): 260.198039
> ITER: 1 | i: 8 | j: 1 Result(i: 2.828427 | j: 1.000000 | i*j:
> 2.828427): 263.026466
> ITER: 1 | i: 8 | j: 2 Result(i: 2.828427 | j: 1.414214 | i*j:
> 4.000000): 267.026466
> ITER: 1 | i: 8 | j: 3 Result(i: 2.828427 | j: 1.732051 | i*j:
> 4.898979): 271.925446
> ITER: 1 | i: 8 | j: 4 Result(i: 2.828427 | j: 2.000000 | i*j:
> 5.656854): 277.582300
> ITER: 1 | i: 8 | j: 5 Result(i: 2.828427 | j: 2.236068 | i*j:
> 6.324555): 283.906855
> ITER: 1 | i: 8 | j: 6 Result(i: 2.828427 | j: 2.449490 | i*j:
> 6.928203): 290.835059
> ITER: 1 | i: 8 | j: 7 Result(i: 2.828427 | j: 2.645751 | i*j:
> 7.483315): 298.318373
> ITER: 1 | i: 8 | j: 8 Result(i: 2.828427 | j: 2.828427 | i*j:
> 8.000000): 306.318373
> ITER: 1 | i: 8 | j: 9 Result(i: 2.828427 | j: 3.000000 | i*j:
> 8.485281): 314.803655
> ITER: 1 | i: 9 | j: 0 Result(i: 3.000000 | j: 0.000000 | i*j:
> 0.000000): 314.803655
> ITER: 1 | i: 9 | j: 1 Result(i: 3.000000 | j: 1.000000 | i*j:
> 3.000000): 317.803655
> ITER: 1 | i: 9 | j: 2 Result(i: 3.000000 | j: 1.414214 | i*j:
> 4.242641): 322.046295
> ITER: 1 | i: 9 | j: 3 Result(i: 3.000000 | j: 1.732051 | i*j:
> 5.196152): 327.242448
> ITER: 1 | i: 9 | j: 4 Result(i: 3.000000 | j: 2.000000 | i*j:
> 6.000000): 333.242448
> ITER: 1 | i: 9 | j: 5 Result(i: 3.000000 | j: 2.236068 | i*j:
> 6.708204): 339.950652
> ITER: 1 | i: 9 | j: 6 Result(i: 3.000000 | j: 2.449490 | i*j:
> 7.348469): 347.299121
> ITER: 1 | i: 9 | j: 7 Result(i: 3.000000 | j: 2.645751 | i*j:
> 7.937254): 355.236375
> ITER: 1 | i: 9 | j: 8 Result(i: 3.000000 | j: 2.828427 | i*j:
> 8.485281): 363.721656
> ITER: 1 | i: 9 | j: 9 Result(i: 3.000000 | j: 3.000000 | i*j:
> 9.000000): 372.721656
> Final Result: 372.721656
>
>
>
> As we can see in the following iterations the sqrt(1) as well as the
> result is set to zero for some reason.
>
> ITER: 0 | i: 1 | j: 4 Result(i: 0.000000 | j: 2.000000 | i*j:
> 0.000000): 0.000000
> ITER: 0 | i: 1 | j: 5 Result(i: 0.000000 | j: 2.236068 | i*j:
> 0.000000): 0.000000
> ITER: 0 | i: 1 | j: 6 Result(i: 0.000000 | j: 2.449490 | i*j:
> 0.000000): 0.000000
> ITER: 0 | i: 1 | j: 7 Result(i: 0.000000 | j: 2.645751 | i*j:
> 0.000000): 0.000000
> ITER: 0 | i: 1 | j: 8 Result(i: 0.000000 | j: 2.828427 | i*j:
> 0.000000): 0.000000
> ITER: 0 | i: 1 | j: 9 Result(i: 0.000000 | j: 3.000000 | i*j:
> 0.000000): 0.000000
>
> Please help me to resolve the accuracy issue! I think that it will
> be very useful for gem5 community.
>
> To be noticed, I find the correct simulated tick in which the
> application started in FS (using m5 dumpstats), and I start the
> --debug-start, but the trace file which is generated is 10x larger
> than SE mode for the same application. How can I compare them?
>
> Thank you in advance!
> Best regards,
> Nikos
>
> Quoting Νικόλαος Ταμπουρατζής <ntampouratzis@ece.auth.gr>:
>
>> Dear Jason,
>>
>> I am trying to use --debug-start but in FS mode it is very
>> difficult to find the tick on which the application is started!
>>
>> However, I am writing the following very simple c++ program:
>>
>> #include <cmath>
>> #include <stdio.h>
>>
>> int main(){
>>
>> int dim = 4096;
>>
>> double result;
>>
>> for (int iter = 0; iter < 2; iter++){
>> result = 0;
>> for (int i = 0; i < dim; i++){
>> for (int j = 0; j < dim; j++){
>> result += sqrt(i) * sqrt(j);
>> }
>> }
>> printf("Result: %lf\n", result); //Result: 30530733453.127449
>> }
>> }
>>
>> I cross-compile it using: riscv64-linux-gnu-g++ -static -O3 -o
>> test_riscv test_riscv.cpp
>>
>>
>> While in X86 (without cross-compilation of course), QEMU-RISCV,
>> GEM5-SE the result is the same (30530733453.127449), in GEM5-FS the
>> result is different! In addition, the result is also different
>> between the 2 iterations.
>>
>> Please reproduce the error if you want in order to verify my result.
>> Ηow can the issue be resolved?
>>
>> Thank you in advance!
>>
>> Best regards,
>> Nikos
>>
>>
>> Quoting Jason Lowe-Power <jason@lowepower.com>:
>>
>>> Hi Nikos,
>>>
>>> You can use --debug-start to start the debugging after some number of
>>> ticks. Also, I would expect that the difference should come up quickly, so
>>> no need to run the program to the end.
>>>
>>> For the FS mode one, you will want to just start the trace as the
>>> application starts. This could be a bit of a pain.
>>>
>>> I'm not really sure what fundamentally could be different. FS and SE mode
>>> use the exact same code for executing instructions, so I don't think that's
>>> the problem. Have you tried running for smaller inputs or just one
>>> iteration?
>>>
>>> Jason
>>>
>>>
>>>
>>> On Wed, Sep 21, 2022 at 9:04 AM Νικόλαος Ταμπουρατζής <
>>> ntampouratzis@ece.auth.gr> wrote:
>>>
>>>> Dear Bobby,
>>>>
>>>> Iam trying to add --debug-flags=Exec (building the gem5 for gem5.opt
>>>> not for gem5.fast which I had) but the debug traces exceed the 20GB
>>>> (and it is not finished yet) for less than 1 simulated second. How can
>>>> I reduce the size of the debug-flags (or set something more specific)?
>>>>
>>>> In contrast I build the HPCG benchmark with DHPCG_DEBUG flag. If you
>>>> want, you can compare these two output files
>>>> (hpcg20010909T014640_SE_Mode & HPCG-Benchmark_3.1_FS_Mode). As you can
>>>> see, something goes wrong with the accuracy of calculations in FS mode
>>>> (benchmark uses double precission). You can find the files here:
>>>> http://kition.mhl.tuc.gr:8000/d/68d82f3533/
>>>>
>>>> Best regards,
>>>> Nikos
>>>>
>>>> Quoting Jason Lowe-Power <jason@lowepower.com>:
>>>>
>>>>> That's quite odd that it works in SE mode but not FS mode!
>>>>>
>>>>> I would suggest running with --debug-flags=Exec for both and then
>>>> perform a
>>>>> diff to see how they differ.
>>>>>
>>>>> Cheers,
>>>>> Jason
>>>>>
>>>>> On Tue, Sep 20, 2022 at 2:45 PM Νικόλαος Ταμπουρατζής <
>>>>> ntampouratzis@ece.auth.gr> wrote:
>>>>>
>>>>>> Dear Bobby,
>>>>>>
>>>>>> In QEMU I get the same (correct) results that I get in SE mode
>>>>>> simulation. I get invalid results in FS simulation (in both
>>>>>> riscv-fs.py and riscv-ubuntu-run.py). I cannot access real RISCV
>>>>>> hardware at this moment, however, if you want you may execute my xhpcg
>>>>>> binary (http://kition.mhl.tuc.gr:8000/f/4ca25fdd3c/) with the
>>>>>> following configuration:
>>>>>>
>>>>>> ./xhpcg --nx=16 --ny=16 --nz=16 --npx=1 --npy=1 --npz=1 --rt=0.1
>>>>>>
>>>>>> Please let me know if you have any updates!
>>>>>>
>>>>>> Best regards,
>>>>>> Nikos
>>>>>>
>>>>>>
>>>>>> Quoting Jason Lowe-Power <jason@lowepower.com>:
>>>>>>
>>>>>>> Hi Nikos,
>>>>>>>
>>>>>>> I notice you said the following in your original email:
>>>>>>>
>>>>>>> In addition, I used the RISCV Ubuntu image
>>>>>>>> (https://github.com/gem5/gem5-resources/tree/stable/src/riscv-ubuntu
>>>> ),
>>>>>>>> I installed the gcc compiler, compile it (through qemu) and I get
>>>>>>>> wrong results too.
>>>>>>>
>>>>>>>
>>>>>>> Is this saying you get the wrong results is QEMU? If so, the bug is in
>>>>>> GCC
>>>>>>> or the HPCG workload, not in gem5. If not, I would test in QEMU to
>>>> make
>>>>>>> sure the binary works there. Another way you could test to see if the
>>>>>>> problem is your binary or gem5 would be to run it on real hardware. We
>>>>>> have
>>>>>>> access to some RISC-V hardware here at UC Davis, if you don't have
>>>> access
>>>>>>> to it.
>>>>>>>
>>>>>>> Cheers,
>>>>>>> Jason
>>>>>>>
>>>>>>> On Tue, Sep 20, 2022 at 12:58 AM Νικόλαος Ταμπουρατζής <
>>>>>>> ntampouratzis@ece.auth.gr> wrote:
>>>>>>>
>>>>>>>> Dear Bobby,
>>>>>>>>
>>>>>>>> 1) I use the original riscv-fs.py which is provided in the latest
>>>> gem5
>>>>>>>> release.
>>>>>>>> I run the gem5 once (./build/RISCV/gem5.fast -d ./HPCG_FS_results
>>>>>>>> ./configs/example/gem5_library/riscv-fs.py) in order to download the
>>>>>>>> riscv-bootloader-vmlinux-5.10 and riscv-disk-img.
>>>>>>>> After this I mount the riscv-disk-img (sudo mount -o loop
>>>>>>>> riscv-disk-img /mnt), put the xhpcg executable and I do the following
>>>>>>>> changes in riscv-fs.py to boot the riscv-disk-img with executable:
>>>>>>>>
>>>>>>>> image = CustomDiskImageResource(
>>>>>>>> local_path = "/home/cossim/.cache/gem5/riscv-disk-img",
>>>>>>>> )
>>>>>>>>
>>>>>>>> # Set the Full System workload.
>>>>>>>> board.set_kernel_disk_workload(
>>>>>>>> kernel=Resource("riscv-bootloader-vmlinux-5.10"),
>>>>>>>> disk_image=image,
>>>>>>>> )
>>>>>>>>
>>>>>>>> Finally, in the gem5/src/python/gem5/components/boards/riscv_board.py
>>>>>>>> I change the last line to "return ["console=ttyS0",
>>>>>>>> "root={root_value}", "rw"]" in order to allow the write permissions
>>>> in
>>>>>>>> the image.
>>>>>>>>
>>>>>>>>
>>>>>>>> 2) The HPCG benchmark after some iterations calculates if the results
>>>>>>>> are valid or not valid. In the case of FS it gives invalid results.
>>>> As
>>>>>>>> I see from the results, one (at least) problem is that produces
>>>>>>>> different results in each HPCG execution (with the same
>>>> configuration).
>>>>>>>>
>>>>>>>> Here is the HPCG output and riscv-fs.py
>>>>>>>> (http://kition.mhl.tuc.gr:8000/d/68d82f3533/). You may reproduce the
>>>>>>>> results in the video if you use the xhpcg executable
>>>>>>>> (http://kition.mhl.tuc.gr:8000/f/4ca25fdd3c/)
>>>>>>>>
>>>>>>>> Please help me in order to solve it!
>>>>>>>>
>>>>>>>> Finally, I get invalid results in the HPL benchmark in FS mode too.
>>>>>>>>
>>>>>>>> Best regards,
>>>>>>>> Nikos
>>>>>>>>
>>>>>>>>
>>>>>>>> Quoting Bobby Bruce <bbruce@ucdavis.edu>:
>>>>>>>>
>>>>>>>> > I'm going to need a bit more information to help:
>>>>>>>> >
>>>>>>>> > 1. In what way have you modified
>>>>>>>> > ./configs/example/gem5_library/riscv-fs.py? Can you attach the
>>>> script
>>>>>>>> here?
>>>>>>>> > 2. What error are you getting or in what way are the results
>>>> invalid?
>>>>>>>> >
>>>>>>>> > -
>>>>>>>> > Dr. Bobby R. Bruce
>>>>>>>> > Room 3050,
>>>>>>>> > Kemper Hall, UC Davis
>>>>>>>> > Davis,
>>>>>>>> > CA, 95616
>>>>>>>> >
>>>>>>>> > web: https://www.bobbybruce.net
>>>>>>>> >
>>>>>>>> >
>>>>>>>> > On Mon, Sep 19, 2022 at 1:43 PM Νικόλαος Ταμπουρατζής <
>>>>>>>> > ntampouratzis@ece.auth.gr> wrote:
>>>>>>>> >
>>>>>>>> >>
>>>>>>>> >> Dear gem5 community,
>>>>>>>> >>
>>>>>>>> >> I have successfully cross-compile the HPCG benchmark for RISCV
>>>>>> (Serial
>>>>>>>> >> version, without MPI and OpenMP). While it working properly in
>>>> gem5
>>>>>> SE
>>>>>>>> >> mode (./build/RISCV/gem5.fast -d ./HPCG_SE_results
>>>>>>>> >> ./configs/example/se.py -c xhpcg --options '--nx=16 --ny=16
>>>> --nz=16
>>>>>>>> >> --npx=1 --npy=1 --npz=1 --rt=0.1'), I get invalid results in FS
>>>>>>>> >> simulation using "./build/RISCV/gem5.fast -d ./HPCG_FS_results
>>>>>>>> >> ./configs/example/gem5_library/riscv-fs.py" (I mount the riscv
>>>> image
>>>>>>>> >> and put it).
>>>>>>>> >>
>>>>>>>> >> Can you help me please?
>>>>>>>> >>
>>>>>>>> >> In addition, I used the RISCV Ubuntu image
>>>>>>>> >> (
>>>> https://github.com/gem5/gem5-resources/tree/stable/src/riscv-ubuntu
>>>>>> ),
>>>>>>>> >> I installed the gcc compiler, compile it (through qemu) and I get
>>>>>>>> >> wrong results too.
>>>>>>>> >>
>>>>>>>> >> Here is the Makefile which I use, the hpcg executable for RISCV
>>>>>>>> >> (xhpcg), and a video that shows the results
>>>>>>>> >> (http://kition.mhl.tuc.gr:8000/f/4ca25fdd3c/).
>>>>>>>> >>
>>>>>>>> >> P.S. I use the latest gem5 version.
>>>>>>>> >>
>>>>>>>> >> Thank you in advance! :)
>>>>>>>> >>
>>>>>>>> >> Best regards,
>>>>>>>> >> Nikos
>>>>>>>> >> _______________________________________________
>>>>>>>> >> gem5-users mailing list -- gem5-users@gem5.org
>>>>>>>> >> To unsubscribe send an email to gem5-users-leave@gem5.org
>>>>>>>> >>
>>>>>>>>
>>>>>>>>
>>>>>>>> _______________________________________________
>>>>>>>> gem5-users mailing list -- gem5-users@gem5.org
>>>>>>>> To unsubscribe send an email to gem5-users-leave@gem5.org
>>>>>>>>
>>>>>>
>>>>>>
>>>>>> _______________________________________________
>>>>>> gem5-users mailing list -- gem5-users@gem5.org
>>>>>> To unsubscribe send an email to gem5-users-leave@gem5.org
>>>>>>
>>>>
>>>>
>>>> _______________________________________________
>>>> gem5-users mailing list -- gem5-users@gem5.org
>>>> To unsubscribe send an email to gem5-users-leave@gem5.org
>>>>
>>
>>
>> _______________________________________________
>> gem5-users mailing list -- gem5-users@gem5.org
>> To unsubscribe send an email to gem5-users-leave@gem5.org
>
>
> _______________________________________________
> gem5-users mailing list -- gem5-users@gem5.org
> To unsubscribe send an email to gem5-users-leave@gem5.org
BB
Bobby Bruce
Fri, Oct 7, 2022 1:04 AM
Hey Niko,
Thanks for this analysis. I jumped a little into this today but didn't get
as far as you did. I wanted to find a quick way to recreate the following:
https://gem5-review.googlesource.com/c/public/gem5/+/64211. Please feel
free to use this, if it helps any.
It's very strange to me that this bug hasn't manifested itself before but
it's undeniably there. I'll try to spend more time looking at this tomorrow
with some traces and debug flags and see if I can narrow down the problem.
--
Dr. Bobby R. Bruce
Room 3050,
Kemper Hall, UC Davis
Davis,
CA, 95616
web: https://www.bobbybruce.net
On Wed, Oct 5, 2022 at 2:26 PM Νικόλαος Ταμπουρατζής <
ntampouratzis@ece.auth.gr> wrote:
In my previous results, I had used double (not float) for the
following variables: result, sq_i and sq_j. In the case of float
instead of double I get "nan" and not 0.000000.
Quoting Νικόλαος Ταμπουρατζής ntampouratzis@ece.auth.gr:
Dear Jason, all,
I am trying to find the accuracy problem with RISCV-FS and I observe
that the problem is created (at least in my dummy example) because
the variables (double) are set to zero in random simulated time (for
this reason I get different results among executions of the same
code). Specifically for the following dummy code:
#include <cmath>
#include <stdio.h>
int main(){
int dim = 10;
float result;
for (int iter = 0; iter < 2; iter++){
result = 0;
for (int i = 0; i < dim; i++){
for (int j = 0; j < dim; j++){
float sq_i = sqrt(i);
float sq_j = sqrt(j);
result += sq_i * sq_j;
printf("ITER: %d | i: %d | j: %d Result(i: %f | j:
%f | i*j: %f): %f\n", iter, i , j, sq_i, sq_j, sq_i * sq_j, result);
}
}
printf("Final Result: %lf\n", result);
}
}
The correct Final Result in both iterations is 372.721656. However,
I get the following results in FS:
ITER: 0 | i: 0 | j: 0 Result(i: 0.000000 | j: 0.000000 | ij:
0.000000): 0.000000
ITER: 0 | i: 0 | j: 1 Result(i: 0.000000 | j: 1.000000 | ij:
0.000000): 0.000000
ITER: 0 | i: 0 | j: 2 Result(i: 0.000000 | j: 1.414214 | ij:
0.000000): 0.000000
ITER: 0 | i: 0 | j: 3 Result(i: 0.000000 | j: 1.732051 | ij:
0.000000): 0.000000
ITER: 0 | i: 0 | j: 4 Result(i: 0.000000 | j: 2.000000 | ij:
0.000000): 0.000000
ITER: 0 | i: 0 | j: 5 Result(i: 0.000000 | j: 2.236068 | ij:
0.000000): 0.000000
ITER: 0 | i: 0 | j: 6 Result(i: 0.000000 | j: 2.449490 | ij:
0.000000): 0.000000
ITER: 0 | i: 0 | j: 7 Result(i: 0.000000 | j: 2.645751 | ij:
0.000000): 0.000000
ITER: 0 | i: 0 | j: 8 Result(i: 0.000000 | j: 2.828427 | ij:
0.000000): 0.000000
ITER: 0 | i: 0 | j: 9 Result(i: 0.000000 | j: 3.000000 | ij:
0.000000): 0.000000
ITER: 0 | i: 1 | j: 0 Result(i: 1.000000 | j: 0.000000 | ij:
0.000000): 0.000000
ITER: 0 | i: 1 | j: 1 Result(i: 1.000000 | j: 1.000000 | ij:
1.000000): 1.000000
ITER: 0 | i: 1 | j: 2 Result(i: 1.000000 | j: 1.414214 | ij:
1.414214): 2.414214
ITER: 0 | i: 1 | j: 3 Result(i: 1.000000 | j: 1.732051 | ij:
1.732051): 4.146264
ITER: 0 | i: 1 | j: 4 Result(i: 0.000000 | j: 2.000000 | ij:
0.000000): 0.000000
ITER: 0 | i: 1 | j: 5 Result(i: 0.000000 | j: 2.236068 | ij:
0.000000): 0.000000
ITER: 0 | i: 1 | j: 6 Result(i: 0.000000 | j: 2.449490 | ij:
0.000000): 0.000000
ITER: 0 | i: 1 | j: 7 Result(i: 0.000000 | j: 2.645751 | ij:
0.000000): 0.000000
ITER: 0 | i: 1 | j: 8 Result(i: 0.000000 | j: 2.828427 | ij:
0.000000): 0.000000
ITER: 0 | i: 1 | j: 9 Result(i: 0.000000 | j: 3.000000 | ij:
0.000000): 0.000000
ITER: 0 | i: 2 | j: 0 Result(i: 1.414214 | j: 0.000000 | ij:
0.000000): 0.000000
ITER: 0 | i: 2 | j: 1 Result(i: 1.414214 | j: 1.000000 | ij:
1.414214): 1.414214
ITER: 0 | i: 2 | j: 2 Result(i: 1.414214 | j: 1.414214 | ij:
2.000000): 3.414214
ITER: 0 | i: 2 | j: 3 Result(i: 1.414214 | j: 1.732051 | ij:
2.449490): 5.863703
ITER: 0 | i: 2 | j: 4 Result(i: 1.414214 | j: 2.000000 | ij:
2.828427): 8.692130
ITER: 0 | i: 2 | j: 5 Result(i: 1.414214 | j: 2.236068 | ij:
3.162278): 11.854408
ITER: 0 | i: 2 | j: 6 Result(i: 1.414214 | j: 2.449490 | ij:
3.464102): 15.318510
ITER: 0 | i: 2 | j: 7 Result(i: 1.414214 | j: 2.645751 | ij:
3.741657): 19.060167
ITER: 0 | i: 2 | j: 8 Result(i: 1.414214 | j: 2.828427 | ij:
4.000000): 23.060167
ITER: 0 | i: 2 | j: 9 Result(i: 1.414214 | j: 3.000000 | ij:
4.242641): 27.302808
ITER: 0 | i: 3 | j: 0 Result(i: 1.732051 | j: 0.000000 | ij:
0.000000): 27.302808
ITER: 0 | i: 3 | j: 1 Result(i: 1.732051 | j: 1.000000 | ij:
1.732051): 29.034859
ITER: 0 | i: 3 | j: 2 Result(i: 1.732051 | j: 1.414214 | ij:
2.449490): 31.484348
ITER: 0 | i: 3 | j: 3 Result(i: 1.732051 | j: 1.732051 | ij:
3.000000): 34.484348
ITER: 0 | i: 3 | j: 4 Result(i: 1.732051 | j: 2.000000 | ij:
3.464102): 37.948450
ITER: 0 | i: 3 | j: 5 Result(i: 1.732051 | j: 2.236068 | ij:
3.872983): 41.821433
ITER: 0 | i: 3 | j: 6 Result(i: 1.732051 | j: 2.449490 | ij:
4.242641): 46.064074
ITER: 0 | i: 3 | j: 7 Result(i: 1.732051 | j: 2.645751 | ij:
4.582576): 50.646650
ITER: 0 | i: 3 | j: 8 Result(i: 1.732051 | j: 2.828427 | ij:
4.898979): 55.545629
ITER: 0 | i: 3 | j: 9 Result(i: 1.732051 | j: 3.000000 | ij:
5.196152): 60.741782
ITER: 0 | i: 4 | j: 0 Result(i: 2.000000 | j: 0.000000 | ij:
0.000000): 60.741782
ITER: 0 | i: 4 | j: 1 Result(i: 2.000000 | j: 1.000000 | ij:
2.000000): 62.741782
ITER: 0 | i: 4 | j: 2 Result(i: 2.000000 | j: 1.414214 | ij:
2.828427): 65.570209
ITER: 0 | i: 4 | j: 3 Result(i: 2.000000 | j: 1.732051 | ij:
3.464102): 69.034310
ITER: 0 | i: 4 | j: 4 Result(i: 2.000000 | j: 2.000000 | ij:
4.000000): 73.034310
ITER: 0 | i: 4 | j: 5 Result(i: 2.000000 | j: 2.236068 | ij:
4.472136): 77.506446
ITER: 0 | i: 4 | j: 6 Result(i: 2.000000 | j: 2.449490 | ij:
4.898979): 82.405426
ITER: 0 | i: 4 | j: 7 Result(i: 2.000000 | j: 2.645751 | ij:
5.291503): 87.696928
ITER: 0 | i: 4 | j: 8 Result(i: 2.000000 | j: 2.828427 | ij:
5.656854): 93.353783
ITER: 0 | i: 4 | j: 9 Result(i: 2.000000 | j: 3.000000 | ij:
6.000000): 99.353783
ITER: 0 | i: 5 | j: 0 Result(i: 2.236068 | j: 0.000000 | ij:
0.000000): 99.353783
ITER: 0 | i: 5 | j: 1 Result(i: 2.236068 | j: 1.000000 | ij:
2.236068): 101.589851
ITER: 0 | i: 5 | j: 2 Result(i: 2.236068 | j: 1.414214 | ij:
3.162278): 104.752128
ITER: 0 | i: 5 | j: 3 Result(i: 2.236068 | j: 1.732051 | ij:
3.872983): 108.625112
ITER: 0 | i: 5 | j: 4 Result(i: 2.236068 | j: 2.000000 | ij:
4.472136): 113.097248
ITER: 0 | i: 5 | j: 5 Result(i: 2.236068 | j: 2.236068 | ij:
5.000000): 118.097248
ITER: 0 | i: 5 | j: 6 Result(i: 2.236068 | j: 2.449490 | ij:
5.477226): 123.574473
ITER: 0 | i: 5 | j: 7 Result(i: 2.236068 | j: 2.645751 | ij:
5.916080): 129.490553
ITER: 0 | i: 5 | j: 8 Result(i: 2.236068 | j: 2.828427 | ij:
6.324555): 135.815108
ITER: 0 | i: 5 | j: 9 Result(i: 2.236068 | j: 3.000000 | ij:
6.708204): 142.523312
ITER: 0 | i: 6 | j: 0 Result(i: 2.449490 | j: 0.000000 | ij:
0.000000): 142.523312
ITER: 0 | i: 6 | j: 1 Result(i: 2.449490 | j: 1.000000 | ij:
2.449490): 144.972802
ITER: 0 | i: 6 | j: 2 Result(i: 2.449490 | j: 1.414214 | ij:
3.464102): 148.436904
ITER: 0 | i: 6 | j: 3 Result(i: 2.449490 | j: 1.732051 | ij:
4.242641): 152.679544
ITER: 0 | i: 6 | j: 4 Result(i: 2.449490 | j: 2.000000 | ij:
4.898979): 157.578524
ITER: 0 | i: 6 | j: 5 Result(i: 2.449490 | j: 2.236068 | ij:
5.477226): 163.055749
ITER: 0 | i: 6 | j: 6 Result(i: 2.449490 | j: 2.449490 | ij:
6.000000): 169.055749
ITER: 0 | i: 6 | j: 7 Result(i: 2.449490 | j: 2.645751 | ij:
6.480741): 175.536490
ITER: 0 | i: 6 | j: 8 Result(i: 2.449490 | j: 2.828427 | ij:
6.928203): 182.464693
ITER: 0 | i: 6 | j: 9 Result(i: 2.449490 | j: 3.000000 | ij:
7.348469): 189.813162
ITER: 0 | i: 7 | j: 0 Result(i: 2.645751 | j: 0.000000 | ij:
0.000000): 189.813162
ITER: 0 | i: 7 | j: 1 Result(i: 2.645751 | j: 1.000000 | ij:
2.645751): 192.458914
ITER: 0 | i: 7 | j: 2 Result(i: 2.645751 | j: 1.414214 | ij:
3.741657): 196.200571
ITER: 0 | i: 7 | j: 3 Result(i: 2.645751 | j: 1.732051 | ij:
4.582576): 200.783147
ITER: 0 | i: 7 | j: 4 Result(i: 2.645751 | j: 2.000000 | ij:
5.291503): 206.074649
ITER: 0 | i: 7 | j: 5 Result(i: 2.645751 | j: 2.236068 | ij:
5.916080): 211.990729
ITER: 0 | i: 7 | j: 6 Result(i: 2.645751 | j: 2.449490 | ij:
6.480741): 218.471470
ITER: 0 | i: 7 | j: 7 Result(i: 2.645751 | j: 2.645751 | ij:
7.000000): 225.471470
ITER: 0 | i: 7 | j: 8 Result(i: 2.645751 | j: 2.828427 | ij:
7.483315): 232.954785
ITER: 0 | i: 7 | j: 9 Result(i: 2.645751 | j: 3.000000 | ij:
7.937254): 240.892039
ITER: 0 | i: 8 | j: 0 Result(i: 2.828427 | j: 0.000000 | ij:
0.000000): 240.892039
ITER: 0 | i: 8 | j: 1 Result(i: 2.828427 | j: 1.000000 | ij:
2.828427): 243.720466
ITER: 0 | i: 8 | j: 2 Result(i: 2.828427 | j: 1.414214 | ij:
4.000000): 247.720466
ITER: 0 | i: 8 | j: 3 Result(i: 2.828427 | j: 1.732051 | ij:
4.898979): 252.619445
ITER: 0 | i: 8 | j: 4 Result(i: 2.828427 | j: 2.000000 | ij:
5.656854): 258.276300
ITER: 0 | i: 8 | j: 5 Result(i: 2.828427 | j: 2.236068 | ij:
6.324555): 264.600855
ITER: 0 | i: 8 | j: 6 Result(i: 2.828427 | j: 2.449490 | ij:
6.928203): 271.529058
ITER: 0 | i: 8 | j: 7 Result(i: 2.828427 | j: 2.645751 | ij:
7.483315): 279.012373
ITER: 0 | i: 8 | j: 8 Result(i: 2.828427 | j: 2.828427 | ij:
8.000000): 287.012373
ITER: 0 | i: 8 | j: 9 Result(i: 2.828427 | j: 3.000000 | ij:
8.485281): 295.497654
ITER: 0 | i: 9 | j: 0 Result(i: 3.000000 | j: 0.000000 | ij:
0.000000): 295.497654
ITER: 0 | i: 9 | j: 1 Result(i: 3.000000 | j: 1.000000 | ij:
3.000000): 298.497654
ITER: 0 | i: 9 | j: 2 Result(i: 3.000000 | j: 1.414214 | ij:
4.242641): 302.740295
ITER: 0 | i: 9 | j: 3 Result(i: 3.000000 | j: 1.732051 | ij:
5.196152): 307.936447
ITER: 0 | i: 9 | j: 4 Result(i: 3.000000 | j: 2.000000 | ij:
6.000000): 313.936447
ITER: 0 | i: 9 | j: 5 Result(i: 3.000000 | j: 2.236068 | ij:
6.708204): 320.644651
ITER: 0 | i: 9 | j: 6 Result(i: 3.000000 | j: 2.449490 | ij:
7.348469): 327.993120
ITER: 0 | i: 9 | j: 7 Result(i: 3.000000 | j: 2.645751 | ij:
7.937254): 335.930374
ITER: 0 | i: 9 | j: 8 Result(i: 3.000000 | j: 2.828427 | ij:
8.485281): 344.415656
ITER: 0 | i: 9 | j: 9 Result(i: 3.000000 | j: 3.000000 | ij:
9.000000): 353.415656
Final Result: 353.415656
ITER: 1 | i: 0 | j: 0 Result(i: 0.000000 | j: 0.000000 | ij:
0.000000): 0.000000
ITER: 1 | i: 0 | j: 1 Result(i: 0.000000 | j: 1.000000 | ij:
0.000000): 0.000000
ITER: 1 | i: 0 | j: 2 Result(i: 0.000000 | j: 1.414214 | ij:
0.000000): 0.000000
ITER: 1 | i: 0 | j: 3 Result(i: 0.000000 | j: 1.732051 | ij:
0.000000): 0.000000
ITER: 1 | i: 0 | j: 4 Result(i: 0.000000 | j: 2.000000 | ij:
0.000000): 0.000000
ITER: 1 | i: 0 | j: 5 Result(i: 0.000000 | j: 2.236068 | ij:
0.000000): 0.000000
ITER: 1 | i: 0 | j: 6 Result(i: 0.000000 | j: 2.449490 | ij:
0.000000): 0.000000
ITER: 1 | i: 0 | j: 7 Result(i: 0.000000 | j: 2.645751 | ij:
0.000000): 0.000000
ITER: 1 | i: 0 | j: 8 Result(i: 0.000000 | j: 2.828427 | ij:
0.000000): 0.000000
ITER: 1 | i: 0 | j: 9 Result(i: 0.000000 | j: 3.000000 | ij:
0.000000): 0.000000
ITER: 1 | i: 1 | j: 0 Result(i: 1.000000 | j: 0.000000 | ij:
0.000000): 0.000000
ITER: 1 | i: 1 | j: 1 Result(i: 1.000000 | j: 1.000000 | ij:
1.000000): 1.000000
ITER: 1 | i: 1 | j: 2 Result(i: 1.000000 | j: 1.414214 | ij:
1.414214): 2.414214
ITER: 1 | i: 1 | j: 3 Result(i: 1.000000 | j: 1.732051 | ij:
1.732051): 4.146264
ITER: 1 | i: 1 | j: 4 Result(i: 1.000000 | j: 2.000000 | ij:
2.000000): 6.146264
ITER: 1 | i: 1 | j: 5 Result(i: 1.000000 | j: 2.236068 | ij:
2.236068): 8.382332
ITER: 1 | i: 1 | j: 6 Result(i: 1.000000 | j: 2.449490 | ij:
2.449490): 10.831822
ITER: 1 | i: 1 | j: 7 Result(i: 1.000000 | j: 2.645751 | ij:
2.645751): 13.477573
ITER: 1 | i: 1 | j: 8 Result(i: 1.000000 | j: 2.828427 | ij:
2.828427): 16.306001
ITER: 1 | i: 1 | j: 9 Result(i: 1.000000 | j: 3.000000 | ij:
3.000000): 19.306001
ITER: 1 | i: 2 | j: 0 Result(i: 1.414214 | j: 0.000000 | ij:
0.000000): 19.306001
ITER: 1 | i: 2 | j: 1 Result(i: 1.414214 | j: 1.000000 | ij:
1.414214): 20.720214
ITER: 1 | i: 2 | j: 2 Result(i: 1.414214 | j: 1.414214 | ij:
2.000000): 22.720214
ITER: 1 | i: 2 | j: 3 Result(i: 1.414214 | j: 1.732051 | ij:
2.449490): 25.169704
ITER: 1 | i: 2 | j: 4 Result(i: 1.414214 | j: 2.000000 | ij:
2.828427): 27.998131
ITER: 1 | i: 2 | j: 5 Result(i: 1.414214 | j: 2.236068 | ij:
3.162278): 31.160409
ITER: 1 | i: 2 | j: 6 Result(i: 1.414214 | j: 2.449490 | ij:
3.464102): 34.624510
ITER: 1 | i: 2 | j: 7 Result(i: 1.414214 | j: 2.645751 | ij:
3.741657): 38.366168
ITER: 1 | i: 2 | j: 8 Result(i: 1.414214 | j: 2.828427 | ij:
4.000000): 42.366168
ITER: 1 | i: 2 | j: 9 Result(i: 1.414214 | j: 3.000000 | ij:
4.242641): 46.608808
ITER: 1 | i: 3 | j: 0 Result(i: 1.732051 | j: 0.000000 | ij:
0.000000): 46.608808
ITER: 1 | i: 3 | j: 1 Result(i: 1.732051 | j: 1.000000 | ij:
1.732051): 48.340859
ITER: 1 | i: 3 | j: 2 Result(i: 1.732051 | j: 1.414214 | ij:
2.449490): 50.790349
ITER: 1 | i: 3 | j: 3 Result(i: 1.732051 | j: 1.732051 | ij:
3.000000): 53.790349
ITER: 1 | i: 3 | j: 4 Result(i: 1.732051 | j: 2.000000 | ij:
3.464102): 57.254450
ITER: 1 | i: 3 | j: 5 Result(i: 1.732051 | j: 2.236068 | ij:
3.872983): 61.127434
ITER: 1 | i: 3 | j: 6 Result(i: 1.732051 | j: 2.449490 | ij:
4.242641): 65.370075
ITER: 1 | i: 3 | j: 7 Result(i: 1.732051 | j: 2.645751 | ij:
4.582576): 69.952650
ITER: 1 | i: 3 | j: 8 Result(i: 1.732051 | j: 2.828427 | ij:
4.898979): 74.851630
ITER: 1 | i: 3 | j: 9 Result(i: 1.732051 | j: 3.000000 | ij:
5.196152): 80.047782
ITER: 1 | i: 4 | j: 0 Result(i: 2.000000 | j: 0.000000 | ij:
0.000000): 80.047782
ITER: 1 | i: 4 | j: 1 Result(i: 2.000000 | j: 1.000000 | ij:
2.000000): 82.047782
ITER: 1 | i: 4 | j: 2 Result(i: 2.000000 | j: 1.414214 | ij:
2.828427): 84.876209
ITER: 1 | i: 4 | j: 3 Result(i: 2.000000 | j: 1.732051 | ij:
3.464102): 88.340311
ITER: 1 | i: 4 | j: 4 Result(i: 2.000000 | j: 2.000000 | ij:
4.000000): 92.340311
ITER: 1 | i: 4 | j: 5 Result(i: 2.000000 | j: 2.236068 | ij:
4.472136): 96.812447
ITER: 1 | i: 4 | j: 6 Result(i: 2.000000 | j: 2.449490 | ij:
4.898979): 101.711426
ITER: 1 | i: 4 | j: 7 Result(i: 2.000000 | j: 2.645751 | ij:
5.291503): 107.002929
ITER: 1 | i: 4 | j: 8 Result(i: 2.000000 | j: 2.828427 | ij:
5.656854): 112.659783
ITER: 1 | i: 4 | j: 9 Result(i: 2.000000 | j: 3.000000 | ij:
6.000000): 118.659783
ITER: 1 | i: 5 | j: 0 Result(i: 2.236068 | j: 0.000000 | ij:
0.000000): 118.659783
ITER: 1 | i: 5 | j: 1 Result(i: 2.236068 | j: 1.000000 | ij:
2.236068): 120.895851
ITER: 1 | i: 5 | j: 2 Result(i: 2.236068 | j: 1.414214 | ij:
3.162278): 124.058129
ITER: 1 | i: 5 | j: 3 Result(i: 2.236068 | j: 1.732051 | ij:
3.872983): 127.931112
ITER: 1 | i: 5 | j: 4 Result(i: 2.236068 | j: 2.000000 | ij:
4.472136): 132.403248
ITER: 1 | i: 5 | j: 5 Result(i: 2.236068 | j: 2.236068 | ij:
5.000000): 137.403248
ITER: 1 | i: 5 | j: 6 Result(i: 2.236068 | j: 2.449490 | ij:
5.477226): 142.880474
ITER: 1 | i: 5 | j: 7 Result(i: 2.236068 | j: 2.645751 | ij:
5.916080): 148.796553
ITER: 1 | i: 5 | j: 8 Result(i: 2.236068 | j: 2.828427 | ij:
6.324555): 155.121109
ITER: 1 | i: 5 | j: 9 Result(i: 2.236068 | j: 3.000000 | ij:
6.708204): 161.829313
ITER: 1 | i: 6 | j: 0 Result(i: 2.449490 | j: 0.000000 | ij:
0.000000): 161.829313
ITER: 1 | i: 6 | j: 1 Result(i: 2.449490 | j: 1.000000 | ij:
2.449490): 164.278802
ITER: 1 | i: 6 | j: 2 Result(i: 2.449490 | j: 1.414214 | ij:
3.464102): 167.742904
ITER: 1 | i: 6 | j: 3 Result(i: 2.449490 | j: 1.732051 | ij:
4.242641): 171.985545
ITER: 1 | i: 6 | j: 4 Result(i: 2.449490 | j: 2.000000 | ij:
4.898979): 176.884524
ITER: 1 | i: 6 | j: 5 Result(i: 2.449490 | j: 2.236068 | ij:
5.477226): 182.361750
ITER: 1 | i: 6 | j: 6 Result(i: 2.449490 | j: 2.449490 | ij:
6.000000): 188.361750
ITER: 1 | i: 6 | j: 7 Result(i: 2.449490 | j: 2.645751 | ij:
6.480741): 194.842491
ITER: 1 | i: 6 | j: 8 Result(i: 2.449490 | j: 2.828427 | ij:
6.928203): 201.770694
ITER: 1 | i: 6 | j: 9 Result(i: 2.449490 | j: 3.000000 | ij:
7.348469): 209.119163
ITER: 1 | i: 7 | j: 0 Result(i: 2.645751 | j: 0.000000 | ij:
0.000000): 209.119163
ITER: 1 | i: 7 | j: 1 Result(i: 2.645751 | j: 1.000000 | ij:
2.645751): 211.764914
ITER: 1 | i: 7 | j: 2 Result(i: 2.645751 | j: 1.414214 | ij:
3.741657): 215.506572
ITER: 1 | i: 7 | j: 3 Result(i: 2.645751 | j: 1.732051 | ij:
4.582576): 220.089147
ITER: 1 | i: 7 | j: 4 Result(i: 2.645751 | j: 2.000000 | ij:
5.291503): 225.380650
ITER: 1 | i: 7 | j: 5 Result(i: 2.645751 | j: 2.236068 | ij:
5.916080): 231.296730
ITER: 1 | i: 7 | j: 6 Result(i: 2.645751 | j: 2.449490 | ij:
6.480741): 237.777470
ITER: 1 | i: 7 | j: 7 Result(i: 2.645751 | j: 2.645751 | ij:
7.000000): 244.777470
ITER: 1 | i: 7 | j: 8 Result(i: 2.645751 | j: 2.828427 | ij:
7.483315): 252.260785
ITER: 1 | i: 7 | j: 9 Result(i: 2.645751 | j: 3.000000 | ij:
7.937254): 260.198039
ITER: 1 | i: 8 | j: 0 Result(i: 2.828427 | j: 0.000000 | ij:
0.000000): 260.198039
ITER: 1 | i: 8 | j: 1 Result(i: 2.828427 | j: 1.000000 | ij:
2.828427): 263.026466
ITER: 1 | i: 8 | j: 2 Result(i: 2.828427 | j: 1.414214 | ij:
4.000000): 267.026466
ITER: 1 | i: 8 | j: 3 Result(i: 2.828427 | j: 1.732051 | ij:
4.898979): 271.925446
ITER: 1 | i: 8 | j: 4 Result(i: 2.828427 | j: 2.000000 | ij:
5.656854): 277.582300
ITER: 1 | i: 8 | j: 5 Result(i: 2.828427 | j: 2.236068 | ij:
6.324555): 283.906855
ITER: 1 | i: 8 | j: 6 Result(i: 2.828427 | j: 2.449490 | ij:
6.928203): 290.835059
ITER: 1 | i: 8 | j: 7 Result(i: 2.828427 | j: 2.645751 | ij:
7.483315): 298.318373
ITER: 1 | i: 8 | j: 8 Result(i: 2.828427 | j: 2.828427 | ij:
8.000000): 306.318373
ITER: 1 | i: 8 | j: 9 Result(i: 2.828427 | j: 3.000000 | ij:
8.485281): 314.803655
ITER: 1 | i: 9 | j: 0 Result(i: 3.000000 | j: 0.000000 | ij:
0.000000): 314.803655
ITER: 1 | i: 9 | j: 1 Result(i: 3.000000 | j: 1.000000 | ij:
3.000000): 317.803655
ITER: 1 | i: 9 | j: 2 Result(i: 3.000000 | j: 1.414214 | ij:
4.242641): 322.046295
ITER: 1 | i: 9 | j: 3 Result(i: 3.000000 | j: 1.732051 | ij:
5.196152): 327.242448
ITER: 1 | i: 9 | j: 4 Result(i: 3.000000 | j: 2.000000 | ij:
6.000000): 333.242448
ITER: 1 | i: 9 | j: 5 Result(i: 3.000000 | j: 2.236068 | ij:
6.708204): 339.950652
ITER: 1 | i: 9 | j: 6 Result(i: 3.000000 | j: 2.449490 | ij:
7.348469): 347.299121
ITER: 1 | i: 9 | j: 7 Result(i: 3.000000 | j: 2.645751 | ij:
7.937254): 355.236375
ITER: 1 | i: 9 | j: 8 Result(i: 3.000000 | j: 2.828427 | ij:
8.485281): 363.721656
ITER: 1 | i: 9 | j: 9 Result(i: 3.000000 | j: 3.000000 | ij:
9.000000): 372.721656
Final Result: 372.721656
As we can see in the following iterations the sqrt(1) as well as the
result is set to zero for some reason.
ITER: 0 | i: 1 | j: 4 Result(i: 0.000000 | j: 2.000000 | ij:
0.000000): 0.000000
ITER: 0 | i: 1 | j: 5 Result(i: 0.000000 | j: 2.236068 | ij:
0.000000): 0.000000
ITER: 0 | i: 1 | j: 6 Result(i: 0.000000 | j: 2.449490 | ij:
0.000000): 0.000000
ITER: 0 | i: 1 | j: 7 Result(i: 0.000000 | j: 2.645751 | ij:
0.000000): 0.000000
ITER: 0 | i: 1 | j: 8 Result(i: 0.000000 | j: 2.828427 | ij:
0.000000): 0.000000
ITER: 0 | i: 1 | j: 9 Result(i: 0.000000 | j: 3.000000 | ij:
0.000000): 0.000000
Please help me to resolve the accuracy issue! I think that it will
be very useful for gem5 community.
To be noticed, I find the correct simulated tick in which the
application started in FS (using m5 dumpstats), and I start the
--debug-start, but the trace file which is generated is 10x larger
than SE mode for the same application. How can I compare them?
Thank you in advance!
Best regards,
Nikos
Quoting Νικόλαος Ταμπουρατζής ntampouratzis@ece.auth.gr:
Dear Jason,
I am trying to use --debug-start but in FS mode it is very
difficult to find the tick on which the application is started!
However, I am writing the following very simple c++ program:
#include <cmath>
#include <stdio.h>
int main(){
int dim = 4096;
double result;
for (int iter = 0; iter < 2; iter++){
result = 0;
for (int i = 0; i < dim; i++){
for (int j = 0; j < dim; j++){
result += sqrt(i) * sqrt(j);
}
}
printf("Result: %lf\n", result); //Result: 30530733453.127449
}
}
I cross-compile it using: riscv64-linux-gnu-g++ -static -O3 -o
test_riscv test_riscv.cpp
While in X86 (without cross-compilation of course), QEMU-RISCV,
GEM5-SE the result is the same (30530733453.127449), in GEM5-FS the
result is different! In addition, the result is also different
between the 2 iterations.
Please reproduce the error if you want in order to verify my result.
Ηow can the issue be resolved?
Thank you in advance!
Best regards,
Nikos
Quoting Jason Lowe-Power jason@lowepower.com:
Hi Nikos,
You can use --debug-start to start the debugging after some number of
ticks. Also, I would expect that the difference should come up
no need to run the program to the end.
For the FS mode one, you will want to just start the trace as the
application starts. This could be a bit of a pain.
I'm not really sure what fundamentally could be different. FS and SE
use the exact same code for executing instructions, so I don't think
the problem. Have you tried running for smaller inputs or just one
iteration?
Jason
On Wed, Sep 21, 2022 at 9:04 AM Νικόλαος Ταμπουρατζής <
ntampouratzis@ece.auth.gr> wrote:
Dear Bobby,
Iam trying to add --debug-flags=Exec (building the gem5 for gem5.opt
not for gem5.fast which I had) but the debug traces exceed the 20GB
(and it is not finished yet) for less than 1 simulated second. How can
I reduce the size of the debug-flags (or set something more specific)?
In contrast I build the HPCG benchmark with DHPCG_DEBUG flag. If you
want, you can compare these two output files
(hpcg20010909T014640_SE_Mode & HPCG-Benchmark_3.1_FS_Mode). As you can
see, something goes wrong with the accuracy of calculations in FS mode
(benchmark uses double precission). You can find the files here:
http://kition.mhl.tuc.gr:8000/d/68d82f3533/
Best regards,
Nikos
Quoting Jason Lowe-Power jason@lowepower.com:
That's quite odd that it works in SE mode but not FS mode!
I would suggest running with --debug-flags=Exec for both and then
diff to see how they differ.
Cheers,
Jason
On Tue, Sep 20, 2022 at 2:45 PM Νικόλαος Ταμπουρατζής <
ntampouratzis@ece.auth.gr> wrote:
Dear Bobby,
In QEMU I get the same (correct) results that I get in SE mode
simulation. I get invalid results in FS simulation (in both
riscv-fs.py and riscv-ubuntu-run.py). I cannot access real RISCV
hardware at this moment, however, if you want you may execute my
Hi Nikos,
I notice you said the following in your original email:
In addition, I used the RISCV Ubuntu image
I installed the gcc compiler, compile it (through qemu) and I get
wrong results too.
Is this saying you get the wrong results is QEMU? If so, the bug
or the HPCG workload, not in gem5. If not, I would test in QEMU to
sure the binary works there. Another way you could test to see if
problem is your binary or gem5 would be to run it on real
access to some RISC-V hardware here at UC Davis, if you don't have
Dear Bobby,
- I use the original riscv-fs.py which is provided in the latest
release.
I run the gem5 once (./build/RISCV/gem5.fast -d ./HPCG_FS_results
./configs/example/gem5_library/riscv-fs.py) in order to download
riscv-bootloader-vmlinux-5.10 and riscv-disk-img.
After this I mount the riscv-disk-img (sudo mount -o loop
riscv-disk-img /mnt), put the xhpcg executable and I do the
changes in riscv-fs.py to boot the riscv-disk-img with executable:
image = CustomDiskImageResource(
local_path = "/home/cossim/.cache/gem5/riscv-disk-img",
)
Set the Full System workload.
board.set_kernel_disk_workload(
kernel=Resource("riscv-bootloader-vmlinux-5.10"),
disk_image=image,
)
Finally, in the
gem5/src/python/gem5/components/boards/riscv_board.py
I change the last line to "return ["console=ttyS0",
"root={root_value}", "rw"]" in order to allow the write
the image.
- The HPCG benchmark after some iterations calculates if the
are valid or not valid. In the case of FS it gives invalid
I see from the results, one (at least) problem is that produces
different results in each HPCG execution (with the same
I'm going to need a bit more information to help:
- In what way have you modified
./configs/example/gem5_library/riscv-fs.py? Can you attach the
- What error are you getting or in what way are the results
Dear gem5 community,
I have successfully cross-compile the HPCG benchmark for RISCV
version, without MPI and OpenMP). While it working properly in
mode (./build/RISCV/gem5.fast -d ./HPCG_SE_results
./configs/example/se.py -c xhpcg --options '--nx=16 --ny=16
--npx=1 --npy=1 --npz=1 --rt=0.1'), I get invalid results in FS
simulation using "./build/RISCV/gem5.fast -d ./HPCG_FS_results
./configs/example/gem5_library/riscv-fs.py" (I mount the riscv
and put it).
Can you help me please?
In addition, I used the RISCV Ubuntu image
(
I installed the gcc compiler, compile it (through qemu) and I
Hey Niko,
Thanks for this analysis. I jumped a little into this today but didn't get
as far as you did. I wanted to find a quick way to recreate the following:
https://gem5-review.googlesource.com/c/public/gem5/+/64211. Please feel
free to use this, if it helps any.
It's very strange to me that this bug hasn't manifested itself before but
it's undeniably there. I'll try to spend more time looking at this tomorrow
with some traces and debug flags and see if I can narrow down the problem.
--
Dr. Bobby R. Bruce
Room 3050,
Kemper Hall, UC Davis
Davis,
CA, 95616
web: https://www.bobbybruce.net
On Wed, Oct 5, 2022 at 2:26 PM Νικόλαος Ταμπουρατζής <
ntampouratzis@ece.auth.gr> wrote:
> In my previous results, I had used double (not float) for the
> following variables: result, sq_i and sq_j. In the case of float
> instead of double I get "nan" and not 0.000000.
>
> Quoting Νικόλαος Ταμπουρατζής <ntampouratzis@ece.auth.gr>:
>
> > Dear Jason, all,
> >
> > I am trying to find the accuracy problem with RISCV-FS and I observe
> > that the problem is created (at least in my dummy example) because
> > the variables (double) are set to zero in random simulated time (for
> > this reason I get different results among executions of the same
> > code). Specifically for the following dummy code:
> >
> >
> > #include <cmath>
> > #include <stdio.h>
> >
> > int main(){
> >
> > int dim = 10;
> >
> > float result;
> >
> > for (int iter = 0; iter < 2; iter++){
> > result = 0;
> > for (int i = 0; i < dim; i++){
> > for (int j = 0; j < dim; j++){
> > float sq_i = sqrt(i);
> > float sq_j = sqrt(j);
> > result += sq_i * sq_j;
> > printf("ITER: %d | i: %d | j: %d Result(i: %f | j:
> > %f | i*j: %f): %f\n", iter, i , j, sq_i, sq_j, sq_i * sq_j, result);
> > }
> > }
> > printf("Final Result: %lf\n", result);
> > }
> > }
> >
> >
> > The correct Final Result in both iterations is 372.721656. However,
> > I get the following results in FS:
> >
> > ITER: 0 | i: 0 | j: 0 Result(i: 0.000000 | j: 0.000000 | i*j:
> > 0.000000): 0.000000
> > ITER: 0 | i: 0 | j: 1 Result(i: 0.000000 | j: 1.000000 | i*j:
> > 0.000000): 0.000000
> > ITER: 0 | i: 0 | j: 2 Result(i: 0.000000 | j: 1.414214 | i*j:
> > 0.000000): 0.000000
> > ITER: 0 | i: 0 | j: 3 Result(i: 0.000000 | j: 1.732051 | i*j:
> > 0.000000): 0.000000
> > ITER: 0 | i: 0 | j: 4 Result(i: 0.000000 | j: 2.000000 | i*j:
> > 0.000000): 0.000000
> > ITER: 0 | i: 0 | j: 5 Result(i: 0.000000 | j: 2.236068 | i*j:
> > 0.000000): 0.000000
> > ITER: 0 | i: 0 | j: 6 Result(i: 0.000000 | j: 2.449490 | i*j:
> > 0.000000): 0.000000
> > ITER: 0 | i: 0 | j: 7 Result(i: 0.000000 | j: 2.645751 | i*j:
> > 0.000000): 0.000000
> > ITER: 0 | i: 0 | j: 8 Result(i: 0.000000 | j: 2.828427 | i*j:
> > 0.000000): 0.000000
> > ITER: 0 | i: 0 | j: 9 Result(i: 0.000000 | j: 3.000000 | i*j:
> > 0.000000): 0.000000
> > ITER: 0 | i: 1 | j: 0 Result(i: 1.000000 | j: 0.000000 | i*j:
> > 0.000000): 0.000000
> > ITER: 0 | i: 1 | j: 1 Result(i: 1.000000 | j: 1.000000 | i*j:
> > 1.000000): 1.000000
> > ITER: 0 | i: 1 | j: 2 Result(i: 1.000000 | j: 1.414214 | i*j:
> > 1.414214): 2.414214
> > ITER: 0 | i: 1 | j: 3 Result(i: 1.000000 | j: 1.732051 | i*j:
> > 1.732051): 4.146264
> > ITER: 0 | i: 1 | j: 4 Result(i: 0.000000 | j: 2.000000 | i*j:
> > 0.000000): 0.000000
> > ITER: 0 | i: 1 | j: 5 Result(i: 0.000000 | j: 2.236068 | i*j:
> > 0.000000): 0.000000
> > ITER: 0 | i: 1 | j: 6 Result(i: 0.000000 | j: 2.449490 | i*j:
> > 0.000000): 0.000000
> > ITER: 0 | i: 1 | j: 7 Result(i: 0.000000 | j: 2.645751 | i*j:
> > 0.000000): 0.000000
> > ITER: 0 | i: 1 | j: 8 Result(i: 0.000000 | j: 2.828427 | i*j:
> > 0.000000): 0.000000
> > ITER: 0 | i: 1 | j: 9 Result(i: 0.000000 | j: 3.000000 | i*j:
> > 0.000000): 0.000000
> > ITER: 0 | i: 2 | j: 0 Result(i: 1.414214 | j: 0.000000 | i*j:
> > 0.000000): 0.000000
> > ITER: 0 | i: 2 | j: 1 Result(i: 1.414214 | j: 1.000000 | i*j:
> > 1.414214): 1.414214
> > ITER: 0 | i: 2 | j: 2 Result(i: 1.414214 | j: 1.414214 | i*j:
> > 2.000000): 3.414214
> > ITER: 0 | i: 2 | j: 3 Result(i: 1.414214 | j: 1.732051 | i*j:
> > 2.449490): 5.863703
> > ITER: 0 | i: 2 | j: 4 Result(i: 1.414214 | j: 2.000000 | i*j:
> > 2.828427): 8.692130
> > ITER: 0 | i: 2 | j: 5 Result(i: 1.414214 | j: 2.236068 | i*j:
> > 3.162278): 11.854408
> > ITER: 0 | i: 2 | j: 6 Result(i: 1.414214 | j: 2.449490 | i*j:
> > 3.464102): 15.318510
> > ITER: 0 | i: 2 | j: 7 Result(i: 1.414214 | j: 2.645751 | i*j:
> > 3.741657): 19.060167
> > ITER: 0 | i: 2 | j: 8 Result(i: 1.414214 | j: 2.828427 | i*j:
> > 4.000000): 23.060167
> > ITER: 0 | i: 2 | j: 9 Result(i: 1.414214 | j: 3.000000 | i*j:
> > 4.242641): 27.302808
> > ITER: 0 | i: 3 | j: 0 Result(i: 1.732051 | j: 0.000000 | i*j:
> > 0.000000): 27.302808
> > ITER: 0 | i: 3 | j: 1 Result(i: 1.732051 | j: 1.000000 | i*j:
> > 1.732051): 29.034859
> > ITER: 0 | i: 3 | j: 2 Result(i: 1.732051 | j: 1.414214 | i*j:
> > 2.449490): 31.484348
> > ITER: 0 | i: 3 | j: 3 Result(i: 1.732051 | j: 1.732051 | i*j:
> > 3.000000): 34.484348
> > ITER: 0 | i: 3 | j: 4 Result(i: 1.732051 | j: 2.000000 | i*j:
> > 3.464102): 37.948450
> > ITER: 0 | i: 3 | j: 5 Result(i: 1.732051 | j: 2.236068 | i*j:
> > 3.872983): 41.821433
> > ITER: 0 | i: 3 | j: 6 Result(i: 1.732051 | j: 2.449490 | i*j:
> > 4.242641): 46.064074
> > ITER: 0 | i: 3 | j: 7 Result(i: 1.732051 | j: 2.645751 | i*j:
> > 4.582576): 50.646650
> > ITER: 0 | i: 3 | j: 8 Result(i: 1.732051 | j: 2.828427 | i*j:
> > 4.898979): 55.545629
> > ITER: 0 | i: 3 | j: 9 Result(i: 1.732051 | j: 3.000000 | i*j:
> > 5.196152): 60.741782
> > ITER: 0 | i: 4 | j: 0 Result(i: 2.000000 | j: 0.000000 | i*j:
> > 0.000000): 60.741782
> > ITER: 0 | i: 4 | j: 1 Result(i: 2.000000 | j: 1.000000 | i*j:
> > 2.000000): 62.741782
> > ITER: 0 | i: 4 | j: 2 Result(i: 2.000000 | j: 1.414214 | i*j:
> > 2.828427): 65.570209
> > ITER: 0 | i: 4 | j: 3 Result(i: 2.000000 | j: 1.732051 | i*j:
> > 3.464102): 69.034310
> > ITER: 0 | i: 4 | j: 4 Result(i: 2.000000 | j: 2.000000 | i*j:
> > 4.000000): 73.034310
> > ITER: 0 | i: 4 | j: 5 Result(i: 2.000000 | j: 2.236068 | i*j:
> > 4.472136): 77.506446
> > ITER: 0 | i: 4 | j: 6 Result(i: 2.000000 | j: 2.449490 | i*j:
> > 4.898979): 82.405426
> > ITER: 0 | i: 4 | j: 7 Result(i: 2.000000 | j: 2.645751 | i*j:
> > 5.291503): 87.696928
> > ITER: 0 | i: 4 | j: 8 Result(i: 2.000000 | j: 2.828427 | i*j:
> > 5.656854): 93.353783
> > ITER: 0 | i: 4 | j: 9 Result(i: 2.000000 | j: 3.000000 | i*j:
> > 6.000000): 99.353783
> > ITER: 0 | i: 5 | j: 0 Result(i: 2.236068 | j: 0.000000 | i*j:
> > 0.000000): 99.353783
> > ITER: 0 | i: 5 | j: 1 Result(i: 2.236068 | j: 1.000000 | i*j:
> > 2.236068): 101.589851
> > ITER: 0 | i: 5 | j: 2 Result(i: 2.236068 | j: 1.414214 | i*j:
> > 3.162278): 104.752128
> > ITER: 0 | i: 5 | j: 3 Result(i: 2.236068 | j: 1.732051 | i*j:
> > 3.872983): 108.625112
> > ITER: 0 | i: 5 | j: 4 Result(i: 2.236068 | j: 2.000000 | i*j:
> > 4.472136): 113.097248
> > ITER: 0 | i: 5 | j: 5 Result(i: 2.236068 | j: 2.236068 | i*j:
> > 5.000000): 118.097248
> > ITER: 0 | i: 5 | j: 6 Result(i: 2.236068 | j: 2.449490 | i*j:
> > 5.477226): 123.574473
> > ITER: 0 | i: 5 | j: 7 Result(i: 2.236068 | j: 2.645751 | i*j:
> > 5.916080): 129.490553
> > ITER: 0 | i: 5 | j: 8 Result(i: 2.236068 | j: 2.828427 | i*j:
> > 6.324555): 135.815108
> > ITER: 0 | i: 5 | j: 9 Result(i: 2.236068 | j: 3.000000 | i*j:
> > 6.708204): 142.523312
> > ITER: 0 | i: 6 | j: 0 Result(i: 2.449490 | j: 0.000000 | i*j:
> > 0.000000): 142.523312
> > ITER: 0 | i: 6 | j: 1 Result(i: 2.449490 | j: 1.000000 | i*j:
> > 2.449490): 144.972802
> > ITER: 0 | i: 6 | j: 2 Result(i: 2.449490 | j: 1.414214 | i*j:
> > 3.464102): 148.436904
> > ITER: 0 | i: 6 | j: 3 Result(i: 2.449490 | j: 1.732051 | i*j:
> > 4.242641): 152.679544
> > ITER: 0 | i: 6 | j: 4 Result(i: 2.449490 | j: 2.000000 | i*j:
> > 4.898979): 157.578524
> > ITER: 0 | i: 6 | j: 5 Result(i: 2.449490 | j: 2.236068 | i*j:
> > 5.477226): 163.055749
> > ITER: 0 | i: 6 | j: 6 Result(i: 2.449490 | j: 2.449490 | i*j:
> > 6.000000): 169.055749
> > ITER: 0 | i: 6 | j: 7 Result(i: 2.449490 | j: 2.645751 | i*j:
> > 6.480741): 175.536490
> > ITER: 0 | i: 6 | j: 8 Result(i: 2.449490 | j: 2.828427 | i*j:
> > 6.928203): 182.464693
> > ITER: 0 | i: 6 | j: 9 Result(i: 2.449490 | j: 3.000000 | i*j:
> > 7.348469): 189.813162
> > ITER: 0 | i: 7 | j: 0 Result(i: 2.645751 | j: 0.000000 | i*j:
> > 0.000000): 189.813162
> > ITER: 0 | i: 7 | j: 1 Result(i: 2.645751 | j: 1.000000 | i*j:
> > 2.645751): 192.458914
> > ITER: 0 | i: 7 | j: 2 Result(i: 2.645751 | j: 1.414214 | i*j:
> > 3.741657): 196.200571
> > ITER: 0 | i: 7 | j: 3 Result(i: 2.645751 | j: 1.732051 | i*j:
> > 4.582576): 200.783147
> > ITER: 0 | i: 7 | j: 4 Result(i: 2.645751 | j: 2.000000 | i*j:
> > 5.291503): 206.074649
> > ITER: 0 | i: 7 | j: 5 Result(i: 2.645751 | j: 2.236068 | i*j:
> > 5.916080): 211.990729
> > ITER: 0 | i: 7 | j: 6 Result(i: 2.645751 | j: 2.449490 | i*j:
> > 6.480741): 218.471470
> > ITER: 0 | i: 7 | j: 7 Result(i: 2.645751 | j: 2.645751 | i*j:
> > 7.000000): 225.471470
> > ITER: 0 | i: 7 | j: 8 Result(i: 2.645751 | j: 2.828427 | i*j:
> > 7.483315): 232.954785
> > ITER: 0 | i: 7 | j: 9 Result(i: 2.645751 | j: 3.000000 | i*j:
> > 7.937254): 240.892039
> > ITER: 0 | i: 8 | j: 0 Result(i: 2.828427 | j: 0.000000 | i*j:
> > 0.000000): 240.892039
> > ITER: 0 | i: 8 | j: 1 Result(i: 2.828427 | j: 1.000000 | i*j:
> > 2.828427): 243.720466
> > ITER: 0 | i: 8 | j: 2 Result(i: 2.828427 | j: 1.414214 | i*j:
> > 4.000000): 247.720466
> > ITER: 0 | i: 8 | j: 3 Result(i: 2.828427 | j: 1.732051 | i*j:
> > 4.898979): 252.619445
> > ITER: 0 | i: 8 | j: 4 Result(i: 2.828427 | j: 2.000000 | i*j:
> > 5.656854): 258.276300
> > ITER: 0 | i: 8 | j: 5 Result(i: 2.828427 | j: 2.236068 | i*j:
> > 6.324555): 264.600855
> > ITER: 0 | i: 8 | j: 6 Result(i: 2.828427 | j: 2.449490 | i*j:
> > 6.928203): 271.529058
> > ITER: 0 | i: 8 | j: 7 Result(i: 2.828427 | j: 2.645751 | i*j:
> > 7.483315): 279.012373
> > ITER: 0 | i: 8 | j: 8 Result(i: 2.828427 | j: 2.828427 | i*j:
> > 8.000000): 287.012373
> > ITER: 0 | i: 8 | j: 9 Result(i: 2.828427 | j: 3.000000 | i*j:
> > 8.485281): 295.497654
> > ITER: 0 | i: 9 | j: 0 Result(i: 3.000000 | j: 0.000000 | i*j:
> > 0.000000): 295.497654
> > ITER: 0 | i: 9 | j: 1 Result(i: 3.000000 | j: 1.000000 | i*j:
> > 3.000000): 298.497654
> > ITER: 0 | i: 9 | j: 2 Result(i: 3.000000 | j: 1.414214 | i*j:
> > 4.242641): 302.740295
> > ITER: 0 | i: 9 | j: 3 Result(i: 3.000000 | j: 1.732051 | i*j:
> > 5.196152): 307.936447
> > ITER: 0 | i: 9 | j: 4 Result(i: 3.000000 | j: 2.000000 | i*j:
> > 6.000000): 313.936447
> > ITER: 0 | i: 9 | j: 5 Result(i: 3.000000 | j: 2.236068 | i*j:
> > 6.708204): 320.644651
> > ITER: 0 | i: 9 | j: 6 Result(i: 3.000000 | j: 2.449490 | i*j:
> > 7.348469): 327.993120
> > ITER: 0 | i: 9 | j: 7 Result(i: 3.000000 | j: 2.645751 | i*j:
> > 7.937254): 335.930374
> > ITER: 0 | i: 9 | j: 8 Result(i: 3.000000 | j: 2.828427 | i*j:
> > 8.485281): 344.415656
> > ITER: 0 | i: 9 | j: 9 Result(i: 3.000000 | j: 3.000000 | i*j:
> > 9.000000): 353.415656
> > Final Result: 353.415656
> > ITER: 1 | i: 0 | j: 0 Result(i: 0.000000 | j: 0.000000 | i*j:
> > 0.000000): 0.000000
> > ITER: 1 | i: 0 | j: 1 Result(i: 0.000000 | j: 1.000000 | i*j:
> > 0.000000): 0.000000
> > ITER: 1 | i: 0 | j: 2 Result(i: 0.000000 | j: 1.414214 | i*j:
> > 0.000000): 0.000000
> > ITER: 1 | i: 0 | j: 3 Result(i: 0.000000 | j: 1.732051 | i*j:
> > 0.000000): 0.000000
> > ITER: 1 | i: 0 | j: 4 Result(i: 0.000000 | j: 2.000000 | i*j:
> > 0.000000): 0.000000
> > ITER: 1 | i: 0 | j: 5 Result(i: 0.000000 | j: 2.236068 | i*j:
> > 0.000000): 0.000000
> > ITER: 1 | i: 0 | j: 6 Result(i: 0.000000 | j: 2.449490 | i*j:
> > 0.000000): 0.000000
> > ITER: 1 | i: 0 | j: 7 Result(i: 0.000000 | j: 2.645751 | i*j:
> > 0.000000): 0.000000
> > ITER: 1 | i: 0 | j: 8 Result(i: 0.000000 | j: 2.828427 | i*j:
> > 0.000000): 0.000000
> > ITER: 1 | i: 0 | j: 9 Result(i: 0.000000 | j: 3.000000 | i*j:
> > 0.000000): 0.000000
> > ITER: 1 | i: 1 | j: 0 Result(i: 1.000000 | j: 0.000000 | i*j:
> > 0.000000): 0.000000
> > ITER: 1 | i: 1 | j: 1 Result(i: 1.000000 | j: 1.000000 | i*j:
> > 1.000000): 1.000000
> > ITER: 1 | i: 1 | j: 2 Result(i: 1.000000 | j: 1.414214 | i*j:
> > 1.414214): 2.414214
> > ITER: 1 | i: 1 | j: 3 Result(i: 1.000000 | j: 1.732051 | i*j:
> > 1.732051): 4.146264
> > ITER: 1 | i: 1 | j: 4 Result(i: 1.000000 | j: 2.000000 | i*j:
> > 2.000000): 6.146264
> > ITER: 1 | i: 1 | j: 5 Result(i: 1.000000 | j: 2.236068 | i*j:
> > 2.236068): 8.382332
> > ITER: 1 | i: 1 | j: 6 Result(i: 1.000000 | j: 2.449490 | i*j:
> > 2.449490): 10.831822
> > ITER: 1 | i: 1 | j: 7 Result(i: 1.000000 | j: 2.645751 | i*j:
> > 2.645751): 13.477573
> > ITER: 1 | i: 1 | j: 8 Result(i: 1.000000 | j: 2.828427 | i*j:
> > 2.828427): 16.306001
> > ITER: 1 | i: 1 | j: 9 Result(i: 1.000000 | j: 3.000000 | i*j:
> > 3.000000): 19.306001
> > ITER: 1 | i: 2 | j: 0 Result(i: 1.414214 | j: 0.000000 | i*j:
> > 0.000000): 19.306001
> > ITER: 1 | i: 2 | j: 1 Result(i: 1.414214 | j: 1.000000 | i*j:
> > 1.414214): 20.720214
> > ITER: 1 | i: 2 | j: 2 Result(i: 1.414214 | j: 1.414214 | i*j:
> > 2.000000): 22.720214
> > ITER: 1 | i: 2 | j: 3 Result(i: 1.414214 | j: 1.732051 | i*j:
> > 2.449490): 25.169704
> > ITER: 1 | i: 2 | j: 4 Result(i: 1.414214 | j: 2.000000 | i*j:
> > 2.828427): 27.998131
> > ITER: 1 | i: 2 | j: 5 Result(i: 1.414214 | j: 2.236068 | i*j:
> > 3.162278): 31.160409
> > ITER: 1 | i: 2 | j: 6 Result(i: 1.414214 | j: 2.449490 | i*j:
> > 3.464102): 34.624510
> > ITER: 1 | i: 2 | j: 7 Result(i: 1.414214 | j: 2.645751 | i*j:
> > 3.741657): 38.366168
> > ITER: 1 | i: 2 | j: 8 Result(i: 1.414214 | j: 2.828427 | i*j:
> > 4.000000): 42.366168
> > ITER: 1 | i: 2 | j: 9 Result(i: 1.414214 | j: 3.000000 | i*j:
> > 4.242641): 46.608808
> > ITER: 1 | i: 3 | j: 0 Result(i: 1.732051 | j: 0.000000 | i*j:
> > 0.000000): 46.608808
> > ITER: 1 | i: 3 | j: 1 Result(i: 1.732051 | j: 1.000000 | i*j:
> > 1.732051): 48.340859
> > ITER: 1 | i: 3 | j: 2 Result(i: 1.732051 | j: 1.414214 | i*j:
> > 2.449490): 50.790349
> > ITER: 1 | i: 3 | j: 3 Result(i: 1.732051 | j: 1.732051 | i*j:
> > 3.000000): 53.790349
> > ITER: 1 | i: 3 | j: 4 Result(i: 1.732051 | j: 2.000000 | i*j:
> > 3.464102): 57.254450
> > ITER: 1 | i: 3 | j: 5 Result(i: 1.732051 | j: 2.236068 | i*j:
> > 3.872983): 61.127434
> > ITER: 1 | i: 3 | j: 6 Result(i: 1.732051 | j: 2.449490 | i*j:
> > 4.242641): 65.370075
> > ITER: 1 | i: 3 | j: 7 Result(i: 1.732051 | j: 2.645751 | i*j:
> > 4.582576): 69.952650
> > ITER: 1 | i: 3 | j: 8 Result(i: 1.732051 | j: 2.828427 | i*j:
> > 4.898979): 74.851630
> > ITER: 1 | i: 3 | j: 9 Result(i: 1.732051 | j: 3.000000 | i*j:
> > 5.196152): 80.047782
> > ITER: 1 | i: 4 | j: 0 Result(i: 2.000000 | j: 0.000000 | i*j:
> > 0.000000): 80.047782
> > ITER: 1 | i: 4 | j: 1 Result(i: 2.000000 | j: 1.000000 | i*j:
> > 2.000000): 82.047782
> > ITER: 1 | i: 4 | j: 2 Result(i: 2.000000 | j: 1.414214 | i*j:
> > 2.828427): 84.876209
> > ITER: 1 | i: 4 | j: 3 Result(i: 2.000000 | j: 1.732051 | i*j:
> > 3.464102): 88.340311
> > ITER: 1 | i: 4 | j: 4 Result(i: 2.000000 | j: 2.000000 | i*j:
> > 4.000000): 92.340311
> > ITER: 1 | i: 4 | j: 5 Result(i: 2.000000 | j: 2.236068 | i*j:
> > 4.472136): 96.812447
> > ITER: 1 | i: 4 | j: 6 Result(i: 2.000000 | j: 2.449490 | i*j:
> > 4.898979): 101.711426
> > ITER: 1 | i: 4 | j: 7 Result(i: 2.000000 | j: 2.645751 | i*j:
> > 5.291503): 107.002929
> > ITER: 1 | i: 4 | j: 8 Result(i: 2.000000 | j: 2.828427 | i*j:
> > 5.656854): 112.659783
> > ITER: 1 | i: 4 | j: 9 Result(i: 2.000000 | j: 3.000000 | i*j:
> > 6.000000): 118.659783
> > ITER: 1 | i: 5 | j: 0 Result(i: 2.236068 | j: 0.000000 | i*j:
> > 0.000000): 118.659783
> > ITER: 1 | i: 5 | j: 1 Result(i: 2.236068 | j: 1.000000 | i*j:
> > 2.236068): 120.895851
> > ITER: 1 | i: 5 | j: 2 Result(i: 2.236068 | j: 1.414214 | i*j:
> > 3.162278): 124.058129
> > ITER: 1 | i: 5 | j: 3 Result(i: 2.236068 | j: 1.732051 | i*j:
> > 3.872983): 127.931112
> > ITER: 1 | i: 5 | j: 4 Result(i: 2.236068 | j: 2.000000 | i*j:
> > 4.472136): 132.403248
> > ITER: 1 | i: 5 | j: 5 Result(i: 2.236068 | j: 2.236068 | i*j:
> > 5.000000): 137.403248
> > ITER: 1 | i: 5 | j: 6 Result(i: 2.236068 | j: 2.449490 | i*j:
> > 5.477226): 142.880474
> > ITER: 1 | i: 5 | j: 7 Result(i: 2.236068 | j: 2.645751 | i*j:
> > 5.916080): 148.796553
> > ITER: 1 | i: 5 | j: 8 Result(i: 2.236068 | j: 2.828427 | i*j:
> > 6.324555): 155.121109
> > ITER: 1 | i: 5 | j: 9 Result(i: 2.236068 | j: 3.000000 | i*j:
> > 6.708204): 161.829313
> > ITER: 1 | i: 6 | j: 0 Result(i: 2.449490 | j: 0.000000 | i*j:
> > 0.000000): 161.829313
> > ITER: 1 | i: 6 | j: 1 Result(i: 2.449490 | j: 1.000000 | i*j:
> > 2.449490): 164.278802
> > ITER: 1 | i: 6 | j: 2 Result(i: 2.449490 | j: 1.414214 | i*j:
> > 3.464102): 167.742904
> > ITER: 1 | i: 6 | j: 3 Result(i: 2.449490 | j: 1.732051 | i*j:
> > 4.242641): 171.985545
> > ITER: 1 | i: 6 | j: 4 Result(i: 2.449490 | j: 2.000000 | i*j:
> > 4.898979): 176.884524
> > ITER: 1 | i: 6 | j: 5 Result(i: 2.449490 | j: 2.236068 | i*j:
> > 5.477226): 182.361750
> > ITER: 1 | i: 6 | j: 6 Result(i: 2.449490 | j: 2.449490 | i*j:
> > 6.000000): 188.361750
> > ITER: 1 | i: 6 | j: 7 Result(i: 2.449490 | j: 2.645751 | i*j:
> > 6.480741): 194.842491
> > ITER: 1 | i: 6 | j: 8 Result(i: 2.449490 | j: 2.828427 | i*j:
> > 6.928203): 201.770694
> > ITER: 1 | i: 6 | j: 9 Result(i: 2.449490 | j: 3.000000 | i*j:
> > 7.348469): 209.119163
> > ITER: 1 | i: 7 | j: 0 Result(i: 2.645751 | j: 0.000000 | i*j:
> > 0.000000): 209.119163
> > ITER: 1 | i: 7 | j: 1 Result(i: 2.645751 | j: 1.000000 | i*j:
> > 2.645751): 211.764914
> > ITER: 1 | i: 7 | j: 2 Result(i: 2.645751 | j: 1.414214 | i*j:
> > 3.741657): 215.506572
> > ITER: 1 | i: 7 | j: 3 Result(i: 2.645751 | j: 1.732051 | i*j:
> > 4.582576): 220.089147
> > ITER: 1 | i: 7 | j: 4 Result(i: 2.645751 | j: 2.000000 | i*j:
> > 5.291503): 225.380650
> > ITER: 1 | i: 7 | j: 5 Result(i: 2.645751 | j: 2.236068 | i*j:
> > 5.916080): 231.296730
> > ITER: 1 | i: 7 | j: 6 Result(i: 2.645751 | j: 2.449490 | i*j:
> > 6.480741): 237.777470
> > ITER: 1 | i: 7 | j: 7 Result(i: 2.645751 | j: 2.645751 | i*j:
> > 7.000000): 244.777470
> > ITER: 1 | i: 7 | j: 8 Result(i: 2.645751 | j: 2.828427 | i*j:
> > 7.483315): 252.260785
> > ITER: 1 | i: 7 | j: 9 Result(i: 2.645751 | j: 3.000000 | i*j:
> > 7.937254): 260.198039
> > ITER: 1 | i: 8 | j: 0 Result(i: 2.828427 | j: 0.000000 | i*j:
> > 0.000000): 260.198039
> > ITER: 1 | i: 8 | j: 1 Result(i: 2.828427 | j: 1.000000 | i*j:
> > 2.828427): 263.026466
> > ITER: 1 | i: 8 | j: 2 Result(i: 2.828427 | j: 1.414214 | i*j:
> > 4.000000): 267.026466
> > ITER: 1 | i: 8 | j: 3 Result(i: 2.828427 | j: 1.732051 | i*j:
> > 4.898979): 271.925446
> > ITER: 1 | i: 8 | j: 4 Result(i: 2.828427 | j: 2.000000 | i*j:
> > 5.656854): 277.582300
> > ITER: 1 | i: 8 | j: 5 Result(i: 2.828427 | j: 2.236068 | i*j:
> > 6.324555): 283.906855
> > ITER: 1 | i: 8 | j: 6 Result(i: 2.828427 | j: 2.449490 | i*j:
> > 6.928203): 290.835059
> > ITER: 1 | i: 8 | j: 7 Result(i: 2.828427 | j: 2.645751 | i*j:
> > 7.483315): 298.318373
> > ITER: 1 | i: 8 | j: 8 Result(i: 2.828427 | j: 2.828427 | i*j:
> > 8.000000): 306.318373
> > ITER: 1 | i: 8 | j: 9 Result(i: 2.828427 | j: 3.000000 | i*j:
> > 8.485281): 314.803655
> > ITER: 1 | i: 9 | j: 0 Result(i: 3.000000 | j: 0.000000 | i*j:
> > 0.000000): 314.803655
> > ITER: 1 | i: 9 | j: 1 Result(i: 3.000000 | j: 1.000000 | i*j:
> > 3.000000): 317.803655
> > ITER: 1 | i: 9 | j: 2 Result(i: 3.000000 | j: 1.414214 | i*j:
> > 4.242641): 322.046295
> > ITER: 1 | i: 9 | j: 3 Result(i: 3.000000 | j: 1.732051 | i*j:
> > 5.196152): 327.242448
> > ITER: 1 | i: 9 | j: 4 Result(i: 3.000000 | j: 2.000000 | i*j:
> > 6.000000): 333.242448
> > ITER: 1 | i: 9 | j: 5 Result(i: 3.000000 | j: 2.236068 | i*j:
> > 6.708204): 339.950652
> > ITER: 1 | i: 9 | j: 6 Result(i: 3.000000 | j: 2.449490 | i*j:
> > 7.348469): 347.299121
> > ITER: 1 | i: 9 | j: 7 Result(i: 3.000000 | j: 2.645751 | i*j:
> > 7.937254): 355.236375
> > ITER: 1 | i: 9 | j: 8 Result(i: 3.000000 | j: 2.828427 | i*j:
> > 8.485281): 363.721656
> > ITER: 1 | i: 9 | j: 9 Result(i: 3.000000 | j: 3.000000 | i*j:
> > 9.000000): 372.721656
> > Final Result: 372.721656
> >
> >
> >
> > As we can see in the following iterations the sqrt(1) as well as the
> > result is set to zero for some reason.
> >
> > ITER: 0 | i: 1 | j: 4 Result(i: 0.000000 | j: 2.000000 | i*j:
> > 0.000000): 0.000000
> > ITER: 0 | i: 1 | j: 5 Result(i: 0.000000 | j: 2.236068 | i*j:
> > 0.000000): 0.000000
> > ITER: 0 | i: 1 | j: 6 Result(i: 0.000000 | j: 2.449490 | i*j:
> > 0.000000): 0.000000
> > ITER: 0 | i: 1 | j: 7 Result(i: 0.000000 | j: 2.645751 | i*j:
> > 0.000000): 0.000000
> > ITER: 0 | i: 1 | j: 8 Result(i: 0.000000 | j: 2.828427 | i*j:
> > 0.000000): 0.000000
> > ITER: 0 | i: 1 | j: 9 Result(i: 0.000000 | j: 3.000000 | i*j:
> > 0.000000): 0.000000
> >
> > Please help me to resolve the accuracy issue! I think that it will
> > be very useful for gem5 community.
> >
> > To be noticed, I find the correct simulated tick in which the
> > application started in FS (using m5 dumpstats), and I start the
> > --debug-start, but the trace file which is generated is 10x larger
> > than SE mode for the same application. How can I compare them?
> >
> > Thank you in advance!
> > Best regards,
> > Nikos
> >
> > Quoting Νικόλαος Ταμπουρατζής <ntampouratzis@ece.auth.gr>:
> >
> >> Dear Jason,
> >>
> >> I am trying to use --debug-start but in FS mode it is very
> >> difficult to find the tick on which the application is started!
> >>
> >> However, I am writing the following very simple c++ program:
> >>
> >> #include <cmath>
> >> #include <stdio.h>
> >>
> >> int main(){
> >>
> >> int dim = 4096;
> >>
> >> double result;
> >>
> >> for (int iter = 0; iter < 2; iter++){
> >> result = 0;
> >> for (int i = 0; i < dim; i++){
> >> for (int j = 0; j < dim; j++){
> >> result += sqrt(i) * sqrt(j);
> >> }
> >> }
> >> printf("Result: %lf\n", result); //Result: 30530733453.127449
> >> }
> >> }
> >>
> >> I cross-compile it using: riscv64-linux-gnu-g++ -static -O3 -o
> >> test_riscv test_riscv.cpp
> >>
> >>
> >> While in X86 (without cross-compilation of course), QEMU-RISCV,
> >> GEM5-SE the result is the same (30530733453.127449), in GEM5-FS the
> >> result is different! In addition, the result is also different
> >> between the 2 iterations.
> >>
> >> Please reproduce the error if you want in order to verify my result.
> >> Ηow can the issue be resolved?
> >>
> >> Thank you in advance!
> >>
> >> Best regards,
> >> Nikos
> >>
> >>
> >> Quoting Jason Lowe-Power <jason@lowepower.com>:
> >>
> >>> Hi Nikos,
> >>>
> >>> You can use --debug-start to start the debugging after some number of
> >>> ticks. Also, I would expect that the difference should come up
> quickly, so
> >>> no need to run the program to the end.
> >>>
> >>> For the FS mode one, you will want to just start the trace as the
> >>> application starts. This could be a bit of a pain.
> >>>
> >>> I'm not really sure what fundamentally could be different. FS and SE
> mode
> >>> use the exact same code for executing instructions, so I don't think
> that's
> >>> the problem. Have you tried running for smaller inputs or just one
> >>> iteration?
> >>>
> >>> Jason
> >>>
> >>>
> >>>
> >>> On Wed, Sep 21, 2022 at 9:04 AM Νικόλαος Ταμπουρατζής <
> >>> ntampouratzis@ece.auth.gr> wrote:
> >>>
> >>>> Dear Bobby,
> >>>>
> >>>> Iam trying to add --debug-flags=Exec (building the gem5 for gem5.opt
> >>>> not for gem5.fast which I had) but the debug traces exceed the 20GB
> >>>> (and it is not finished yet) for less than 1 simulated second. How can
> >>>> I reduce the size of the debug-flags (or set something more specific)?
> >>>>
> >>>> In contrast I build the HPCG benchmark with DHPCG_DEBUG flag. If you
> >>>> want, you can compare these two output files
> >>>> (hpcg20010909T014640_SE_Mode & HPCG-Benchmark_3.1_FS_Mode). As you can
> >>>> see, something goes wrong with the accuracy of calculations in FS mode
> >>>> (benchmark uses double precission). You can find the files here:
> >>>> http://kition.mhl.tuc.gr:8000/d/68d82f3533/
> >>>>
> >>>> Best regards,
> >>>> Nikos
> >>>>
> >>>> Quoting Jason Lowe-Power <jason@lowepower.com>:
> >>>>
> >>>>> That's quite odd that it works in SE mode but not FS mode!
> >>>>>
> >>>>> I would suggest running with --debug-flags=Exec for both and then
> >>>> perform a
> >>>>> diff to see how they differ.
> >>>>>
> >>>>> Cheers,
> >>>>> Jason
> >>>>>
> >>>>> On Tue, Sep 20, 2022 at 2:45 PM Νικόλαος Ταμπουρατζής <
> >>>>> ntampouratzis@ece.auth.gr> wrote:
> >>>>>
> >>>>>> Dear Bobby,
> >>>>>>
> >>>>>> In QEMU I get the same (correct) results that I get in SE mode
> >>>>>> simulation. I get invalid results in FS simulation (in both
> >>>>>> riscv-fs.py and riscv-ubuntu-run.py). I cannot access real RISCV
> >>>>>> hardware at this moment, however, if you want you may execute my
> xhpcg
> >>>>>> binary (http://kition.mhl.tuc.gr:8000/f/4ca25fdd3c/) with the
> >>>>>> following configuration:
> >>>>>>
> >>>>>> ./xhpcg --nx=16 --ny=16 --nz=16 --npx=1 --npy=1 --npz=1 --rt=0.1
> >>>>>>
> >>>>>> Please let me know if you have any updates!
> >>>>>>
> >>>>>> Best regards,
> >>>>>> Nikos
> >>>>>>
> >>>>>>
> >>>>>> Quoting Jason Lowe-Power <jason@lowepower.com>:
> >>>>>>
> >>>>>>> Hi Nikos,
> >>>>>>>
> >>>>>>> I notice you said the following in your original email:
> >>>>>>>
> >>>>>>> In addition, I used the RISCV Ubuntu image
> >>>>>>>> (
> https://github.com/gem5/gem5-resources/tree/stable/src/riscv-ubuntu
> >>>> ),
> >>>>>>>> I installed the gcc compiler, compile it (through qemu) and I get
> >>>>>>>> wrong results too.
> >>>>>>>
> >>>>>>>
> >>>>>>> Is this saying you get the wrong results is QEMU? If so, the bug
> is in
> >>>>>> GCC
> >>>>>>> or the HPCG workload, not in gem5. If not, I would test in QEMU to
> >>>> make
> >>>>>>> sure the binary works there. Another way you could test to see if
> the
> >>>>>>> problem is your binary or gem5 would be to run it on real
> hardware. We
> >>>>>> have
> >>>>>>> access to some RISC-V hardware here at UC Davis, if you don't have
> >>>> access
> >>>>>>> to it.
> >>>>>>>
> >>>>>>> Cheers,
> >>>>>>> Jason
> >>>>>>>
> >>>>>>> On Tue, Sep 20, 2022 at 12:58 AM Νικόλαος Ταμπουρατζής <
> >>>>>>> ntampouratzis@ece.auth.gr> wrote:
> >>>>>>>
> >>>>>>>> Dear Bobby,
> >>>>>>>>
> >>>>>>>> 1) I use the original riscv-fs.py which is provided in the latest
> >>>> gem5
> >>>>>>>> release.
> >>>>>>>> I run the gem5 once (./build/RISCV/gem5.fast -d ./HPCG_FS_results
> >>>>>>>> ./configs/example/gem5_library/riscv-fs.py) in order to download
> the
> >>>>>>>> riscv-bootloader-vmlinux-5.10 and riscv-disk-img.
> >>>>>>>> After this I mount the riscv-disk-img (sudo mount -o loop
> >>>>>>>> riscv-disk-img /mnt), put the xhpcg executable and I do the
> following
> >>>>>>>> changes in riscv-fs.py to boot the riscv-disk-img with executable:
> >>>>>>>>
> >>>>>>>> image = CustomDiskImageResource(
> >>>>>>>> local_path = "/home/cossim/.cache/gem5/riscv-disk-img",
> >>>>>>>> )
> >>>>>>>>
> >>>>>>>> # Set the Full System workload.
> >>>>>>>> board.set_kernel_disk_workload(
> >>>>>>>>
> kernel=Resource("riscv-bootloader-vmlinux-5.10"),
> >>>>>>>> disk_image=image,
> >>>>>>>> )
> >>>>>>>>
> >>>>>>>> Finally, in the
> gem5/src/python/gem5/components/boards/riscv_board.py
> >>>>>>>> I change the last line to "return ["console=ttyS0",
> >>>>>>>> "root={root_value}", "rw"]" in order to allow the write
> permissions
> >>>> in
> >>>>>>>> the image.
> >>>>>>>>
> >>>>>>>>
> >>>>>>>> 2) The HPCG benchmark after some iterations calculates if the
> results
> >>>>>>>> are valid or not valid. In the case of FS it gives invalid
> results.
> >>>> As
> >>>>>>>> I see from the results, one (at least) problem is that produces
> >>>>>>>> different results in each HPCG execution (with the same
> >>>> configuration).
> >>>>>>>>
> >>>>>>>> Here is the HPCG output and riscv-fs.py
> >>>>>>>> (http://kition.mhl.tuc.gr:8000/d/68d82f3533/). You may reproduce
> the
> >>>>>>>> results in the video if you use the xhpcg executable
> >>>>>>>> (http://kition.mhl.tuc.gr:8000/f/4ca25fdd3c/)
> >>>>>>>>
> >>>>>>>> Please help me in order to solve it!
> >>>>>>>>
> >>>>>>>> Finally, I get invalid results in the HPL benchmark in FS mode
> too.
> >>>>>>>>
> >>>>>>>> Best regards,
> >>>>>>>> Nikos
> >>>>>>>>
> >>>>>>>>
> >>>>>>>> Quoting Bobby Bruce <bbruce@ucdavis.edu>:
> >>>>>>>>
> >>>>>>>> > I'm going to need a bit more information to help:
> >>>>>>>> >
> >>>>>>>> > 1. In what way have you modified
> >>>>>>>> > ./configs/example/gem5_library/riscv-fs.py? Can you attach the
> >>>> script
> >>>>>>>> here?
> >>>>>>>> > 2. What error are you getting or in what way are the results
> >>>> invalid?
> >>>>>>>> >
> >>>>>>>> > -
> >>>>>>>> > Dr. Bobby R. Bruce
> >>>>>>>> > Room 3050,
> >>>>>>>> > Kemper Hall, UC Davis
> >>>>>>>> > Davis,
> >>>>>>>> > CA, 95616
> >>>>>>>> >
> >>>>>>>> > web: https://www.bobbybruce.net
> >>>>>>>> >
> >>>>>>>> >
> >>>>>>>> > On Mon, Sep 19, 2022 at 1:43 PM Νικόλαος Ταμπουρατζής <
> >>>>>>>> > ntampouratzis@ece.auth.gr> wrote:
> >>>>>>>> >
> >>>>>>>> >>
> >>>>>>>> >> Dear gem5 community,
> >>>>>>>> >>
> >>>>>>>> >> I have successfully cross-compile the HPCG benchmark for RISCV
> >>>>>> (Serial
> >>>>>>>> >> version, without MPI and OpenMP). While it working properly in
> >>>> gem5
> >>>>>> SE
> >>>>>>>> >> mode (./build/RISCV/gem5.fast -d ./HPCG_SE_results
> >>>>>>>> >> ./configs/example/se.py -c xhpcg --options '--nx=16 --ny=16
> >>>> --nz=16
> >>>>>>>> >> --npx=1 --npy=1 --npz=1 --rt=0.1'), I get invalid results in FS
> >>>>>>>> >> simulation using "./build/RISCV/gem5.fast -d ./HPCG_FS_results
> >>>>>>>> >> ./configs/example/gem5_library/riscv-fs.py" (I mount the riscv
> >>>> image
> >>>>>>>> >> and put it).
> >>>>>>>> >>
> >>>>>>>> >> Can you help me please?
> >>>>>>>> >>
> >>>>>>>> >> In addition, I used the RISCV Ubuntu image
> >>>>>>>> >> (
> >>>> https://github.com/gem5/gem5-resources/tree/stable/src/riscv-ubuntu
> >>>>>> ),
> >>>>>>>> >> I installed the gcc compiler, compile it (through qemu) and I
> get
> >>>>>>>> >> wrong results too.
> >>>>>>>> >>
> >>>>>>>> >> Here is the Makefile which I use, the hpcg executable for RISCV
> >>>>>>>> >> (xhpcg), and a video that shows the results
> >>>>>>>> >> (http://kition.mhl.tuc.gr:8000/f/4ca25fdd3c/).
> >>>>>>>> >>
> >>>>>>>> >> P.S. I use the latest gem5 version.
> >>>>>>>> >>
> >>>>>>>> >> Thank you in advance! :)
> >>>>>>>> >>
> >>>>>>>> >> Best regards,
> >>>>>>>> >> Nikos
> >>>>>>>> >> _______________________________________________
> >>>>>>>> >> gem5-users mailing list -- gem5-users@gem5.org
> >>>>>>>> >> To unsubscribe send an email to gem5-users-leave@gem5.org
> >>>>>>>> >>
> >>>>>>>>
> >>>>>>>>
> >>>>>>>> _______________________________________________
> >>>>>>>> gem5-users mailing list -- gem5-users@gem5.org
> >>>>>>>> To unsubscribe send an email to gem5-users-leave@gem5.org
> >>>>>>>>
> >>>>>>
> >>>>>>
> >>>>>> _______________________________________________
> >>>>>> gem5-users mailing list -- gem5-users@gem5.org
> >>>>>> To unsubscribe send an email to gem5-users-leave@gem5.org
> >>>>>>
> >>>>
> >>>>
> >>>> _______________________________________________
> >>>> gem5-users mailing list -- gem5-users@gem5.org
> >>>> To unsubscribe send an email to gem5-users-leave@gem5.org
> >>>>
> >>
> >>
> >> _______________________________________________
> >> gem5-users mailing list -- gem5-users@gem5.org
> >> To unsubscribe send an email to gem5-users-leave@gem5.org
> >
> >
> > _______________________________________________
> > gem5-users mailing list -- gem5-users@gem5.org
> > To unsubscribe send an email to gem5-users-leave@gem5.org
>
>
> _______________________________________________
> gem5-users mailing list -- gem5-users@gem5.org
> To unsubscribe send an email to gem5-users-leave@gem5.org
>
Dear Boddy,
Thanks a lot for the effort! I looked in detail and I observe that the
problem is created only using float and double variables (in the case
of int it is working properly in FS mode). Specifically, in the case
of float the variables are set to "nan", while in the case of double
the variables are set to 0.000000 (in random time - probably from some
instruction of simulated OS?). You may use a simple c/c++ example in
order to get some traces before going to HPCG...
Thank you in advance!!
Best regards,
Nikos
Quoting Bobby Bruce bbruce@ucdavis.edu:
Hey Niko,
Thanks for this analysis. I jumped a little into this today but didn't get
as far as you did. I wanted to find a quick way to recreate the following:
https://gem5-review.googlesource.com/c/public/gem5/+/64211. Please feel
free to use this, if it helps any.
It's very strange to me that this bug hasn't manifested itself before but
it's undeniably there. I'll try to spend more time looking at this tomorrow
with some traces and debug flags and see if I can narrow down the problem.
--
Dr. Bobby R. Bruce
Room 3050,
Kemper Hall, UC Davis
Davis,
CA, 95616
web: https://www.bobbybruce.net
On Wed, Oct 5, 2022 at 2:26 PM Νικόλαος Ταμπουρατζής <
ntampouratzis@ece.auth.gr> wrote:
In my previous results, I had used double (not float) for the
following variables: result, sq_i and sq_j. In the case of float
instead of double I get "nan" and not 0.000000.
Quoting Νικόλαος Ταμπουρατζής ntampouratzis@ece.auth.gr:
Dear Jason, all,
I am trying to find the accuracy problem with RISCV-FS and I observe
that the problem is created (at least in my dummy example) because
the variables (double) are set to zero in random simulated time (for
this reason I get different results among executions of the same
code). Specifically for the following dummy code:
#include <cmath>
#include <stdio.h>
int main(){
int dim = 10;
float result;
for (int iter = 0; iter < 2; iter++){
result = 0;
for (int i = 0; i < dim; i++){
for (int j = 0; j < dim; j++){
float sq_i = sqrt(i);
float sq_j = sqrt(j);
result += sq_i * sq_j;
printf("ITER: %d | i: %d | j: %d Result(i: %f | j:
%f | i*j: %f): %f\n", iter, i , j, sq_i, sq_j, sq_i * sq_j, result);
}
}
printf("Final Result: %lf\n", result);
}
}
The correct Final Result in both iterations is 372.721656. However,
I get the following results in FS:
ITER: 0 | i: 0 | j: 0 Result(i: 0.000000 | j: 0.000000 | ij:
0.000000): 0.000000
ITER: 0 | i: 0 | j: 1 Result(i: 0.000000 | j: 1.000000 | ij:
0.000000): 0.000000
ITER: 0 | i: 0 | j: 2 Result(i: 0.000000 | j: 1.414214 | ij:
0.000000): 0.000000
ITER: 0 | i: 0 | j: 3 Result(i: 0.000000 | j: 1.732051 | ij:
0.000000): 0.000000
ITER: 0 | i: 0 | j: 4 Result(i: 0.000000 | j: 2.000000 | ij:
0.000000): 0.000000
ITER: 0 | i: 0 | j: 5 Result(i: 0.000000 | j: 2.236068 | ij:
0.000000): 0.000000
ITER: 0 | i: 0 | j: 6 Result(i: 0.000000 | j: 2.449490 | ij:
0.000000): 0.000000
ITER: 0 | i: 0 | j: 7 Result(i: 0.000000 | j: 2.645751 | ij:
0.000000): 0.000000
ITER: 0 | i: 0 | j: 8 Result(i: 0.000000 | j: 2.828427 | ij:
0.000000): 0.000000
ITER: 0 | i: 0 | j: 9 Result(i: 0.000000 | j: 3.000000 | ij:
0.000000): 0.000000
ITER: 0 | i: 1 | j: 0 Result(i: 1.000000 | j: 0.000000 | ij:
0.000000): 0.000000
ITER: 0 | i: 1 | j: 1 Result(i: 1.000000 | j: 1.000000 | ij:
1.000000): 1.000000
ITER: 0 | i: 1 | j: 2 Result(i: 1.000000 | j: 1.414214 | ij:
1.414214): 2.414214
ITER: 0 | i: 1 | j: 3 Result(i: 1.000000 | j: 1.732051 | ij:
1.732051): 4.146264
ITER: 0 | i: 1 | j: 4 Result(i: 0.000000 | j: 2.000000 | ij:
0.000000): 0.000000
ITER: 0 | i: 1 | j: 5 Result(i: 0.000000 | j: 2.236068 | ij:
0.000000): 0.000000
ITER: 0 | i: 1 | j: 6 Result(i: 0.000000 | j: 2.449490 | ij:
0.000000): 0.000000
ITER: 0 | i: 1 | j: 7 Result(i: 0.000000 | j: 2.645751 | ij:
0.000000): 0.000000
ITER: 0 | i: 1 | j: 8 Result(i: 0.000000 | j: 2.828427 | ij:
0.000000): 0.000000
ITER: 0 | i: 1 | j: 9 Result(i: 0.000000 | j: 3.000000 | ij:
0.000000): 0.000000
ITER: 0 | i: 2 | j: 0 Result(i: 1.414214 | j: 0.000000 | ij:
0.000000): 0.000000
ITER: 0 | i: 2 | j: 1 Result(i: 1.414214 | j: 1.000000 | ij:
1.414214): 1.414214
ITER: 0 | i: 2 | j: 2 Result(i: 1.414214 | j: 1.414214 | ij:
2.000000): 3.414214
ITER: 0 | i: 2 | j: 3 Result(i: 1.414214 | j: 1.732051 | ij:
2.449490): 5.863703
ITER: 0 | i: 2 | j: 4 Result(i: 1.414214 | j: 2.000000 | ij:
2.828427): 8.692130
ITER: 0 | i: 2 | j: 5 Result(i: 1.414214 | j: 2.236068 | ij:
3.162278): 11.854408
ITER: 0 | i: 2 | j: 6 Result(i: 1.414214 | j: 2.449490 | ij:
3.464102): 15.318510
ITER: 0 | i: 2 | j: 7 Result(i: 1.414214 | j: 2.645751 | ij:
3.741657): 19.060167
ITER: 0 | i: 2 | j: 8 Result(i: 1.414214 | j: 2.828427 | ij:
4.000000): 23.060167
ITER: 0 | i: 2 | j: 9 Result(i: 1.414214 | j: 3.000000 | ij:
4.242641): 27.302808
ITER: 0 | i: 3 | j: 0 Result(i: 1.732051 | j: 0.000000 | ij:
0.000000): 27.302808
ITER: 0 | i: 3 | j: 1 Result(i: 1.732051 | j: 1.000000 | ij:
1.732051): 29.034859
ITER: 0 | i: 3 | j: 2 Result(i: 1.732051 | j: 1.414214 | ij:
2.449490): 31.484348
ITER: 0 | i: 3 | j: 3 Result(i: 1.732051 | j: 1.732051 | ij:
3.000000): 34.484348
ITER: 0 | i: 3 | j: 4 Result(i: 1.732051 | j: 2.000000 | ij:
3.464102): 37.948450
ITER: 0 | i: 3 | j: 5 Result(i: 1.732051 | j: 2.236068 | ij:
3.872983): 41.821433
ITER: 0 | i: 3 | j: 6 Result(i: 1.732051 | j: 2.449490 | ij:
4.242641): 46.064074
ITER: 0 | i: 3 | j: 7 Result(i: 1.732051 | j: 2.645751 | ij:
4.582576): 50.646650
ITER: 0 | i: 3 | j: 8 Result(i: 1.732051 | j: 2.828427 | ij:
4.898979): 55.545629
ITER: 0 | i: 3 | j: 9 Result(i: 1.732051 | j: 3.000000 | ij:
5.196152): 60.741782
ITER: 0 | i: 4 | j: 0 Result(i: 2.000000 | j: 0.000000 | ij:
0.000000): 60.741782
ITER: 0 | i: 4 | j: 1 Result(i: 2.000000 | j: 1.000000 | ij:
2.000000): 62.741782
ITER: 0 | i: 4 | j: 2 Result(i: 2.000000 | j: 1.414214 | ij:
2.828427): 65.570209
ITER: 0 | i: 4 | j: 3 Result(i: 2.000000 | j: 1.732051 | ij:
3.464102): 69.034310
ITER: 0 | i: 4 | j: 4 Result(i: 2.000000 | j: 2.000000 | ij:
4.000000): 73.034310
ITER: 0 | i: 4 | j: 5 Result(i: 2.000000 | j: 2.236068 | ij:
4.472136): 77.506446
ITER: 0 | i: 4 | j: 6 Result(i: 2.000000 | j: 2.449490 | ij:
4.898979): 82.405426
ITER: 0 | i: 4 | j: 7 Result(i: 2.000000 | j: 2.645751 | ij:
5.291503): 87.696928
ITER: 0 | i: 4 | j: 8 Result(i: 2.000000 | j: 2.828427 | ij:
5.656854): 93.353783
ITER: 0 | i: 4 | j: 9 Result(i: 2.000000 | j: 3.000000 | ij:
6.000000): 99.353783
ITER: 0 | i: 5 | j: 0 Result(i: 2.236068 | j: 0.000000 | ij:
0.000000): 99.353783
ITER: 0 | i: 5 | j: 1 Result(i: 2.236068 | j: 1.000000 | ij:
2.236068): 101.589851
ITER: 0 | i: 5 | j: 2 Result(i: 2.236068 | j: 1.414214 | ij:
3.162278): 104.752128
ITER: 0 | i: 5 | j: 3 Result(i: 2.236068 | j: 1.732051 | ij:
3.872983): 108.625112
ITER: 0 | i: 5 | j: 4 Result(i: 2.236068 | j: 2.000000 | ij:
4.472136): 113.097248
ITER: 0 | i: 5 | j: 5 Result(i: 2.236068 | j: 2.236068 | ij:
5.000000): 118.097248
ITER: 0 | i: 5 | j: 6 Result(i: 2.236068 | j: 2.449490 | ij:
5.477226): 123.574473
ITER: 0 | i: 5 | j: 7 Result(i: 2.236068 | j: 2.645751 | ij:
5.916080): 129.490553
ITER: 0 | i: 5 | j: 8 Result(i: 2.236068 | j: 2.828427 | ij:
6.324555): 135.815108
ITER: 0 | i: 5 | j: 9 Result(i: 2.236068 | j: 3.000000 | ij:
6.708204): 142.523312
ITER: 0 | i: 6 | j: 0 Result(i: 2.449490 | j: 0.000000 | ij:
0.000000): 142.523312
ITER: 0 | i: 6 | j: 1 Result(i: 2.449490 | j: 1.000000 | ij:
2.449490): 144.972802
ITER: 0 | i: 6 | j: 2 Result(i: 2.449490 | j: 1.414214 | ij:
3.464102): 148.436904
ITER: 0 | i: 6 | j: 3 Result(i: 2.449490 | j: 1.732051 | ij:
4.242641): 152.679544
ITER: 0 | i: 6 | j: 4 Result(i: 2.449490 | j: 2.000000 | ij:
4.898979): 157.578524
ITER: 0 | i: 6 | j: 5 Result(i: 2.449490 | j: 2.236068 | ij:
5.477226): 163.055749
ITER: 0 | i: 6 | j: 6 Result(i: 2.449490 | j: 2.449490 | ij:
6.000000): 169.055749
ITER: 0 | i: 6 | j: 7 Result(i: 2.449490 | j: 2.645751 | ij:
6.480741): 175.536490
ITER: 0 | i: 6 | j: 8 Result(i: 2.449490 | j: 2.828427 | ij:
6.928203): 182.464693
ITER: 0 | i: 6 | j: 9 Result(i: 2.449490 | j: 3.000000 | ij:
7.348469): 189.813162
ITER: 0 | i: 7 | j: 0 Result(i: 2.645751 | j: 0.000000 | ij:
0.000000): 189.813162
ITER: 0 | i: 7 | j: 1 Result(i: 2.645751 | j: 1.000000 | ij:
2.645751): 192.458914
ITER: 0 | i: 7 | j: 2 Result(i: 2.645751 | j: 1.414214 | ij:
3.741657): 196.200571
ITER: 0 | i: 7 | j: 3 Result(i: 2.645751 | j: 1.732051 | ij:
4.582576): 200.783147
ITER: 0 | i: 7 | j: 4 Result(i: 2.645751 | j: 2.000000 | ij:
5.291503): 206.074649
ITER: 0 | i: 7 | j: 5 Result(i: 2.645751 | j: 2.236068 | ij:
5.916080): 211.990729
ITER: 0 | i: 7 | j: 6 Result(i: 2.645751 | j: 2.449490 | ij:
6.480741): 218.471470
ITER: 0 | i: 7 | j: 7 Result(i: 2.645751 | j: 2.645751 | ij:
7.000000): 225.471470
ITER: 0 | i: 7 | j: 8 Result(i: 2.645751 | j: 2.828427 | ij:
7.483315): 232.954785
ITER: 0 | i: 7 | j: 9 Result(i: 2.645751 | j: 3.000000 | ij:
7.937254): 240.892039
ITER: 0 | i: 8 | j: 0 Result(i: 2.828427 | j: 0.000000 | ij:
0.000000): 240.892039
ITER: 0 | i: 8 | j: 1 Result(i: 2.828427 | j: 1.000000 | ij:
2.828427): 243.720466
ITER: 0 | i: 8 | j: 2 Result(i: 2.828427 | j: 1.414214 | ij:
4.000000): 247.720466
ITER: 0 | i: 8 | j: 3 Result(i: 2.828427 | j: 1.732051 | ij:
4.898979): 252.619445
ITER: 0 | i: 8 | j: 4 Result(i: 2.828427 | j: 2.000000 | ij:
5.656854): 258.276300
ITER: 0 | i: 8 | j: 5 Result(i: 2.828427 | j: 2.236068 | ij:
6.324555): 264.600855
ITER: 0 | i: 8 | j: 6 Result(i: 2.828427 | j: 2.449490 | ij:
6.928203): 271.529058
ITER: 0 | i: 8 | j: 7 Result(i: 2.828427 | j: 2.645751 | ij:
7.483315): 279.012373
ITER: 0 | i: 8 | j: 8 Result(i: 2.828427 | j: 2.828427 | ij:
8.000000): 287.012373
ITER: 0 | i: 8 | j: 9 Result(i: 2.828427 | j: 3.000000 | ij:
8.485281): 295.497654
ITER: 0 | i: 9 | j: 0 Result(i: 3.000000 | j: 0.000000 | ij:
0.000000): 295.497654
ITER: 0 | i: 9 | j: 1 Result(i: 3.000000 | j: 1.000000 | ij:
3.000000): 298.497654
ITER: 0 | i: 9 | j: 2 Result(i: 3.000000 | j: 1.414214 | ij:
4.242641): 302.740295
ITER: 0 | i: 9 | j: 3 Result(i: 3.000000 | j: 1.732051 | ij:
5.196152): 307.936447
ITER: 0 | i: 9 | j: 4 Result(i: 3.000000 | j: 2.000000 | ij:
6.000000): 313.936447
ITER: 0 | i: 9 | j: 5 Result(i: 3.000000 | j: 2.236068 | ij:
6.708204): 320.644651
ITER: 0 | i: 9 | j: 6 Result(i: 3.000000 | j: 2.449490 | ij:
7.348469): 327.993120
ITER: 0 | i: 9 | j: 7 Result(i: 3.000000 | j: 2.645751 | ij:
7.937254): 335.930374
ITER: 0 | i: 9 | j: 8 Result(i: 3.000000 | j: 2.828427 | ij:
8.485281): 344.415656
ITER: 0 | i: 9 | j: 9 Result(i: 3.000000 | j: 3.000000 | ij:
9.000000): 353.415656
Final Result: 353.415656
ITER: 1 | i: 0 | j: 0 Result(i: 0.000000 | j: 0.000000 | ij:
0.000000): 0.000000
ITER: 1 | i: 0 | j: 1 Result(i: 0.000000 | j: 1.000000 | ij:
0.000000): 0.000000
ITER: 1 | i: 0 | j: 2 Result(i: 0.000000 | j: 1.414214 | ij:
0.000000): 0.000000
ITER: 1 | i: 0 | j: 3 Result(i: 0.000000 | j: 1.732051 | ij:
0.000000): 0.000000
ITER: 1 | i: 0 | j: 4 Result(i: 0.000000 | j: 2.000000 | ij:
0.000000): 0.000000
ITER: 1 | i: 0 | j: 5 Result(i: 0.000000 | j: 2.236068 | ij:
0.000000): 0.000000
ITER: 1 | i: 0 | j: 6 Result(i: 0.000000 | j: 2.449490 | ij:
0.000000): 0.000000
ITER: 1 | i: 0 | j: 7 Result(i: 0.000000 | j: 2.645751 | ij:
0.000000): 0.000000
ITER: 1 | i: 0 | j: 8 Result(i: 0.000000 | j: 2.828427 | ij:
0.000000): 0.000000
ITER: 1 | i: 0 | j: 9 Result(i: 0.000000 | j: 3.000000 | ij:
0.000000): 0.000000
ITER: 1 | i: 1 | j: 0 Result(i: 1.000000 | j: 0.000000 | ij:
0.000000): 0.000000
ITER: 1 | i: 1 | j: 1 Result(i: 1.000000 | j: 1.000000 | ij:
1.000000): 1.000000
ITER: 1 | i: 1 | j: 2 Result(i: 1.000000 | j: 1.414214 | ij:
1.414214): 2.414214
ITER: 1 | i: 1 | j: 3 Result(i: 1.000000 | j: 1.732051 | ij:
1.732051): 4.146264
ITER: 1 | i: 1 | j: 4 Result(i: 1.000000 | j: 2.000000 | ij:
2.000000): 6.146264
ITER: 1 | i: 1 | j: 5 Result(i: 1.000000 | j: 2.236068 | ij:
2.236068): 8.382332
ITER: 1 | i: 1 | j: 6 Result(i: 1.000000 | j: 2.449490 | ij:
2.449490): 10.831822
ITER: 1 | i: 1 | j: 7 Result(i: 1.000000 | j: 2.645751 | ij:
2.645751): 13.477573
ITER: 1 | i: 1 | j: 8 Result(i: 1.000000 | j: 2.828427 | ij:
2.828427): 16.306001
ITER: 1 | i: 1 | j: 9 Result(i: 1.000000 | j: 3.000000 | ij:
3.000000): 19.306001
ITER: 1 | i: 2 | j: 0 Result(i: 1.414214 | j: 0.000000 | ij:
0.000000): 19.306001
ITER: 1 | i: 2 | j: 1 Result(i: 1.414214 | j: 1.000000 | ij:
1.414214): 20.720214
ITER: 1 | i: 2 | j: 2 Result(i: 1.414214 | j: 1.414214 | ij:
2.000000): 22.720214
ITER: 1 | i: 2 | j: 3 Result(i: 1.414214 | j: 1.732051 | ij:
2.449490): 25.169704
ITER: 1 | i: 2 | j: 4 Result(i: 1.414214 | j: 2.000000 | ij:
2.828427): 27.998131
ITER: 1 | i: 2 | j: 5 Result(i: 1.414214 | j: 2.236068 | ij:
3.162278): 31.160409
ITER: 1 | i: 2 | j: 6 Result(i: 1.414214 | j: 2.449490 | ij:
3.464102): 34.624510
ITER: 1 | i: 2 | j: 7 Result(i: 1.414214 | j: 2.645751 | ij:
3.741657): 38.366168
ITER: 1 | i: 2 | j: 8 Result(i: 1.414214 | j: 2.828427 | ij:
4.000000): 42.366168
ITER: 1 | i: 2 | j: 9 Result(i: 1.414214 | j: 3.000000 | ij:
4.242641): 46.608808
ITER: 1 | i: 3 | j: 0 Result(i: 1.732051 | j: 0.000000 | ij:
0.000000): 46.608808
ITER: 1 | i: 3 | j: 1 Result(i: 1.732051 | j: 1.000000 | ij:
1.732051): 48.340859
ITER: 1 | i: 3 | j: 2 Result(i: 1.732051 | j: 1.414214 | ij:
2.449490): 50.790349
ITER: 1 | i: 3 | j: 3 Result(i: 1.732051 | j: 1.732051 | ij:
3.000000): 53.790349
ITER: 1 | i: 3 | j: 4 Result(i: 1.732051 | j: 2.000000 | ij:
3.464102): 57.254450
ITER: 1 | i: 3 | j: 5 Result(i: 1.732051 | j: 2.236068 | ij:
3.872983): 61.127434
ITER: 1 | i: 3 | j: 6 Result(i: 1.732051 | j: 2.449490 | ij:
4.242641): 65.370075
ITER: 1 | i: 3 | j: 7 Result(i: 1.732051 | j: 2.645751 | ij:
4.582576): 69.952650
ITER: 1 | i: 3 | j: 8 Result(i: 1.732051 | j: 2.828427 | ij:
4.898979): 74.851630
ITER: 1 | i: 3 | j: 9 Result(i: 1.732051 | j: 3.000000 | ij:
5.196152): 80.047782
ITER: 1 | i: 4 | j: 0 Result(i: 2.000000 | j: 0.000000 | ij:
0.000000): 80.047782
ITER: 1 | i: 4 | j: 1 Result(i: 2.000000 | j: 1.000000 | ij:
2.000000): 82.047782
ITER: 1 | i: 4 | j: 2 Result(i: 2.000000 | j: 1.414214 | ij:
2.828427): 84.876209
ITER: 1 | i: 4 | j: 3 Result(i: 2.000000 | j: 1.732051 | ij:
3.464102): 88.340311
ITER: 1 | i: 4 | j: 4 Result(i: 2.000000 | j: 2.000000 | ij:
4.000000): 92.340311
ITER: 1 | i: 4 | j: 5 Result(i: 2.000000 | j: 2.236068 | ij:
4.472136): 96.812447
ITER: 1 | i: 4 | j: 6 Result(i: 2.000000 | j: 2.449490 | ij:
4.898979): 101.711426
ITER: 1 | i: 4 | j: 7 Result(i: 2.000000 | j: 2.645751 | ij:
5.291503): 107.002929
ITER: 1 | i: 4 | j: 8 Result(i: 2.000000 | j: 2.828427 | ij:
5.656854): 112.659783
ITER: 1 | i: 4 | j: 9 Result(i: 2.000000 | j: 3.000000 | ij:
6.000000): 118.659783
ITER: 1 | i: 5 | j: 0 Result(i: 2.236068 | j: 0.000000 | ij:
0.000000): 118.659783
ITER: 1 | i: 5 | j: 1 Result(i: 2.236068 | j: 1.000000 | ij:
2.236068): 120.895851
ITER: 1 | i: 5 | j: 2 Result(i: 2.236068 | j: 1.414214 | ij:
3.162278): 124.058129
ITER: 1 | i: 5 | j: 3 Result(i: 2.236068 | j: 1.732051 | ij:
3.872983): 127.931112
ITER: 1 | i: 5 | j: 4 Result(i: 2.236068 | j: 2.000000 | ij:
4.472136): 132.403248
ITER: 1 | i: 5 | j: 5 Result(i: 2.236068 | j: 2.236068 | ij:
5.000000): 137.403248
ITER: 1 | i: 5 | j: 6 Result(i: 2.236068 | j: 2.449490 | ij:
5.477226): 142.880474
ITER: 1 | i: 5 | j: 7 Result(i: 2.236068 | j: 2.645751 | ij:
5.916080): 148.796553
ITER: 1 | i: 5 | j: 8 Result(i: 2.236068 | j: 2.828427 | ij:
6.324555): 155.121109
ITER: 1 | i: 5 | j: 9 Result(i: 2.236068 | j: 3.000000 | ij:
6.708204): 161.829313
ITER: 1 | i: 6 | j: 0 Result(i: 2.449490 | j: 0.000000 | ij:
0.000000): 161.829313
ITER: 1 | i: 6 | j: 1 Result(i: 2.449490 | j: 1.000000 | ij:
2.449490): 164.278802
ITER: 1 | i: 6 | j: 2 Result(i: 2.449490 | j: 1.414214 | ij:
3.464102): 167.742904
ITER: 1 | i: 6 | j: 3 Result(i: 2.449490 | j: 1.732051 | ij:
4.242641): 171.985545
ITER: 1 | i: 6 | j: 4 Result(i: 2.449490 | j: 2.000000 | ij:
4.898979): 176.884524
ITER: 1 | i: 6 | j: 5 Result(i: 2.449490 | j: 2.236068 | ij:
5.477226): 182.361750
ITER: 1 | i: 6 | j: 6 Result(i: 2.449490 | j: 2.449490 | ij:
6.000000): 188.361750
ITER: 1 | i: 6 | j: 7 Result(i: 2.449490 | j: 2.645751 | ij:
6.480741): 194.842491
ITER: 1 | i: 6 | j: 8 Result(i: 2.449490 | j: 2.828427 | ij:
6.928203): 201.770694
ITER: 1 | i: 6 | j: 9 Result(i: 2.449490 | j: 3.000000 | ij:
7.348469): 209.119163
ITER: 1 | i: 7 | j: 0 Result(i: 2.645751 | j: 0.000000 | ij:
0.000000): 209.119163
ITER: 1 | i: 7 | j: 1 Result(i: 2.645751 | j: 1.000000 | ij:
2.645751): 211.764914
ITER: 1 | i: 7 | j: 2 Result(i: 2.645751 | j: 1.414214 | ij:
3.741657): 215.506572
ITER: 1 | i: 7 | j: 3 Result(i: 2.645751 | j: 1.732051 | ij:
4.582576): 220.089147
ITER: 1 | i: 7 | j: 4 Result(i: 2.645751 | j: 2.000000 | ij:
5.291503): 225.380650
ITER: 1 | i: 7 | j: 5 Result(i: 2.645751 | j: 2.236068 | ij:
5.916080): 231.296730
ITER: 1 | i: 7 | j: 6 Result(i: 2.645751 | j: 2.449490 | ij:
6.480741): 237.777470
ITER: 1 | i: 7 | j: 7 Result(i: 2.645751 | j: 2.645751 | ij:
7.000000): 244.777470
ITER: 1 | i: 7 | j: 8 Result(i: 2.645751 | j: 2.828427 | ij:
7.483315): 252.260785
ITER: 1 | i: 7 | j: 9 Result(i: 2.645751 | j: 3.000000 | ij:
7.937254): 260.198039
ITER: 1 | i: 8 | j: 0 Result(i: 2.828427 | j: 0.000000 | ij:
0.000000): 260.198039
ITER: 1 | i: 8 | j: 1 Result(i: 2.828427 | j: 1.000000 | ij:
2.828427): 263.026466
ITER: 1 | i: 8 | j: 2 Result(i: 2.828427 | j: 1.414214 | ij:
4.000000): 267.026466
ITER: 1 | i: 8 | j: 3 Result(i: 2.828427 | j: 1.732051 | ij:
4.898979): 271.925446
ITER: 1 | i: 8 | j: 4 Result(i: 2.828427 | j: 2.000000 | ij:
5.656854): 277.582300
ITER: 1 | i: 8 | j: 5 Result(i: 2.828427 | j: 2.236068 | ij:
6.324555): 283.906855
ITER: 1 | i: 8 | j: 6 Result(i: 2.828427 | j: 2.449490 | ij:
6.928203): 290.835059
ITER: 1 | i: 8 | j: 7 Result(i: 2.828427 | j: 2.645751 | ij:
7.483315): 298.318373
ITER: 1 | i: 8 | j: 8 Result(i: 2.828427 | j: 2.828427 | ij:
8.000000): 306.318373
ITER: 1 | i: 8 | j: 9 Result(i: 2.828427 | j: 3.000000 | ij:
8.485281): 314.803655
ITER: 1 | i: 9 | j: 0 Result(i: 3.000000 | j: 0.000000 | ij:
0.000000): 314.803655
ITER: 1 | i: 9 | j: 1 Result(i: 3.000000 | j: 1.000000 | ij:
3.000000): 317.803655
ITER: 1 | i: 9 | j: 2 Result(i: 3.000000 | j: 1.414214 | ij:
4.242641): 322.046295
ITER: 1 | i: 9 | j: 3 Result(i: 3.000000 | j: 1.732051 | ij:
5.196152): 327.242448
ITER: 1 | i: 9 | j: 4 Result(i: 3.000000 | j: 2.000000 | ij:
6.000000): 333.242448
ITER: 1 | i: 9 | j: 5 Result(i: 3.000000 | j: 2.236068 | ij:
6.708204): 339.950652
ITER: 1 | i: 9 | j: 6 Result(i: 3.000000 | j: 2.449490 | ij:
7.348469): 347.299121
ITER: 1 | i: 9 | j: 7 Result(i: 3.000000 | j: 2.645751 | ij:
7.937254): 355.236375
ITER: 1 | i: 9 | j: 8 Result(i: 3.000000 | j: 2.828427 | ij:
8.485281): 363.721656
ITER: 1 | i: 9 | j: 9 Result(i: 3.000000 | j: 3.000000 | ij:
9.000000): 372.721656
Final Result: 372.721656
As we can see in the following iterations the sqrt(1) as well as the
result is set to zero for some reason.
ITER: 0 | i: 1 | j: 4 Result(i: 0.000000 | j: 2.000000 | ij:
0.000000): 0.000000
ITER: 0 | i: 1 | j: 5 Result(i: 0.000000 | j: 2.236068 | ij:
0.000000): 0.000000
ITER: 0 | i: 1 | j: 6 Result(i: 0.000000 | j: 2.449490 | ij:
0.000000): 0.000000
ITER: 0 | i: 1 | j: 7 Result(i: 0.000000 | j: 2.645751 | ij:
0.000000): 0.000000
ITER: 0 | i: 1 | j: 8 Result(i: 0.000000 | j: 2.828427 | ij:
0.000000): 0.000000
ITER: 0 | i: 1 | j: 9 Result(i: 0.000000 | j: 3.000000 | ij:
0.000000): 0.000000
Please help me to resolve the accuracy issue! I think that it will
be very useful for gem5 community.
To be noticed, I find the correct simulated tick in which the
application started in FS (using m5 dumpstats), and I start the
--debug-start, but the trace file which is generated is 10x larger
than SE mode for the same application. How can I compare them?
Thank you in advance!
Best regards,
Nikos
Quoting Νικόλαος Ταμπουρατζής ntampouratzis@ece.auth.gr:
Dear Jason,
I am trying to use --debug-start but in FS mode it is very
difficult to find the tick on which the application is started!
However, I am writing the following very simple c++ program:
#include <cmath>
#include <stdio.h>
int main(){
int dim = 4096;
double result;
for (int iter = 0; iter < 2; iter++){
result = 0;
for (int i = 0; i < dim; i++){
for (int j = 0; j < dim; j++){
result += sqrt(i) * sqrt(j);
}
}
printf("Result: %lf\n", result); //Result: 30530733453.127449
}
}
I cross-compile it using: riscv64-linux-gnu-g++ -static -O3 -o
test_riscv test_riscv.cpp
While in X86 (without cross-compilation of course), QEMU-RISCV,
GEM5-SE the result is the same (30530733453.127449), in GEM5-FS the
result is different! In addition, the result is also different
between the 2 iterations.
Please reproduce the error if you want in order to verify my result.
Ηow can the issue be resolved?
Thank you in advance!
Best regards,
Nikos
Quoting Jason Lowe-Power jason@lowepower.com:
Hi Nikos,
You can use --debug-start to start the debugging after some number of
ticks. Also, I would expect that the difference should come up
no need to run the program to the end.
For the FS mode one, you will want to just start the trace as the
application starts. This could be a bit of a pain.
I'm not really sure what fundamentally could be different. FS and SE
use the exact same code for executing instructions, so I don't think
the problem. Have you tried running for smaller inputs or just one
iteration?
Jason
On Wed, Sep 21, 2022 at 9:04 AM Νικόλαος Ταμπουρατζής <
ntampouratzis@ece.auth.gr> wrote:
Dear Bobby,
Iam trying to add --debug-flags=Exec (building the gem5 for gem5.opt
not for gem5.fast which I had) but the debug traces exceed the 20GB
(and it is not finished yet) for less than 1 simulated second. How can
I reduce the size of the debug-flags (or set something more specific)?
In contrast I build the HPCG benchmark with DHPCG_DEBUG flag. If you
want, you can compare these two output files
(hpcg20010909T014640_SE_Mode & HPCG-Benchmark_3.1_FS_Mode). As you can
see, something goes wrong with the accuracy of calculations in FS mode
(benchmark uses double precission). You can find the files here:
http://kition.mhl.tuc.gr:8000/d/68d82f3533/
Best regards,
Nikos
Quoting Jason Lowe-Power jason@lowepower.com:
That's quite odd that it works in SE mode but not FS mode!
I would suggest running with --debug-flags=Exec for both and then
diff to see how they differ.
Cheers,
Jason
On Tue, Sep 20, 2022 at 2:45 PM Νικόλαος Ταμπουρατζής <
ntampouratzis@ece.auth.gr> wrote:
Dear Bobby,
In QEMU I get the same (correct) results that I get in SE mode
simulation. I get invalid results in FS simulation (in both
riscv-fs.py and riscv-ubuntu-run.py). I cannot access real RISCV
hardware at this moment, however, if you want you may execute my
Hi Nikos,
I notice you said the following in your original email:
In addition, I used the RISCV Ubuntu image
I installed the gcc compiler, compile it (through qemu) and I get
wrong results too.
Is this saying you get the wrong results is QEMU? If so, the bug
or the HPCG workload, not in gem5. If not, I would test in QEMU to
sure the binary works there. Another way you could test to see if
problem is your binary or gem5 would be to run it on real
access to some RISC-V hardware here at UC Davis, if you don't have
Dear Bobby,
- I use the original riscv-fs.py which is provided in the latest
release.
I run the gem5 once (./build/RISCV/gem5.fast -d ./HPCG_FS_results
./configs/example/gem5_library/riscv-fs.py) in order to download
riscv-bootloader-vmlinux-5.10 and riscv-disk-img.
After this I mount the riscv-disk-img (sudo mount -o loop
riscv-disk-img /mnt), put the xhpcg executable and I do the
changes in riscv-fs.py to boot the riscv-disk-img with executable:
image = CustomDiskImageResource(
local_path = "/home/cossim/.cache/gem5/riscv-disk-img",
)
Set the Full System workload.
board.set_kernel_disk_workload(
kernel=Resource("riscv-bootloader-vmlinux-5.10"),
disk_image=image,
)
Finally, in the
gem5/src/python/gem5/components/boards/riscv_board.py
I change the last line to "return ["console=ttyS0",
"root={root_value}", "rw"]" in order to allow the write
the image.
- The HPCG benchmark after some iterations calculates if the
are valid or not valid. In the case of FS it gives invalid
I see from the results, one (at least) problem is that produces
different results in each HPCG execution (with the same
I'm going to need a bit more information to help:
- In what way have you modified
./configs/example/gem5_library/riscv-fs.py? Can you attach the
- What error are you getting or in what way are the results
Dear gem5 community,
I have successfully cross-compile the HPCG benchmark for RISCV
version, without MPI and OpenMP). While it working properly in
mode (./build/RISCV/gem5.fast -d ./HPCG_SE_results
./configs/example/se.py -c xhpcg --options '--nx=16 --ny=16
--npx=1 --npy=1 --npz=1 --rt=0.1'), I get invalid results in FS
simulation using "./build/RISCV/gem5.fast -d ./HPCG_FS_results
./configs/example/gem5_library/riscv-fs.py" (I mount the riscv
and put it).
Can you help me please?
In addition, I used the RISCV Ubuntu image
(
I installed the gcc compiler, compile it (through qemu) and I
Dear Boddy,
Thanks a lot for the effort! I looked in detail and I observe that the
problem is created only using float and double variables (in the case
of int it is working properly in FS mode). Specifically, in the case
of float the variables are set to "nan", while in the case of double
the variables are set to 0.000000 (in random time - probably from some
instruction of simulated OS?). You may use a simple c/c++ example in
order to get some traces before going to HPCG...
Thank you in advance!!
Best regards,
Nikos
Quoting Bobby Bruce <bbruce@ucdavis.edu>:
> Hey Niko,
>
> Thanks for this analysis. I jumped a little into this today but didn't get
> as far as you did. I wanted to find a quick way to recreate the following:
> https://gem5-review.googlesource.com/c/public/gem5/+/64211. Please feel
> free to use this, if it helps any.
>
> It's very strange to me that this bug hasn't manifested itself before but
> it's undeniably there. I'll try to spend more time looking at this tomorrow
> with some traces and debug flags and see if I can narrow down the problem.
>
> --
> Dr. Bobby R. Bruce
> Room 3050,
> Kemper Hall, UC Davis
> Davis,
> CA, 95616
>
> web: https://www.bobbybruce.net
>
>
> On Wed, Oct 5, 2022 at 2:26 PM Νικόλαος Ταμπουρατζής <
> ntampouratzis@ece.auth.gr> wrote:
>
>> In my previous results, I had used double (not float) for the
>> following variables: result, sq_i and sq_j. In the case of float
>> instead of double I get "nan" and not 0.000000.
>>
>> Quoting Νικόλαος Ταμπουρατζής <ntampouratzis@ece.auth.gr>:
>>
>> > Dear Jason, all,
>> >
>> > I am trying to find the accuracy problem with RISCV-FS and I observe
>> > that the problem is created (at least in my dummy example) because
>> > the variables (double) are set to zero in random simulated time (for
>> > this reason I get different results among executions of the same
>> > code). Specifically for the following dummy code:
>> >
>> >
>> > #include <cmath>
>> > #include <stdio.h>
>> >
>> > int main(){
>> >
>> > int dim = 10;
>> >
>> > float result;
>> >
>> > for (int iter = 0; iter < 2; iter++){
>> > result = 0;
>> > for (int i = 0; i < dim; i++){
>> > for (int j = 0; j < dim; j++){
>> > float sq_i = sqrt(i);
>> > float sq_j = sqrt(j);
>> > result += sq_i * sq_j;
>> > printf("ITER: %d | i: %d | j: %d Result(i: %f | j:
>> > %f | i*j: %f): %f\n", iter, i , j, sq_i, sq_j, sq_i * sq_j, result);
>> > }
>> > }
>> > printf("Final Result: %lf\n", result);
>> > }
>> > }
>> >
>> >
>> > The correct Final Result in both iterations is 372.721656. However,
>> > I get the following results in FS:
>> >
>> > ITER: 0 | i: 0 | j: 0 Result(i: 0.000000 | j: 0.000000 | i*j:
>> > 0.000000): 0.000000
>> > ITER: 0 | i: 0 | j: 1 Result(i: 0.000000 | j: 1.000000 | i*j:
>> > 0.000000): 0.000000
>> > ITER: 0 | i: 0 | j: 2 Result(i: 0.000000 | j: 1.414214 | i*j:
>> > 0.000000): 0.000000
>> > ITER: 0 | i: 0 | j: 3 Result(i: 0.000000 | j: 1.732051 | i*j:
>> > 0.000000): 0.000000
>> > ITER: 0 | i: 0 | j: 4 Result(i: 0.000000 | j: 2.000000 | i*j:
>> > 0.000000): 0.000000
>> > ITER: 0 | i: 0 | j: 5 Result(i: 0.000000 | j: 2.236068 | i*j:
>> > 0.000000): 0.000000
>> > ITER: 0 | i: 0 | j: 6 Result(i: 0.000000 | j: 2.449490 | i*j:
>> > 0.000000): 0.000000
>> > ITER: 0 | i: 0 | j: 7 Result(i: 0.000000 | j: 2.645751 | i*j:
>> > 0.000000): 0.000000
>> > ITER: 0 | i: 0 | j: 8 Result(i: 0.000000 | j: 2.828427 | i*j:
>> > 0.000000): 0.000000
>> > ITER: 0 | i: 0 | j: 9 Result(i: 0.000000 | j: 3.000000 | i*j:
>> > 0.000000): 0.000000
>> > ITER: 0 | i: 1 | j: 0 Result(i: 1.000000 | j: 0.000000 | i*j:
>> > 0.000000): 0.000000
>> > ITER: 0 | i: 1 | j: 1 Result(i: 1.000000 | j: 1.000000 | i*j:
>> > 1.000000): 1.000000
>> > ITER: 0 | i: 1 | j: 2 Result(i: 1.000000 | j: 1.414214 | i*j:
>> > 1.414214): 2.414214
>> > ITER: 0 | i: 1 | j: 3 Result(i: 1.000000 | j: 1.732051 | i*j:
>> > 1.732051): 4.146264
>> > ITER: 0 | i: 1 | j: 4 Result(i: 0.000000 | j: 2.000000 | i*j:
>> > 0.000000): 0.000000
>> > ITER: 0 | i: 1 | j: 5 Result(i: 0.000000 | j: 2.236068 | i*j:
>> > 0.000000): 0.000000
>> > ITER: 0 | i: 1 | j: 6 Result(i: 0.000000 | j: 2.449490 | i*j:
>> > 0.000000): 0.000000
>> > ITER: 0 | i: 1 | j: 7 Result(i: 0.000000 | j: 2.645751 | i*j:
>> > 0.000000): 0.000000
>> > ITER: 0 | i: 1 | j: 8 Result(i: 0.000000 | j: 2.828427 | i*j:
>> > 0.000000): 0.000000
>> > ITER: 0 | i: 1 | j: 9 Result(i: 0.000000 | j: 3.000000 | i*j:
>> > 0.000000): 0.000000
>> > ITER: 0 | i: 2 | j: 0 Result(i: 1.414214 | j: 0.000000 | i*j:
>> > 0.000000): 0.000000
>> > ITER: 0 | i: 2 | j: 1 Result(i: 1.414214 | j: 1.000000 | i*j:
>> > 1.414214): 1.414214
>> > ITER: 0 | i: 2 | j: 2 Result(i: 1.414214 | j: 1.414214 | i*j:
>> > 2.000000): 3.414214
>> > ITER: 0 | i: 2 | j: 3 Result(i: 1.414214 | j: 1.732051 | i*j:
>> > 2.449490): 5.863703
>> > ITER: 0 | i: 2 | j: 4 Result(i: 1.414214 | j: 2.000000 | i*j:
>> > 2.828427): 8.692130
>> > ITER: 0 | i: 2 | j: 5 Result(i: 1.414214 | j: 2.236068 | i*j:
>> > 3.162278): 11.854408
>> > ITER: 0 | i: 2 | j: 6 Result(i: 1.414214 | j: 2.449490 | i*j:
>> > 3.464102): 15.318510
>> > ITER: 0 | i: 2 | j: 7 Result(i: 1.414214 | j: 2.645751 | i*j:
>> > 3.741657): 19.060167
>> > ITER: 0 | i: 2 | j: 8 Result(i: 1.414214 | j: 2.828427 | i*j:
>> > 4.000000): 23.060167
>> > ITER: 0 | i: 2 | j: 9 Result(i: 1.414214 | j: 3.000000 | i*j:
>> > 4.242641): 27.302808
>> > ITER: 0 | i: 3 | j: 0 Result(i: 1.732051 | j: 0.000000 | i*j:
>> > 0.000000): 27.302808
>> > ITER: 0 | i: 3 | j: 1 Result(i: 1.732051 | j: 1.000000 | i*j:
>> > 1.732051): 29.034859
>> > ITER: 0 | i: 3 | j: 2 Result(i: 1.732051 | j: 1.414214 | i*j:
>> > 2.449490): 31.484348
>> > ITER: 0 | i: 3 | j: 3 Result(i: 1.732051 | j: 1.732051 | i*j:
>> > 3.000000): 34.484348
>> > ITER: 0 | i: 3 | j: 4 Result(i: 1.732051 | j: 2.000000 | i*j:
>> > 3.464102): 37.948450
>> > ITER: 0 | i: 3 | j: 5 Result(i: 1.732051 | j: 2.236068 | i*j:
>> > 3.872983): 41.821433
>> > ITER: 0 | i: 3 | j: 6 Result(i: 1.732051 | j: 2.449490 | i*j:
>> > 4.242641): 46.064074
>> > ITER: 0 | i: 3 | j: 7 Result(i: 1.732051 | j: 2.645751 | i*j:
>> > 4.582576): 50.646650
>> > ITER: 0 | i: 3 | j: 8 Result(i: 1.732051 | j: 2.828427 | i*j:
>> > 4.898979): 55.545629
>> > ITER: 0 | i: 3 | j: 9 Result(i: 1.732051 | j: 3.000000 | i*j:
>> > 5.196152): 60.741782
>> > ITER: 0 | i: 4 | j: 0 Result(i: 2.000000 | j: 0.000000 | i*j:
>> > 0.000000): 60.741782
>> > ITER: 0 | i: 4 | j: 1 Result(i: 2.000000 | j: 1.000000 | i*j:
>> > 2.000000): 62.741782
>> > ITER: 0 | i: 4 | j: 2 Result(i: 2.000000 | j: 1.414214 | i*j:
>> > 2.828427): 65.570209
>> > ITER: 0 | i: 4 | j: 3 Result(i: 2.000000 | j: 1.732051 | i*j:
>> > 3.464102): 69.034310
>> > ITER: 0 | i: 4 | j: 4 Result(i: 2.000000 | j: 2.000000 | i*j:
>> > 4.000000): 73.034310
>> > ITER: 0 | i: 4 | j: 5 Result(i: 2.000000 | j: 2.236068 | i*j:
>> > 4.472136): 77.506446
>> > ITER: 0 | i: 4 | j: 6 Result(i: 2.000000 | j: 2.449490 | i*j:
>> > 4.898979): 82.405426
>> > ITER: 0 | i: 4 | j: 7 Result(i: 2.000000 | j: 2.645751 | i*j:
>> > 5.291503): 87.696928
>> > ITER: 0 | i: 4 | j: 8 Result(i: 2.000000 | j: 2.828427 | i*j:
>> > 5.656854): 93.353783
>> > ITER: 0 | i: 4 | j: 9 Result(i: 2.000000 | j: 3.000000 | i*j:
>> > 6.000000): 99.353783
>> > ITER: 0 | i: 5 | j: 0 Result(i: 2.236068 | j: 0.000000 | i*j:
>> > 0.000000): 99.353783
>> > ITER: 0 | i: 5 | j: 1 Result(i: 2.236068 | j: 1.000000 | i*j:
>> > 2.236068): 101.589851
>> > ITER: 0 | i: 5 | j: 2 Result(i: 2.236068 | j: 1.414214 | i*j:
>> > 3.162278): 104.752128
>> > ITER: 0 | i: 5 | j: 3 Result(i: 2.236068 | j: 1.732051 | i*j:
>> > 3.872983): 108.625112
>> > ITER: 0 | i: 5 | j: 4 Result(i: 2.236068 | j: 2.000000 | i*j:
>> > 4.472136): 113.097248
>> > ITER: 0 | i: 5 | j: 5 Result(i: 2.236068 | j: 2.236068 | i*j:
>> > 5.000000): 118.097248
>> > ITER: 0 | i: 5 | j: 6 Result(i: 2.236068 | j: 2.449490 | i*j:
>> > 5.477226): 123.574473
>> > ITER: 0 | i: 5 | j: 7 Result(i: 2.236068 | j: 2.645751 | i*j:
>> > 5.916080): 129.490553
>> > ITER: 0 | i: 5 | j: 8 Result(i: 2.236068 | j: 2.828427 | i*j:
>> > 6.324555): 135.815108
>> > ITER: 0 | i: 5 | j: 9 Result(i: 2.236068 | j: 3.000000 | i*j:
>> > 6.708204): 142.523312
>> > ITER: 0 | i: 6 | j: 0 Result(i: 2.449490 | j: 0.000000 | i*j:
>> > 0.000000): 142.523312
>> > ITER: 0 | i: 6 | j: 1 Result(i: 2.449490 | j: 1.000000 | i*j:
>> > 2.449490): 144.972802
>> > ITER: 0 | i: 6 | j: 2 Result(i: 2.449490 | j: 1.414214 | i*j:
>> > 3.464102): 148.436904
>> > ITER: 0 | i: 6 | j: 3 Result(i: 2.449490 | j: 1.732051 | i*j:
>> > 4.242641): 152.679544
>> > ITER: 0 | i: 6 | j: 4 Result(i: 2.449490 | j: 2.000000 | i*j:
>> > 4.898979): 157.578524
>> > ITER: 0 | i: 6 | j: 5 Result(i: 2.449490 | j: 2.236068 | i*j:
>> > 5.477226): 163.055749
>> > ITER: 0 | i: 6 | j: 6 Result(i: 2.449490 | j: 2.449490 | i*j:
>> > 6.000000): 169.055749
>> > ITER: 0 | i: 6 | j: 7 Result(i: 2.449490 | j: 2.645751 | i*j:
>> > 6.480741): 175.536490
>> > ITER: 0 | i: 6 | j: 8 Result(i: 2.449490 | j: 2.828427 | i*j:
>> > 6.928203): 182.464693
>> > ITER: 0 | i: 6 | j: 9 Result(i: 2.449490 | j: 3.000000 | i*j:
>> > 7.348469): 189.813162
>> > ITER: 0 | i: 7 | j: 0 Result(i: 2.645751 | j: 0.000000 | i*j:
>> > 0.000000): 189.813162
>> > ITER: 0 | i: 7 | j: 1 Result(i: 2.645751 | j: 1.000000 | i*j:
>> > 2.645751): 192.458914
>> > ITER: 0 | i: 7 | j: 2 Result(i: 2.645751 | j: 1.414214 | i*j:
>> > 3.741657): 196.200571
>> > ITER: 0 | i: 7 | j: 3 Result(i: 2.645751 | j: 1.732051 | i*j:
>> > 4.582576): 200.783147
>> > ITER: 0 | i: 7 | j: 4 Result(i: 2.645751 | j: 2.000000 | i*j:
>> > 5.291503): 206.074649
>> > ITER: 0 | i: 7 | j: 5 Result(i: 2.645751 | j: 2.236068 | i*j:
>> > 5.916080): 211.990729
>> > ITER: 0 | i: 7 | j: 6 Result(i: 2.645751 | j: 2.449490 | i*j:
>> > 6.480741): 218.471470
>> > ITER: 0 | i: 7 | j: 7 Result(i: 2.645751 | j: 2.645751 | i*j:
>> > 7.000000): 225.471470
>> > ITER: 0 | i: 7 | j: 8 Result(i: 2.645751 | j: 2.828427 | i*j:
>> > 7.483315): 232.954785
>> > ITER: 0 | i: 7 | j: 9 Result(i: 2.645751 | j: 3.000000 | i*j:
>> > 7.937254): 240.892039
>> > ITER: 0 | i: 8 | j: 0 Result(i: 2.828427 | j: 0.000000 | i*j:
>> > 0.000000): 240.892039
>> > ITER: 0 | i: 8 | j: 1 Result(i: 2.828427 | j: 1.000000 | i*j:
>> > 2.828427): 243.720466
>> > ITER: 0 | i: 8 | j: 2 Result(i: 2.828427 | j: 1.414214 | i*j:
>> > 4.000000): 247.720466
>> > ITER: 0 | i: 8 | j: 3 Result(i: 2.828427 | j: 1.732051 | i*j:
>> > 4.898979): 252.619445
>> > ITER: 0 | i: 8 | j: 4 Result(i: 2.828427 | j: 2.000000 | i*j:
>> > 5.656854): 258.276300
>> > ITER: 0 | i: 8 | j: 5 Result(i: 2.828427 | j: 2.236068 | i*j:
>> > 6.324555): 264.600855
>> > ITER: 0 | i: 8 | j: 6 Result(i: 2.828427 | j: 2.449490 | i*j:
>> > 6.928203): 271.529058
>> > ITER: 0 | i: 8 | j: 7 Result(i: 2.828427 | j: 2.645751 | i*j:
>> > 7.483315): 279.012373
>> > ITER: 0 | i: 8 | j: 8 Result(i: 2.828427 | j: 2.828427 | i*j:
>> > 8.000000): 287.012373
>> > ITER: 0 | i: 8 | j: 9 Result(i: 2.828427 | j: 3.000000 | i*j:
>> > 8.485281): 295.497654
>> > ITER: 0 | i: 9 | j: 0 Result(i: 3.000000 | j: 0.000000 | i*j:
>> > 0.000000): 295.497654
>> > ITER: 0 | i: 9 | j: 1 Result(i: 3.000000 | j: 1.000000 | i*j:
>> > 3.000000): 298.497654
>> > ITER: 0 | i: 9 | j: 2 Result(i: 3.000000 | j: 1.414214 | i*j:
>> > 4.242641): 302.740295
>> > ITER: 0 | i: 9 | j: 3 Result(i: 3.000000 | j: 1.732051 | i*j:
>> > 5.196152): 307.936447
>> > ITER: 0 | i: 9 | j: 4 Result(i: 3.000000 | j: 2.000000 | i*j:
>> > 6.000000): 313.936447
>> > ITER: 0 | i: 9 | j: 5 Result(i: 3.000000 | j: 2.236068 | i*j:
>> > 6.708204): 320.644651
>> > ITER: 0 | i: 9 | j: 6 Result(i: 3.000000 | j: 2.449490 | i*j:
>> > 7.348469): 327.993120
>> > ITER: 0 | i: 9 | j: 7 Result(i: 3.000000 | j: 2.645751 | i*j:
>> > 7.937254): 335.930374
>> > ITER: 0 | i: 9 | j: 8 Result(i: 3.000000 | j: 2.828427 | i*j:
>> > 8.485281): 344.415656
>> > ITER: 0 | i: 9 | j: 9 Result(i: 3.000000 | j: 3.000000 | i*j:
>> > 9.000000): 353.415656
>> > Final Result: 353.415656
>> > ITER: 1 | i: 0 | j: 0 Result(i: 0.000000 | j: 0.000000 | i*j:
>> > 0.000000): 0.000000
>> > ITER: 1 | i: 0 | j: 1 Result(i: 0.000000 | j: 1.000000 | i*j:
>> > 0.000000): 0.000000
>> > ITER: 1 | i: 0 | j: 2 Result(i: 0.000000 | j: 1.414214 | i*j:
>> > 0.000000): 0.000000
>> > ITER: 1 | i: 0 | j: 3 Result(i: 0.000000 | j: 1.732051 | i*j:
>> > 0.000000): 0.000000
>> > ITER: 1 | i: 0 | j: 4 Result(i: 0.000000 | j: 2.000000 | i*j:
>> > 0.000000): 0.000000
>> > ITER: 1 | i: 0 | j: 5 Result(i: 0.000000 | j: 2.236068 | i*j:
>> > 0.000000): 0.000000
>> > ITER: 1 | i: 0 | j: 6 Result(i: 0.000000 | j: 2.449490 | i*j:
>> > 0.000000): 0.000000
>> > ITER: 1 | i: 0 | j: 7 Result(i: 0.000000 | j: 2.645751 | i*j:
>> > 0.000000): 0.000000
>> > ITER: 1 | i: 0 | j: 8 Result(i: 0.000000 | j: 2.828427 | i*j:
>> > 0.000000): 0.000000
>> > ITER: 1 | i: 0 | j: 9 Result(i: 0.000000 | j: 3.000000 | i*j:
>> > 0.000000): 0.000000
>> > ITER: 1 | i: 1 | j: 0 Result(i: 1.000000 | j: 0.000000 | i*j:
>> > 0.000000): 0.000000
>> > ITER: 1 | i: 1 | j: 1 Result(i: 1.000000 | j: 1.000000 | i*j:
>> > 1.000000): 1.000000
>> > ITER: 1 | i: 1 | j: 2 Result(i: 1.000000 | j: 1.414214 | i*j:
>> > 1.414214): 2.414214
>> > ITER: 1 | i: 1 | j: 3 Result(i: 1.000000 | j: 1.732051 | i*j:
>> > 1.732051): 4.146264
>> > ITER: 1 | i: 1 | j: 4 Result(i: 1.000000 | j: 2.000000 | i*j:
>> > 2.000000): 6.146264
>> > ITER: 1 | i: 1 | j: 5 Result(i: 1.000000 | j: 2.236068 | i*j:
>> > 2.236068): 8.382332
>> > ITER: 1 | i: 1 | j: 6 Result(i: 1.000000 | j: 2.449490 | i*j:
>> > 2.449490): 10.831822
>> > ITER: 1 | i: 1 | j: 7 Result(i: 1.000000 | j: 2.645751 | i*j:
>> > 2.645751): 13.477573
>> > ITER: 1 | i: 1 | j: 8 Result(i: 1.000000 | j: 2.828427 | i*j:
>> > 2.828427): 16.306001
>> > ITER: 1 | i: 1 | j: 9 Result(i: 1.000000 | j: 3.000000 | i*j:
>> > 3.000000): 19.306001
>> > ITER: 1 | i: 2 | j: 0 Result(i: 1.414214 | j: 0.000000 | i*j:
>> > 0.000000): 19.306001
>> > ITER: 1 | i: 2 | j: 1 Result(i: 1.414214 | j: 1.000000 | i*j:
>> > 1.414214): 20.720214
>> > ITER: 1 | i: 2 | j: 2 Result(i: 1.414214 | j: 1.414214 | i*j:
>> > 2.000000): 22.720214
>> > ITER: 1 | i: 2 | j: 3 Result(i: 1.414214 | j: 1.732051 | i*j:
>> > 2.449490): 25.169704
>> > ITER: 1 | i: 2 | j: 4 Result(i: 1.414214 | j: 2.000000 | i*j:
>> > 2.828427): 27.998131
>> > ITER: 1 | i: 2 | j: 5 Result(i: 1.414214 | j: 2.236068 | i*j:
>> > 3.162278): 31.160409
>> > ITER: 1 | i: 2 | j: 6 Result(i: 1.414214 | j: 2.449490 | i*j:
>> > 3.464102): 34.624510
>> > ITER: 1 | i: 2 | j: 7 Result(i: 1.414214 | j: 2.645751 | i*j:
>> > 3.741657): 38.366168
>> > ITER: 1 | i: 2 | j: 8 Result(i: 1.414214 | j: 2.828427 | i*j:
>> > 4.000000): 42.366168
>> > ITER: 1 | i: 2 | j: 9 Result(i: 1.414214 | j: 3.000000 | i*j:
>> > 4.242641): 46.608808
>> > ITER: 1 | i: 3 | j: 0 Result(i: 1.732051 | j: 0.000000 | i*j:
>> > 0.000000): 46.608808
>> > ITER: 1 | i: 3 | j: 1 Result(i: 1.732051 | j: 1.000000 | i*j:
>> > 1.732051): 48.340859
>> > ITER: 1 | i: 3 | j: 2 Result(i: 1.732051 | j: 1.414214 | i*j:
>> > 2.449490): 50.790349
>> > ITER: 1 | i: 3 | j: 3 Result(i: 1.732051 | j: 1.732051 | i*j:
>> > 3.000000): 53.790349
>> > ITER: 1 | i: 3 | j: 4 Result(i: 1.732051 | j: 2.000000 | i*j:
>> > 3.464102): 57.254450
>> > ITER: 1 | i: 3 | j: 5 Result(i: 1.732051 | j: 2.236068 | i*j:
>> > 3.872983): 61.127434
>> > ITER: 1 | i: 3 | j: 6 Result(i: 1.732051 | j: 2.449490 | i*j:
>> > 4.242641): 65.370075
>> > ITER: 1 | i: 3 | j: 7 Result(i: 1.732051 | j: 2.645751 | i*j:
>> > 4.582576): 69.952650
>> > ITER: 1 | i: 3 | j: 8 Result(i: 1.732051 | j: 2.828427 | i*j:
>> > 4.898979): 74.851630
>> > ITER: 1 | i: 3 | j: 9 Result(i: 1.732051 | j: 3.000000 | i*j:
>> > 5.196152): 80.047782
>> > ITER: 1 | i: 4 | j: 0 Result(i: 2.000000 | j: 0.000000 | i*j:
>> > 0.000000): 80.047782
>> > ITER: 1 | i: 4 | j: 1 Result(i: 2.000000 | j: 1.000000 | i*j:
>> > 2.000000): 82.047782
>> > ITER: 1 | i: 4 | j: 2 Result(i: 2.000000 | j: 1.414214 | i*j:
>> > 2.828427): 84.876209
>> > ITER: 1 | i: 4 | j: 3 Result(i: 2.000000 | j: 1.732051 | i*j:
>> > 3.464102): 88.340311
>> > ITER: 1 | i: 4 | j: 4 Result(i: 2.000000 | j: 2.000000 | i*j:
>> > 4.000000): 92.340311
>> > ITER: 1 | i: 4 | j: 5 Result(i: 2.000000 | j: 2.236068 | i*j:
>> > 4.472136): 96.812447
>> > ITER: 1 | i: 4 | j: 6 Result(i: 2.000000 | j: 2.449490 | i*j:
>> > 4.898979): 101.711426
>> > ITER: 1 | i: 4 | j: 7 Result(i: 2.000000 | j: 2.645751 | i*j:
>> > 5.291503): 107.002929
>> > ITER: 1 | i: 4 | j: 8 Result(i: 2.000000 | j: 2.828427 | i*j:
>> > 5.656854): 112.659783
>> > ITER: 1 | i: 4 | j: 9 Result(i: 2.000000 | j: 3.000000 | i*j:
>> > 6.000000): 118.659783
>> > ITER: 1 | i: 5 | j: 0 Result(i: 2.236068 | j: 0.000000 | i*j:
>> > 0.000000): 118.659783
>> > ITER: 1 | i: 5 | j: 1 Result(i: 2.236068 | j: 1.000000 | i*j:
>> > 2.236068): 120.895851
>> > ITER: 1 | i: 5 | j: 2 Result(i: 2.236068 | j: 1.414214 | i*j:
>> > 3.162278): 124.058129
>> > ITER: 1 | i: 5 | j: 3 Result(i: 2.236068 | j: 1.732051 | i*j:
>> > 3.872983): 127.931112
>> > ITER: 1 | i: 5 | j: 4 Result(i: 2.236068 | j: 2.000000 | i*j:
>> > 4.472136): 132.403248
>> > ITER: 1 | i: 5 | j: 5 Result(i: 2.236068 | j: 2.236068 | i*j:
>> > 5.000000): 137.403248
>> > ITER: 1 | i: 5 | j: 6 Result(i: 2.236068 | j: 2.449490 | i*j:
>> > 5.477226): 142.880474
>> > ITER: 1 | i: 5 | j: 7 Result(i: 2.236068 | j: 2.645751 | i*j:
>> > 5.916080): 148.796553
>> > ITER: 1 | i: 5 | j: 8 Result(i: 2.236068 | j: 2.828427 | i*j:
>> > 6.324555): 155.121109
>> > ITER: 1 | i: 5 | j: 9 Result(i: 2.236068 | j: 3.000000 | i*j:
>> > 6.708204): 161.829313
>> > ITER: 1 | i: 6 | j: 0 Result(i: 2.449490 | j: 0.000000 | i*j:
>> > 0.000000): 161.829313
>> > ITER: 1 | i: 6 | j: 1 Result(i: 2.449490 | j: 1.000000 | i*j:
>> > 2.449490): 164.278802
>> > ITER: 1 | i: 6 | j: 2 Result(i: 2.449490 | j: 1.414214 | i*j:
>> > 3.464102): 167.742904
>> > ITER: 1 | i: 6 | j: 3 Result(i: 2.449490 | j: 1.732051 | i*j:
>> > 4.242641): 171.985545
>> > ITER: 1 | i: 6 | j: 4 Result(i: 2.449490 | j: 2.000000 | i*j:
>> > 4.898979): 176.884524
>> > ITER: 1 | i: 6 | j: 5 Result(i: 2.449490 | j: 2.236068 | i*j:
>> > 5.477226): 182.361750
>> > ITER: 1 | i: 6 | j: 6 Result(i: 2.449490 | j: 2.449490 | i*j:
>> > 6.000000): 188.361750
>> > ITER: 1 | i: 6 | j: 7 Result(i: 2.449490 | j: 2.645751 | i*j:
>> > 6.480741): 194.842491
>> > ITER: 1 | i: 6 | j: 8 Result(i: 2.449490 | j: 2.828427 | i*j:
>> > 6.928203): 201.770694
>> > ITER: 1 | i: 6 | j: 9 Result(i: 2.449490 | j: 3.000000 | i*j:
>> > 7.348469): 209.119163
>> > ITER: 1 | i: 7 | j: 0 Result(i: 2.645751 | j: 0.000000 | i*j:
>> > 0.000000): 209.119163
>> > ITER: 1 | i: 7 | j: 1 Result(i: 2.645751 | j: 1.000000 | i*j:
>> > 2.645751): 211.764914
>> > ITER: 1 | i: 7 | j: 2 Result(i: 2.645751 | j: 1.414214 | i*j:
>> > 3.741657): 215.506572
>> > ITER: 1 | i: 7 | j: 3 Result(i: 2.645751 | j: 1.732051 | i*j:
>> > 4.582576): 220.089147
>> > ITER: 1 | i: 7 | j: 4 Result(i: 2.645751 | j: 2.000000 | i*j:
>> > 5.291503): 225.380650
>> > ITER: 1 | i: 7 | j: 5 Result(i: 2.645751 | j: 2.236068 | i*j:
>> > 5.916080): 231.296730
>> > ITER: 1 | i: 7 | j: 6 Result(i: 2.645751 | j: 2.449490 | i*j:
>> > 6.480741): 237.777470
>> > ITER: 1 | i: 7 | j: 7 Result(i: 2.645751 | j: 2.645751 | i*j:
>> > 7.000000): 244.777470
>> > ITER: 1 | i: 7 | j: 8 Result(i: 2.645751 | j: 2.828427 | i*j:
>> > 7.483315): 252.260785
>> > ITER: 1 | i: 7 | j: 9 Result(i: 2.645751 | j: 3.000000 | i*j:
>> > 7.937254): 260.198039
>> > ITER: 1 | i: 8 | j: 0 Result(i: 2.828427 | j: 0.000000 | i*j:
>> > 0.000000): 260.198039
>> > ITER: 1 | i: 8 | j: 1 Result(i: 2.828427 | j: 1.000000 | i*j:
>> > 2.828427): 263.026466
>> > ITER: 1 | i: 8 | j: 2 Result(i: 2.828427 | j: 1.414214 | i*j:
>> > 4.000000): 267.026466
>> > ITER: 1 | i: 8 | j: 3 Result(i: 2.828427 | j: 1.732051 | i*j:
>> > 4.898979): 271.925446
>> > ITER: 1 | i: 8 | j: 4 Result(i: 2.828427 | j: 2.000000 | i*j:
>> > 5.656854): 277.582300
>> > ITER: 1 | i: 8 | j: 5 Result(i: 2.828427 | j: 2.236068 | i*j:
>> > 6.324555): 283.906855
>> > ITER: 1 | i: 8 | j: 6 Result(i: 2.828427 | j: 2.449490 | i*j:
>> > 6.928203): 290.835059
>> > ITER: 1 | i: 8 | j: 7 Result(i: 2.828427 | j: 2.645751 | i*j:
>> > 7.483315): 298.318373
>> > ITER: 1 | i: 8 | j: 8 Result(i: 2.828427 | j: 2.828427 | i*j:
>> > 8.000000): 306.318373
>> > ITER: 1 | i: 8 | j: 9 Result(i: 2.828427 | j: 3.000000 | i*j:
>> > 8.485281): 314.803655
>> > ITER: 1 | i: 9 | j: 0 Result(i: 3.000000 | j: 0.000000 | i*j:
>> > 0.000000): 314.803655
>> > ITER: 1 | i: 9 | j: 1 Result(i: 3.000000 | j: 1.000000 | i*j:
>> > 3.000000): 317.803655
>> > ITER: 1 | i: 9 | j: 2 Result(i: 3.000000 | j: 1.414214 | i*j:
>> > 4.242641): 322.046295
>> > ITER: 1 | i: 9 | j: 3 Result(i: 3.000000 | j: 1.732051 | i*j:
>> > 5.196152): 327.242448
>> > ITER: 1 | i: 9 | j: 4 Result(i: 3.000000 | j: 2.000000 | i*j:
>> > 6.000000): 333.242448
>> > ITER: 1 | i: 9 | j: 5 Result(i: 3.000000 | j: 2.236068 | i*j:
>> > 6.708204): 339.950652
>> > ITER: 1 | i: 9 | j: 6 Result(i: 3.000000 | j: 2.449490 | i*j:
>> > 7.348469): 347.299121
>> > ITER: 1 | i: 9 | j: 7 Result(i: 3.000000 | j: 2.645751 | i*j:
>> > 7.937254): 355.236375
>> > ITER: 1 | i: 9 | j: 8 Result(i: 3.000000 | j: 2.828427 | i*j:
>> > 8.485281): 363.721656
>> > ITER: 1 | i: 9 | j: 9 Result(i: 3.000000 | j: 3.000000 | i*j:
>> > 9.000000): 372.721656
>> > Final Result: 372.721656
>> >
>> >
>> >
>> > As we can see in the following iterations the sqrt(1) as well as the
>> > result is set to zero for some reason.
>> >
>> > ITER: 0 | i: 1 | j: 4 Result(i: 0.000000 | j: 2.000000 | i*j:
>> > 0.000000): 0.000000
>> > ITER: 0 | i: 1 | j: 5 Result(i: 0.000000 | j: 2.236068 | i*j:
>> > 0.000000): 0.000000
>> > ITER: 0 | i: 1 | j: 6 Result(i: 0.000000 | j: 2.449490 | i*j:
>> > 0.000000): 0.000000
>> > ITER: 0 | i: 1 | j: 7 Result(i: 0.000000 | j: 2.645751 | i*j:
>> > 0.000000): 0.000000
>> > ITER: 0 | i: 1 | j: 8 Result(i: 0.000000 | j: 2.828427 | i*j:
>> > 0.000000): 0.000000
>> > ITER: 0 | i: 1 | j: 9 Result(i: 0.000000 | j: 3.000000 | i*j:
>> > 0.000000): 0.000000
>> >
>> > Please help me to resolve the accuracy issue! I think that it will
>> > be very useful for gem5 community.
>> >
>> > To be noticed, I find the correct simulated tick in which the
>> > application started in FS (using m5 dumpstats), and I start the
>> > --debug-start, but the trace file which is generated is 10x larger
>> > than SE mode for the same application. How can I compare them?
>> >
>> > Thank you in advance!
>> > Best regards,
>> > Nikos
>> >
>> > Quoting Νικόλαος Ταμπουρατζής <ntampouratzis@ece.auth.gr>:
>> >
>> >> Dear Jason,
>> >>
>> >> I am trying to use --debug-start but in FS mode it is very
>> >> difficult to find the tick on which the application is started!
>> >>
>> >> However, I am writing the following very simple c++ program:
>> >>
>> >> #include <cmath>
>> >> #include <stdio.h>
>> >>
>> >> int main(){
>> >>
>> >> int dim = 4096;
>> >>
>> >> double result;
>> >>
>> >> for (int iter = 0; iter < 2; iter++){
>> >> result = 0;
>> >> for (int i = 0; i < dim; i++){
>> >> for (int j = 0; j < dim; j++){
>> >> result += sqrt(i) * sqrt(j);
>> >> }
>> >> }
>> >> printf("Result: %lf\n", result); //Result: 30530733453.127449
>> >> }
>> >> }
>> >>
>> >> I cross-compile it using: riscv64-linux-gnu-g++ -static -O3 -o
>> >> test_riscv test_riscv.cpp
>> >>
>> >>
>> >> While in X86 (without cross-compilation of course), QEMU-RISCV,
>> >> GEM5-SE the result is the same (30530733453.127449), in GEM5-FS the
>> >> result is different! In addition, the result is also different
>> >> between the 2 iterations.
>> >>
>> >> Please reproduce the error if you want in order to verify my result.
>> >> Ηow can the issue be resolved?
>> >>
>> >> Thank you in advance!
>> >>
>> >> Best regards,
>> >> Nikos
>> >>
>> >>
>> >> Quoting Jason Lowe-Power <jason@lowepower.com>:
>> >>
>> >>> Hi Nikos,
>> >>>
>> >>> You can use --debug-start to start the debugging after some number of
>> >>> ticks. Also, I would expect that the difference should come up
>> quickly, so
>> >>> no need to run the program to the end.
>> >>>
>> >>> For the FS mode one, you will want to just start the trace as the
>> >>> application starts. This could be a bit of a pain.
>> >>>
>> >>> I'm not really sure what fundamentally could be different. FS and SE
>> mode
>> >>> use the exact same code for executing instructions, so I don't think
>> that's
>> >>> the problem. Have you tried running for smaller inputs or just one
>> >>> iteration?
>> >>>
>> >>> Jason
>> >>>
>> >>>
>> >>>
>> >>> On Wed, Sep 21, 2022 at 9:04 AM Νικόλαος Ταμπουρατζής <
>> >>> ntampouratzis@ece.auth.gr> wrote:
>> >>>
>> >>>> Dear Bobby,
>> >>>>
>> >>>> Iam trying to add --debug-flags=Exec (building the gem5 for gem5.opt
>> >>>> not for gem5.fast which I had) but the debug traces exceed the 20GB
>> >>>> (and it is not finished yet) for less than 1 simulated second. How can
>> >>>> I reduce the size of the debug-flags (or set something more specific)?
>> >>>>
>> >>>> In contrast I build the HPCG benchmark with DHPCG_DEBUG flag. If you
>> >>>> want, you can compare these two output files
>> >>>> (hpcg20010909T014640_SE_Mode & HPCG-Benchmark_3.1_FS_Mode). As you can
>> >>>> see, something goes wrong with the accuracy of calculations in FS mode
>> >>>> (benchmark uses double precission). You can find the files here:
>> >>>> http://kition.mhl.tuc.gr:8000/d/68d82f3533/
>> >>>>
>> >>>> Best regards,
>> >>>> Nikos
>> >>>>
>> >>>> Quoting Jason Lowe-Power <jason@lowepower.com>:
>> >>>>
>> >>>>> That's quite odd that it works in SE mode but not FS mode!
>> >>>>>
>> >>>>> I would suggest running with --debug-flags=Exec for both and then
>> >>>> perform a
>> >>>>> diff to see how they differ.
>> >>>>>
>> >>>>> Cheers,
>> >>>>> Jason
>> >>>>>
>> >>>>> On Tue, Sep 20, 2022 at 2:45 PM Νικόλαος Ταμπουρατζής <
>> >>>>> ntampouratzis@ece.auth.gr> wrote:
>> >>>>>
>> >>>>>> Dear Bobby,
>> >>>>>>
>> >>>>>> In QEMU I get the same (correct) results that I get in SE mode
>> >>>>>> simulation. I get invalid results in FS simulation (in both
>> >>>>>> riscv-fs.py and riscv-ubuntu-run.py). I cannot access real RISCV
>> >>>>>> hardware at this moment, however, if you want you may execute my
>> xhpcg
>> >>>>>> binary (http://kition.mhl.tuc.gr:8000/f/4ca25fdd3c/) with the
>> >>>>>> following configuration:
>> >>>>>>
>> >>>>>> ./xhpcg --nx=16 --ny=16 --nz=16 --npx=1 --npy=1 --npz=1 --rt=0.1
>> >>>>>>
>> >>>>>> Please let me know if you have any updates!
>> >>>>>>
>> >>>>>> Best regards,
>> >>>>>> Nikos
>> >>>>>>
>> >>>>>>
>> >>>>>> Quoting Jason Lowe-Power <jason@lowepower.com>:
>> >>>>>>
>> >>>>>>> Hi Nikos,
>> >>>>>>>
>> >>>>>>> I notice you said the following in your original email:
>> >>>>>>>
>> >>>>>>> In addition, I used the RISCV Ubuntu image
>> >>>>>>>> (
>> https://github.com/gem5/gem5-resources/tree/stable/src/riscv-ubuntu
>> >>>> ),
>> >>>>>>>> I installed the gcc compiler, compile it (through qemu) and I get
>> >>>>>>>> wrong results too.
>> >>>>>>>
>> >>>>>>>
>> >>>>>>> Is this saying you get the wrong results is QEMU? If so, the bug
>> is in
>> >>>>>> GCC
>> >>>>>>> or the HPCG workload, not in gem5. If not, I would test in QEMU to
>> >>>> make
>> >>>>>>> sure the binary works there. Another way you could test to see if
>> the
>> >>>>>>> problem is your binary or gem5 would be to run it on real
>> hardware. We
>> >>>>>> have
>> >>>>>>> access to some RISC-V hardware here at UC Davis, if you don't have
>> >>>> access
>> >>>>>>> to it.
>> >>>>>>>
>> >>>>>>> Cheers,
>> >>>>>>> Jason
>> >>>>>>>
>> >>>>>>> On Tue, Sep 20, 2022 at 12:58 AM Νικόλαος Ταμπουρατζής <
>> >>>>>>> ntampouratzis@ece.auth.gr> wrote:
>> >>>>>>>
>> >>>>>>>> Dear Bobby,
>> >>>>>>>>
>> >>>>>>>> 1) I use the original riscv-fs.py which is provided in the latest
>> >>>> gem5
>> >>>>>>>> release.
>> >>>>>>>> I run the gem5 once (./build/RISCV/gem5.fast -d ./HPCG_FS_results
>> >>>>>>>> ./configs/example/gem5_library/riscv-fs.py) in order to download
>> the
>> >>>>>>>> riscv-bootloader-vmlinux-5.10 and riscv-disk-img.
>> >>>>>>>> After this I mount the riscv-disk-img (sudo mount -o loop
>> >>>>>>>> riscv-disk-img /mnt), put the xhpcg executable and I do the
>> following
>> >>>>>>>> changes in riscv-fs.py to boot the riscv-disk-img with executable:
>> >>>>>>>>
>> >>>>>>>> image = CustomDiskImageResource(
>> >>>>>>>> local_path = "/home/cossim/.cache/gem5/riscv-disk-img",
>> >>>>>>>> )
>> >>>>>>>>
>> >>>>>>>> # Set the Full System workload.
>> >>>>>>>> board.set_kernel_disk_workload(
>> >>>>>>>>
>> kernel=Resource("riscv-bootloader-vmlinux-5.10"),
>> >>>>>>>> disk_image=image,
>> >>>>>>>> )
>> >>>>>>>>
>> >>>>>>>> Finally, in the
>> gem5/src/python/gem5/components/boards/riscv_board.py
>> >>>>>>>> I change the last line to "return ["console=ttyS0",
>> >>>>>>>> "root={root_value}", "rw"]" in order to allow the write
>> permissions
>> >>>> in
>> >>>>>>>> the image.
>> >>>>>>>>
>> >>>>>>>>
>> >>>>>>>> 2) The HPCG benchmark after some iterations calculates if the
>> results
>> >>>>>>>> are valid or not valid. In the case of FS it gives invalid
>> results.
>> >>>> As
>> >>>>>>>> I see from the results, one (at least) problem is that produces
>> >>>>>>>> different results in each HPCG execution (with the same
>> >>>> configuration).
>> >>>>>>>>
>> >>>>>>>> Here is the HPCG output and riscv-fs.py
>> >>>>>>>> (http://kition.mhl.tuc.gr:8000/d/68d82f3533/). You may reproduce
>> the
>> >>>>>>>> results in the video if you use the xhpcg executable
>> >>>>>>>> (http://kition.mhl.tuc.gr:8000/f/4ca25fdd3c/)
>> >>>>>>>>
>> >>>>>>>> Please help me in order to solve it!
>> >>>>>>>>
>> >>>>>>>> Finally, I get invalid results in the HPL benchmark in FS mode
>> too.
>> >>>>>>>>
>> >>>>>>>> Best regards,
>> >>>>>>>> Nikos
>> >>>>>>>>
>> >>>>>>>>
>> >>>>>>>> Quoting Bobby Bruce <bbruce@ucdavis.edu>:
>> >>>>>>>>
>> >>>>>>>> > I'm going to need a bit more information to help:
>> >>>>>>>> >
>> >>>>>>>> > 1. In what way have you modified
>> >>>>>>>> > ./configs/example/gem5_library/riscv-fs.py? Can you attach the
>> >>>> script
>> >>>>>>>> here?
>> >>>>>>>> > 2. What error are you getting or in what way are the results
>> >>>> invalid?
>> >>>>>>>> >
>> >>>>>>>> > -
>> >>>>>>>> > Dr. Bobby R. Bruce
>> >>>>>>>> > Room 3050,
>> >>>>>>>> > Kemper Hall, UC Davis
>> >>>>>>>> > Davis,
>> >>>>>>>> > CA, 95616
>> >>>>>>>> >
>> >>>>>>>> > web: https://www.bobbybruce.net
>> >>>>>>>> >
>> >>>>>>>> >
>> >>>>>>>> > On Mon, Sep 19, 2022 at 1:43 PM Νικόλαος Ταμπουρατζής <
>> >>>>>>>> > ntampouratzis@ece.auth.gr> wrote:
>> >>>>>>>> >
>> >>>>>>>> >>
>> >>>>>>>> >> Dear gem5 community,
>> >>>>>>>> >>
>> >>>>>>>> >> I have successfully cross-compile the HPCG benchmark for RISCV
>> >>>>>> (Serial
>> >>>>>>>> >> version, without MPI and OpenMP). While it working properly in
>> >>>> gem5
>> >>>>>> SE
>> >>>>>>>> >> mode (./build/RISCV/gem5.fast -d ./HPCG_SE_results
>> >>>>>>>> >> ./configs/example/se.py -c xhpcg --options '--nx=16 --ny=16
>> >>>> --nz=16
>> >>>>>>>> >> --npx=1 --npy=1 --npz=1 --rt=0.1'), I get invalid results in FS
>> >>>>>>>> >> simulation using "./build/RISCV/gem5.fast -d ./HPCG_FS_results
>> >>>>>>>> >> ./configs/example/gem5_library/riscv-fs.py" (I mount the riscv
>> >>>> image
>> >>>>>>>> >> and put it).
>> >>>>>>>> >>
>> >>>>>>>> >> Can you help me please?
>> >>>>>>>> >>
>> >>>>>>>> >> In addition, I used the RISCV Ubuntu image
>> >>>>>>>> >> (
>> >>>> https://github.com/gem5/gem5-resources/tree/stable/src/riscv-ubuntu
>> >>>>>> ),
>> >>>>>>>> >> I installed the gcc compiler, compile it (through qemu) and I
>> get
>> >>>>>>>> >> wrong results too.
>> >>>>>>>> >>
>> >>>>>>>> >> Here is the Makefile which I use, the hpcg executable for RISCV
>> >>>>>>>> >> (xhpcg), and a video that shows the results
>> >>>>>>>> >> (http://kition.mhl.tuc.gr:8000/f/4ca25fdd3c/).
>> >>>>>>>> >>
>> >>>>>>>> >> P.S. I use the latest gem5 version.
>> >>>>>>>> >>
>> >>>>>>>> >> Thank you in advance! :)
>> >>>>>>>> >>
>> >>>>>>>> >> Best regards,
>> >>>>>>>> >> Nikos
>> >>>>>>>> >> _______________________________________________
>> >>>>>>>> >> gem5-users mailing list -- gem5-users@gem5.org
>> >>>>>>>> >> To unsubscribe send an email to gem5-users-leave@gem5.org
>> >>>>>>>> >>
>> >>>>>>>>
>> >>>>>>>>
>> >>>>>>>> _______________________________________________
>> >>>>>>>> gem5-users mailing list -- gem5-users@gem5.org
>> >>>>>>>> To unsubscribe send an email to gem5-users-leave@gem5.org
>> >>>>>>>>
>> >>>>>>
>> >>>>>>
>> >>>>>> _______________________________________________
>> >>>>>> gem5-users mailing list -- gem5-users@gem5.org
>> >>>>>> To unsubscribe send an email to gem5-users-leave@gem5.org
>> >>>>>>
>> >>>>
>> >>>>
>> >>>> _______________________________________________
>> >>>> gem5-users mailing list -- gem5-users@gem5.org
>> >>>> To unsubscribe send an email to gem5-users-leave@gem5.org
>> >>>>
>> >>
>> >>
>> >> _______________________________________________
>> >> gem5-users mailing list -- gem5-users@gem5.org
>> >> To unsubscribe send an email to gem5-users-leave@gem5.org
>> >
>> >
>> > _______________________________________________
>> > gem5-users mailing list -- gem5-users@gem5.org
>> > To unsubscribe send an email to gem5-users-leave@gem5.org
>>
>>
>> _______________________________________________
>> gem5-users mailing list -- gem5-users@gem5.org
>> To unsubscribe send an email to gem5-users-leave@gem5.org
>>
JL
Jason Lowe-Power
Fri, Oct 7, 2022 4:01 PM
I have an idea...
Have you put a breakpoint in the implementation of the fsqrt_d function? I
would like to know if when running in SE mode and running in FS mode we are
using the same rounding mode. My hypothesis is that in FS mode the rounding
mode is set differently.
Cheers,
Jason
On Fri, Oct 7, 2022 at 12:15 AM Νικόλαος Ταμπουρατζής <
ntampouratzis@ece.auth.gr> wrote:
Dear Boddy,
Thanks a lot for the effort! I looked in detail and I observe that the
problem is created only using float and double variables (in the case
of int it is working properly in FS mode). Specifically, in the case
of float the variables are set to "nan", while in the case of double
the variables are set to 0.000000 (in random time - probably from some
instruction of simulated OS?). You may use a simple c/c++ example in
order to get some traces before going to HPCG...
Thank you in advance!!
Best regards,
Nikos
Quoting Bobby Bruce bbruce@ucdavis.edu:
Hey Niko,
Thanks for this analysis. I jumped a little into this today but didn't
as far as you did. I wanted to find a quick way to recreate the
with some traces and debug flags and see if I can narrow down the
In my previous results, I had used double (not float) for the
following variables: result, sq_i and sq_j. In the case of float
instead of double I get "nan" and not 0.000000.
Quoting Νικόλαος Ταμπουρατζής ntampouratzis@ece.auth.gr:
Dear Jason, all,
I am trying to find the accuracy problem with RISCV-FS and I observe
that the problem is created (at least in my dummy example) because
the variables (double) are set to zero in random simulated time (for
this reason I get different results among executions of the same
code). Specifically for the following dummy code:
#include <cmath>
#include <stdio.h>
int main(){
int dim = 10;
float result;
for (int iter = 0; iter < 2; iter++){
result = 0;
for (int i = 0; i < dim; i++){
for (int j = 0; j < dim; j++){
float sq_i = sqrt(i);
float sq_j = sqrt(j);
result += sq_i * sq_j;
printf("ITER: %d | i: %d | j: %d Result(i: %f | j:
%f | i*j: %f): %f\n", iter, i , j, sq_i, sq_j, sq_i * sq_j, result);
}
}
printf("Final Result: %lf\n", result);
}
}
The correct Final Result in both iterations is 372.721656. However,
I get the following results in FS:
ITER: 0 | i: 0 | j: 0 Result(i: 0.000000 | j: 0.000000 | ij:
0.000000): 0.000000
ITER: 0 | i: 0 | j: 1 Result(i: 0.000000 | j: 1.000000 | ij:
0.000000): 0.000000
ITER: 0 | i: 0 | j: 2 Result(i: 0.000000 | j: 1.414214 | ij:
0.000000): 0.000000
ITER: 0 | i: 0 | j: 3 Result(i: 0.000000 | j: 1.732051 | ij:
0.000000): 0.000000
ITER: 0 | i: 0 | j: 4 Result(i: 0.000000 | j: 2.000000 | ij:
0.000000): 0.000000
ITER: 0 | i: 0 | j: 5 Result(i: 0.000000 | j: 2.236068 | ij:
0.000000): 0.000000
ITER: 0 | i: 0 | j: 6 Result(i: 0.000000 | j: 2.449490 | ij:
0.000000): 0.000000
ITER: 0 | i: 0 | j: 7 Result(i: 0.000000 | j: 2.645751 | ij:
0.000000): 0.000000
ITER: 0 | i: 0 | j: 8 Result(i: 0.000000 | j: 2.828427 | ij:
0.000000): 0.000000
ITER: 0 | i: 0 | j: 9 Result(i: 0.000000 | j: 3.000000 | ij:
0.000000): 0.000000
ITER: 0 | i: 1 | j: 0 Result(i: 1.000000 | j: 0.000000 | ij:
0.000000): 0.000000
ITER: 0 | i: 1 | j: 1 Result(i: 1.000000 | j: 1.000000 | ij:
1.000000): 1.000000
ITER: 0 | i: 1 | j: 2 Result(i: 1.000000 | j: 1.414214 | ij:
1.414214): 2.414214
ITER: 0 | i: 1 | j: 3 Result(i: 1.000000 | j: 1.732051 | ij:
1.732051): 4.146264
ITER: 0 | i: 1 | j: 4 Result(i: 0.000000 | j: 2.000000 | ij:
0.000000): 0.000000
ITER: 0 | i: 1 | j: 5 Result(i: 0.000000 | j: 2.236068 | ij:
0.000000): 0.000000
ITER: 0 | i: 1 | j: 6 Result(i: 0.000000 | j: 2.449490 | ij:
0.000000): 0.000000
ITER: 0 | i: 1 | j: 7 Result(i: 0.000000 | j: 2.645751 | ij:
0.000000): 0.000000
ITER: 0 | i: 1 | j: 8 Result(i: 0.000000 | j: 2.828427 | ij:
0.000000): 0.000000
ITER: 0 | i: 1 | j: 9 Result(i: 0.000000 | j: 3.000000 | ij:
0.000000): 0.000000
ITER: 0 | i: 2 | j: 0 Result(i: 1.414214 | j: 0.000000 | ij:
0.000000): 0.000000
ITER: 0 | i: 2 | j: 1 Result(i: 1.414214 | j: 1.000000 | ij:
1.414214): 1.414214
ITER: 0 | i: 2 | j: 2 Result(i: 1.414214 | j: 1.414214 | ij:
2.000000): 3.414214
ITER: 0 | i: 2 | j: 3 Result(i: 1.414214 | j: 1.732051 | ij:
2.449490): 5.863703
ITER: 0 | i: 2 | j: 4 Result(i: 1.414214 | j: 2.000000 | ij:
2.828427): 8.692130
ITER: 0 | i: 2 | j: 5 Result(i: 1.414214 | j: 2.236068 | ij:
3.162278): 11.854408
ITER: 0 | i: 2 | j: 6 Result(i: 1.414214 | j: 2.449490 | ij:
3.464102): 15.318510
ITER: 0 | i: 2 | j: 7 Result(i: 1.414214 | j: 2.645751 | ij:
3.741657): 19.060167
ITER: 0 | i: 2 | j: 8 Result(i: 1.414214 | j: 2.828427 | ij:
4.000000): 23.060167
ITER: 0 | i: 2 | j: 9 Result(i: 1.414214 | j: 3.000000 | ij:
4.242641): 27.302808
ITER: 0 | i: 3 | j: 0 Result(i: 1.732051 | j: 0.000000 | ij:
0.000000): 27.302808
ITER: 0 | i: 3 | j: 1 Result(i: 1.732051 | j: 1.000000 | ij:
1.732051): 29.034859
ITER: 0 | i: 3 | j: 2 Result(i: 1.732051 | j: 1.414214 | ij:
2.449490): 31.484348
ITER: 0 | i: 3 | j: 3 Result(i: 1.732051 | j: 1.732051 | ij:
3.000000): 34.484348
ITER: 0 | i: 3 | j: 4 Result(i: 1.732051 | j: 2.000000 | ij:
3.464102): 37.948450
ITER: 0 | i: 3 | j: 5 Result(i: 1.732051 | j: 2.236068 | ij:
3.872983): 41.821433
ITER: 0 | i: 3 | j: 6 Result(i: 1.732051 | j: 2.449490 | ij:
4.242641): 46.064074
ITER: 0 | i: 3 | j: 7 Result(i: 1.732051 | j: 2.645751 | ij:
4.582576): 50.646650
ITER: 0 | i: 3 | j: 8 Result(i: 1.732051 | j: 2.828427 | ij:
4.898979): 55.545629
ITER: 0 | i: 3 | j: 9 Result(i: 1.732051 | j: 3.000000 | ij:
5.196152): 60.741782
ITER: 0 | i: 4 | j: 0 Result(i: 2.000000 | j: 0.000000 | ij:
0.000000): 60.741782
ITER: 0 | i: 4 | j: 1 Result(i: 2.000000 | j: 1.000000 | ij:
2.000000): 62.741782
ITER: 0 | i: 4 | j: 2 Result(i: 2.000000 | j: 1.414214 | ij:
2.828427): 65.570209
ITER: 0 | i: 4 | j: 3 Result(i: 2.000000 | j: 1.732051 | ij:
3.464102): 69.034310
ITER: 0 | i: 4 | j: 4 Result(i: 2.000000 | j: 2.000000 | ij:
4.000000): 73.034310
ITER: 0 | i: 4 | j: 5 Result(i: 2.000000 | j: 2.236068 | ij:
4.472136): 77.506446
ITER: 0 | i: 4 | j: 6 Result(i: 2.000000 | j: 2.449490 | ij:
4.898979): 82.405426
ITER: 0 | i: 4 | j: 7 Result(i: 2.000000 | j: 2.645751 | ij:
5.291503): 87.696928
ITER: 0 | i: 4 | j: 8 Result(i: 2.000000 | j: 2.828427 | ij:
5.656854): 93.353783
ITER: 0 | i: 4 | j: 9 Result(i: 2.000000 | j: 3.000000 | ij:
6.000000): 99.353783
ITER: 0 | i: 5 | j: 0 Result(i: 2.236068 | j: 0.000000 | ij:
0.000000): 99.353783
ITER: 0 | i: 5 | j: 1 Result(i: 2.236068 | j: 1.000000 | ij:
2.236068): 101.589851
ITER: 0 | i: 5 | j: 2 Result(i: 2.236068 | j: 1.414214 | ij:
3.162278): 104.752128
ITER: 0 | i: 5 | j: 3 Result(i: 2.236068 | j: 1.732051 | ij:
3.872983): 108.625112
ITER: 0 | i: 5 | j: 4 Result(i: 2.236068 | j: 2.000000 | ij:
4.472136): 113.097248
ITER: 0 | i: 5 | j: 5 Result(i: 2.236068 | j: 2.236068 | ij:
5.000000): 118.097248
ITER: 0 | i: 5 | j: 6 Result(i: 2.236068 | j: 2.449490 | ij:
5.477226): 123.574473
ITER: 0 | i: 5 | j: 7 Result(i: 2.236068 | j: 2.645751 | ij:
5.916080): 129.490553
ITER: 0 | i: 5 | j: 8 Result(i: 2.236068 | j: 2.828427 | ij:
6.324555): 135.815108
ITER: 0 | i: 5 | j: 9 Result(i: 2.236068 | j: 3.000000 | ij:
6.708204): 142.523312
ITER: 0 | i: 6 | j: 0 Result(i: 2.449490 | j: 0.000000 | ij:
0.000000): 142.523312
ITER: 0 | i: 6 | j: 1 Result(i: 2.449490 | j: 1.000000 | ij:
2.449490): 144.972802
ITER: 0 | i: 6 | j: 2 Result(i: 2.449490 | j: 1.414214 | ij:
3.464102): 148.436904
ITER: 0 | i: 6 | j: 3 Result(i: 2.449490 | j: 1.732051 | ij:
4.242641): 152.679544
ITER: 0 | i: 6 | j: 4 Result(i: 2.449490 | j: 2.000000 | ij:
4.898979): 157.578524
ITER: 0 | i: 6 | j: 5 Result(i: 2.449490 | j: 2.236068 | ij:
5.477226): 163.055749
ITER: 0 | i: 6 | j: 6 Result(i: 2.449490 | j: 2.449490 | ij:
6.000000): 169.055749
ITER: 0 | i: 6 | j: 7 Result(i: 2.449490 | j: 2.645751 | ij:
6.480741): 175.536490
ITER: 0 | i: 6 | j: 8 Result(i: 2.449490 | j: 2.828427 | ij:
6.928203): 182.464693
ITER: 0 | i: 6 | j: 9 Result(i: 2.449490 | j: 3.000000 | ij:
7.348469): 189.813162
ITER: 0 | i: 7 | j: 0 Result(i: 2.645751 | j: 0.000000 | ij:
0.000000): 189.813162
ITER: 0 | i: 7 | j: 1 Result(i: 2.645751 | j: 1.000000 | ij:
2.645751): 192.458914
ITER: 0 | i: 7 | j: 2 Result(i: 2.645751 | j: 1.414214 | ij:
3.741657): 196.200571
ITER: 0 | i: 7 | j: 3 Result(i: 2.645751 | j: 1.732051 | ij:
4.582576): 200.783147
ITER: 0 | i: 7 | j: 4 Result(i: 2.645751 | j: 2.000000 | ij:
5.291503): 206.074649
ITER: 0 | i: 7 | j: 5 Result(i: 2.645751 | j: 2.236068 | ij:
5.916080): 211.990729
ITER: 0 | i: 7 | j: 6 Result(i: 2.645751 | j: 2.449490 | ij:
6.480741): 218.471470
ITER: 0 | i: 7 | j: 7 Result(i: 2.645751 | j: 2.645751 | ij:
7.000000): 225.471470
ITER: 0 | i: 7 | j: 8 Result(i: 2.645751 | j: 2.828427 | ij:
7.483315): 232.954785
ITER: 0 | i: 7 | j: 9 Result(i: 2.645751 | j: 3.000000 | ij:
7.937254): 240.892039
ITER: 0 | i: 8 | j: 0 Result(i: 2.828427 | j: 0.000000 | ij:
0.000000): 240.892039
ITER: 0 | i: 8 | j: 1 Result(i: 2.828427 | j: 1.000000 | ij:
2.828427): 243.720466
ITER: 0 | i: 8 | j: 2 Result(i: 2.828427 | j: 1.414214 | ij:
4.000000): 247.720466
ITER: 0 | i: 8 | j: 3 Result(i: 2.828427 | j: 1.732051 | ij:
4.898979): 252.619445
ITER: 0 | i: 8 | j: 4 Result(i: 2.828427 | j: 2.000000 | ij:
5.656854): 258.276300
ITER: 0 | i: 8 | j: 5 Result(i: 2.828427 | j: 2.236068 | ij:
6.324555): 264.600855
ITER: 0 | i: 8 | j: 6 Result(i: 2.828427 | j: 2.449490 | ij:
6.928203): 271.529058
ITER: 0 | i: 8 | j: 7 Result(i: 2.828427 | j: 2.645751 | ij:
7.483315): 279.012373
ITER: 0 | i: 8 | j: 8 Result(i: 2.828427 | j: 2.828427 | ij:
8.000000): 287.012373
ITER: 0 | i: 8 | j: 9 Result(i: 2.828427 | j: 3.000000 | ij:
8.485281): 295.497654
ITER: 0 | i: 9 | j: 0 Result(i: 3.000000 | j: 0.000000 | ij:
0.000000): 295.497654
ITER: 0 | i: 9 | j: 1 Result(i: 3.000000 | j: 1.000000 | ij:
3.000000): 298.497654
ITER: 0 | i: 9 | j: 2 Result(i: 3.000000 | j: 1.414214 | ij:
4.242641): 302.740295
ITER: 0 | i: 9 | j: 3 Result(i: 3.000000 | j: 1.732051 | ij:
5.196152): 307.936447
ITER: 0 | i: 9 | j: 4 Result(i: 3.000000 | j: 2.000000 | ij:
6.000000): 313.936447
ITER: 0 | i: 9 | j: 5 Result(i: 3.000000 | j: 2.236068 | ij:
6.708204): 320.644651
ITER: 0 | i: 9 | j: 6 Result(i: 3.000000 | j: 2.449490 | ij:
7.348469): 327.993120
ITER: 0 | i: 9 | j: 7 Result(i: 3.000000 | j: 2.645751 | ij:
7.937254): 335.930374
ITER: 0 | i: 9 | j: 8 Result(i: 3.000000 | j: 2.828427 | ij:
8.485281): 344.415656
ITER: 0 | i: 9 | j: 9 Result(i: 3.000000 | j: 3.000000 | ij:
9.000000): 353.415656
Final Result: 353.415656
ITER: 1 | i: 0 | j: 0 Result(i: 0.000000 | j: 0.000000 | ij:
0.000000): 0.000000
ITER: 1 | i: 0 | j: 1 Result(i: 0.000000 | j: 1.000000 | ij:
0.000000): 0.000000
ITER: 1 | i: 0 | j: 2 Result(i: 0.000000 | j: 1.414214 | ij:
0.000000): 0.000000
ITER: 1 | i: 0 | j: 3 Result(i: 0.000000 | j: 1.732051 | ij:
0.000000): 0.000000
ITER: 1 | i: 0 | j: 4 Result(i: 0.000000 | j: 2.000000 | ij:
0.000000): 0.000000
ITER: 1 | i: 0 | j: 5 Result(i: 0.000000 | j: 2.236068 | ij:
0.000000): 0.000000
ITER: 1 | i: 0 | j: 6 Result(i: 0.000000 | j: 2.449490 | ij:
0.000000): 0.000000
ITER: 1 | i: 0 | j: 7 Result(i: 0.000000 | j: 2.645751 | ij:
0.000000): 0.000000
ITER: 1 | i: 0 | j: 8 Result(i: 0.000000 | j: 2.828427 | ij:
0.000000): 0.000000
ITER: 1 | i: 0 | j: 9 Result(i: 0.000000 | j: 3.000000 | ij:
0.000000): 0.000000
ITER: 1 | i: 1 | j: 0 Result(i: 1.000000 | j: 0.000000 | ij:
0.000000): 0.000000
ITER: 1 | i: 1 | j: 1 Result(i: 1.000000 | j: 1.000000 | ij:
1.000000): 1.000000
ITER: 1 | i: 1 | j: 2 Result(i: 1.000000 | j: 1.414214 | ij:
1.414214): 2.414214
ITER: 1 | i: 1 | j: 3 Result(i: 1.000000 | j: 1.732051 | ij:
1.732051): 4.146264
ITER: 1 | i: 1 | j: 4 Result(i: 1.000000 | j: 2.000000 | ij:
2.000000): 6.146264
ITER: 1 | i: 1 | j: 5 Result(i: 1.000000 | j: 2.236068 | ij:
2.236068): 8.382332
ITER: 1 | i: 1 | j: 6 Result(i: 1.000000 | j: 2.449490 | ij:
2.449490): 10.831822
ITER: 1 | i: 1 | j: 7 Result(i: 1.000000 | j: 2.645751 | ij:
2.645751): 13.477573
ITER: 1 | i: 1 | j: 8 Result(i: 1.000000 | j: 2.828427 | ij:
2.828427): 16.306001
ITER: 1 | i: 1 | j: 9 Result(i: 1.000000 | j: 3.000000 | ij:
3.000000): 19.306001
ITER: 1 | i: 2 | j: 0 Result(i: 1.414214 | j: 0.000000 | ij:
0.000000): 19.306001
ITER: 1 | i: 2 | j: 1 Result(i: 1.414214 | j: 1.000000 | ij:
1.414214): 20.720214
ITER: 1 | i: 2 | j: 2 Result(i: 1.414214 | j: 1.414214 | ij:
2.000000): 22.720214
ITER: 1 | i: 2 | j: 3 Result(i: 1.414214 | j: 1.732051 | ij:
2.449490): 25.169704
ITER: 1 | i: 2 | j: 4 Result(i: 1.414214 | j: 2.000000 | ij:
2.828427): 27.998131
ITER: 1 | i: 2 | j: 5 Result(i: 1.414214 | j: 2.236068 | ij:
3.162278): 31.160409
ITER: 1 | i: 2 | j: 6 Result(i: 1.414214 | j: 2.449490 | ij:
3.464102): 34.624510
ITER: 1 | i: 2 | j: 7 Result(i: 1.414214 | j: 2.645751 | ij:
3.741657): 38.366168
ITER: 1 | i: 2 | j: 8 Result(i: 1.414214 | j: 2.828427 | ij:
4.000000): 42.366168
ITER: 1 | i: 2 | j: 9 Result(i: 1.414214 | j: 3.000000 | ij:
4.242641): 46.608808
ITER: 1 | i: 3 | j: 0 Result(i: 1.732051 | j: 0.000000 | ij:
0.000000): 46.608808
ITER: 1 | i: 3 | j: 1 Result(i: 1.732051 | j: 1.000000 | ij:
1.732051): 48.340859
ITER: 1 | i: 3 | j: 2 Result(i: 1.732051 | j: 1.414214 | ij:
2.449490): 50.790349
ITER: 1 | i: 3 | j: 3 Result(i: 1.732051 | j: 1.732051 | ij:
3.000000): 53.790349
ITER: 1 | i: 3 | j: 4 Result(i: 1.732051 | j: 2.000000 | ij:
3.464102): 57.254450
ITER: 1 | i: 3 | j: 5 Result(i: 1.732051 | j: 2.236068 | ij:
3.872983): 61.127434
ITER: 1 | i: 3 | j: 6 Result(i: 1.732051 | j: 2.449490 | ij:
4.242641): 65.370075
ITER: 1 | i: 3 | j: 7 Result(i: 1.732051 | j: 2.645751 | ij:
4.582576): 69.952650
ITER: 1 | i: 3 | j: 8 Result(i: 1.732051 | j: 2.828427 | ij:
4.898979): 74.851630
ITER: 1 | i: 3 | j: 9 Result(i: 1.732051 | j: 3.000000 | ij:
5.196152): 80.047782
ITER: 1 | i: 4 | j: 0 Result(i: 2.000000 | j: 0.000000 | ij:
0.000000): 80.047782
ITER: 1 | i: 4 | j: 1 Result(i: 2.000000 | j: 1.000000 | ij:
2.000000): 82.047782
ITER: 1 | i: 4 | j: 2 Result(i: 2.000000 | j: 1.414214 | ij:
2.828427): 84.876209
ITER: 1 | i: 4 | j: 3 Result(i: 2.000000 | j: 1.732051 | ij:
3.464102): 88.340311
ITER: 1 | i: 4 | j: 4 Result(i: 2.000000 | j: 2.000000 | ij:
4.000000): 92.340311
ITER: 1 | i: 4 | j: 5 Result(i: 2.000000 | j: 2.236068 | ij:
4.472136): 96.812447
ITER: 1 | i: 4 | j: 6 Result(i: 2.000000 | j: 2.449490 | ij:
4.898979): 101.711426
ITER: 1 | i: 4 | j: 7 Result(i: 2.000000 | j: 2.645751 | ij:
5.291503): 107.002929
ITER: 1 | i: 4 | j: 8 Result(i: 2.000000 | j: 2.828427 | ij:
5.656854): 112.659783
ITER: 1 | i: 4 | j: 9 Result(i: 2.000000 | j: 3.000000 | ij:
6.000000): 118.659783
ITER: 1 | i: 5 | j: 0 Result(i: 2.236068 | j: 0.000000 | ij:
0.000000): 118.659783
ITER: 1 | i: 5 | j: 1 Result(i: 2.236068 | j: 1.000000 | ij:
2.236068): 120.895851
ITER: 1 | i: 5 | j: 2 Result(i: 2.236068 | j: 1.414214 | ij:
3.162278): 124.058129
ITER: 1 | i: 5 | j: 3 Result(i: 2.236068 | j: 1.732051 | ij:
3.872983): 127.931112
ITER: 1 | i: 5 | j: 4 Result(i: 2.236068 | j: 2.000000 | ij:
4.472136): 132.403248
ITER: 1 | i: 5 | j: 5 Result(i: 2.236068 | j: 2.236068 | ij:
5.000000): 137.403248
ITER: 1 | i: 5 | j: 6 Result(i: 2.236068 | j: 2.449490 | ij:
5.477226): 142.880474
ITER: 1 | i: 5 | j: 7 Result(i: 2.236068 | j: 2.645751 | ij:
5.916080): 148.796553
ITER: 1 | i: 5 | j: 8 Result(i: 2.236068 | j: 2.828427 | ij:
6.324555): 155.121109
ITER: 1 | i: 5 | j: 9 Result(i: 2.236068 | j: 3.000000 | ij:
6.708204): 161.829313
ITER: 1 | i: 6 | j: 0 Result(i: 2.449490 | j: 0.000000 | ij:
0.000000): 161.829313
ITER: 1 | i: 6 | j: 1 Result(i: 2.449490 | j: 1.000000 | ij:
2.449490): 164.278802
ITER: 1 | i: 6 | j: 2 Result(i: 2.449490 | j: 1.414214 | ij:
3.464102): 167.742904
ITER: 1 | i: 6 | j: 3 Result(i: 2.449490 | j: 1.732051 | ij:
4.242641): 171.985545
ITER: 1 | i: 6 | j: 4 Result(i: 2.449490 | j: 2.000000 | ij:
4.898979): 176.884524
ITER: 1 | i: 6 | j: 5 Result(i: 2.449490 | j: 2.236068 | ij:
5.477226): 182.361750
ITER: 1 | i: 6 | j: 6 Result(i: 2.449490 | j: 2.449490 | ij:
6.000000): 188.361750
ITER: 1 | i: 6 | j: 7 Result(i: 2.449490 | j: 2.645751 | ij:
6.480741): 194.842491
ITER: 1 | i: 6 | j: 8 Result(i: 2.449490 | j: 2.828427 | ij:
6.928203): 201.770694
ITER: 1 | i: 6 | j: 9 Result(i: 2.449490 | j: 3.000000 | ij:
7.348469): 209.119163
ITER: 1 | i: 7 | j: 0 Result(i: 2.645751 | j: 0.000000 | ij:
0.000000): 209.119163
ITER: 1 | i: 7 | j: 1 Result(i: 2.645751 | j: 1.000000 | ij:
2.645751): 211.764914
ITER: 1 | i: 7 | j: 2 Result(i: 2.645751 | j: 1.414214 | ij:
3.741657): 215.506572
ITER: 1 | i: 7 | j: 3 Result(i: 2.645751 | j: 1.732051 | ij:
4.582576): 220.089147
ITER: 1 | i: 7 | j: 4 Result(i: 2.645751 | j: 2.000000 | ij:
5.291503): 225.380650
ITER: 1 | i: 7 | j: 5 Result(i: 2.645751 | j: 2.236068 | ij:
5.916080): 231.296730
ITER: 1 | i: 7 | j: 6 Result(i: 2.645751 | j: 2.449490 | ij:
6.480741): 237.777470
ITER: 1 | i: 7 | j: 7 Result(i: 2.645751 | j: 2.645751 | ij:
7.000000): 244.777470
ITER: 1 | i: 7 | j: 8 Result(i: 2.645751 | j: 2.828427 | ij:
7.483315): 252.260785
ITER: 1 | i: 7 | j: 9 Result(i: 2.645751 | j: 3.000000 | ij:
7.937254): 260.198039
ITER: 1 | i: 8 | j: 0 Result(i: 2.828427 | j: 0.000000 | ij:
0.000000): 260.198039
ITER: 1 | i: 8 | j: 1 Result(i: 2.828427 | j: 1.000000 | ij:
2.828427): 263.026466
ITER: 1 | i: 8 | j: 2 Result(i: 2.828427 | j: 1.414214 | ij:
4.000000): 267.026466
ITER: 1 | i: 8 | j: 3 Result(i: 2.828427 | j: 1.732051 | ij:
4.898979): 271.925446
ITER: 1 | i: 8 | j: 4 Result(i: 2.828427 | j: 2.000000 | ij:
5.656854): 277.582300
ITER: 1 | i: 8 | j: 5 Result(i: 2.828427 | j: 2.236068 | ij:
6.324555): 283.906855
ITER: 1 | i: 8 | j: 6 Result(i: 2.828427 | j: 2.449490 | ij:
6.928203): 290.835059
ITER: 1 | i: 8 | j: 7 Result(i: 2.828427 | j: 2.645751 | ij:
7.483315): 298.318373
ITER: 1 | i: 8 | j: 8 Result(i: 2.828427 | j: 2.828427 | ij:
8.000000): 306.318373
ITER: 1 | i: 8 | j: 9 Result(i: 2.828427 | j: 3.000000 | ij:
8.485281): 314.803655
ITER: 1 | i: 9 | j: 0 Result(i: 3.000000 | j: 0.000000 | ij:
0.000000): 314.803655
ITER: 1 | i: 9 | j: 1 Result(i: 3.000000 | j: 1.000000 | ij:
3.000000): 317.803655
ITER: 1 | i: 9 | j: 2 Result(i: 3.000000 | j: 1.414214 | ij:
4.242641): 322.046295
ITER: 1 | i: 9 | j: 3 Result(i: 3.000000 | j: 1.732051 | ij:
5.196152): 327.242448
ITER: 1 | i: 9 | j: 4 Result(i: 3.000000 | j: 2.000000 | ij:
6.000000): 333.242448
ITER: 1 | i: 9 | j: 5 Result(i: 3.000000 | j: 2.236068 | ij:
6.708204): 339.950652
ITER: 1 | i: 9 | j: 6 Result(i: 3.000000 | j: 2.449490 | ij:
7.348469): 347.299121
ITER: 1 | i: 9 | j: 7 Result(i: 3.000000 | j: 2.645751 | ij:
7.937254): 355.236375
ITER: 1 | i: 9 | j: 8 Result(i: 3.000000 | j: 2.828427 | ij:
8.485281): 363.721656
ITER: 1 | i: 9 | j: 9 Result(i: 3.000000 | j: 3.000000 | ij:
9.000000): 372.721656
Final Result: 372.721656
As we can see in the following iterations the sqrt(1) as well as the
result is set to zero for some reason.
ITER: 0 | i: 1 | j: 4 Result(i: 0.000000 | j: 2.000000 | ij:
0.000000): 0.000000
ITER: 0 | i: 1 | j: 5 Result(i: 0.000000 | j: 2.236068 | ij:
0.000000): 0.000000
ITER: 0 | i: 1 | j: 6 Result(i: 0.000000 | j: 2.449490 | ij:
0.000000): 0.000000
ITER: 0 | i: 1 | j: 7 Result(i: 0.000000 | j: 2.645751 | ij:
0.000000): 0.000000
ITER: 0 | i: 1 | j: 8 Result(i: 0.000000 | j: 2.828427 | ij:
0.000000): 0.000000
ITER: 0 | i: 1 | j: 9 Result(i: 0.000000 | j: 3.000000 | ij:
0.000000): 0.000000
Please help me to resolve the accuracy issue! I think that it will
be very useful for gem5 community.
To be noticed, I find the correct simulated tick in which the
application started in FS (using m5 dumpstats), and I start the
--debug-start, but the trace file which is generated is 10x larger
than SE mode for the same application. How can I compare them?
Thank you in advance!
Best regards,
Nikos
Quoting Νικόλαος Ταμπουρατζής ntampouratzis@ece.auth.gr:
Dear Jason,
I am trying to use --debug-start but in FS mode it is very
difficult to find the tick on which the application is started!
However, I am writing the following very simple c++ program:
#include <cmath>
#include <stdio.h>
int main(){
int dim = 4096;
double result;
for (int iter = 0; iter < 2; iter++){
result = 0;
for (int i = 0; i < dim; i++){
for (int j = 0; j < dim; j++){
result += sqrt(i) * sqrt(j);
}
}
printf("Result: %lf\n", result); //Result: 30530733453.127449
}
}
I cross-compile it using: riscv64-linux-gnu-g++ -static -O3 -o
test_riscv test_riscv.cpp
While in X86 (without cross-compilation of course), QEMU-RISCV,
GEM5-SE the result is the same (30530733453.127449), in GEM5-FS the
result is different! In addition, the result is also different
between the 2 iterations.
Please reproduce the error if you want in order to verify my result.
Ηow can the issue be resolved?
Thank you in advance!
Best regards,
Nikos
Quoting Jason Lowe-Power jason@lowepower.com:
Hi Nikos,
You can use --debug-start to start the debugging after some number
ticks. Also, I would expect that the difference should come up
no need to run the program to the end.
For the FS mode one, you will want to just start the trace as the
application starts. This could be a bit of a pain.
I'm not really sure what fundamentally could be different. FS and SE
use the exact same code for executing instructions, so I don't think
the problem. Have you tried running for smaller inputs or just one
iteration?
Jason
On Wed, Sep 21, 2022 at 9:04 AM Νικόλαος Ταμπουρατζής <
ntampouratzis@ece.auth.gr> wrote:
Dear Bobby,
Iam trying to add --debug-flags=Exec (building the gem5 for
not for gem5.fast which I had) but the debug traces exceed the 20GB
(and it is not finished yet) for less than 1 simulated second. How
I reduce the size of the debug-flags (or set something more
In contrast I build the HPCG benchmark with DHPCG_DEBUG flag. If
want, you can compare these two output files
(hpcg20010909T014640_SE_Mode & HPCG-Benchmark_3.1_FS_Mode). As you
see, something goes wrong with the accuracy of calculations in FS
That's quite odd that it works in SE mode but not FS mode!
I would suggest running with --debug-flags=Exec for both and then
diff to see how they differ.
Cheers,
Jason
On Tue, Sep 20, 2022 at 2:45 PM Νικόλαος Ταμπουρατζής <
ntampouratzis@ece.auth.gr> wrote:
Dear Bobby,
In QEMU I get the same (correct) results that I get in SE mode
simulation. I get invalid results in FS simulation (in both
riscv-fs.py and riscv-ubuntu-run.py). I cannot access real RISCV
hardware at this moment, however, if you want you may execute my
Hi Nikos,
I notice you said the following in your original email:
In addition, I used the RISCV Ubuntu image
I installed the gcc compiler, compile it (through qemu) and I
Is this saying you get the wrong results is QEMU? If so, the bug
or the HPCG workload, not in gem5. If not, I would test in QEMU
sure the binary works there. Another way you could test to see
problem is your binary or gem5 would be to run it on real
access to some RISC-V hardware here at UC Davis, if you don't
Dear Bobby,
- I use the original riscv-fs.py which is provided in the
release.
I run the gem5 once (./build/RISCV/gem5.fast -d
./configs/example/gem5_library/riscv-fs.py) in order to
riscv-bootloader-vmlinux-5.10 and riscv-disk-img.
After this I mount the riscv-disk-img (sudo mount -o loop
riscv-disk-img /mnt), put the xhpcg executable and I do the
changes in riscv-fs.py to boot the riscv-disk-img with
image = CustomDiskImageResource(
local_path = "/home/cossim/.cache/gem5/riscv-disk-img",
)
Set the Full System workload.
board.set_kernel_disk_workload(
kernel=Resource("riscv-bootloader-vmlinux-5.10"),
disk_image=image,
)
Finally, in the
gem5/src/python/gem5/components/boards/riscv_board.py
I change the last line to "return ["console=ttyS0",
"root={root_value}", "rw"]" in order to allow the write
the image.
- The HPCG benchmark after some iterations calculates if the
are valid or not valid. In the case of FS it gives invalid
I see from the results, one (at least) problem is that produces
different results in each HPCG execution (with the same
I'm going to need a bit more information to help:
- In what way have you modified
./configs/example/gem5_library/riscv-fs.py? Can you attach
- What error are you getting or in what way are the results
Dear gem5 community,
I have successfully cross-compile the HPCG benchmark for
version, without MPI and OpenMP). While it working properly
mode (./build/RISCV/gem5.fast -d ./HPCG_SE_results
./configs/example/se.py -c xhpcg --options '--nx=16 --ny=16
--npx=1 --npy=1 --npz=1 --rt=0.1'), I get invalid results
simulation using "./build/RISCV/gem5.fast -d
./configs/example/gem5_library/riscv-fs.py" (I mount the
and put it).
Can you help me please?
In addition, I used the RISCV Ubuntu image
(
I installed the gcc compiler, compile it (through qemu) and
wrong results too.
Here is the Makefile which I use, the hpcg executable for
I have an idea...
Have you put a breakpoint in the implementation of the fsqrt_d function? I
would like to know if when running in SE mode and running in FS mode we are
using the same rounding mode. My hypothesis is that in FS mode the rounding
mode is set differently.
Cheers,
Jason
On Fri, Oct 7, 2022 at 12:15 AM Νικόλαος Ταμπουρατζής <
ntampouratzis@ece.auth.gr> wrote:
> Dear Boddy,
>
> Thanks a lot for the effort! I looked in detail and I observe that the
> problem is created only using float and double variables (in the case
> of int it is working properly in FS mode). Specifically, in the case
> of float the variables are set to "nan", while in the case of double
> the variables are set to 0.000000 (in random time - probably from some
> instruction of simulated OS?). You may use a simple c/c++ example in
> order to get some traces before going to HPCG...
>
> Thank you in advance!!
> Best regards,
> Nikos
>
>
> Quoting Bobby Bruce <bbruce@ucdavis.edu>:
>
> > Hey Niko,
> >
> > Thanks for this analysis. I jumped a little into this today but didn't
> get
> > as far as you did. I wanted to find a quick way to recreate the
> following:
> > https://gem5-review.googlesource.com/c/public/gem5/+/64211. Please feel
> > free to use this, if it helps any.
> >
> > It's very strange to me that this bug hasn't manifested itself before but
> > it's undeniably there. I'll try to spend more time looking at this
> tomorrow
> > with some traces and debug flags and see if I can narrow down the
> problem.
> >
> > --
> > Dr. Bobby R. Bruce
> > Room 3050,
> > Kemper Hall, UC Davis
> > Davis,
> > CA, 95616
> >
> > web: https://www.bobbybruce.net
> >
> >
> > On Wed, Oct 5, 2022 at 2:26 PM Νικόλαος Ταμπουρατζής <
> > ntampouratzis@ece.auth.gr> wrote:
> >
> >> In my previous results, I had used double (not float) for the
> >> following variables: result, sq_i and sq_j. In the case of float
> >> instead of double I get "nan" and not 0.000000.
> >>
> >> Quoting Νικόλαος Ταμπουρατζής <ntampouratzis@ece.auth.gr>:
> >>
> >> > Dear Jason, all,
> >> >
> >> > I am trying to find the accuracy problem with RISCV-FS and I observe
> >> > that the problem is created (at least in my dummy example) because
> >> > the variables (double) are set to zero in random simulated time (for
> >> > this reason I get different results among executions of the same
> >> > code). Specifically for the following dummy code:
> >> >
> >> >
> >> > #include <cmath>
> >> > #include <stdio.h>
> >> >
> >> > int main(){
> >> >
> >> > int dim = 10;
> >> >
> >> > float result;
> >> >
> >> > for (int iter = 0; iter < 2; iter++){
> >> > result = 0;
> >> > for (int i = 0; i < dim; i++){
> >> > for (int j = 0; j < dim; j++){
> >> > float sq_i = sqrt(i);
> >> > float sq_j = sqrt(j);
> >> > result += sq_i * sq_j;
> >> > printf("ITER: %d | i: %d | j: %d Result(i: %f | j:
> >> > %f | i*j: %f): %f\n", iter, i , j, sq_i, sq_j, sq_i * sq_j, result);
> >> > }
> >> > }
> >> > printf("Final Result: %lf\n", result);
> >> > }
> >> > }
> >> >
> >> >
> >> > The correct Final Result in both iterations is 372.721656. However,
> >> > I get the following results in FS:
> >> >
> >> > ITER: 0 | i: 0 | j: 0 Result(i: 0.000000 | j: 0.000000 | i*j:
> >> > 0.000000): 0.000000
> >> > ITER: 0 | i: 0 | j: 1 Result(i: 0.000000 | j: 1.000000 | i*j:
> >> > 0.000000): 0.000000
> >> > ITER: 0 | i: 0 | j: 2 Result(i: 0.000000 | j: 1.414214 | i*j:
> >> > 0.000000): 0.000000
> >> > ITER: 0 | i: 0 | j: 3 Result(i: 0.000000 | j: 1.732051 | i*j:
> >> > 0.000000): 0.000000
> >> > ITER: 0 | i: 0 | j: 4 Result(i: 0.000000 | j: 2.000000 | i*j:
> >> > 0.000000): 0.000000
> >> > ITER: 0 | i: 0 | j: 5 Result(i: 0.000000 | j: 2.236068 | i*j:
> >> > 0.000000): 0.000000
> >> > ITER: 0 | i: 0 | j: 6 Result(i: 0.000000 | j: 2.449490 | i*j:
> >> > 0.000000): 0.000000
> >> > ITER: 0 | i: 0 | j: 7 Result(i: 0.000000 | j: 2.645751 | i*j:
> >> > 0.000000): 0.000000
> >> > ITER: 0 | i: 0 | j: 8 Result(i: 0.000000 | j: 2.828427 | i*j:
> >> > 0.000000): 0.000000
> >> > ITER: 0 | i: 0 | j: 9 Result(i: 0.000000 | j: 3.000000 | i*j:
> >> > 0.000000): 0.000000
> >> > ITER: 0 | i: 1 | j: 0 Result(i: 1.000000 | j: 0.000000 | i*j:
> >> > 0.000000): 0.000000
> >> > ITER: 0 | i: 1 | j: 1 Result(i: 1.000000 | j: 1.000000 | i*j:
> >> > 1.000000): 1.000000
> >> > ITER: 0 | i: 1 | j: 2 Result(i: 1.000000 | j: 1.414214 | i*j:
> >> > 1.414214): 2.414214
> >> > ITER: 0 | i: 1 | j: 3 Result(i: 1.000000 | j: 1.732051 | i*j:
> >> > 1.732051): 4.146264
> >> > ITER: 0 | i: 1 | j: 4 Result(i: 0.000000 | j: 2.000000 | i*j:
> >> > 0.000000): 0.000000
> >> > ITER: 0 | i: 1 | j: 5 Result(i: 0.000000 | j: 2.236068 | i*j:
> >> > 0.000000): 0.000000
> >> > ITER: 0 | i: 1 | j: 6 Result(i: 0.000000 | j: 2.449490 | i*j:
> >> > 0.000000): 0.000000
> >> > ITER: 0 | i: 1 | j: 7 Result(i: 0.000000 | j: 2.645751 | i*j:
> >> > 0.000000): 0.000000
> >> > ITER: 0 | i: 1 | j: 8 Result(i: 0.000000 | j: 2.828427 | i*j:
> >> > 0.000000): 0.000000
> >> > ITER: 0 | i: 1 | j: 9 Result(i: 0.000000 | j: 3.000000 | i*j:
> >> > 0.000000): 0.000000
> >> > ITER: 0 | i: 2 | j: 0 Result(i: 1.414214 | j: 0.000000 | i*j:
> >> > 0.000000): 0.000000
> >> > ITER: 0 | i: 2 | j: 1 Result(i: 1.414214 | j: 1.000000 | i*j:
> >> > 1.414214): 1.414214
> >> > ITER: 0 | i: 2 | j: 2 Result(i: 1.414214 | j: 1.414214 | i*j:
> >> > 2.000000): 3.414214
> >> > ITER: 0 | i: 2 | j: 3 Result(i: 1.414214 | j: 1.732051 | i*j:
> >> > 2.449490): 5.863703
> >> > ITER: 0 | i: 2 | j: 4 Result(i: 1.414214 | j: 2.000000 | i*j:
> >> > 2.828427): 8.692130
> >> > ITER: 0 | i: 2 | j: 5 Result(i: 1.414214 | j: 2.236068 | i*j:
> >> > 3.162278): 11.854408
> >> > ITER: 0 | i: 2 | j: 6 Result(i: 1.414214 | j: 2.449490 | i*j:
> >> > 3.464102): 15.318510
> >> > ITER: 0 | i: 2 | j: 7 Result(i: 1.414214 | j: 2.645751 | i*j:
> >> > 3.741657): 19.060167
> >> > ITER: 0 | i: 2 | j: 8 Result(i: 1.414214 | j: 2.828427 | i*j:
> >> > 4.000000): 23.060167
> >> > ITER: 0 | i: 2 | j: 9 Result(i: 1.414214 | j: 3.000000 | i*j:
> >> > 4.242641): 27.302808
> >> > ITER: 0 | i: 3 | j: 0 Result(i: 1.732051 | j: 0.000000 | i*j:
> >> > 0.000000): 27.302808
> >> > ITER: 0 | i: 3 | j: 1 Result(i: 1.732051 | j: 1.000000 | i*j:
> >> > 1.732051): 29.034859
> >> > ITER: 0 | i: 3 | j: 2 Result(i: 1.732051 | j: 1.414214 | i*j:
> >> > 2.449490): 31.484348
> >> > ITER: 0 | i: 3 | j: 3 Result(i: 1.732051 | j: 1.732051 | i*j:
> >> > 3.000000): 34.484348
> >> > ITER: 0 | i: 3 | j: 4 Result(i: 1.732051 | j: 2.000000 | i*j:
> >> > 3.464102): 37.948450
> >> > ITER: 0 | i: 3 | j: 5 Result(i: 1.732051 | j: 2.236068 | i*j:
> >> > 3.872983): 41.821433
> >> > ITER: 0 | i: 3 | j: 6 Result(i: 1.732051 | j: 2.449490 | i*j:
> >> > 4.242641): 46.064074
> >> > ITER: 0 | i: 3 | j: 7 Result(i: 1.732051 | j: 2.645751 | i*j:
> >> > 4.582576): 50.646650
> >> > ITER: 0 | i: 3 | j: 8 Result(i: 1.732051 | j: 2.828427 | i*j:
> >> > 4.898979): 55.545629
> >> > ITER: 0 | i: 3 | j: 9 Result(i: 1.732051 | j: 3.000000 | i*j:
> >> > 5.196152): 60.741782
> >> > ITER: 0 | i: 4 | j: 0 Result(i: 2.000000 | j: 0.000000 | i*j:
> >> > 0.000000): 60.741782
> >> > ITER: 0 | i: 4 | j: 1 Result(i: 2.000000 | j: 1.000000 | i*j:
> >> > 2.000000): 62.741782
> >> > ITER: 0 | i: 4 | j: 2 Result(i: 2.000000 | j: 1.414214 | i*j:
> >> > 2.828427): 65.570209
> >> > ITER: 0 | i: 4 | j: 3 Result(i: 2.000000 | j: 1.732051 | i*j:
> >> > 3.464102): 69.034310
> >> > ITER: 0 | i: 4 | j: 4 Result(i: 2.000000 | j: 2.000000 | i*j:
> >> > 4.000000): 73.034310
> >> > ITER: 0 | i: 4 | j: 5 Result(i: 2.000000 | j: 2.236068 | i*j:
> >> > 4.472136): 77.506446
> >> > ITER: 0 | i: 4 | j: 6 Result(i: 2.000000 | j: 2.449490 | i*j:
> >> > 4.898979): 82.405426
> >> > ITER: 0 | i: 4 | j: 7 Result(i: 2.000000 | j: 2.645751 | i*j:
> >> > 5.291503): 87.696928
> >> > ITER: 0 | i: 4 | j: 8 Result(i: 2.000000 | j: 2.828427 | i*j:
> >> > 5.656854): 93.353783
> >> > ITER: 0 | i: 4 | j: 9 Result(i: 2.000000 | j: 3.000000 | i*j:
> >> > 6.000000): 99.353783
> >> > ITER: 0 | i: 5 | j: 0 Result(i: 2.236068 | j: 0.000000 | i*j:
> >> > 0.000000): 99.353783
> >> > ITER: 0 | i: 5 | j: 1 Result(i: 2.236068 | j: 1.000000 | i*j:
> >> > 2.236068): 101.589851
> >> > ITER: 0 | i: 5 | j: 2 Result(i: 2.236068 | j: 1.414214 | i*j:
> >> > 3.162278): 104.752128
> >> > ITER: 0 | i: 5 | j: 3 Result(i: 2.236068 | j: 1.732051 | i*j:
> >> > 3.872983): 108.625112
> >> > ITER: 0 | i: 5 | j: 4 Result(i: 2.236068 | j: 2.000000 | i*j:
> >> > 4.472136): 113.097248
> >> > ITER: 0 | i: 5 | j: 5 Result(i: 2.236068 | j: 2.236068 | i*j:
> >> > 5.000000): 118.097248
> >> > ITER: 0 | i: 5 | j: 6 Result(i: 2.236068 | j: 2.449490 | i*j:
> >> > 5.477226): 123.574473
> >> > ITER: 0 | i: 5 | j: 7 Result(i: 2.236068 | j: 2.645751 | i*j:
> >> > 5.916080): 129.490553
> >> > ITER: 0 | i: 5 | j: 8 Result(i: 2.236068 | j: 2.828427 | i*j:
> >> > 6.324555): 135.815108
> >> > ITER: 0 | i: 5 | j: 9 Result(i: 2.236068 | j: 3.000000 | i*j:
> >> > 6.708204): 142.523312
> >> > ITER: 0 | i: 6 | j: 0 Result(i: 2.449490 | j: 0.000000 | i*j:
> >> > 0.000000): 142.523312
> >> > ITER: 0 | i: 6 | j: 1 Result(i: 2.449490 | j: 1.000000 | i*j:
> >> > 2.449490): 144.972802
> >> > ITER: 0 | i: 6 | j: 2 Result(i: 2.449490 | j: 1.414214 | i*j:
> >> > 3.464102): 148.436904
> >> > ITER: 0 | i: 6 | j: 3 Result(i: 2.449490 | j: 1.732051 | i*j:
> >> > 4.242641): 152.679544
> >> > ITER: 0 | i: 6 | j: 4 Result(i: 2.449490 | j: 2.000000 | i*j:
> >> > 4.898979): 157.578524
> >> > ITER: 0 | i: 6 | j: 5 Result(i: 2.449490 | j: 2.236068 | i*j:
> >> > 5.477226): 163.055749
> >> > ITER: 0 | i: 6 | j: 6 Result(i: 2.449490 | j: 2.449490 | i*j:
> >> > 6.000000): 169.055749
> >> > ITER: 0 | i: 6 | j: 7 Result(i: 2.449490 | j: 2.645751 | i*j:
> >> > 6.480741): 175.536490
> >> > ITER: 0 | i: 6 | j: 8 Result(i: 2.449490 | j: 2.828427 | i*j:
> >> > 6.928203): 182.464693
> >> > ITER: 0 | i: 6 | j: 9 Result(i: 2.449490 | j: 3.000000 | i*j:
> >> > 7.348469): 189.813162
> >> > ITER: 0 | i: 7 | j: 0 Result(i: 2.645751 | j: 0.000000 | i*j:
> >> > 0.000000): 189.813162
> >> > ITER: 0 | i: 7 | j: 1 Result(i: 2.645751 | j: 1.000000 | i*j:
> >> > 2.645751): 192.458914
> >> > ITER: 0 | i: 7 | j: 2 Result(i: 2.645751 | j: 1.414214 | i*j:
> >> > 3.741657): 196.200571
> >> > ITER: 0 | i: 7 | j: 3 Result(i: 2.645751 | j: 1.732051 | i*j:
> >> > 4.582576): 200.783147
> >> > ITER: 0 | i: 7 | j: 4 Result(i: 2.645751 | j: 2.000000 | i*j:
> >> > 5.291503): 206.074649
> >> > ITER: 0 | i: 7 | j: 5 Result(i: 2.645751 | j: 2.236068 | i*j:
> >> > 5.916080): 211.990729
> >> > ITER: 0 | i: 7 | j: 6 Result(i: 2.645751 | j: 2.449490 | i*j:
> >> > 6.480741): 218.471470
> >> > ITER: 0 | i: 7 | j: 7 Result(i: 2.645751 | j: 2.645751 | i*j:
> >> > 7.000000): 225.471470
> >> > ITER: 0 | i: 7 | j: 8 Result(i: 2.645751 | j: 2.828427 | i*j:
> >> > 7.483315): 232.954785
> >> > ITER: 0 | i: 7 | j: 9 Result(i: 2.645751 | j: 3.000000 | i*j:
> >> > 7.937254): 240.892039
> >> > ITER: 0 | i: 8 | j: 0 Result(i: 2.828427 | j: 0.000000 | i*j:
> >> > 0.000000): 240.892039
> >> > ITER: 0 | i: 8 | j: 1 Result(i: 2.828427 | j: 1.000000 | i*j:
> >> > 2.828427): 243.720466
> >> > ITER: 0 | i: 8 | j: 2 Result(i: 2.828427 | j: 1.414214 | i*j:
> >> > 4.000000): 247.720466
> >> > ITER: 0 | i: 8 | j: 3 Result(i: 2.828427 | j: 1.732051 | i*j:
> >> > 4.898979): 252.619445
> >> > ITER: 0 | i: 8 | j: 4 Result(i: 2.828427 | j: 2.000000 | i*j:
> >> > 5.656854): 258.276300
> >> > ITER: 0 | i: 8 | j: 5 Result(i: 2.828427 | j: 2.236068 | i*j:
> >> > 6.324555): 264.600855
> >> > ITER: 0 | i: 8 | j: 6 Result(i: 2.828427 | j: 2.449490 | i*j:
> >> > 6.928203): 271.529058
> >> > ITER: 0 | i: 8 | j: 7 Result(i: 2.828427 | j: 2.645751 | i*j:
> >> > 7.483315): 279.012373
> >> > ITER: 0 | i: 8 | j: 8 Result(i: 2.828427 | j: 2.828427 | i*j:
> >> > 8.000000): 287.012373
> >> > ITER: 0 | i: 8 | j: 9 Result(i: 2.828427 | j: 3.000000 | i*j:
> >> > 8.485281): 295.497654
> >> > ITER: 0 | i: 9 | j: 0 Result(i: 3.000000 | j: 0.000000 | i*j:
> >> > 0.000000): 295.497654
> >> > ITER: 0 | i: 9 | j: 1 Result(i: 3.000000 | j: 1.000000 | i*j:
> >> > 3.000000): 298.497654
> >> > ITER: 0 | i: 9 | j: 2 Result(i: 3.000000 | j: 1.414214 | i*j:
> >> > 4.242641): 302.740295
> >> > ITER: 0 | i: 9 | j: 3 Result(i: 3.000000 | j: 1.732051 | i*j:
> >> > 5.196152): 307.936447
> >> > ITER: 0 | i: 9 | j: 4 Result(i: 3.000000 | j: 2.000000 | i*j:
> >> > 6.000000): 313.936447
> >> > ITER: 0 | i: 9 | j: 5 Result(i: 3.000000 | j: 2.236068 | i*j:
> >> > 6.708204): 320.644651
> >> > ITER: 0 | i: 9 | j: 6 Result(i: 3.000000 | j: 2.449490 | i*j:
> >> > 7.348469): 327.993120
> >> > ITER: 0 | i: 9 | j: 7 Result(i: 3.000000 | j: 2.645751 | i*j:
> >> > 7.937254): 335.930374
> >> > ITER: 0 | i: 9 | j: 8 Result(i: 3.000000 | j: 2.828427 | i*j:
> >> > 8.485281): 344.415656
> >> > ITER: 0 | i: 9 | j: 9 Result(i: 3.000000 | j: 3.000000 | i*j:
> >> > 9.000000): 353.415656
> >> > Final Result: 353.415656
> >> > ITER: 1 | i: 0 | j: 0 Result(i: 0.000000 | j: 0.000000 | i*j:
> >> > 0.000000): 0.000000
> >> > ITER: 1 | i: 0 | j: 1 Result(i: 0.000000 | j: 1.000000 | i*j:
> >> > 0.000000): 0.000000
> >> > ITER: 1 | i: 0 | j: 2 Result(i: 0.000000 | j: 1.414214 | i*j:
> >> > 0.000000): 0.000000
> >> > ITER: 1 | i: 0 | j: 3 Result(i: 0.000000 | j: 1.732051 | i*j:
> >> > 0.000000): 0.000000
> >> > ITER: 1 | i: 0 | j: 4 Result(i: 0.000000 | j: 2.000000 | i*j:
> >> > 0.000000): 0.000000
> >> > ITER: 1 | i: 0 | j: 5 Result(i: 0.000000 | j: 2.236068 | i*j:
> >> > 0.000000): 0.000000
> >> > ITER: 1 | i: 0 | j: 6 Result(i: 0.000000 | j: 2.449490 | i*j:
> >> > 0.000000): 0.000000
> >> > ITER: 1 | i: 0 | j: 7 Result(i: 0.000000 | j: 2.645751 | i*j:
> >> > 0.000000): 0.000000
> >> > ITER: 1 | i: 0 | j: 8 Result(i: 0.000000 | j: 2.828427 | i*j:
> >> > 0.000000): 0.000000
> >> > ITER: 1 | i: 0 | j: 9 Result(i: 0.000000 | j: 3.000000 | i*j:
> >> > 0.000000): 0.000000
> >> > ITER: 1 | i: 1 | j: 0 Result(i: 1.000000 | j: 0.000000 | i*j:
> >> > 0.000000): 0.000000
> >> > ITER: 1 | i: 1 | j: 1 Result(i: 1.000000 | j: 1.000000 | i*j:
> >> > 1.000000): 1.000000
> >> > ITER: 1 | i: 1 | j: 2 Result(i: 1.000000 | j: 1.414214 | i*j:
> >> > 1.414214): 2.414214
> >> > ITER: 1 | i: 1 | j: 3 Result(i: 1.000000 | j: 1.732051 | i*j:
> >> > 1.732051): 4.146264
> >> > ITER: 1 | i: 1 | j: 4 Result(i: 1.000000 | j: 2.000000 | i*j:
> >> > 2.000000): 6.146264
> >> > ITER: 1 | i: 1 | j: 5 Result(i: 1.000000 | j: 2.236068 | i*j:
> >> > 2.236068): 8.382332
> >> > ITER: 1 | i: 1 | j: 6 Result(i: 1.000000 | j: 2.449490 | i*j:
> >> > 2.449490): 10.831822
> >> > ITER: 1 | i: 1 | j: 7 Result(i: 1.000000 | j: 2.645751 | i*j:
> >> > 2.645751): 13.477573
> >> > ITER: 1 | i: 1 | j: 8 Result(i: 1.000000 | j: 2.828427 | i*j:
> >> > 2.828427): 16.306001
> >> > ITER: 1 | i: 1 | j: 9 Result(i: 1.000000 | j: 3.000000 | i*j:
> >> > 3.000000): 19.306001
> >> > ITER: 1 | i: 2 | j: 0 Result(i: 1.414214 | j: 0.000000 | i*j:
> >> > 0.000000): 19.306001
> >> > ITER: 1 | i: 2 | j: 1 Result(i: 1.414214 | j: 1.000000 | i*j:
> >> > 1.414214): 20.720214
> >> > ITER: 1 | i: 2 | j: 2 Result(i: 1.414214 | j: 1.414214 | i*j:
> >> > 2.000000): 22.720214
> >> > ITER: 1 | i: 2 | j: 3 Result(i: 1.414214 | j: 1.732051 | i*j:
> >> > 2.449490): 25.169704
> >> > ITER: 1 | i: 2 | j: 4 Result(i: 1.414214 | j: 2.000000 | i*j:
> >> > 2.828427): 27.998131
> >> > ITER: 1 | i: 2 | j: 5 Result(i: 1.414214 | j: 2.236068 | i*j:
> >> > 3.162278): 31.160409
> >> > ITER: 1 | i: 2 | j: 6 Result(i: 1.414214 | j: 2.449490 | i*j:
> >> > 3.464102): 34.624510
> >> > ITER: 1 | i: 2 | j: 7 Result(i: 1.414214 | j: 2.645751 | i*j:
> >> > 3.741657): 38.366168
> >> > ITER: 1 | i: 2 | j: 8 Result(i: 1.414214 | j: 2.828427 | i*j:
> >> > 4.000000): 42.366168
> >> > ITER: 1 | i: 2 | j: 9 Result(i: 1.414214 | j: 3.000000 | i*j:
> >> > 4.242641): 46.608808
> >> > ITER: 1 | i: 3 | j: 0 Result(i: 1.732051 | j: 0.000000 | i*j:
> >> > 0.000000): 46.608808
> >> > ITER: 1 | i: 3 | j: 1 Result(i: 1.732051 | j: 1.000000 | i*j:
> >> > 1.732051): 48.340859
> >> > ITER: 1 | i: 3 | j: 2 Result(i: 1.732051 | j: 1.414214 | i*j:
> >> > 2.449490): 50.790349
> >> > ITER: 1 | i: 3 | j: 3 Result(i: 1.732051 | j: 1.732051 | i*j:
> >> > 3.000000): 53.790349
> >> > ITER: 1 | i: 3 | j: 4 Result(i: 1.732051 | j: 2.000000 | i*j:
> >> > 3.464102): 57.254450
> >> > ITER: 1 | i: 3 | j: 5 Result(i: 1.732051 | j: 2.236068 | i*j:
> >> > 3.872983): 61.127434
> >> > ITER: 1 | i: 3 | j: 6 Result(i: 1.732051 | j: 2.449490 | i*j:
> >> > 4.242641): 65.370075
> >> > ITER: 1 | i: 3 | j: 7 Result(i: 1.732051 | j: 2.645751 | i*j:
> >> > 4.582576): 69.952650
> >> > ITER: 1 | i: 3 | j: 8 Result(i: 1.732051 | j: 2.828427 | i*j:
> >> > 4.898979): 74.851630
> >> > ITER: 1 | i: 3 | j: 9 Result(i: 1.732051 | j: 3.000000 | i*j:
> >> > 5.196152): 80.047782
> >> > ITER: 1 | i: 4 | j: 0 Result(i: 2.000000 | j: 0.000000 | i*j:
> >> > 0.000000): 80.047782
> >> > ITER: 1 | i: 4 | j: 1 Result(i: 2.000000 | j: 1.000000 | i*j:
> >> > 2.000000): 82.047782
> >> > ITER: 1 | i: 4 | j: 2 Result(i: 2.000000 | j: 1.414214 | i*j:
> >> > 2.828427): 84.876209
> >> > ITER: 1 | i: 4 | j: 3 Result(i: 2.000000 | j: 1.732051 | i*j:
> >> > 3.464102): 88.340311
> >> > ITER: 1 | i: 4 | j: 4 Result(i: 2.000000 | j: 2.000000 | i*j:
> >> > 4.000000): 92.340311
> >> > ITER: 1 | i: 4 | j: 5 Result(i: 2.000000 | j: 2.236068 | i*j:
> >> > 4.472136): 96.812447
> >> > ITER: 1 | i: 4 | j: 6 Result(i: 2.000000 | j: 2.449490 | i*j:
> >> > 4.898979): 101.711426
> >> > ITER: 1 | i: 4 | j: 7 Result(i: 2.000000 | j: 2.645751 | i*j:
> >> > 5.291503): 107.002929
> >> > ITER: 1 | i: 4 | j: 8 Result(i: 2.000000 | j: 2.828427 | i*j:
> >> > 5.656854): 112.659783
> >> > ITER: 1 | i: 4 | j: 9 Result(i: 2.000000 | j: 3.000000 | i*j:
> >> > 6.000000): 118.659783
> >> > ITER: 1 | i: 5 | j: 0 Result(i: 2.236068 | j: 0.000000 | i*j:
> >> > 0.000000): 118.659783
> >> > ITER: 1 | i: 5 | j: 1 Result(i: 2.236068 | j: 1.000000 | i*j:
> >> > 2.236068): 120.895851
> >> > ITER: 1 | i: 5 | j: 2 Result(i: 2.236068 | j: 1.414214 | i*j:
> >> > 3.162278): 124.058129
> >> > ITER: 1 | i: 5 | j: 3 Result(i: 2.236068 | j: 1.732051 | i*j:
> >> > 3.872983): 127.931112
> >> > ITER: 1 | i: 5 | j: 4 Result(i: 2.236068 | j: 2.000000 | i*j:
> >> > 4.472136): 132.403248
> >> > ITER: 1 | i: 5 | j: 5 Result(i: 2.236068 | j: 2.236068 | i*j:
> >> > 5.000000): 137.403248
> >> > ITER: 1 | i: 5 | j: 6 Result(i: 2.236068 | j: 2.449490 | i*j:
> >> > 5.477226): 142.880474
> >> > ITER: 1 | i: 5 | j: 7 Result(i: 2.236068 | j: 2.645751 | i*j:
> >> > 5.916080): 148.796553
> >> > ITER: 1 | i: 5 | j: 8 Result(i: 2.236068 | j: 2.828427 | i*j:
> >> > 6.324555): 155.121109
> >> > ITER: 1 | i: 5 | j: 9 Result(i: 2.236068 | j: 3.000000 | i*j:
> >> > 6.708204): 161.829313
> >> > ITER: 1 | i: 6 | j: 0 Result(i: 2.449490 | j: 0.000000 | i*j:
> >> > 0.000000): 161.829313
> >> > ITER: 1 | i: 6 | j: 1 Result(i: 2.449490 | j: 1.000000 | i*j:
> >> > 2.449490): 164.278802
> >> > ITER: 1 | i: 6 | j: 2 Result(i: 2.449490 | j: 1.414214 | i*j:
> >> > 3.464102): 167.742904
> >> > ITER: 1 | i: 6 | j: 3 Result(i: 2.449490 | j: 1.732051 | i*j:
> >> > 4.242641): 171.985545
> >> > ITER: 1 | i: 6 | j: 4 Result(i: 2.449490 | j: 2.000000 | i*j:
> >> > 4.898979): 176.884524
> >> > ITER: 1 | i: 6 | j: 5 Result(i: 2.449490 | j: 2.236068 | i*j:
> >> > 5.477226): 182.361750
> >> > ITER: 1 | i: 6 | j: 6 Result(i: 2.449490 | j: 2.449490 | i*j:
> >> > 6.000000): 188.361750
> >> > ITER: 1 | i: 6 | j: 7 Result(i: 2.449490 | j: 2.645751 | i*j:
> >> > 6.480741): 194.842491
> >> > ITER: 1 | i: 6 | j: 8 Result(i: 2.449490 | j: 2.828427 | i*j:
> >> > 6.928203): 201.770694
> >> > ITER: 1 | i: 6 | j: 9 Result(i: 2.449490 | j: 3.000000 | i*j:
> >> > 7.348469): 209.119163
> >> > ITER: 1 | i: 7 | j: 0 Result(i: 2.645751 | j: 0.000000 | i*j:
> >> > 0.000000): 209.119163
> >> > ITER: 1 | i: 7 | j: 1 Result(i: 2.645751 | j: 1.000000 | i*j:
> >> > 2.645751): 211.764914
> >> > ITER: 1 | i: 7 | j: 2 Result(i: 2.645751 | j: 1.414214 | i*j:
> >> > 3.741657): 215.506572
> >> > ITER: 1 | i: 7 | j: 3 Result(i: 2.645751 | j: 1.732051 | i*j:
> >> > 4.582576): 220.089147
> >> > ITER: 1 | i: 7 | j: 4 Result(i: 2.645751 | j: 2.000000 | i*j:
> >> > 5.291503): 225.380650
> >> > ITER: 1 | i: 7 | j: 5 Result(i: 2.645751 | j: 2.236068 | i*j:
> >> > 5.916080): 231.296730
> >> > ITER: 1 | i: 7 | j: 6 Result(i: 2.645751 | j: 2.449490 | i*j:
> >> > 6.480741): 237.777470
> >> > ITER: 1 | i: 7 | j: 7 Result(i: 2.645751 | j: 2.645751 | i*j:
> >> > 7.000000): 244.777470
> >> > ITER: 1 | i: 7 | j: 8 Result(i: 2.645751 | j: 2.828427 | i*j:
> >> > 7.483315): 252.260785
> >> > ITER: 1 | i: 7 | j: 9 Result(i: 2.645751 | j: 3.000000 | i*j:
> >> > 7.937254): 260.198039
> >> > ITER: 1 | i: 8 | j: 0 Result(i: 2.828427 | j: 0.000000 | i*j:
> >> > 0.000000): 260.198039
> >> > ITER: 1 | i: 8 | j: 1 Result(i: 2.828427 | j: 1.000000 | i*j:
> >> > 2.828427): 263.026466
> >> > ITER: 1 | i: 8 | j: 2 Result(i: 2.828427 | j: 1.414214 | i*j:
> >> > 4.000000): 267.026466
> >> > ITER: 1 | i: 8 | j: 3 Result(i: 2.828427 | j: 1.732051 | i*j:
> >> > 4.898979): 271.925446
> >> > ITER: 1 | i: 8 | j: 4 Result(i: 2.828427 | j: 2.000000 | i*j:
> >> > 5.656854): 277.582300
> >> > ITER: 1 | i: 8 | j: 5 Result(i: 2.828427 | j: 2.236068 | i*j:
> >> > 6.324555): 283.906855
> >> > ITER: 1 | i: 8 | j: 6 Result(i: 2.828427 | j: 2.449490 | i*j:
> >> > 6.928203): 290.835059
> >> > ITER: 1 | i: 8 | j: 7 Result(i: 2.828427 | j: 2.645751 | i*j:
> >> > 7.483315): 298.318373
> >> > ITER: 1 | i: 8 | j: 8 Result(i: 2.828427 | j: 2.828427 | i*j:
> >> > 8.000000): 306.318373
> >> > ITER: 1 | i: 8 | j: 9 Result(i: 2.828427 | j: 3.000000 | i*j:
> >> > 8.485281): 314.803655
> >> > ITER: 1 | i: 9 | j: 0 Result(i: 3.000000 | j: 0.000000 | i*j:
> >> > 0.000000): 314.803655
> >> > ITER: 1 | i: 9 | j: 1 Result(i: 3.000000 | j: 1.000000 | i*j:
> >> > 3.000000): 317.803655
> >> > ITER: 1 | i: 9 | j: 2 Result(i: 3.000000 | j: 1.414214 | i*j:
> >> > 4.242641): 322.046295
> >> > ITER: 1 | i: 9 | j: 3 Result(i: 3.000000 | j: 1.732051 | i*j:
> >> > 5.196152): 327.242448
> >> > ITER: 1 | i: 9 | j: 4 Result(i: 3.000000 | j: 2.000000 | i*j:
> >> > 6.000000): 333.242448
> >> > ITER: 1 | i: 9 | j: 5 Result(i: 3.000000 | j: 2.236068 | i*j:
> >> > 6.708204): 339.950652
> >> > ITER: 1 | i: 9 | j: 6 Result(i: 3.000000 | j: 2.449490 | i*j:
> >> > 7.348469): 347.299121
> >> > ITER: 1 | i: 9 | j: 7 Result(i: 3.000000 | j: 2.645751 | i*j:
> >> > 7.937254): 355.236375
> >> > ITER: 1 | i: 9 | j: 8 Result(i: 3.000000 | j: 2.828427 | i*j:
> >> > 8.485281): 363.721656
> >> > ITER: 1 | i: 9 | j: 9 Result(i: 3.000000 | j: 3.000000 | i*j:
> >> > 9.000000): 372.721656
> >> > Final Result: 372.721656
> >> >
> >> >
> >> >
> >> > As we can see in the following iterations the sqrt(1) as well as the
> >> > result is set to zero for some reason.
> >> >
> >> > ITER: 0 | i: 1 | j: 4 Result(i: 0.000000 | j: 2.000000 | i*j:
> >> > 0.000000): 0.000000
> >> > ITER: 0 | i: 1 | j: 5 Result(i: 0.000000 | j: 2.236068 | i*j:
> >> > 0.000000): 0.000000
> >> > ITER: 0 | i: 1 | j: 6 Result(i: 0.000000 | j: 2.449490 | i*j:
> >> > 0.000000): 0.000000
> >> > ITER: 0 | i: 1 | j: 7 Result(i: 0.000000 | j: 2.645751 | i*j:
> >> > 0.000000): 0.000000
> >> > ITER: 0 | i: 1 | j: 8 Result(i: 0.000000 | j: 2.828427 | i*j:
> >> > 0.000000): 0.000000
> >> > ITER: 0 | i: 1 | j: 9 Result(i: 0.000000 | j: 3.000000 | i*j:
> >> > 0.000000): 0.000000
> >> >
> >> > Please help me to resolve the accuracy issue! I think that it will
> >> > be very useful for gem5 community.
> >> >
> >> > To be noticed, I find the correct simulated tick in which the
> >> > application started in FS (using m5 dumpstats), and I start the
> >> > --debug-start, but the trace file which is generated is 10x larger
> >> > than SE mode for the same application. How can I compare them?
> >> >
> >> > Thank you in advance!
> >> > Best regards,
> >> > Nikos
> >> >
> >> > Quoting Νικόλαος Ταμπουρατζής <ntampouratzis@ece.auth.gr>:
> >> >
> >> >> Dear Jason,
> >> >>
> >> >> I am trying to use --debug-start but in FS mode it is very
> >> >> difficult to find the tick on which the application is started!
> >> >>
> >> >> However, I am writing the following very simple c++ program:
> >> >>
> >> >> #include <cmath>
> >> >> #include <stdio.h>
> >> >>
> >> >> int main(){
> >> >>
> >> >> int dim = 4096;
> >> >>
> >> >> double result;
> >> >>
> >> >> for (int iter = 0; iter < 2; iter++){
> >> >> result = 0;
> >> >> for (int i = 0; i < dim; i++){
> >> >> for (int j = 0; j < dim; j++){
> >> >> result += sqrt(i) * sqrt(j);
> >> >> }
> >> >> }
> >> >> printf("Result: %lf\n", result); //Result: 30530733453.127449
> >> >> }
> >> >> }
> >> >>
> >> >> I cross-compile it using: riscv64-linux-gnu-g++ -static -O3 -o
> >> >> test_riscv test_riscv.cpp
> >> >>
> >> >>
> >> >> While in X86 (without cross-compilation of course), QEMU-RISCV,
> >> >> GEM5-SE the result is the same (30530733453.127449), in GEM5-FS the
> >> >> result is different! In addition, the result is also different
> >> >> between the 2 iterations.
> >> >>
> >> >> Please reproduce the error if you want in order to verify my result.
> >> >> Ηow can the issue be resolved?
> >> >>
> >> >> Thank you in advance!
> >> >>
> >> >> Best regards,
> >> >> Nikos
> >> >>
> >> >>
> >> >> Quoting Jason Lowe-Power <jason@lowepower.com>:
> >> >>
> >> >>> Hi Nikos,
> >> >>>
> >> >>> You can use --debug-start to start the debugging after some number
> of
> >> >>> ticks. Also, I would expect that the difference should come up
> >> quickly, so
> >> >>> no need to run the program to the end.
> >> >>>
> >> >>> For the FS mode one, you will want to just start the trace as the
> >> >>> application starts. This could be a bit of a pain.
> >> >>>
> >> >>> I'm not really sure what fundamentally could be different. FS and SE
> >> mode
> >> >>> use the exact same code for executing instructions, so I don't think
> >> that's
> >> >>> the problem. Have you tried running for smaller inputs or just one
> >> >>> iteration?
> >> >>>
> >> >>> Jason
> >> >>>
> >> >>>
> >> >>>
> >> >>> On Wed, Sep 21, 2022 at 9:04 AM Νικόλαος Ταμπουρατζής <
> >> >>> ntampouratzis@ece.auth.gr> wrote:
> >> >>>
> >> >>>> Dear Bobby,
> >> >>>>
> >> >>>> Iam trying to add --debug-flags=Exec (building the gem5 for
> gem5.opt
> >> >>>> not for gem5.fast which I had) but the debug traces exceed the 20GB
> >> >>>> (and it is not finished yet) for less than 1 simulated second. How
> can
> >> >>>> I reduce the size of the debug-flags (or set something more
> specific)?
> >> >>>>
> >> >>>> In contrast I build the HPCG benchmark with DHPCG_DEBUG flag. If
> you
> >> >>>> want, you can compare these two output files
> >> >>>> (hpcg20010909T014640_SE_Mode & HPCG-Benchmark_3.1_FS_Mode). As you
> can
> >> >>>> see, something goes wrong with the accuracy of calculations in FS
> mode
> >> >>>> (benchmark uses double precission). You can find the files here:
> >> >>>> http://kition.mhl.tuc.gr:8000/d/68d82f3533/
> >> >>>>
> >> >>>> Best regards,
> >> >>>> Nikos
> >> >>>>
> >> >>>> Quoting Jason Lowe-Power <jason@lowepower.com>:
> >> >>>>
> >> >>>>> That's quite odd that it works in SE mode but not FS mode!
> >> >>>>>
> >> >>>>> I would suggest running with --debug-flags=Exec for both and then
> >> >>>> perform a
> >> >>>>> diff to see how they differ.
> >> >>>>>
> >> >>>>> Cheers,
> >> >>>>> Jason
> >> >>>>>
> >> >>>>> On Tue, Sep 20, 2022 at 2:45 PM Νικόλαος Ταμπουρατζής <
> >> >>>>> ntampouratzis@ece.auth.gr> wrote:
> >> >>>>>
> >> >>>>>> Dear Bobby,
> >> >>>>>>
> >> >>>>>> In QEMU I get the same (correct) results that I get in SE mode
> >> >>>>>> simulation. I get invalid results in FS simulation (in both
> >> >>>>>> riscv-fs.py and riscv-ubuntu-run.py). I cannot access real RISCV
> >> >>>>>> hardware at this moment, however, if you want you may execute my
> >> xhpcg
> >> >>>>>> binary (http://kition.mhl.tuc.gr:8000/f/4ca25fdd3c/) with the
> >> >>>>>> following configuration:
> >> >>>>>>
> >> >>>>>> ./xhpcg --nx=16 --ny=16 --nz=16 --npx=1 --npy=1 --npz=1 --rt=0.1
> >> >>>>>>
> >> >>>>>> Please let me know if you have any updates!
> >> >>>>>>
> >> >>>>>> Best regards,
> >> >>>>>> Nikos
> >> >>>>>>
> >> >>>>>>
> >> >>>>>> Quoting Jason Lowe-Power <jason@lowepower.com>:
> >> >>>>>>
> >> >>>>>>> Hi Nikos,
> >> >>>>>>>
> >> >>>>>>> I notice you said the following in your original email:
> >> >>>>>>>
> >> >>>>>>> In addition, I used the RISCV Ubuntu image
> >> >>>>>>>> (
> >> https://github.com/gem5/gem5-resources/tree/stable/src/riscv-ubuntu
> >> >>>> ),
> >> >>>>>>>> I installed the gcc compiler, compile it (through qemu) and I
> get
> >> >>>>>>>> wrong results too.
> >> >>>>>>>
> >> >>>>>>>
> >> >>>>>>> Is this saying you get the wrong results is QEMU? If so, the bug
> >> is in
> >> >>>>>> GCC
> >> >>>>>>> or the HPCG workload, not in gem5. If not, I would test in QEMU
> to
> >> >>>> make
> >> >>>>>>> sure the binary works there. Another way you could test to see
> if
> >> the
> >> >>>>>>> problem is your binary or gem5 would be to run it on real
> >> hardware. We
> >> >>>>>> have
> >> >>>>>>> access to some RISC-V hardware here at UC Davis, if you don't
> have
> >> >>>> access
> >> >>>>>>> to it.
> >> >>>>>>>
> >> >>>>>>> Cheers,
> >> >>>>>>> Jason
> >> >>>>>>>
> >> >>>>>>> On Tue, Sep 20, 2022 at 12:58 AM Νικόλαος Ταμπουρατζής <
> >> >>>>>>> ntampouratzis@ece.auth.gr> wrote:
> >> >>>>>>>
> >> >>>>>>>> Dear Bobby,
> >> >>>>>>>>
> >> >>>>>>>> 1) I use the original riscv-fs.py which is provided in the
> latest
> >> >>>> gem5
> >> >>>>>>>> release.
> >> >>>>>>>> I run the gem5 once (./build/RISCV/gem5.fast -d
> ./HPCG_FS_results
> >> >>>>>>>> ./configs/example/gem5_library/riscv-fs.py) in order to
> download
> >> the
> >> >>>>>>>> riscv-bootloader-vmlinux-5.10 and riscv-disk-img.
> >> >>>>>>>> After this I mount the riscv-disk-img (sudo mount -o loop
> >> >>>>>>>> riscv-disk-img /mnt), put the xhpcg executable and I do the
> >> following
> >> >>>>>>>> changes in riscv-fs.py to boot the riscv-disk-img with
> executable:
> >> >>>>>>>>
> >> >>>>>>>> image = CustomDiskImageResource(
> >> >>>>>>>> local_path = "/home/cossim/.cache/gem5/riscv-disk-img",
> >> >>>>>>>> )
> >> >>>>>>>>
> >> >>>>>>>> # Set the Full System workload.
> >> >>>>>>>> board.set_kernel_disk_workload(
> >> >>>>>>>>
> >> kernel=Resource("riscv-bootloader-vmlinux-5.10"),
> >> >>>>>>>> disk_image=image,
> >> >>>>>>>> )
> >> >>>>>>>>
> >> >>>>>>>> Finally, in the
> >> gem5/src/python/gem5/components/boards/riscv_board.py
> >> >>>>>>>> I change the last line to "return ["console=ttyS0",
> >> >>>>>>>> "root={root_value}", "rw"]" in order to allow the write
> >> permissions
> >> >>>> in
> >> >>>>>>>> the image.
> >> >>>>>>>>
> >> >>>>>>>>
> >> >>>>>>>> 2) The HPCG benchmark after some iterations calculates if the
> >> results
> >> >>>>>>>> are valid or not valid. In the case of FS it gives invalid
> >> results.
> >> >>>> As
> >> >>>>>>>> I see from the results, one (at least) problem is that produces
> >> >>>>>>>> different results in each HPCG execution (with the same
> >> >>>> configuration).
> >> >>>>>>>>
> >> >>>>>>>> Here is the HPCG output and riscv-fs.py
> >> >>>>>>>> (http://kition.mhl.tuc.gr:8000/d/68d82f3533/). You may
> reproduce
> >> the
> >> >>>>>>>> results in the video if you use the xhpcg executable
> >> >>>>>>>> (http://kition.mhl.tuc.gr:8000/f/4ca25fdd3c/)
> >> >>>>>>>>
> >> >>>>>>>> Please help me in order to solve it!
> >> >>>>>>>>
> >> >>>>>>>> Finally, I get invalid results in the HPL benchmark in FS mode
> >> too.
> >> >>>>>>>>
> >> >>>>>>>> Best regards,
> >> >>>>>>>> Nikos
> >> >>>>>>>>
> >> >>>>>>>>
> >> >>>>>>>> Quoting Bobby Bruce <bbruce@ucdavis.edu>:
> >> >>>>>>>>
> >> >>>>>>>> > I'm going to need a bit more information to help:
> >> >>>>>>>> >
> >> >>>>>>>> > 1. In what way have you modified
> >> >>>>>>>> > ./configs/example/gem5_library/riscv-fs.py? Can you attach
> the
> >> >>>> script
> >> >>>>>>>> here?
> >> >>>>>>>> > 2. What error are you getting or in what way are the results
> >> >>>> invalid?
> >> >>>>>>>> >
> >> >>>>>>>> > -
> >> >>>>>>>> > Dr. Bobby R. Bruce
> >> >>>>>>>> > Room 3050,
> >> >>>>>>>> > Kemper Hall, UC Davis
> >> >>>>>>>> > Davis,
> >> >>>>>>>> > CA, 95616
> >> >>>>>>>> >
> >> >>>>>>>> > web: https://www.bobbybruce.net
> >> >>>>>>>> >
> >> >>>>>>>> >
> >> >>>>>>>> > On Mon, Sep 19, 2022 at 1:43 PM Νικόλαος Ταμπουρατζής <
> >> >>>>>>>> > ntampouratzis@ece.auth.gr> wrote:
> >> >>>>>>>> >
> >> >>>>>>>> >>
> >> >>>>>>>> >> Dear gem5 community,
> >> >>>>>>>> >>
> >> >>>>>>>> >> I have successfully cross-compile the HPCG benchmark for
> RISCV
> >> >>>>>> (Serial
> >> >>>>>>>> >> version, without MPI and OpenMP). While it working properly
> in
> >> >>>> gem5
> >> >>>>>> SE
> >> >>>>>>>> >> mode (./build/RISCV/gem5.fast -d ./HPCG_SE_results
> >> >>>>>>>> >> ./configs/example/se.py -c xhpcg --options '--nx=16 --ny=16
> >> >>>> --nz=16
> >> >>>>>>>> >> --npx=1 --npy=1 --npz=1 --rt=0.1'), I get invalid results
> in FS
> >> >>>>>>>> >> simulation using "./build/RISCV/gem5.fast -d
> ./HPCG_FS_results
> >> >>>>>>>> >> ./configs/example/gem5_library/riscv-fs.py" (I mount the
> riscv
> >> >>>> image
> >> >>>>>>>> >> and put it).
> >> >>>>>>>> >>
> >> >>>>>>>> >> Can you help me please?
> >> >>>>>>>> >>
> >> >>>>>>>> >> In addition, I used the RISCV Ubuntu image
> >> >>>>>>>> >> (
> >> >>>>
> https://github.com/gem5/gem5-resources/tree/stable/src/riscv-ubuntu
> >> >>>>>> ),
> >> >>>>>>>> >> I installed the gcc compiler, compile it (through qemu) and
> I
> >> get
> >> >>>>>>>> >> wrong results too.
> >> >>>>>>>> >>
> >> >>>>>>>> >> Here is the Makefile which I use, the hpcg executable for
> RISCV
> >> >>>>>>>> >> (xhpcg), and a video that shows the results
> >> >>>>>>>> >> (http://kition.mhl.tuc.gr:8000/f/4ca25fdd3c/).
> >> >>>>>>>> >>
> >> >>>>>>>> >> P.S. I use the latest gem5 version.
> >> >>>>>>>> >>
> >> >>>>>>>> >> Thank you in advance! :)
> >> >>>>>>>> >>
> >> >>>>>>>> >> Best regards,
> >> >>>>>>>> >> Nikos
> >> >>>>>>>> >> _______________________________________________
> >> >>>>>>>> >> gem5-users mailing list -- gem5-users@gem5.org
> >> >>>>>>>> >> To unsubscribe send an email to gem5-users-leave@gem5.org
> >> >>>>>>>> >>
> >> >>>>>>>>
> >> >>>>>>>>
> >> >>>>>>>> _______________________________________________
> >> >>>>>>>> gem5-users mailing list -- gem5-users@gem5.org
> >> >>>>>>>> To unsubscribe send an email to gem5-users-leave@gem5.org
> >> >>>>>>>>
> >> >>>>>>
> >> >>>>>>
> >> >>>>>> _______________________________________________
> >> >>>>>> gem5-users mailing list -- gem5-users@gem5.org
> >> >>>>>> To unsubscribe send an email to gem5-users-leave@gem5.org
> >> >>>>>>
> >> >>>>
> >> >>>>
> >> >>>> _______________________________________________
> >> >>>> gem5-users mailing list -- gem5-users@gem5.org
> >> >>>> To unsubscribe send an email to gem5-users-leave@gem5.org
> >> >>>>
> >> >>
> >> >>
> >> >> _______________________________________________
> >> >> gem5-users mailing list -- gem5-users@gem5.org
> >> >> To unsubscribe send an email to gem5-users-leave@gem5.org
> >> >
> >> >
> >> > _______________________________________________
> >> > gem5-users mailing list -- gem5-users@gem5.org
> >> > To unsubscribe send an email to gem5-users-leave@gem5.org
> >>
> >>
> >> _______________________________________________
> >> gem5-users mailing list -- gem5-users@gem5.org
> >> To unsubscribe send an email to gem5-users-leave@gem5.org
> >>
>
>
> _______________________________________________
> gem5-users mailing list -- gem5-users@gem5.org
> To unsubscribe send an email to gem5-users-leave@gem5.org
>
Dear Jason & Boddy,
Unfortunately, I have tried my simple example without the sqrt
function and the problem remains. Specifically, I have the following
simple code:
#include <cmath>
#include <stdio.h>
int main(){
int dim = 1024;
double result;
for (int iter = 0; iter < 2; iter++){
result = 0;
for (int i = 0; i < dim; i++){
for (int j = 0; j < dim; j++){
result += i * j;
}
}
printf("Final Result: %lf\n", result);
}
}
In the above code, the correct result is 274341298176.000000 (from
RISCV-SE mode and x86), while in FS mode I get sometimes the correct
result and other times a different number.
Best regards,
Nikos
Quoting Jason Lowe-Power jason@lowepower.com:
I have an idea...
Have you put a breakpoint in the implementation of the fsqrt_d function? I
would like to know if when running in SE mode and running in FS mode we are
using the same rounding mode. My hypothesis is that in FS mode the rounding
mode is set differently.
Cheers,
Jason
On Fri, Oct 7, 2022 at 12:15 AM Νικόλαος Ταμπουρατζής <
ntampouratzis@ece.auth.gr> wrote:
Dear Boddy,
Thanks a lot for the effort! I looked in detail and I observe that the
problem is created only using float and double variables (in the case
of int it is working properly in FS mode). Specifically, in the case
of float the variables are set to "nan", while in the case of double
the variables are set to 0.000000 (in random time - probably from some
instruction of simulated OS?). You may use a simple c/c++ example in
order to get some traces before going to HPCG...
Thank you in advance!!
Best regards,
Nikos
Quoting Bobby Bruce bbruce@ucdavis.edu:
Hey Niko,
Thanks for this analysis. I jumped a little into this today but didn't
as far as you did. I wanted to find a quick way to recreate the
with some traces and debug flags and see if I can narrow down the
In my previous results, I had used double (not float) for the
following variables: result, sq_i and sq_j. In the case of float
instead of double I get "nan" and not 0.000000.
Quoting Νικόλαος Ταμπουρατζής ntampouratzis@ece.auth.gr:
Dear Jason, all,
I am trying to find the accuracy problem with RISCV-FS and I observe
that the problem is created (at least in my dummy example) because
the variables (double) are set to zero in random simulated time (for
this reason I get different results among executions of the same
code). Specifically for the following dummy code:
#include <cmath>
#include <stdio.h>
int main(){
int dim = 10;
float result;
for (int iter = 0; iter < 2; iter++){
result = 0;
for (int i = 0; i < dim; i++){
for (int j = 0; j < dim; j++){
float sq_i = sqrt(i);
float sq_j = sqrt(j);
result += sq_i * sq_j;
printf("ITER: %d | i: %d | j: %d Result(i: %f | j:
%f | i*j: %f): %f\n", iter, i , j, sq_i, sq_j, sq_i * sq_j, result);
}
}
printf("Final Result: %lf\n", result);
}
}
The correct Final Result in both iterations is 372.721656. However,
I get the following results in FS:
ITER: 0 | i: 0 | j: 0 Result(i: 0.000000 | j: 0.000000 | ij:
0.000000): 0.000000
ITER: 0 | i: 0 | j: 1 Result(i: 0.000000 | j: 1.000000 | ij:
0.000000): 0.000000
ITER: 0 | i: 0 | j: 2 Result(i: 0.000000 | j: 1.414214 | ij:
0.000000): 0.000000
ITER: 0 | i: 0 | j: 3 Result(i: 0.000000 | j: 1.732051 | ij:
0.000000): 0.000000
ITER: 0 | i: 0 | j: 4 Result(i: 0.000000 | j: 2.000000 | ij:
0.000000): 0.000000
ITER: 0 | i: 0 | j: 5 Result(i: 0.000000 | j: 2.236068 | ij:
0.000000): 0.000000
ITER: 0 | i: 0 | j: 6 Result(i: 0.000000 | j: 2.449490 | ij:
0.000000): 0.000000
ITER: 0 | i: 0 | j: 7 Result(i: 0.000000 | j: 2.645751 | ij:
0.000000): 0.000000
ITER: 0 | i: 0 | j: 8 Result(i: 0.000000 | j: 2.828427 | ij:
0.000000): 0.000000
ITER: 0 | i: 0 | j: 9 Result(i: 0.000000 | j: 3.000000 | ij:
0.000000): 0.000000
ITER: 0 | i: 1 | j: 0 Result(i: 1.000000 | j: 0.000000 | ij:
0.000000): 0.000000
ITER: 0 | i: 1 | j: 1 Result(i: 1.000000 | j: 1.000000 | ij:
1.000000): 1.000000
ITER: 0 | i: 1 | j: 2 Result(i: 1.000000 | j: 1.414214 | ij:
1.414214): 2.414214
ITER: 0 | i: 1 | j: 3 Result(i: 1.000000 | j: 1.732051 | ij:
1.732051): 4.146264
ITER: 0 | i: 1 | j: 4 Result(i: 0.000000 | j: 2.000000 | ij:
0.000000): 0.000000
ITER: 0 | i: 1 | j: 5 Result(i: 0.000000 | j: 2.236068 | ij:
0.000000): 0.000000
ITER: 0 | i: 1 | j: 6 Result(i: 0.000000 | j: 2.449490 | ij:
0.000000): 0.000000
ITER: 0 | i: 1 | j: 7 Result(i: 0.000000 | j: 2.645751 | ij:
0.000000): 0.000000
ITER: 0 | i: 1 | j: 8 Result(i: 0.000000 | j: 2.828427 | ij:
0.000000): 0.000000
ITER: 0 | i: 1 | j: 9 Result(i: 0.000000 | j: 3.000000 | ij:
0.000000): 0.000000
ITER: 0 | i: 2 | j: 0 Result(i: 1.414214 | j: 0.000000 | ij:
0.000000): 0.000000
ITER: 0 | i: 2 | j: 1 Result(i: 1.414214 | j: 1.000000 | ij:
1.414214): 1.414214
ITER: 0 | i: 2 | j: 2 Result(i: 1.414214 | j: 1.414214 | ij:
2.000000): 3.414214
ITER: 0 | i: 2 | j: 3 Result(i: 1.414214 | j: 1.732051 | ij:
2.449490): 5.863703
ITER: 0 | i: 2 | j: 4 Result(i: 1.414214 | j: 2.000000 | ij:
2.828427): 8.692130
ITER: 0 | i: 2 | j: 5 Result(i: 1.414214 | j: 2.236068 | ij:
3.162278): 11.854408
ITER: 0 | i: 2 | j: 6 Result(i: 1.414214 | j: 2.449490 | ij:
3.464102): 15.318510
ITER: 0 | i: 2 | j: 7 Result(i: 1.414214 | j: 2.645751 | ij:
3.741657): 19.060167
ITER: 0 | i: 2 | j: 8 Result(i: 1.414214 | j: 2.828427 | ij:
4.000000): 23.060167
ITER: 0 | i: 2 | j: 9 Result(i: 1.414214 | j: 3.000000 | ij:
4.242641): 27.302808
ITER: 0 | i: 3 | j: 0 Result(i: 1.732051 | j: 0.000000 | ij:
0.000000): 27.302808
ITER: 0 | i: 3 | j: 1 Result(i: 1.732051 | j: 1.000000 | ij:
1.732051): 29.034859
ITER: 0 | i: 3 | j: 2 Result(i: 1.732051 | j: 1.414214 | ij:
2.449490): 31.484348
ITER: 0 | i: 3 | j: 3 Result(i: 1.732051 | j: 1.732051 | ij:
3.000000): 34.484348
ITER: 0 | i: 3 | j: 4 Result(i: 1.732051 | j: 2.000000 | ij:
3.464102): 37.948450
ITER: 0 | i: 3 | j: 5 Result(i: 1.732051 | j: 2.236068 | ij:
3.872983): 41.821433
ITER: 0 | i: 3 | j: 6 Result(i: 1.732051 | j: 2.449490 | ij:
4.242641): 46.064074
ITER: 0 | i: 3 | j: 7 Result(i: 1.732051 | j: 2.645751 | ij:
4.582576): 50.646650
ITER: 0 | i: 3 | j: 8 Result(i: 1.732051 | j: 2.828427 | ij:
4.898979): 55.545629
ITER: 0 | i: 3 | j: 9 Result(i: 1.732051 | j: 3.000000 | ij:
5.196152): 60.741782
ITER: 0 | i: 4 | j: 0 Result(i: 2.000000 | j: 0.000000 | ij:
0.000000): 60.741782
ITER: 0 | i: 4 | j: 1 Result(i: 2.000000 | j: 1.000000 | ij:
2.000000): 62.741782
ITER: 0 | i: 4 | j: 2 Result(i: 2.000000 | j: 1.414214 | ij:
2.828427): 65.570209
ITER: 0 | i: 4 | j: 3 Result(i: 2.000000 | j: 1.732051 | ij:
3.464102): 69.034310
ITER: 0 | i: 4 | j: 4 Result(i: 2.000000 | j: 2.000000 | ij:
4.000000): 73.034310
ITER: 0 | i: 4 | j: 5 Result(i: 2.000000 | j: 2.236068 | ij:
4.472136): 77.506446
ITER: 0 | i: 4 | j: 6 Result(i: 2.000000 | j: 2.449490 | ij:
4.898979): 82.405426
ITER: 0 | i: 4 | j: 7 Result(i: 2.000000 | j: 2.645751 | ij:
5.291503): 87.696928
ITER: 0 | i: 4 | j: 8 Result(i: 2.000000 | j: 2.828427 | ij:
5.656854): 93.353783
ITER: 0 | i: 4 | j: 9 Result(i: 2.000000 | j: 3.000000 | ij:
6.000000): 99.353783
ITER: 0 | i: 5 | j: 0 Result(i: 2.236068 | j: 0.000000 | ij:
0.000000): 99.353783
ITER: 0 | i: 5 | j: 1 Result(i: 2.236068 | j: 1.000000 | ij:
2.236068): 101.589851
ITER: 0 | i: 5 | j: 2 Result(i: 2.236068 | j: 1.414214 | ij:
3.162278): 104.752128
ITER: 0 | i: 5 | j: 3 Result(i: 2.236068 | j: 1.732051 | ij:
3.872983): 108.625112
ITER: 0 | i: 5 | j: 4 Result(i: 2.236068 | j: 2.000000 | ij:
4.472136): 113.097248
ITER: 0 | i: 5 | j: 5 Result(i: 2.236068 | j: 2.236068 | ij:
5.000000): 118.097248
ITER: 0 | i: 5 | j: 6 Result(i: 2.236068 | j: 2.449490 | ij:
5.477226): 123.574473
ITER: 0 | i: 5 | j: 7 Result(i: 2.236068 | j: 2.645751 | ij:
5.916080): 129.490553
ITER: 0 | i: 5 | j: 8 Result(i: 2.236068 | j: 2.828427 | ij:
6.324555): 135.815108
ITER: 0 | i: 5 | j: 9 Result(i: 2.236068 | j: 3.000000 | ij:
6.708204): 142.523312
ITER: 0 | i: 6 | j: 0 Result(i: 2.449490 | j: 0.000000 | ij:
0.000000): 142.523312
ITER: 0 | i: 6 | j: 1 Result(i: 2.449490 | j: 1.000000 | ij:
2.449490): 144.972802
ITER: 0 | i: 6 | j: 2 Result(i: 2.449490 | j: 1.414214 | ij:
3.464102): 148.436904
ITER: 0 | i: 6 | j: 3 Result(i: 2.449490 | j: 1.732051 | ij:
4.242641): 152.679544
ITER: 0 | i: 6 | j: 4 Result(i: 2.449490 | j: 2.000000 | ij:
4.898979): 157.578524
ITER: 0 | i: 6 | j: 5 Result(i: 2.449490 | j: 2.236068 | ij:
5.477226): 163.055749
ITER: 0 | i: 6 | j: 6 Result(i: 2.449490 | j: 2.449490 | ij:
6.000000): 169.055749
ITER: 0 | i: 6 | j: 7 Result(i: 2.449490 | j: 2.645751 | ij:
6.480741): 175.536490
ITER: 0 | i: 6 | j: 8 Result(i: 2.449490 | j: 2.828427 | ij:
6.928203): 182.464693
ITER: 0 | i: 6 | j: 9 Result(i: 2.449490 | j: 3.000000 | ij:
7.348469): 189.813162
ITER: 0 | i: 7 | j: 0 Result(i: 2.645751 | j: 0.000000 | ij:
0.000000): 189.813162
ITER: 0 | i: 7 | j: 1 Result(i: 2.645751 | j: 1.000000 | ij:
2.645751): 192.458914
ITER: 0 | i: 7 | j: 2 Result(i: 2.645751 | j: 1.414214 | ij:
3.741657): 196.200571
ITER: 0 | i: 7 | j: 3 Result(i: 2.645751 | j: 1.732051 | ij:
4.582576): 200.783147
ITER: 0 | i: 7 | j: 4 Result(i: 2.645751 | j: 2.000000 | ij:
5.291503): 206.074649
ITER: 0 | i: 7 | j: 5 Result(i: 2.645751 | j: 2.236068 | ij:
5.916080): 211.990729
ITER: 0 | i: 7 | j: 6 Result(i: 2.645751 | j: 2.449490 | ij:
6.480741): 218.471470
ITER: 0 | i: 7 | j: 7 Result(i: 2.645751 | j: 2.645751 | ij:
7.000000): 225.471470
ITER: 0 | i: 7 | j: 8 Result(i: 2.645751 | j: 2.828427 | ij:
7.483315): 232.954785
ITER: 0 | i: 7 | j: 9 Result(i: 2.645751 | j: 3.000000 | ij:
7.937254): 240.892039
ITER: 0 | i: 8 | j: 0 Result(i: 2.828427 | j: 0.000000 | ij:
0.000000): 240.892039
ITER: 0 | i: 8 | j: 1 Result(i: 2.828427 | j: 1.000000 | ij:
2.828427): 243.720466
ITER: 0 | i: 8 | j: 2 Result(i: 2.828427 | j: 1.414214 | ij:
4.000000): 247.720466
ITER: 0 | i: 8 | j: 3 Result(i: 2.828427 | j: 1.732051 | ij:
4.898979): 252.619445
ITER: 0 | i: 8 | j: 4 Result(i: 2.828427 | j: 2.000000 | ij:
5.656854): 258.276300
ITER: 0 | i: 8 | j: 5 Result(i: 2.828427 | j: 2.236068 | ij:
6.324555): 264.600855
ITER: 0 | i: 8 | j: 6 Result(i: 2.828427 | j: 2.449490 | ij:
6.928203): 271.529058
ITER: 0 | i: 8 | j: 7 Result(i: 2.828427 | j: 2.645751 | ij:
7.483315): 279.012373
ITER: 0 | i: 8 | j: 8 Result(i: 2.828427 | j: 2.828427 | ij:
8.000000): 287.012373
ITER: 0 | i: 8 | j: 9 Result(i: 2.828427 | j: 3.000000 | ij:
8.485281): 295.497654
ITER: 0 | i: 9 | j: 0 Result(i: 3.000000 | j: 0.000000 | ij:
0.000000): 295.497654
ITER: 0 | i: 9 | j: 1 Result(i: 3.000000 | j: 1.000000 | ij:
3.000000): 298.497654
ITER: 0 | i: 9 | j: 2 Result(i: 3.000000 | j: 1.414214 | ij:
4.242641): 302.740295
ITER: 0 | i: 9 | j: 3 Result(i: 3.000000 | j: 1.732051 | ij:
5.196152): 307.936447
ITER: 0 | i: 9 | j: 4 Result(i: 3.000000 | j: 2.000000 | ij:
6.000000): 313.936447
ITER: 0 | i: 9 | j: 5 Result(i: 3.000000 | j: 2.236068 | ij:
6.708204): 320.644651
ITER: 0 | i: 9 | j: 6 Result(i: 3.000000 | j: 2.449490 | ij:
7.348469): 327.993120
ITER: 0 | i: 9 | j: 7 Result(i: 3.000000 | j: 2.645751 | ij:
7.937254): 335.930374
ITER: 0 | i: 9 | j: 8 Result(i: 3.000000 | j: 2.828427 | ij:
8.485281): 344.415656
ITER: 0 | i: 9 | j: 9 Result(i: 3.000000 | j: 3.000000 | ij:
9.000000): 353.415656
Final Result: 353.415656
ITER: 1 | i: 0 | j: 0 Result(i: 0.000000 | j: 0.000000 | ij:
0.000000): 0.000000
ITER: 1 | i: 0 | j: 1 Result(i: 0.000000 | j: 1.000000 | ij:
0.000000): 0.000000
ITER: 1 | i: 0 | j: 2 Result(i: 0.000000 | j: 1.414214 | ij:
0.000000): 0.000000
ITER: 1 | i: 0 | j: 3 Result(i: 0.000000 | j: 1.732051 | ij:
0.000000): 0.000000
ITER: 1 | i: 0 | j: 4 Result(i: 0.000000 | j: 2.000000 | ij:
0.000000): 0.000000
ITER: 1 | i: 0 | j: 5 Result(i: 0.000000 | j: 2.236068 | ij:
0.000000): 0.000000
ITER: 1 | i: 0 | j: 6 Result(i: 0.000000 | j: 2.449490 | ij:
0.000000): 0.000000
ITER: 1 | i: 0 | j: 7 Result(i: 0.000000 | j: 2.645751 | ij:
0.000000): 0.000000
ITER: 1 | i: 0 | j: 8 Result(i: 0.000000 | j: 2.828427 | ij:
0.000000): 0.000000
ITER: 1 | i: 0 | j: 9 Result(i: 0.000000 | j: 3.000000 | ij:
0.000000): 0.000000
ITER: 1 | i: 1 | j: 0 Result(i: 1.000000 | j: 0.000000 | ij:
0.000000): 0.000000
ITER: 1 | i: 1 | j: 1 Result(i: 1.000000 | j: 1.000000 | ij:
1.000000): 1.000000
ITER: 1 | i: 1 | j: 2 Result(i: 1.000000 | j: 1.414214 | ij:
1.414214): 2.414214
ITER: 1 | i: 1 | j: 3 Result(i: 1.000000 | j: 1.732051 | ij:
1.732051): 4.146264
ITER: 1 | i: 1 | j: 4 Result(i: 1.000000 | j: 2.000000 | ij:
2.000000): 6.146264
ITER: 1 | i: 1 | j: 5 Result(i: 1.000000 | j: 2.236068 | ij:
2.236068): 8.382332
ITER: 1 | i: 1 | j: 6 Result(i: 1.000000 | j: 2.449490 | ij:
2.449490): 10.831822
ITER: 1 | i: 1 | j: 7 Result(i: 1.000000 | j: 2.645751 | ij:
2.645751): 13.477573
ITER: 1 | i: 1 | j: 8 Result(i: 1.000000 | j: 2.828427 | ij:
2.828427): 16.306001
ITER: 1 | i: 1 | j: 9 Result(i: 1.000000 | j: 3.000000 | ij:
3.000000): 19.306001
ITER: 1 | i: 2 | j: 0 Result(i: 1.414214 | j: 0.000000 | ij:
0.000000): 19.306001
ITER: 1 | i: 2 | j: 1 Result(i: 1.414214 | j: 1.000000 | ij:
1.414214): 20.720214
ITER: 1 | i: 2 | j: 2 Result(i: 1.414214 | j: 1.414214 | ij:
2.000000): 22.720214
ITER: 1 | i: 2 | j: 3 Result(i: 1.414214 | j: 1.732051 | ij:
2.449490): 25.169704
ITER: 1 | i: 2 | j: 4 Result(i: 1.414214 | j: 2.000000 | ij:
2.828427): 27.998131
ITER: 1 | i: 2 | j: 5 Result(i: 1.414214 | j: 2.236068 | ij:
3.162278): 31.160409
ITER: 1 | i: 2 | j: 6 Result(i: 1.414214 | j: 2.449490 | ij:
3.464102): 34.624510
ITER: 1 | i: 2 | j: 7 Result(i: 1.414214 | j: 2.645751 | ij:
3.741657): 38.366168
ITER: 1 | i: 2 | j: 8 Result(i: 1.414214 | j: 2.828427 | ij:
4.000000): 42.366168
ITER: 1 | i: 2 | j: 9 Result(i: 1.414214 | j: 3.000000 | ij:
4.242641): 46.608808
ITER: 1 | i: 3 | j: 0 Result(i: 1.732051 | j: 0.000000 | ij:
0.000000): 46.608808
ITER: 1 | i: 3 | j: 1 Result(i: 1.732051 | j: 1.000000 | ij:
1.732051): 48.340859
ITER: 1 | i: 3 | j: 2 Result(i: 1.732051 | j: 1.414214 | ij:
2.449490): 50.790349
ITER: 1 | i: 3 | j: 3 Result(i: 1.732051 | j: 1.732051 | ij:
3.000000): 53.790349
ITER: 1 | i: 3 | j: 4 Result(i: 1.732051 | j: 2.000000 | ij:
3.464102): 57.254450
ITER: 1 | i: 3 | j: 5 Result(i: 1.732051 | j: 2.236068 | ij:
3.872983): 61.127434
ITER: 1 | i: 3 | j: 6 Result(i: 1.732051 | j: 2.449490 | ij:
4.242641): 65.370075
ITER: 1 | i: 3 | j: 7 Result(i: 1.732051 | j: 2.645751 | ij:
4.582576): 69.952650
ITER: 1 | i: 3 | j: 8 Result(i: 1.732051 | j: 2.828427 | ij:
4.898979): 74.851630
ITER: 1 | i: 3 | j: 9 Result(i: 1.732051 | j: 3.000000 | ij:
5.196152): 80.047782
ITER: 1 | i: 4 | j: 0 Result(i: 2.000000 | j: 0.000000 | ij:
0.000000): 80.047782
ITER: 1 | i: 4 | j: 1 Result(i: 2.000000 | j: 1.000000 | ij:
2.000000): 82.047782
ITER: 1 | i: 4 | j: 2 Result(i: 2.000000 | j: 1.414214 | ij:
2.828427): 84.876209
ITER: 1 | i: 4 | j: 3 Result(i: 2.000000 | j: 1.732051 | ij:
3.464102): 88.340311
ITER: 1 | i: 4 | j: 4 Result(i: 2.000000 | j: 2.000000 | ij:
4.000000): 92.340311
ITER: 1 | i: 4 | j: 5 Result(i: 2.000000 | j: 2.236068 | ij:
4.472136): 96.812447
ITER: 1 | i: 4 | j: 6 Result(i: 2.000000 | j: 2.449490 | ij:
4.898979): 101.711426
ITER: 1 | i: 4 | j: 7 Result(i: 2.000000 | j: 2.645751 | ij:
5.291503): 107.002929
ITER: 1 | i: 4 | j: 8 Result(i: 2.000000 | j: 2.828427 | ij:
5.656854): 112.659783
ITER: 1 | i: 4 | j: 9 Result(i: 2.000000 | j: 3.000000 | ij:
6.000000): 118.659783
ITER: 1 | i: 5 | j: 0 Result(i: 2.236068 | j: 0.000000 | ij:
0.000000): 118.659783
ITER: 1 | i: 5 | j: 1 Result(i: 2.236068 | j: 1.000000 | ij:
2.236068): 120.895851
ITER: 1 | i: 5 | j: 2 Result(i: 2.236068 | j: 1.414214 | ij:
3.162278): 124.058129
ITER: 1 | i: 5 | j: 3 Result(i: 2.236068 | j: 1.732051 | ij:
3.872983): 127.931112
ITER: 1 | i: 5 | j: 4 Result(i: 2.236068 | j: 2.000000 | ij:
4.472136): 132.403248
ITER: 1 | i: 5 | j: 5 Result(i: 2.236068 | j: 2.236068 | ij:
5.000000): 137.403248
ITER: 1 | i: 5 | j: 6 Result(i: 2.236068 | j: 2.449490 | ij:
5.477226): 142.880474
ITER: 1 | i: 5 | j: 7 Result(i: 2.236068 | j: 2.645751 | ij:
5.916080): 148.796553
ITER: 1 | i: 5 | j: 8 Result(i: 2.236068 | j: 2.828427 | ij:
6.324555): 155.121109
ITER: 1 | i: 5 | j: 9 Result(i: 2.236068 | j: 3.000000 | ij:
6.708204): 161.829313
ITER: 1 | i: 6 | j: 0 Result(i: 2.449490 | j: 0.000000 | ij:
0.000000): 161.829313
ITER: 1 | i: 6 | j: 1 Result(i: 2.449490 | j: 1.000000 | ij:
2.449490): 164.278802
ITER: 1 | i: 6 | j: 2 Result(i: 2.449490 | j: 1.414214 | ij:
3.464102): 167.742904
ITER: 1 | i: 6 | j: 3 Result(i: 2.449490 | j: 1.732051 | ij:
4.242641): 171.985545
ITER: 1 | i: 6 | j: 4 Result(i: 2.449490 | j: 2.000000 | ij:
4.898979): 176.884524
ITER: 1 | i: 6 | j: 5 Result(i: 2.449490 | j: 2.236068 | ij:
5.477226): 182.361750
ITER: 1 | i: 6 | j: 6 Result(i: 2.449490 | j: 2.449490 | ij:
6.000000): 188.361750
ITER: 1 | i: 6 | j: 7 Result(i: 2.449490 | j: 2.645751 | ij:
6.480741): 194.842491
ITER: 1 | i: 6 | j: 8 Result(i: 2.449490 | j: 2.828427 | ij:
6.928203): 201.770694
ITER: 1 | i: 6 | j: 9 Result(i: 2.449490 | j: 3.000000 | ij:
7.348469): 209.119163
ITER: 1 | i: 7 | j: 0 Result(i: 2.645751 | j: 0.000000 | ij:
0.000000): 209.119163
ITER: 1 | i: 7 | j: 1 Result(i: 2.645751 | j: 1.000000 | ij:
2.645751): 211.764914
ITER: 1 | i: 7 | j: 2 Result(i: 2.645751 | j: 1.414214 | ij:
3.741657): 215.506572
ITER: 1 | i: 7 | j: 3 Result(i: 2.645751 | j: 1.732051 | ij:
4.582576): 220.089147
ITER: 1 | i: 7 | j: 4 Result(i: 2.645751 | j: 2.000000 | ij:
5.291503): 225.380650
ITER: 1 | i: 7 | j: 5 Result(i: 2.645751 | j: 2.236068 | ij:
5.916080): 231.296730
ITER: 1 | i: 7 | j: 6 Result(i: 2.645751 | j: 2.449490 | ij:
6.480741): 237.777470
ITER: 1 | i: 7 | j: 7 Result(i: 2.645751 | j: 2.645751 | ij:
7.000000): 244.777470
ITER: 1 | i: 7 | j: 8 Result(i: 2.645751 | j: 2.828427 | ij:
7.483315): 252.260785
ITER: 1 | i: 7 | j: 9 Result(i: 2.645751 | j: 3.000000 | ij:
7.937254): 260.198039
ITER: 1 | i: 8 | j: 0 Result(i: 2.828427 | j: 0.000000 | ij:
0.000000): 260.198039
ITER: 1 | i: 8 | j: 1 Result(i: 2.828427 | j: 1.000000 | ij:
2.828427): 263.026466
ITER: 1 | i: 8 | j: 2 Result(i: 2.828427 | j: 1.414214 | ij:
4.000000): 267.026466
ITER: 1 | i: 8 | j: 3 Result(i: 2.828427 | j: 1.732051 | ij:
4.898979): 271.925446
ITER: 1 | i: 8 | j: 4 Result(i: 2.828427 | j: 2.000000 | ij:
5.656854): 277.582300
ITER: 1 | i: 8 | j: 5 Result(i: 2.828427 | j: 2.236068 | ij:
6.324555): 283.906855
ITER: 1 | i: 8 | j: 6 Result(i: 2.828427 | j: 2.449490 | ij:
6.928203): 290.835059
ITER: 1 | i: 8 | j: 7 Result(i: 2.828427 | j: 2.645751 | ij:
7.483315): 298.318373
ITER: 1 | i: 8 | j: 8 Result(i: 2.828427 | j: 2.828427 | ij:
8.000000): 306.318373
ITER: 1 | i: 8 | j: 9 Result(i: 2.828427 | j: 3.000000 | ij:
8.485281): 314.803655
ITER: 1 | i: 9 | j: 0 Result(i: 3.000000 | j: 0.000000 | ij:
0.000000): 314.803655
ITER: 1 | i: 9 | j: 1 Result(i: 3.000000 | j: 1.000000 | ij:
3.000000): 317.803655
ITER: 1 | i: 9 | j: 2 Result(i: 3.000000 | j: 1.414214 | ij:
4.242641): 322.046295
ITER: 1 | i: 9 | j: 3 Result(i: 3.000000 | j: 1.732051 | ij:
5.196152): 327.242448
ITER: 1 | i: 9 | j: 4 Result(i: 3.000000 | j: 2.000000 | ij:
6.000000): 333.242448
ITER: 1 | i: 9 | j: 5 Result(i: 3.000000 | j: 2.236068 | ij:
6.708204): 339.950652
ITER: 1 | i: 9 | j: 6 Result(i: 3.000000 | j: 2.449490 | ij:
7.348469): 347.299121
ITER: 1 | i: 9 | j: 7 Result(i: 3.000000 | j: 2.645751 | ij:
7.937254): 355.236375
ITER: 1 | i: 9 | j: 8 Result(i: 3.000000 | j: 2.828427 | ij:
8.485281): 363.721656
ITER: 1 | i: 9 | j: 9 Result(i: 3.000000 | j: 3.000000 | ij:
9.000000): 372.721656
Final Result: 372.721656
As we can see in the following iterations the sqrt(1) as well as the
result is set to zero for some reason.
ITER: 0 | i: 1 | j: 4 Result(i: 0.000000 | j: 2.000000 | ij:
0.000000): 0.000000
ITER: 0 | i: 1 | j: 5 Result(i: 0.000000 | j: 2.236068 | ij:
0.000000): 0.000000
ITER: 0 | i: 1 | j: 6 Result(i: 0.000000 | j: 2.449490 | ij:
0.000000): 0.000000
ITER: 0 | i: 1 | j: 7 Result(i: 0.000000 | j: 2.645751 | ij:
0.000000): 0.000000
ITER: 0 | i: 1 | j: 8 Result(i: 0.000000 | j: 2.828427 | ij:
0.000000): 0.000000
ITER: 0 | i: 1 | j: 9 Result(i: 0.000000 | j: 3.000000 | ij:
0.000000): 0.000000
Please help me to resolve the accuracy issue! I think that it will
be very useful for gem5 community.
To be noticed, I find the correct simulated tick in which the
application started in FS (using m5 dumpstats), and I start the
--debug-start, but the trace file which is generated is 10x larger
than SE mode for the same application. How can I compare them?
Thank you in advance!
Best regards,
Nikos
Quoting Νικόλαος Ταμπουρατζής ntampouratzis@ece.auth.gr:
Dear Jason,
I am trying to use --debug-start but in FS mode it is very
difficult to find the tick on which the application is started!
However, I am writing the following very simple c++ program:
#include <cmath>
#include <stdio.h>
int main(){
int dim = 4096;
double result;
for (int iter = 0; iter < 2; iter++){
result = 0;
for (int i = 0; i < dim; i++){
for (int j = 0; j < dim; j++){
result += sqrt(i) * sqrt(j);
}
}
printf("Result: %lf\n", result); //Result: 30530733453.127449
}
}
I cross-compile it using: riscv64-linux-gnu-g++ -static -O3 -o
test_riscv test_riscv.cpp
While in X86 (without cross-compilation of course), QEMU-RISCV,
GEM5-SE the result is the same (30530733453.127449), in GEM5-FS the
result is different! In addition, the result is also different
between the 2 iterations.
Please reproduce the error if you want in order to verify my result.
Ηow can the issue be resolved?
Thank you in advance!
Best regards,
Nikos
Quoting Jason Lowe-Power jason@lowepower.com:
Hi Nikos,
You can use --debug-start to start the debugging after some number
ticks. Also, I would expect that the difference should come up
no need to run the program to the end.
For the FS mode one, you will want to just start the trace as the
application starts. This could be a bit of a pain.
I'm not really sure what fundamentally could be different. FS and SE
use the exact same code for executing instructions, so I don't think
the problem. Have you tried running for smaller inputs or just one
iteration?
Jason
On Wed, Sep 21, 2022 at 9:04 AM Νικόλαος Ταμπουρατζής <
ntampouratzis@ece.auth.gr> wrote:
Dear Bobby,
Iam trying to add --debug-flags=Exec (building the gem5 for
not for gem5.fast which I had) but the debug traces exceed the 20GB
(and it is not finished yet) for less than 1 simulated second. How
I reduce the size of the debug-flags (or set something more
In contrast I build the HPCG benchmark with DHPCG_DEBUG flag. If
want, you can compare these two output files
(hpcg20010909T014640_SE_Mode & HPCG-Benchmark_3.1_FS_Mode). As you
see, something goes wrong with the accuracy of calculations in FS
That's quite odd that it works in SE mode but not FS mode!
I would suggest running with --debug-flags=Exec for both and then
diff to see how they differ.
Cheers,
Jason
On Tue, Sep 20, 2022 at 2:45 PM Νικόλαος Ταμπουρατζής <
ntampouratzis@ece.auth.gr> wrote:
Dear Bobby,
In QEMU I get the same (correct) results that I get in SE mode
simulation. I get invalid results in FS simulation (in both
riscv-fs.py and riscv-ubuntu-run.py). I cannot access real RISCV
hardware at this moment, however, if you want you may execute my
Hi Nikos,
I notice you said the following in your original email:
In addition, I used the RISCV Ubuntu image
I installed the gcc compiler, compile it (through qemu) and I
Is this saying you get the wrong results is QEMU? If so, the bug
or the HPCG workload, not in gem5. If not, I would test in QEMU
sure the binary works there. Another way you could test to see
problem is your binary or gem5 would be to run it on real
access to some RISC-V hardware here at UC Davis, if you don't
Dear Bobby,
- I use the original riscv-fs.py which is provided in the
release.
I run the gem5 once (./build/RISCV/gem5.fast -d
./configs/example/gem5_library/riscv-fs.py) in order to
riscv-bootloader-vmlinux-5.10 and riscv-disk-img.
After this I mount the riscv-disk-img (sudo mount -o loop
riscv-disk-img /mnt), put the xhpcg executable and I do the
changes in riscv-fs.py to boot the riscv-disk-img with
image = CustomDiskImageResource(
local_path = "/home/cossim/.cache/gem5/riscv-disk-img",
)
Set the Full System workload.
board.set_kernel_disk_workload(
kernel=Resource("riscv-bootloader-vmlinux-5.10"),
disk_image=image,
)
Finally, in the
gem5/src/python/gem5/components/boards/riscv_board.py
I change the last line to "return ["console=ttyS0",
"root={root_value}", "rw"]" in order to allow the write
the image.
- The HPCG benchmark after some iterations calculates if the
are valid or not valid. In the case of FS it gives invalid
I see from the results, one (at least) problem is that produces
different results in each HPCG execution (with the same
I'm going to need a bit more information to help:
- In what way have you modified
./configs/example/gem5_library/riscv-fs.py? Can you attach
- What error are you getting or in what way are the results
Dear gem5 community,
I have successfully cross-compile the HPCG benchmark for
version, without MPI and OpenMP). While it working properly
mode (./build/RISCV/gem5.fast -d ./HPCG_SE_results
./configs/example/se.py -c xhpcg --options '--nx=16 --ny=16
--npx=1 --npy=1 --npz=1 --rt=0.1'), I get invalid results
simulation using "./build/RISCV/gem5.fast -d
./configs/example/gem5_library/riscv-fs.py" (I mount the
and put it).
Can you help me please?
In addition, I used the RISCV Ubuntu image
(
I installed the gcc compiler, compile it (through qemu) and
wrong results too.
Here is the Makefile which I use, the hpcg executable for
Dear Jason & Boddy,
Unfortunately, I have tried my simple example without the sqrt
function and the problem remains. Specifically, I have the following
simple code:
#include <cmath>
#include <stdio.h>
int main(){
int dim = 1024;
double result;
for (int iter = 0; iter < 2; iter++){
result = 0;
for (int i = 0; i < dim; i++){
for (int j = 0; j < dim; j++){
result += i * j;
}
}
printf("Final Result: %lf\n", result);
}
}
In the above code, the correct result is 274341298176.000000 (from
RISCV-SE mode and x86), while in FS mode I get sometimes the correct
result and other times a different number.
Best regards,
Nikos
Quoting Jason Lowe-Power <jason@lowepower.com>:
> I have an idea...
>
> Have you put a breakpoint in the implementation of the fsqrt_d function? I
> would like to know if when running in SE mode and running in FS mode we are
> using the same rounding mode. My hypothesis is that in FS mode the rounding
> mode is set differently.
>
> Cheers,
> Jason
>
> On Fri, Oct 7, 2022 at 12:15 AM Νικόλαος Ταμπουρατζής <
> ntampouratzis@ece.auth.gr> wrote:
>
>> Dear Boddy,
>>
>> Thanks a lot for the effort! I looked in detail and I observe that the
>> problem is created only using float and double variables (in the case
>> of int it is working properly in FS mode). Specifically, in the case
>> of float the variables are set to "nan", while in the case of double
>> the variables are set to 0.000000 (in random time - probably from some
>> instruction of simulated OS?). You may use a simple c/c++ example in
>> order to get some traces before going to HPCG...
>>
>> Thank you in advance!!
>> Best regards,
>> Nikos
>>
>>
>> Quoting Bobby Bruce <bbruce@ucdavis.edu>:
>>
>> > Hey Niko,
>> >
>> > Thanks for this analysis. I jumped a little into this today but didn't
>> get
>> > as far as you did. I wanted to find a quick way to recreate the
>> following:
>> > https://gem5-review.googlesource.com/c/public/gem5/+/64211. Please feel
>> > free to use this, if it helps any.
>> >
>> > It's very strange to me that this bug hasn't manifested itself before but
>> > it's undeniably there. I'll try to spend more time looking at this
>> tomorrow
>> > with some traces and debug flags and see if I can narrow down the
>> problem.
>> >
>> > --
>> > Dr. Bobby R. Bruce
>> > Room 3050,
>> > Kemper Hall, UC Davis
>> > Davis,
>> > CA, 95616
>> >
>> > web: https://www.bobbybruce.net
>> >
>> >
>> > On Wed, Oct 5, 2022 at 2:26 PM Νικόλαος Ταμπουρατζής <
>> > ntampouratzis@ece.auth.gr> wrote:
>> >
>> >> In my previous results, I had used double (not float) for the
>> >> following variables: result, sq_i and sq_j. In the case of float
>> >> instead of double I get "nan" and not 0.000000.
>> >>
>> >> Quoting Νικόλαος Ταμπουρατζής <ntampouratzis@ece.auth.gr>:
>> >>
>> >> > Dear Jason, all,
>> >> >
>> >> > I am trying to find the accuracy problem with RISCV-FS and I observe
>> >> > that the problem is created (at least in my dummy example) because
>> >> > the variables (double) are set to zero in random simulated time (for
>> >> > this reason I get different results among executions of the same
>> >> > code). Specifically for the following dummy code:
>> >> >
>> >> >
>> >> > #include <cmath>
>> >> > #include <stdio.h>
>> >> >
>> >> > int main(){
>> >> >
>> >> > int dim = 10;
>> >> >
>> >> > float result;
>> >> >
>> >> > for (int iter = 0; iter < 2; iter++){
>> >> > result = 0;
>> >> > for (int i = 0; i < dim; i++){
>> >> > for (int j = 0; j < dim; j++){
>> >> > float sq_i = sqrt(i);
>> >> > float sq_j = sqrt(j);
>> >> > result += sq_i * sq_j;
>> >> > printf("ITER: %d | i: %d | j: %d Result(i: %f | j:
>> >> > %f | i*j: %f): %f\n", iter, i , j, sq_i, sq_j, sq_i * sq_j, result);
>> >> > }
>> >> > }
>> >> > printf("Final Result: %lf\n", result);
>> >> > }
>> >> > }
>> >> >
>> >> >
>> >> > The correct Final Result in both iterations is 372.721656. However,
>> >> > I get the following results in FS:
>> >> >
>> >> > ITER: 0 | i: 0 | j: 0 Result(i: 0.000000 | j: 0.000000 | i*j:
>> >> > 0.000000): 0.000000
>> >> > ITER: 0 | i: 0 | j: 1 Result(i: 0.000000 | j: 1.000000 | i*j:
>> >> > 0.000000): 0.000000
>> >> > ITER: 0 | i: 0 | j: 2 Result(i: 0.000000 | j: 1.414214 | i*j:
>> >> > 0.000000): 0.000000
>> >> > ITER: 0 | i: 0 | j: 3 Result(i: 0.000000 | j: 1.732051 | i*j:
>> >> > 0.000000): 0.000000
>> >> > ITER: 0 | i: 0 | j: 4 Result(i: 0.000000 | j: 2.000000 | i*j:
>> >> > 0.000000): 0.000000
>> >> > ITER: 0 | i: 0 | j: 5 Result(i: 0.000000 | j: 2.236068 | i*j:
>> >> > 0.000000): 0.000000
>> >> > ITER: 0 | i: 0 | j: 6 Result(i: 0.000000 | j: 2.449490 | i*j:
>> >> > 0.000000): 0.000000
>> >> > ITER: 0 | i: 0 | j: 7 Result(i: 0.000000 | j: 2.645751 | i*j:
>> >> > 0.000000): 0.000000
>> >> > ITER: 0 | i: 0 | j: 8 Result(i: 0.000000 | j: 2.828427 | i*j:
>> >> > 0.000000): 0.000000
>> >> > ITER: 0 | i: 0 | j: 9 Result(i: 0.000000 | j: 3.000000 | i*j:
>> >> > 0.000000): 0.000000
>> >> > ITER: 0 | i: 1 | j: 0 Result(i: 1.000000 | j: 0.000000 | i*j:
>> >> > 0.000000): 0.000000
>> >> > ITER: 0 | i: 1 | j: 1 Result(i: 1.000000 | j: 1.000000 | i*j:
>> >> > 1.000000): 1.000000
>> >> > ITER: 0 | i: 1 | j: 2 Result(i: 1.000000 | j: 1.414214 | i*j:
>> >> > 1.414214): 2.414214
>> >> > ITER: 0 | i: 1 | j: 3 Result(i: 1.000000 | j: 1.732051 | i*j:
>> >> > 1.732051): 4.146264
>> >> > ITER: 0 | i: 1 | j: 4 Result(i: 0.000000 | j: 2.000000 | i*j:
>> >> > 0.000000): 0.000000
>> >> > ITER: 0 | i: 1 | j: 5 Result(i: 0.000000 | j: 2.236068 | i*j:
>> >> > 0.000000): 0.000000
>> >> > ITER: 0 | i: 1 | j: 6 Result(i: 0.000000 | j: 2.449490 | i*j:
>> >> > 0.000000): 0.000000
>> >> > ITER: 0 | i: 1 | j: 7 Result(i: 0.000000 | j: 2.645751 | i*j:
>> >> > 0.000000): 0.000000
>> >> > ITER: 0 | i: 1 | j: 8 Result(i: 0.000000 | j: 2.828427 | i*j:
>> >> > 0.000000): 0.000000
>> >> > ITER: 0 | i: 1 | j: 9 Result(i: 0.000000 | j: 3.000000 | i*j:
>> >> > 0.000000): 0.000000
>> >> > ITER: 0 | i: 2 | j: 0 Result(i: 1.414214 | j: 0.000000 | i*j:
>> >> > 0.000000): 0.000000
>> >> > ITER: 0 | i: 2 | j: 1 Result(i: 1.414214 | j: 1.000000 | i*j:
>> >> > 1.414214): 1.414214
>> >> > ITER: 0 | i: 2 | j: 2 Result(i: 1.414214 | j: 1.414214 | i*j:
>> >> > 2.000000): 3.414214
>> >> > ITER: 0 | i: 2 | j: 3 Result(i: 1.414214 | j: 1.732051 | i*j:
>> >> > 2.449490): 5.863703
>> >> > ITER: 0 | i: 2 | j: 4 Result(i: 1.414214 | j: 2.000000 | i*j:
>> >> > 2.828427): 8.692130
>> >> > ITER: 0 | i: 2 | j: 5 Result(i: 1.414214 | j: 2.236068 | i*j:
>> >> > 3.162278): 11.854408
>> >> > ITER: 0 | i: 2 | j: 6 Result(i: 1.414214 | j: 2.449490 | i*j:
>> >> > 3.464102): 15.318510
>> >> > ITER: 0 | i: 2 | j: 7 Result(i: 1.414214 | j: 2.645751 | i*j:
>> >> > 3.741657): 19.060167
>> >> > ITER: 0 | i: 2 | j: 8 Result(i: 1.414214 | j: 2.828427 | i*j:
>> >> > 4.000000): 23.060167
>> >> > ITER: 0 | i: 2 | j: 9 Result(i: 1.414214 | j: 3.000000 | i*j:
>> >> > 4.242641): 27.302808
>> >> > ITER: 0 | i: 3 | j: 0 Result(i: 1.732051 | j: 0.000000 | i*j:
>> >> > 0.000000): 27.302808
>> >> > ITER: 0 | i: 3 | j: 1 Result(i: 1.732051 | j: 1.000000 | i*j:
>> >> > 1.732051): 29.034859
>> >> > ITER: 0 | i: 3 | j: 2 Result(i: 1.732051 | j: 1.414214 | i*j:
>> >> > 2.449490): 31.484348
>> >> > ITER: 0 | i: 3 | j: 3 Result(i: 1.732051 | j: 1.732051 | i*j:
>> >> > 3.000000): 34.484348
>> >> > ITER: 0 | i: 3 | j: 4 Result(i: 1.732051 | j: 2.000000 | i*j:
>> >> > 3.464102): 37.948450
>> >> > ITER: 0 | i: 3 | j: 5 Result(i: 1.732051 | j: 2.236068 | i*j:
>> >> > 3.872983): 41.821433
>> >> > ITER: 0 | i: 3 | j: 6 Result(i: 1.732051 | j: 2.449490 | i*j:
>> >> > 4.242641): 46.064074
>> >> > ITER: 0 | i: 3 | j: 7 Result(i: 1.732051 | j: 2.645751 | i*j:
>> >> > 4.582576): 50.646650
>> >> > ITER: 0 | i: 3 | j: 8 Result(i: 1.732051 | j: 2.828427 | i*j:
>> >> > 4.898979): 55.545629
>> >> > ITER: 0 | i: 3 | j: 9 Result(i: 1.732051 | j: 3.000000 | i*j:
>> >> > 5.196152): 60.741782
>> >> > ITER: 0 | i: 4 | j: 0 Result(i: 2.000000 | j: 0.000000 | i*j:
>> >> > 0.000000): 60.741782
>> >> > ITER: 0 | i: 4 | j: 1 Result(i: 2.000000 | j: 1.000000 | i*j:
>> >> > 2.000000): 62.741782
>> >> > ITER: 0 | i: 4 | j: 2 Result(i: 2.000000 | j: 1.414214 | i*j:
>> >> > 2.828427): 65.570209
>> >> > ITER: 0 | i: 4 | j: 3 Result(i: 2.000000 | j: 1.732051 | i*j:
>> >> > 3.464102): 69.034310
>> >> > ITER: 0 | i: 4 | j: 4 Result(i: 2.000000 | j: 2.000000 | i*j:
>> >> > 4.000000): 73.034310
>> >> > ITER: 0 | i: 4 | j: 5 Result(i: 2.000000 | j: 2.236068 | i*j:
>> >> > 4.472136): 77.506446
>> >> > ITER: 0 | i: 4 | j: 6 Result(i: 2.000000 | j: 2.449490 | i*j:
>> >> > 4.898979): 82.405426
>> >> > ITER: 0 | i: 4 | j: 7 Result(i: 2.000000 | j: 2.645751 | i*j:
>> >> > 5.291503): 87.696928
>> >> > ITER: 0 | i: 4 | j: 8 Result(i: 2.000000 | j: 2.828427 | i*j:
>> >> > 5.656854): 93.353783
>> >> > ITER: 0 | i: 4 | j: 9 Result(i: 2.000000 | j: 3.000000 | i*j:
>> >> > 6.000000): 99.353783
>> >> > ITER: 0 | i: 5 | j: 0 Result(i: 2.236068 | j: 0.000000 | i*j:
>> >> > 0.000000): 99.353783
>> >> > ITER: 0 | i: 5 | j: 1 Result(i: 2.236068 | j: 1.000000 | i*j:
>> >> > 2.236068): 101.589851
>> >> > ITER: 0 | i: 5 | j: 2 Result(i: 2.236068 | j: 1.414214 | i*j:
>> >> > 3.162278): 104.752128
>> >> > ITER: 0 | i: 5 | j: 3 Result(i: 2.236068 | j: 1.732051 | i*j:
>> >> > 3.872983): 108.625112
>> >> > ITER: 0 | i: 5 | j: 4 Result(i: 2.236068 | j: 2.000000 | i*j:
>> >> > 4.472136): 113.097248
>> >> > ITER: 0 | i: 5 | j: 5 Result(i: 2.236068 | j: 2.236068 | i*j:
>> >> > 5.000000): 118.097248
>> >> > ITER: 0 | i: 5 | j: 6 Result(i: 2.236068 | j: 2.449490 | i*j:
>> >> > 5.477226): 123.574473
>> >> > ITER: 0 | i: 5 | j: 7 Result(i: 2.236068 | j: 2.645751 | i*j:
>> >> > 5.916080): 129.490553
>> >> > ITER: 0 | i: 5 | j: 8 Result(i: 2.236068 | j: 2.828427 | i*j:
>> >> > 6.324555): 135.815108
>> >> > ITER: 0 | i: 5 | j: 9 Result(i: 2.236068 | j: 3.000000 | i*j:
>> >> > 6.708204): 142.523312
>> >> > ITER: 0 | i: 6 | j: 0 Result(i: 2.449490 | j: 0.000000 | i*j:
>> >> > 0.000000): 142.523312
>> >> > ITER: 0 | i: 6 | j: 1 Result(i: 2.449490 | j: 1.000000 | i*j:
>> >> > 2.449490): 144.972802
>> >> > ITER: 0 | i: 6 | j: 2 Result(i: 2.449490 | j: 1.414214 | i*j:
>> >> > 3.464102): 148.436904
>> >> > ITER: 0 | i: 6 | j: 3 Result(i: 2.449490 | j: 1.732051 | i*j:
>> >> > 4.242641): 152.679544
>> >> > ITER: 0 | i: 6 | j: 4 Result(i: 2.449490 | j: 2.000000 | i*j:
>> >> > 4.898979): 157.578524
>> >> > ITER: 0 | i: 6 | j: 5 Result(i: 2.449490 | j: 2.236068 | i*j:
>> >> > 5.477226): 163.055749
>> >> > ITER: 0 | i: 6 | j: 6 Result(i: 2.449490 | j: 2.449490 | i*j:
>> >> > 6.000000): 169.055749
>> >> > ITER: 0 | i: 6 | j: 7 Result(i: 2.449490 | j: 2.645751 | i*j:
>> >> > 6.480741): 175.536490
>> >> > ITER: 0 | i: 6 | j: 8 Result(i: 2.449490 | j: 2.828427 | i*j:
>> >> > 6.928203): 182.464693
>> >> > ITER: 0 | i: 6 | j: 9 Result(i: 2.449490 | j: 3.000000 | i*j:
>> >> > 7.348469): 189.813162
>> >> > ITER: 0 | i: 7 | j: 0 Result(i: 2.645751 | j: 0.000000 | i*j:
>> >> > 0.000000): 189.813162
>> >> > ITER: 0 | i: 7 | j: 1 Result(i: 2.645751 | j: 1.000000 | i*j:
>> >> > 2.645751): 192.458914
>> >> > ITER: 0 | i: 7 | j: 2 Result(i: 2.645751 | j: 1.414214 | i*j:
>> >> > 3.741657): 196.200571
>> >> > ITER: 0 | i: 7 | j: 3 Result(i: 2.645751 | j: 1.732051 | i*j:
>> >> > 4.582576): 200.783147
>> >> > ITER: 0 | i: 7 | j: 4 Result(i: 2.645751 | j: 2.000000 | i*j:
>> >> > 5.291503): 206.074649
>> >> > ITER: 0 | i: 7 | j: 5 Result(i: 2.645751 | j: 2.236068 | i*j:
>> >> > 5.916080): 211.990729
>> >> > ITER: 0 | i: 7 | j: 6 Result(i: 2.645751 | j: 2.449490 | i*j:
>> >> > 6.480741): 218.471470
>> >> > ITER: 0 | i: 7 | j: 7 Result(i: 2.645751 | j: 2.645751 | i*j:
>> >> > 7.000000): 225.471470
>> >> > ITER: 0 | i: 7 | j: 8 Result(i: 2.645751 | j: 2.828427 | i*j:
>> >> > 7.483315): 232.954785
>> >> > ITER: 0 | i: 7 | j: 9 Result(i: 2.645751 | j: 3.000000 | i*j:
>> >> > 7.937254): 240.892039
>> >> > ITER: 0 | i: 8 | j: 0 Result(i: 2.828427 | j: 0.000000 | i*j:
>> >> > 0.000000): 240.892039
>> >> > ITER: 0 | i: 8 | j: 1 Result(i: 2.828427 | j: 1.000000 | i*j:
>> >> > 2.828427): 243.720466
>> >> > ITER: 0 | i: 8 | j: 2 Result(i: 2.828427 | j: 1.414214 | i*j:
>> >> > 4.000000): 247.720466
>> >> > ITER: 0 | i: 8 | j: 3 Result(i: 2.828427 | j: 1.732051 | i*j:
>> >> > 4.898979): 252.619445
>> >> > ITER: 0 | i: 8 | j: 4 Result(i: 2.828427 | j: 2.000000 | i*j:
>> >> > 5.656854): 258.276300
>> >> > ITER: 0 | i: 8 | j: 5 Result(i: 2.828427 | j: 2.236068 | i*j:
>> >> > 6.324555): 264.600855
>> >> > ITER: 0 | i: 8 | j: 6 Result(i: 2.828427 | j: 2.449490 | i*j:
>> >> > 6.928203): 271.529058
>> >> > ITER: 0 | i: 8 | j: 7 Result(i: 2.828427 | j: 2.645751 | i*j:
>> >> > 7.483315): 279.012373
>> >> > ITER: 0 | i: 8 | j: 8 Result(i: 2.828427 | j: 2.828427 | i*j:
>> >> > 8.000000): 287.012373
>> >> > ITER: 0 | i: 8 | j: 9 Result(i: 2.828427 | j: 3.000000 | i*j:
>> >> > 8.485281): 295.497654
>> >> > ITER: 0 | i: 9 | j: 0 Result(i: 3.000000 | j: 0.000000 | i*j:
>> >> > 0.000000): 295.497654
>> >> > ITER: 0 | i: 9 | j: 1 Result(i: 3.000000 | j: 1.000000 | i*j:
>> >> > 3.000000): 298.497654
>> >> > ITER: 0 | i: 9 | j: 2 Result(i: 3.000000 | j: 1.414214 | i*j:
>> >> > 4.242641): 302.740295
>> >> > ITER: 0 | i: 9 | j: 3 Result(i: 3.000000 | j: 1.732051 | i*j:
>> >> > 5.196152): 307.936447
>> >> > ITER: 0 | i: 9 | j: 4 Result(i: 3.000000 | j: 2.000000 | i*j:
>> >> > 6.000000): 313.936447
>> >> > ITER: 0 | i: 9 | j: 5 Result(i: 3.000000 | j: 2.236068 | i*j:
>> >> > 6.708204): 320.644651
>> >> > ITER: 0 | i: 9 | j: 6 Result(i: 3.000000 | j: 2.449490 | i*j:
>> >> > 7.348469): 327.993120
>> >> > ITER: 0 | i: 9 | j: 7 Result(i: 3.000000 | j: 2.645751 | i*j:
>> >> > 7.937254): 335.930374
>> >> > ITER: 0 | i: 9 | j: 8 Result(i: 3.000000 | j: 2.828427 | i*j:
>> >> > 8.485281): 344.415656
>> >> > ITER: 0 | i: 9 | j: 9 Result(i: 3.000000 | j: 3.000000 | i*j:
>> >> > 9.000000): 353.415656
>> >> > Final Result: 353.415656
>> >> > ITER: 1 | i: 0 | j: 0 Result(i: 0.000000 | j: 0.000000 | i*j:
>> >> > 0.000000): 0.000000
>> >> > ITER: 1 | i: 0 | j: 1 Result(i: 0.000000 | j: 1.000000 | i*j:
>> >> > 0.000000): 0.000000
>> >> > ITER: 1 | i: 0 | j: 2 Result(i: 0.000000 | j: 1.414214 | i*j:
>> >> > 0.000000): 0.000000
>> >> > ITER: 1 | i: 0 | j: 3 Result(i: 0.000000 | j: 1.732051 | i*j:
>> >> > 0.000000): 0.000000
>> >> > ITER: 1 | i: 0 | j: 4 Result(i: 0.000000 | j: 2.000000 | i*j:
>> >> > 0.000000): 0.000000
>> >> > ITER: 1 | i: 0 | j: 5 Result(i: 0.000000 | j: 2.236068 | i*j:
>> >> > 0.000000): 0.000000
>> >> > ITER: 1 | i: 0 | j: 6 Result(i: 0.000000 | j: 2.449490 | i*j:
>> >> > 0.000000): 0.000000
>> >> > ITER: 1 | i: 0 | j: 7 Result(i: 0.000000 | j: 2.645751 | i*j:
>> >> > 0.000000): 0.000000
>> >> > ITER: 1 | i: 0 | j: 8 Result(i: 0.000000 | j: 2.828427 | i*j:
>> >> > 0.000000): 0.000000
>> >> > ITER: 1 | i: 0 | j: 9 Result(i: 0.000000 | j: 3.000000 | i*j:
>> >> > 0.000000): 0.000000
>> >> > ITER: 1 | i: 1 | j: 0 Result(i: 1.000000 | j: 0.000000 | i*j:
>> >> > 0.000000): 0.000000
>> >> > ITER: 1 | i: 1 | j: 1 Result(i: 1.000000 | j: 1.000000 | i*j:
>> >> > 1.000000): 1.000000
>> >> > ITER: 1 | i: 1 | j: 2 Result(i: 1.000000 | j: 1.414214 | i*j:
>> >> > 1.414214): 2.414214
>> >> > ITER: 1 | i: 1 | j: 3 Result(i: 1.000000 | j: 1.732051 | i*j:
>> >> > 1.732051): 4.146264
>> >> > ITER: 1 | i: 1 | j: 4 Result(i: 1.000000 | j: 2.000000 | i*j:
>> >> > 2.000000): 6.146264
>> >> > ITER: 1 | i: 1 | j: 5 Result(i: 1.000000 | j: 2.236068 | i*j:
>> >> > 2.236068): 8.382332
>> >> > ITER: 1 | i: 1 | j: 6 Result(i: 1.000000 | j: 2.449490 | i*j:
>> >> > 2.449490): 10.831822
>> >> > ITER: 1 | i: 1 | j: 7 Result(i: 1.000000 | j: 2.645751 | i*j:
>> >> > 2.645751): 13.477573
>> >> > ITER: 1 | i: 1 | j: 8 Result(i: 1.000000 | j: 2.828427 | i*j:
>> >> > 2.828427): 16.306001
>> >> > ITER: 1 | i: 1 | j: 9 Result(i: 1.000000 | j: 3.000000 | i*j:
>> >> > 3.000000): 19.306001
>> >> > ITER: 1 | i: 2 | j: 0 Result(i: 1.414214 | j: 0.000000 | i*j:
>> >> > 0.000000): 19.306001
>> >> > ITER: 1 | i: 2 | j: 1 Result(i: 1.414214 | j: 1.000000 | i*j:
>> >> > 1.414214): 20.720214
>> >> > ITER: 1 | i: 2 | j: 2 Result(i: 1.414214 | j: 1.414214 | i*j:
>> >> > 2.000000): 22.720214
>> >> > ITER: 1 | i: 2 | j: 3 Result(i: 1.414214 | j: 1.732051 | i*j:
>> >> > 2.449490): 25.169704
>> >> > ITER: 1 | i: 2 | j: 4 Result(i: 1.414214 | j: 2.000000 | i*j:
>> >> > 2.828427): 27.998131
>> >> > ITER: 1 | i: 2 | j: 5 Result(i: 1.414214 | j: 2.236068 | i*j:
>> >> > 3.162278): 31.160409
>> >> > ITER: 1 | i: 2 | j: 6 Result(i: 1.414214 | j: 2.449490 | i*j:
>> >> > 3.464102): 34.624510
>> >> > ITER: 1 | i: 2 | j: 7 Result(i: 1.414214 | j: 2.645751 | i*j:
>> >> > 3.741657): 38.366168
>> >> > ITER: 1 | i: 2 | j: 8 Result(i: 1.414214 | j: 2.828427 | i*j:
>> >> > 4.000000): 42.366168
>> >> > ITER: 1 | i: 2 | j: 9 Result(i: 1.414214 | j: 3.000000 | i*j:
>> >> > 4.242641): 46.608808
>> >> > ITER: 1 | i: 3 | j: 0 Result(i: 1.732051 | j: 0.000000 | i*j:
>> >> > 0.000000): 46.608808
>> >> > ITER: 1 | i: 3 | j: 1 Result(i: 1.732051 | j: 1.000000 | i*j:
>> >> > 1.732051): 48.340859
>> >> > ITER: 1 | i: 3 | j: 2 Result(i: 1.732051 | j: 1.414214 | i*j:
>> >> > 2.449490): 50.790349
>> >> > ITER: 1 | i: 3 | j: 3 Result(i: 1.732051 | j: 1.732051 | i*j:
>> >> > 3.000000): 53.790349
>> >> > ITER: 1 | i: 3 | j: 4 Result(i: 1.732051 | j: 2.000000 | i*j:
>> >> > 3.464102): 57.254450
>> >> > ITER: 1 | i: 3 | j: 5 Result(i: 1.732051 | j: 2.236068 | i*j:
>> >> > 3.872983): 61.127434
>> >> > ITER: 1 | i: 3 | j: 6 Result(i: 1.732051 | j: 2.449490 | i*j:
>> >> > 4.242641): 65.370075
>> >> > ITER: 1 | i: 3 | j: 7 Result(i: 1.732051 | j: 2.645751 | i*j:
>> >> > 4.582576): 69.952650
>> >> > ITER: 1 | i: 3 | j: 8 Result(i: 1.732051 | j: 2.828427 | i*j:
>> >> > 4.898979): 74.851630
>> >> > ITER: 1 | i: 3 | j: 9 Result(i: 1.732051 | j: 3.000000 | i*j:
>> >> > 5.196152): 80.047782
>> >> > ITER: 1 | i: 4 | j: 0 Result(i: 2.000000 | j: 0.000000 | i*j:
>> >> > 0.000000): 80.047782
>> >> > ITER: 1 | i: 4 | j: 1 Result(i: 2.000000 | j: 1.000000 | i*j:
>> >> > 2.000000): 82.047782
>> >> > ITER: 1 | i: 4 | j: 2 Result(i: 2.000000 | j: 1.414214 | i*j:
>> >> > 2.828427): 84.876209
>> >> > ITER: 1 | i: 4 | j: 3 Result(i: 2.000000 | j: 1.732051 | i*j:
>> >> > 3.464102): 88.340311
>> >> > ITER: 1 | i: 4 | j: 4 Result(i: 2.000000 | j: 2.000000 | i*j:
>> >> > 4.000000): 92.340311
>> >> > ITER: 1 | i: 4 | j: 5 Result(i: 2.000000 | j: 2.236068 | i*j:
>> >> > 4.472136): 96.812447
>> >> > ITER: 1 | i: 4 | j: 6 Result(i: 2.000000 | j: 2.449490 | i*j:
>> >> > 4.898979): 101.711426
>> >> > ITER: 1 | i: 4 | j: 7 Result(i: 2.000000 | j: 2.645751 | i*j:
>> >> > 5.291503): 107.002929
>> >> > ITER: 1 | i: 4 | j: 8 Result(i: 2.000000 | j: 2.828427 | i*j:
>> >> > 5.656854): 112.659783
>> >> > ITER: 1 | i: 4 | j: 9 Result(i: 2.000000 | j: 3.000000 | i*j:
>> >> > 6.000000): 118.659783
>> >> > ITER: 1 | i: 5 | j: 0 Result(i: 2.236068 | j: 0.000000 | i*j:
>> >> > 0.000000): 118.659783
>> >> > ITER: 1 | i: 5 | j: 1 Result(i: 2.236068 | j: 1.000000 | i*j:
>> >> > 2.236068): 120.895851
>> >> > ITER: 1 | i: 5 | j: 2 Result(i: 2.236068 | j: 1.414214 | i*j:
>> >> > 3.162278): 124.058129
>> >> > ITER: 1 | i: 5 | j: 3 Result(i: 2.236068 | j: 1.732051 | i*j:
>> >> > 3.872983): 127.931112
>> >> > ITER: 1 | i: 5 | j: 4 Result(i: 2.236068 | j: 2.000000 | i*j:
>> >> > 4.472136): 132.403248
>> >> > ITER: 1 | i: 5 | j: 5 Result(i: 2.236068 | j: 2.236068 | i*j:
>> >> > 5.000000): 137.403248
>> >> > ITER: 1 | i: 5 | j: 6 Result(i: 2.236068 | j: 2.449490 | i*j:
>> >> > 5.477226): 142.880474
>> >> > ITER: 1 | i: 5 | j: 7 Result(i: 2.236068 | j: 2.645751 | i*j:
>> >> > 5.916080): 148.796553
>> >> > ITER: 1 | i: 5 | j: 8 Result(i: 2.236068 | j: 2.828427 | i*j:
>> >> > 6.324555): 155.121109
>> >> > ITER: 1 | i: 5 | j: 9 Result(i: 2.236068 | j: 3.000000 | i*j:
>> >> > 6.708204): 161.829313
>> >> > ITER: 1 | i: 6 | j: 0 Result(i: 2.449490 | j: 0.000000 | i*j:
>> >> > 0.000000): 161.829313
>> >> > ITER: 1 | i: 6 | j: 1 Result(i: 2.449490 | j: 1.000000 | i*j:
>> >> > 2.449490): 164.278802
>> >> > ITER: 1 | i: 6 | j: 2 Result(i: 2.449490 | j: 1.414214 | i*j:
>> >> > 3.464102): 167.742904
>> >> > ITER: 1 | i: 6 | j: 3 Result(i: 2.449490 | j: 1.732051 | i*j:
>> >> > 4.242641): 171.985545
>> >> > ITER: 1 | i: 6 | j: 4 Result(i: 2.449490 | j: 2.000000 | i*j:
>> >> > 4.898979): 176.884524
>> >> > ITER: 1 | i: 6 | j: 5 Result(i: 2.449490 | j: 2.236068 | i*j:
>> >> > 5.477226): 182.361750
>> >> > ITER: 1 | i: 6 | j: 6 Result(i: 2.449490 | j: 2.449490 | i*j:
>> >> > 6.000000): 188.361750
>> >> > ITER: 1 | i: 6 | j: 7 Result(i: 2.449490 | j: 2.645751 | i*j:
>> >> > 6.480741): 194.842491
>> >> > ITER: 1 | i: 6 | j: 8 Result(i: 2.449490 | j: 2.828427 | i*j:
>> >> > 6.928203): 201.770694
>> >> > ITER: 1 | i: 6 | j: 9 Result(i: 2.449490 | j: 3.000000 | i*j:
>> >> > 7.348469): 209.119163
>> >> > ITER: 1 | i: 7 | j: 0 Result(i: 2.645751 | j: 0.000000 | i*j:
>> >> > 0.000000): 209.119163
>> >> > ITER: 1 | i: 7 | j: 1 Result(i: 2.645751 | j: 1.000000 | i*j:
>> >> > 2.645751): 211.764914
>> >> > ITER: 1 | i: 7 | j: 2 Result(i: 2.645751 | j: 1.414214 | i*j:
>> >> > 3.741657): 215.506572
>> >> > ITER: 1 | i: 7 | j: 3 Result(i: 2.645751 | j: 1.732051 | i*j:
>> >> > 4.582576): 220.089147
>> >> > ITER: 1 | i: 7 | j: 4 Result(i: 2.645751 | j: 2.000000 | i*j:
>> >> > 5.291503): 225.380650
>> >> > ITER: 1 | i: 7 | j: 5 Result(i: 2.645751 | j: 2.236068 | i*j:
>> >> > 5.916080): 231.296730
>> >> > ITER: 1 | i: 7 | j: 6 Result(i: 2.645751 | j: 2.449490 | i*j:
>> >> > 6.480741): 237.777470
>> >> > ITER: 1 | i: 7 | j: 7 Result(i: 2.645751 | j: 2.645751 | i*j:
>> >> > 7.000000): 244.777470
>> >> > ITER: 1 | i: 7 | j: 8 Result(i: 2.645751 | j: 2.828427 | i*j:
>> >> > 7.483315): 252.260785
>> >> > ITER: 1 | i: 7 | j: 9 Result(i: 2.645751 | j: 3.000000 | i*j:
>> >> > 7.937254): 260.198039
>> >> > ITER: 1 | i: 8 | j: 0 Result(i: 2.828427 | j: 0.000000 | i*j:
>> >> > 0.000000): 260.198039
>> >> > ITER: 1 | i: 8 | j: 1 Result(i: 2.828427 | j: 1.000000 | i*j:
>> >> > 2.828427): 263.026466
>> >> > ITER: 1 | i: 8 | j: 2 Result(i: 2.828427 | j: 1.414214 | i*j:
>> >> > 4.000000): 267.026466
>> >> > ITER: 1 | i: 8 | j: 3 Result(i: 2.828427 | j: 1.732051 | i*j:
>> >> > 4.898979): 271.925446
>> >> > ITER: 1 | i: 8 | j: 4 Result(i: 2.828427 | j: 2.000000 | i*j:
>> >> > 5.656854): 277.582300
>> >> > ITER: 1 | i: 8 | j: 5 Result(i: 2.828427 | j: 2.236068 | i*j:
>> >> > 6.324555): 283.906855
>> >> > ITER: 1 | i: 8 | j: 6 Result(i: 2.828427 | j: 2.449490 | i*j:
>> >> > 6.928203): 290.835059
>> >> > ITER: 1 | i: 8 | j: 7 Result(i: 2.828427 | j: 2.645751 | i*j:
>> >> > 7.483315): 298.318373
>> >> > ITER: 1 | i: 8 | j: 8 Result(i: 2.828427 | j: 2.828427 | i*j:
>> >> > 8.000000): 306.318373
>> >> > ITER: 1 | i: 8 | j: 9 Result(i: 2.828427 | j: 3.000000 | i*j:
>> >> > 8.485281): 314.803655
>> >> > ITER: 1 | i: 9 | j: 0 Result(i: 3.000000 | j: 0.000000 | i*j:
>> >> > 0.000000): 314.803655
>> >> > ITER: 1 | i: 9 | j: 1 Result(i: 3.000000 | j: 1.000000 | i*j:
>> >> > 3.000000): 317.803655
>> >> > ITER: 1 | i: 9 | j: 2 Result(i: 3.000000 | j: 1.414214 | i*j:
>> >> > 4.242641): 322.046295
>> >> > ITER: 1 | i: 9 | j: 3 Result(i: 3.000000 | j: 1.732051 | i*j:
>> >> > 5.196152): 327.242448
>> >> > ITER: 1 | i: 9 | j: 4 Result(i: 3.000000 | j: 2.000000 | i*j:
>> >> > 6.000000): 333.242448
>> >> > ITER: 1 | i: 9 | j: 5 Result(i: 3.000000 | j: 2.236068 | i*j:
>> >> > 6.708204): 339.950652
>> >> > ITER: 1 | i: 9 | j: 6 Result(i: 3.000000 | j: 2.449490 | i*j:
>> >> > 7.348469): 347.299121
>> >> > ITER: 1 | i: 9 | j: 7 Result(i: 3.000000 | j: 2.645751 | i*j:
>> >> > 7.937254): 355.236375
>> >> > ITER: 1 | i: 9 | j: 8 Result(i: 3.000000 | j: 2.828427 | i*j:
>> >> > 8.485281): 363.721656
>> >> > ITER: 1 | i: 9 | j: 9 Result(i: 3.000000 | j: 3.000000 | i*j:
>> >> > 9.000000): 372.721656
>> >> > Final Result: 372.721656
>> >> >
>> >> >
>> >> >
>> >> > As we can see in the following iterations the sqrt(1) as well as the
>> >> > result is set to zero for some reason.
>> >> >
>> >> > ITER: 0 | i: 1 | j: 4 Result(i: 0.000000 | j: 2.000000 | i*j:
>> >> > 0.000000): 0.000000
>> >> > ITER: 0 | i: 1 | j: 5 Result(i: 0.000000 | j: 2.236068 | i*j:
>> >> > 0.000000): 0.000000
>> >> > ITER: 0 | i: 1 | j: 6 Result(i: 0.000000 | j: 2.449490 | i*j:
>> >> > 0.000000): 0.000000
>> >> > ITER: 0 | i: 1 | j: 7 Result(i: 0.000000 | j: 2.645751 | i*j:
>> >> > 0.000000): 0.000000
>> >> > ITER: 0 | i: 1 | j: 8 Result(i: 0.000000 | j: 2.828427 | i*j:
>> >> > 0.000000): 0.000000
>> >> > ITER: 0 | i: 1 | j: 9 Result(i: 0.000000 | j: 3.000000 | i*j:
>> >> > 0.000000): 0.000000
>> >> >
>> >> > Please help me to resolve the accuracy issue! I think that it will
>> >> > be very useful for gem5 community.
>> >> >
>> >> > To be noticed, I find the correct simulated tick in which the
>> >> > application started in FS (using m5 dumpstats), and I start the
>> >> > --debug-start, but the trace file which is generated is 10x larger
>> >> > than SE mode for the same application. How can I compare them?
>> >> >
>> >> > Thank you in advance!
>> >> > Best regards,
>> >> > Nikos
>> >> >
>> >> > Quoting Νικόλαος Ταμπουρατζής <ntampouratzis@ece.auth.gr>:
>> >> >
>> >> >> Dear Jason,
>> >> >>
>> >> >> I am trying to use --debug-start but in FS mode it is very
>> >> >> difficult to find the tick on which the application is started!
>> >> >>
>> >> >> However, I am writing the following very simple c++ program:
>> >> >>
>> >> >> #include <cmath>
>> >> >> #include <stdio.h>
>> >> >>
>> >> >> int main(){
>> >> >>
>> >> >> int dim = 4096;
>> >> >>
>> >> >> double result;
>> >> >>
>> >> >> for (int iter = 0; iter < 2; iter++){
>> >> >> result = 0;
>> >> >> for (int i = 0; i < dim; i++){
>> >> >> for (int j = 0; j < dim; j++){
>> >> >> result += sqrt(i) * sqrt(j);
>> >> >> }
>> >> >> }
>> >> >> printf("Result: %lf\n", result); //Result: 30530733453.127449
>> >> >> }
>> >> >> }
>> >> >>
>> >> >> I cross-compile it using: riscv64-linux-gnu-g++ -static -O3 -o
>> >> >> test_riscv test_riscv.cpp
>> >> >>
>> >> >>
>> >> >> While in X86 (without cross-compilation of course), QEMU-RISCV,
>> >> >> GEM5-SE the result is the same (30530733453.127449), in GEM5-FS the
>> >> >> result is different! In addition, the result is also different
>> >> >> between the 2 iterations.
>> >> >>
>> >> >> Please reproduce the error if you want in order to verify my result.
>> >> >> Ηow can the issue be resolved?
>> >> >>
>> >> >> Thank you in advance!
>> >> >>
>> >> >> Best regards,
>> >> >> Nikos
>> >> >>
>> >> >>
>> >> >> Quoting Jason Lowe-Power <jason@lowepower.com>:
>> >> >>
>> >> >>> Hi Nikos,
>> >> >>>
>> >> >>> You can use --debug-start to start the debugging after some number
>> of
>> >> >>> ticks. Also, I would expect that the difference should come up
>> >> quickly, so
>> >> >>> no need to run the program to the end.
>> >> >>>
>> >> >>> For the FS mode one, you will want to just start the trace as the
>> >> >>> application starts. This could be a bit of a pain.
>> >> >>>
>> >> >>> I'm not really sure what fundamentally could be different. FS and SE
>> >> mode
>> >> >>> use the exact same code for executing instructions, so I don't think
>> >> that's
>> >> >>> the problem. Have you tried running for smaller inputs or just one
>> >> >>> iteration?
>> >> >>>
>> >> >>> Jason
>> >> >>>
>> >> >>>
>> >> >>>
>> >> >>> On Wed, Sep 21, 2022 at 9:04 AM Νικόλαος Ταμπουρατζής <
>> >> >>> ntampouratzis@ece.auth.gr> wrote:
>> >> >>>
>> >> >>>> Dear Bobby,
>> >> >>>>
>> >> >>>> Iam trying to add --debug-flags=Exec (building the gem5 for
>> gem5.opt
>> >> >>>> not for gem5.fast which I had) but the debug traces exceed the 20GB
>> >> >>>> (and it is not finished yet) for less than 1 simulated second. How
>> can
>> >> >>>> I reduce the size of the debug-flags (or set something more
>> specific)?
>> >> >>>>
>> >> >>>> In contrast I build the HPCG benchmark with DHPCG_DEBUG flag. If
>> you
>> >> >>>> want, you can compare these two output files
>> >> >>>> (hpcg20010909T014640_SE_Mode & HPCG-Benchmark_3.1_FS_Mode). As you
>> can
>> >> >>>> see, something goes wrong with the accuracy of calculations in FS
>> mode
>> >> >>>> (benchmark uses double precission). You can find the files here:
>> >> >>>> http://kition.mhl.tuc.gr:8000/d/68d82f3533/
>> >> >>>>
>> >> >>>> Best regards,
>> >> >>>> Nikos
>> >> >>>>
>> >> >>>> Quoting Jason Lowe-Power <jason@lowepower.com>:
>> >> >>>>
>> >> >>>>> That's quite odd that it works in SE mode but not FS mode!
>> >> >>>>>
>> >> >>>>> I would suggest running with --debug-flags=Exec for both and then
>> >> >>>> perform a
>> >> >>>>> diff to see how they differ.
>> >> >>>>>
>> >> >>>>> Cheers,
>> >> >>>>> Jason
>> >> >>>>>
>> >> >>>>> On Tue, Sep 20, 2022 at 2:45 PM Νικόλαος Ταμπουρατζής <
>> >> >>>>> ntampouratzis@ece.auth.gr> wrote:
>> >> >>>>>
>> >> >>>>>> Dear Bobby,
>> >> >>>>>>
>> >> >>>>>> In QEMU I get the same (correct) results that I get in SE mode
>> >> >>>>>> simulation. I get invalid results in FS simulation (in both
>> >> >>>>>> riscv-fs.py and riscv-ubuntu-run.py). I cannot access real RISCV
>> >> >>>>>> hardware at this moment, however, if you want you may execute my
>> >> xhpcg
>> >> >>>>>> binary (http://kition.mhl.tuc.gr:8000/f/4ca25fdd3c/) with the
>> >> >>>>>> following configuration:
>> >> >>>>>>
>> >> >>>>>> ./xhpcg --nx=16 --ny=16 --nz=16 --npx=1 --npy=1 --npz=1 --rt=0.1
>> >> >>>>>>
>> >> >>>>>> Please let me know if you have any updates!
>> >> >>>>>>
>> >> >>>>>> Best regards,
>> >> >>>>>> Nikos
>> >> >>>>>>
>> >> >>>>>>
>> >> >>>>>> Quoting Jason Lowe-Power <jason@lowepower.com>:
>> >> >>>>>>
>> >> >>>>>>> Hi Nikos,
>> >> >>>>>>>
>> >> >>>>>>> I notice you said the following in your original email:
>> >> >>>>>>>
>> >> >>>>>>> In addition, I used the RISCV Ubuntu image
>> >> >>>>>>>> (
>> >> https://github.com/gem5/gem5-resources/tree/stable/src/riscv-ubuntu
>> >> >>>> ),
>> >> >>>>>>>> I installed the gcc compiler, compile it (through qemu) and I
>> get
>> >> >>>>>>>> wrong results too.
>> >> >>>>>>>
>> >> >>>>>>>
>> >> >>>>>>> Is this saying you get the wrong results is QEMU? If so, the bug
>> >> is in
>> >> >>>>>> GCC
>> >> >>>>>>> or the HPCG workload, not in gem5. If not, I would test in QEMU
>> to
>> >> >>>> make
>> >> >>>>>>> sure the binary works there. Another way you could test to see
>> if
>> >> the
>> >> >>>>>>> problem is your binary or gem5 would be to run it on real
>> >> hardware. We
>> >> >>>>>> have
>> >> >>>>>>> access to some RISC-V hardware here at UC Davis, if you don't
>> have
>> >> >>>> access
>> >> >>>>>>> to it.
>> >> >>>>>>>
>> >> >>>>>>> Cheers,
>> >> >>>>>>> Jason
>> >> >>>>>>>
>> >> >>>>>>> On Tue, Sep 20, 2022 at 12:58 AM Νικόλαος Ταμπουρατζής <
>> >> >>>>>>> ntampouratzis@ece.auth.gr> wrote:
>> >> >>>>>>>
>> >> >>>>>>>> Dear Bobby,
>> >> >>>>>>>>
>> >> >>>>>>>> 1) I use the original riscv-fs.py which is provided in the
>> latest
>> >> >>>> gem5
>> >> >>>>>>>> release.
>> >> >>>>>>>> I run the gem5 once (./build/RISCV/gem5.fast -d
>> ./HPCG_FS_results
>> >> >>>>>>>> ./configs/example/gem5_library/riscv-fs.py) in order to
>> download
>> >> the
>> >> >>>>>>>> riscv-bootloader-vmlinux-5.10 and riscv-disk-img.
>> >> >>>>>>>> After this I mount the riscv-disk-img (sudo mount -o loop
>> >> >>>>>>>> riscv-disk-img /mnt), put the xhpcg executable and I do the
>> >> following
>> >> >>>>>>>> changes in riscv-fs.py to boot the riscv-disk-img with
>> executable:
>> >> >>>>>>>>
>> >> >>>>>>>> image = CustomDiskImageResource(
>> >> >>>>>>>> local_path = "/home/cossim/.cache/gem5/riscv-disk-img",
>> >> >>>>>>>> )
>> >> >>>>>>>>
>> >> >>>>>>>> # Set the Full System workload.
>> >> >>>>>>>> board.set_kernel_disk_workload(
>> >> >>>>>>>>
>> >> kernel=Resource("riscv-bootloader-vmlinux-5.10"),
>> >> >>>>>>>> disk_image=image,
>> >> >>>>>>>> )
>> >> >>>>>>>>
>> >> >>>>>>>> Finally, in the
>> >> gem5/src/python/gem5/components/boards/riscv_board.py
>> >> >>>>>>>> I change the last line to "return ["console=ttyS0",
>> >> >>>>>>>> "root={root_value}", "rw"]" in order to allow the write
>> >> permissions
>> >> >>>> in
>> >> >>>>>>>> the image.
>> >> >>>>>>>>
>> >> >>>>>>>>
>> >> >>>>>>>> 2) The HPCG benchmark after some iterations calculates if the
>> >> results
>> >> >>>>>>>> are valid or not valid. In the case of FS it gives invalid
>> >> results.
>> >> >>>> As
>> >> >>>>>>>> I see from the results, one (at least) problem is that produces
>> >> >>>>>>>> different results in each HPCG execution (with the same
>> >> >>>> configuration).
>> >> >>>>>>>>
>> >> >>>>>>>> Here is the HPCG output and riscv-fs.py
>> >> >>>>>>>> (http://kition.mhl.tuc.gr:8000/d/68d82f3533/). You may
>> reproduce
>> >> the
>> >> >>>>>>>> results in the video if you use the xhpcg executable
>> >> >>>>>>>> (http://kition.mhl.tuc.gr:8000/f/4ca25fdd3c/)
>> >> >>>>>>>>
>> >> >>>>>>>> Please help me in order to solve it!
>> >> >>>>>>>>
>> >> >>>>>>>> Finally, I get invalid results in the HPL benchmark in FS mode
>> >> too.
>> >> >>>>>>>>
>> >> >>>>>>>> Best regards,
>> >> >>>>>>>> Nikos
>> >> >>>>>>>>
>> >> >>>>>>>>
>> >> >>>>>>>> Quoting Bobby Bruce <bbruce@ucdavis.edu>:
>> >> >>>>>>>>
>> >> >>>>>>>> > I'm going to need a bit more information to help:
>> >> >>>>>>>> >
>> >> >>>>>>>> > 1. In what way have you modified
>> >> >>>>>>>> > ./configs/example/gem5_library/riscv-fs.py? Can you attach
>> the
>> >> >>>> script
>> >> >>>>>>>> here?
>> >> >>>>>>>> > 2. What error are you getting or in what way are the results
>> >> >>>> invalid?
>> >> >>>>>>>> >
>> >> >>>>>>>> > -
>> >> >>>>>>>> > Dr. Bobby R. Bruce
>> >> >>>>>>>> > Room 3050,
>> >> >>>>>>>> > Kemper Hall, UC Davis
>> >> >>>>>>>> > Davis,
>> >> >>>>>>>> > CA, 95616
>> >> >>>>>>>> >
>> >> >>>>>>>> > web: https://www.bobbybruce.net
>> >> >>>>>>>> >
>> >> >>>>>>>> >
>> >> >>>>>>>> > On Mon, Sep 19, 2022 at 1:43 PM Νικόλαος Ταμπουρατζής <
>> >> >>>>>>>> > ntampouratzis@ece.auth.gr> wrote:
>> >> >>>>>>>> >
>> >> >>>>>>>> >>
>> >> >>>>>>>> >> Dear gem5 community,
>> >> >>>>>>>> >>
>> >> >>>>>>>> >> I have successfully cross-compile the HPCG benchmark for
>> RISCV
>> >> >>>>>> (Serial
>> >> >>>>>>>> >> version, without MPI and OpenMP). While it working properly
>> in
>> >> >>>> gem5
>> >> >>>>>> SE
>> >> >>>>>>>> >> mode (./build/RISCV/gem5.fast -d ./HPCG_SE_results
>> >> >>>>>>>> >> ./configs/example/se.py -c xhpcg --options '--nx=16 --ny=16
>> >> >>>> --nz=16
>> >> >>>>>>>> >> --npx=1 --npy=1 --npz=1 --rt=0.1'), I get invalid results
>> in FS
>> >> >>>>>>>> >> simulation using "./build/RISCV/gem5.fast -d
>> ./HPCG_FS_results
>> >> >>>>>>>> >> ./configs/example/gem5_library/riscv-fs.py" (I mount the
>> riscv
>> >> >>>> image
>> >> >>>>>>>> >> and put it).
>> >> >>>>>>>> >>
>> >> >>>>>>>> >> Can you help me please?
>> >> >>>>>>>> >>
>> >> >>>>>>>> >> In addition, I used the RISCV Ubuntu image
>> >> >>>>>>>> >> (
>> >> >>>>
>> https://github.com/gem5/gem5-resources/tree/stable/src/riscv-ubuntu
>> >> >>>>>> ),
>> >> >>>>>>>> >> I installed the gcc compiler, compile it (through qemu) and
>> I
>> >> get
>> >> >>>>>>>> >> wrong results too.
>> >> >>>>>>>> >>
>> >> >>>>>>>> >> Here is the Makefile which I use, the hpcg executable for
>> RISCV
>> >> >>>>>>>> >> (xhpcg), and a video that shows the results
>> >> >>>>>>>> >> (http://kition.mhl.tuc.gr:8000/f/4ca25fdd3c/).
>> >> >>>>>>>> >>
>> >> >>>>>>>> >> P.S. I use the latest gem5 version.
>> >> >>>>>>>> >>
>> >> >>>>>>>> >> Thank you in advance! :)
>> >> >>>>>>>> >>
>> >> >>>>>>>> >> Best regards,
>> >> >>>>>>>> >> Nikos
>> >> >>>>>>>> >> _______________________________________________
>> >> >>>>>>>> >> gem5-users mailing list -- gem5-users@gem5.org
>> >> >>>>>>>> >> To unsubscribe send an email to gem5-users-leave@gem5.org
>> >> >>>>>>>> >>
>> >> >>>>>>>>
>> >> >>>>>>>>
>> >> >>>>>>>> _______________________________________________
>> >> >>>>>>>> gem5-users mailing list -- gem5-users@gem5.org
>> >> >>>>>>>> To unsubscribe send an email to gem5-users-leave@gem5.org
>> >> >>>>>>>>
>> >> >>>>>>
>> >> >>>>>>
>> >> >>>>>> _______________________________________________
>> >> >>>>>> gem5-users mailing list -- gem5-users@gem5.org
>> >> >>>>>> To unsubscribe send an email to gem5-users-leave@gem5.org
>> >> >>>>>>
>> >> >>>>
>> >> >>>>
>> >> >>>> _______________________________________________
>> >> >>>> gem5-users mailing list -- gem5-users@gem5.org
>> >> >>>> To unsubscribe send an email to gem5-users-leave@gem5.org
>> >> >>>>
>> >> >>
>> >> >>
>> >> >> _______________________________________________
>> >> >> gem5-users mailing list -- gem5-users@gem5.org
>> >> >> To unsubscribe send an email to gem5-users-leave@gem5.org
>> >> >
>> >> >
>> >> > _______________________________________________
>> >> > gem5-users mailing list -- gem5-users@gem5.org
>> >> > To unsubscribe send an email to gem5-users-leave@gem5.org
>> >>
>> >>
>> >> _______________________________________________
>> >> gem5-users mailing list -- gem5-users@gem5.org
>> >> To unsubscribe send an email to gem5-users-leave@gem5.org
>> >>
>>
>>
>> _______________________________________________
>> gem5-users mailing list -- gem5-users@gem5.org
>> To unsubscribe send an email to gem5-users-leave@gem5.org
>>
HN
Hoa Nguyen
Sat, Oct 8, 2022 1:40 AM
Hi,
It's quite odd that both sqrt_i and result were zeroed out at the same
time. Does the problem appear in other ISA FS mode, e.g. x86 FS mode? Can
you show the objdump of the loop as well?
Regards,
Hoa Nguyen
On Thu, Oct 6, 2022, 04:06 Νικόλαος Ταμπουρατζής ntampouratzis@ece.auth.gr
wrote:
Dear Jason, all,
I am trying to find the accuracy problem with RISCV-FS and I observe
that the problem is created (at least in my dummy example) because the
variables (double) are set to zero in random simulated time (for this
reason I get different results among executions of the same code).
Specifically for the following dummy code:
#include <cmath>
#include <stdio.h>
int main(){
int dim = 10;
float result;
for (int iter = 0; iter < 2; iter++){
result = 0;
for (int i = 0; i < dim; i++){
for (int j = 0; j < dim; j++){
float sq_i = sqrt(i);
float sq_j = sqrt(j);
result += sq_i * sq_j;
printf("ITER: %d | i: %d | j: %d Result(i: %f | j: %f
| i*j: %f): %f\n", iter, i , j, sq_i, sq_j, sq_i * sq_j, result);
}
}
printf("Final Result: %lf\n", result);
}
}
The correct Final Result in both iterations is 372.721656. However, I
get the following results in FS:
ITER: 0 | i: 0 | j: 0 Result(i: 0.000000 | j: 0.000000 | ij:
0.000000): 0.000000
ITER: 0 | i: 0 | j: 1 Result(i: 0.000000 | j: 1.000000 | ij:
0.000000): 0.000000
ITER: 0 | i: 0 | j: 2 Result(i: 0.000000 | j: 1.414214 | ij:
0.000000): 0.000000
ITER: 0 | i: 0 | j: 3 Result(i: 0.000000 | j: 1.732051 | ij:
0.000000): 0.000000
ITER: 0 | i: 0 | j: 4 Result(i: 0.000000 | j: 2.000000 | ij:
0.000000): 0.000000
ITER: 0 | i: 0 | j: 5 Result(i: 0.000000 | j: 2.236068 | ij:
0.000000): 0.000000
ITER: 0 | i: 0 | j: 6 Result(i: 0.000000 | j: 2.449490 | ij:
0.000000): 0.000000
ITER: 0 | i: 0 | j: 7 Result(i: 0.000000 | j: 2.645751 | ij:
0.000000): 0.000000
ITER: 0 | i: 0 | j: 8 Result(i: 0.000000 | j: 2.828427 | ij:
0.000000): 0.000000
ITER: 0 | i: 0 | j: 9 Result(i: 0.000000 | j: 3.000000 | ij:
0.000000): 0.000000
ITER: 0 | i: 1 | j: 0 Result(i: 1.000000 | j: 0.000000 | ij:
0.000000): 0.000000
ITER: 0 | i: 1 | j: 1 Result(i: 1.000000 | j: 1.000000 | ij:
1.000000): 1.000000
ITER: 0 | i: 1 | j: 2 Result(i: 1.000000 | j: 1.414214 | ij:
1.414214): 2.414214
ITER: 0 | i: 1 | j: 3 Result(i: 1.000000 | j: 1.732051 | ij:
1.732051): 4.146264
ITER: 0 | i: 1 | j: 4 Result(i: 0.000000 | j: 2.000000 | ij:
0.000000): 0.000000
ITER: 0 | i: 1 | j: 5 Result(i: 0.000000 | j: 2.236068 | ij:
0.000000): 0.000000
ITER: 0 | i: 1 | j: 6 Result(i: 0.000000 | j: 2.449490 | ij:
0.000000): 0.000000
ITER: 0 | i: 1 | j: 7 Result(i: 0.000000 | j: 2.645751 | ij:
0.000000): 0.000000
ITER: 0 | i: 1 | j: 8 Result(i: 0.000000 | j: 2.828427 | ij:
0.000000): 0.000000
ITER: 0 | i: 1 | j: 9 Result(i: 0.000000 | j: 3.000000 | ij:
0.000000): 0.000000
ITER: 0 | i: 2 | j: 0 Result(i: 1.414214 | j: 0.000000 | ij:
0.000000): 0.000000
ITER: 0 | i: 2 | j: 1 Result(i: 1.414214 | j: 1.000000 | ij:
1.414214): 1.414214
ITER: 0 | i: 2 | j: 2 Result(i: 1.414214 | j: 1.414214 | ij:
2.000000): 3.414214
ITER: 0 | i: 2 | j: 3 Result(i: 1.414214 | j: 1.732051 | ij:
2.449490): 5.863703
ITER: 0 | i: 2 | j: 4 Result(i: 1.414214 | j: 2.000000 | ij:
2.828427): 8.692130
ITER: 0 | i: 2 | j: 5 Result(i: 1.414214 | j: 2.236068 | ij:
3.162278): 11.854408
ITER: 0 | i: 2 | j: 6 Result(i: 1.414214 | j: 2.449490 | ij:
3.464102): 15.318510
ITER: 0 | i: 2 | j: 7 Result(i: 1.414214 | j: 2.645751 | ij:
3.741657): 19.060167
ITER: 0 | i: 2 | j: 8 Result(i: 1.414214 | j: 2.828427 | ij:
4.000000): 23.060167
ITER: 0 | i: 2 | j: 9 Result(i: 1.414214 | j: 3.000000 | ij:
4.242641): 27.302808
ITER: 0 | i: 3 | j: 0 Result(i: 1.732051 | j: 0.000000 | ij:
0.000000): 27.302808
ITER: 0 | i: 3 | j: 1 Result(i: 1.732051 | j: 1.000000 | ij:
1.732051): 29.034859
ITER: 0 | i: 3 | j: 2 Result(i: 1.732051 | j: 1.414214 | ij:
2.449490): 31.484348
ITER: 0 | i: 3 | j: 3 Result(i: 1.732051 | j: 1.732051 | ij:
3.000000): 34.484348
ITER: 0 | i: 3 | j: 4 Result(i: 1.732051 | j: 2.000000 | ij:
3.464102): 37.948450
ITER: 0 | i: 3 | j: 5 Result(i: 1.732051 | j: 2.236068 | ij:
3.872983): 41.821433
ITER: 0 | i: 3 | j: 6 Result(i: 1.732051 | j: 2.449490 | ij:
4.242641): 46.064074
ITER: 0 | i: 3 | j: 7 Result(i: 1.732051 | j: 2.645751 | ij:
4.582576): 50.646650
ITER: 0 | i: 3 | j: 8 Result(i: 1.732051 | j: 2.828427 | ij:
4.898979): 55.545629
ITER: 0 | i: 3 | j: 9 Result(i: 1.732051 | j: 3.000000 | ij:
5.196152): 60.741782
ITER: 0 | i: 4 | j: 0 Result(i: 2.000000 | j: 0.000000 | ij:
0.000000): 60.741782
ITER: 0 | i: 4 | j: 1 Result(i: 2.000000 | j: 1.000000 | ij:
2.000000): 62.741782
ITER: 0 | i: 4 | j: 2 Result(i: 2.000000 | j: 1.414214 | ij:
2.828427): 65.570209
ITER: 0 | i: 4 | j: 3 Result(i: 2.000000 | j: 1.732051 | ij:
3.464102): 69.034310
ITER: 0 | i: 4 | j: 4 Result(i: 2.000000 | j: 2.000000 | ij:
4.000000): 73.034310
ITER: 0 | i: 4 | j: 5 Result(i: 2.000000 | j: 2.236068 | ij:
4.472136): 77.506446
ITER: 0 | i: 4 | j: 6 Result(i: 2.000000 | j: 2.449490 | ij:
4.898979): 82.405426
ITER: 0 | i: 4 | j: 7 Result(i: 2.000000 | j: 2.645751 | ij:
5.291503): 87.696928
ITER: 0 | i: 4 | j: 8 Result(i: 2.000000 | j: 2.828427 | ij:
5.656854): 93.353783
ITER: 0 | i: 4 | j: 9 Result(i: 2.000000 | j: 3.000000 | ij:
6.000000): 99.353783
ITER: 0 | i: 5 | j: 0 Result(i: 2.236068 | j: 0.000000 | ij:
0.000000): 99.353783
ITER: 0 | i: 5 | j: 1 Result(i: 2.236068 | j: 1.000000 | ij:
2.236068): 101.589851
ITER: 0 | i: 5 | j: 2 Result(i: 2.236068 | j: 1.414214 | ij:
3.162278): 104.752128
ITER: 0 | i: 5 | j: 3 Result(i: 2.236068 | j: 1.732051 | ij:
3.872983): 108.625112
ITER: 0 | i: 5 | j: 4 Result(i: 2.236068 | j: 2.000000 | ij:
4.472136): 113.097248
ITER: 0 | i: 5 | j: 5 Result(i: 2.236068 | j: 2.236068 | ij:
5.000000): 118.097248
ITER: 0 | i: 5 | j: 6 Result(i: 2.236068 | j: 2.449490 | ij:
5.477226): 123.574473
ITER: 0 | i: 5 | j: 7 Result(i: 2.236068 | j: 2.645751 | ij:
5.916080): 129.490553
ITER: 0 | i: 5 | j: 8 Result(i: 2.236068 | j: 2.828427 | ij:
6.324555): 135.815108
ITER: 0 | i: 5 | j: 9 Result(i: 2.236068 | j: 3.000000 | ij:
6.708204): 142.523312
ITER: 0 | i: 6 | j: 0 Result(i: 2.449490 | j: 0.000000 | ij:
0.000000): 142.523312
ITER: 0 | i: 6 | j: 1 Result(i: 2.449490 | j: 1.000000 | ij:
2.449490): 144.972802
ITER: 0 | i: 6 | j: 2 Result(i: 2.449490 | j: 1.414214 | ij:
3.464102): 148.436904
ITER: 0 | i: 6 | j: 3 Result(i: 2.449490 | j: 1.732051 | ij:
4.242641): 152.679544
ITER: 0 | i: 6 | j: 4 Result(i: 2.449490 | j: 2.000000 | ij:
4.898979): 157.578524
ITER: 0 | i: 6 | j: 5 Result(i: 2.449490 | j: 2.236068 | ij:
5.477226): 163.055749
ITER: 0 | i: 6 | j: 6 Result(i: 2.449490 | j: 2.449490 | ij:
6.000000): 169.055749
ITER: 0 | i: 6 | j: 7 Result(i: 2.449490 | j: 2.645751 | ij:
6.480741): 175.536490
ITER: 0 | i: 6 | j: 8 Result(i: 2.449490 | j: 2.828427 | ij:
6.928203): 182.464693
ITER: 0 | i: 6 | j: 9 Result(i: 2.449490 | j: 3.000000 | ij:
7.348469): 189.813162
ITER: 0 | i: 7 | j: 0 Result(i: 2.645751 | j: 0.000000 | ij:
0.000000): 189.813162
ITER: 0 | i: 7 | j: 1 Result(i: 2.645751 | j: 1.000000 | ij:
2.645751): 192.458914
ITER: 0 | i: 7 | j: 2 Result(i: 2.645751 | j: 1.414214 | ij:
3.741657): 196.200571
ITER: 0 | i: 7 | j: 3 Result(i: 2.645751 | j: 1.732051 | ij:
4.582576): 200.783147
ITER: 0 | i: 7 | j: 4 Result(i: 2.645751 | j: 2.000000 | ij:
5.291503): 206.074649
ITER: 0 | i: 7 | j: 5 Result(i: 2.645751 | j: 2.236068 | ij:
5.916080): 211.990729
ITER: 0 | i: 7 | j: 6 Result(i: 2.645751 | j: 2.449490 | ij:
6.480741): 218.471470
ITER: 0 | i: 7 | j: 7 Result(i: 2.645751 | j: 2.645751 | ij:
7.000000): 225.471470
ITER: 0 | i: 7 | j: 8 Result(i: 2.645751 | j: 2.828427 | ij:
7.483315): 232.954785
ITER: 0 | i: 7 | j: 9 Result(i: 2.645751 | j: 3.000000 | ij:
7.937254): 240.892039
ITER: 0 | i: 8 | j: 0 Result(i: 2.828427 | j: 0.000000 | ij:
0.000000): 240.892039
ITER: 0 | i: 8 | j: 1 Result(i: 2.828427 | j: 1.000000 | ij:
2.828427): 243.720466
ITER: 0 | i: 8 | j: 2 Result(i: 2.828427 | j: 1.414214 | ij:
4.000000): 247.720466
ITER: 0 | i: 8 | j: 3 Result(i: 2.828427 | j: 1.732051 | ij:
4.898979): 252.619445
ITER: 0 | i: 8 | j: 4 Result(i: 2.828427 | j: 2.000000 | ij:
5.656854): 258.276300
ITER: 0 | i: 8 | j: 5 Result(i: 2.828427 | j: 2.236068 | ij:
6.324555): 264.600855
ITER: 0 | i: 8 | j: 6 Result(i: 2.828427 | j: 2.449490 | ij:
6.928203): 271.529058
ITER: 0 | i: 8 | j: 7 Result(i: 2.828427 | j: 2.645751 | ij:
7.483315): 279.012373
ITER: 0 | i: 8 | j: 8 Result(i: 2.828427 | j: 2.828427 | ij:
8.000000): 287.012373
ITER: 0 | i: 8 | j: 9 Result(i: 2.828427 | j: 3.000000 | ij:
8.485281): 295.497654
ITER: 0 | i: 9 | j: 0 Result(i: 3.000000 | j: 0.000000 | ij:
0.000000): 295.497654
ITER: 0 | i: 9 | j: 1 Result(i: 3.000000 | j: 1.000000 | ij:
3.000000): 298.497654
ITER: 0 | i: 9 | j: 2 Result(i: 3.000000 | j: 1.414214 | ij:
4.242641): 302.740295
ITER: 0 | i: 9 | j: 3 Result(i: 3.000000 | j: 1.732051 | ij:
5.196152): 307.936447
ITER: 0 | i: 9 | j: 4 Result(i: 3.000000 | j: 2.000000 | ij:
6.000000): 313.936447
ITER: 0 | i: 9 | j: 5 Result(i: 3.000000 | j: 2.236068 | ij:
6.708204): 320.644651
ITER: 0 | i: 9 | j: 6 Result(i: 3.000000 | j: 2.449490 | ij:
7.348469): 327.993120
ITER: 0 | i: 9 | j: 7 Result(i: 3.000000 | j: 2.645751 | ij:
7.937254): 335.930374
ITER: 0 | i: 9 | j: 8 Result(i: 3.000000 | j: 2.828427 | ij:
8.485281): 344.415656
ITER: 0 | i: 9 | j: 9 Result(i: 3.000000 | j: 3.000000 | ij:
9.000000): 353.415656
Final Result: 353.415656
ITER: 1 | i: 0 | j: 0 Result(i: 0.000000 | j: 0.000000 | ij:
0.000000): 0.000000
ITER: 1 | i: 0 | j: 1 Result(i: 0.000000 | j: 1.000000 | ij:
0.000000): 0.000000
ITER: 1 | i: 0 | j: 2 Result(i: 0.000000 | j: 1.414214 | ij:
0.000000): 0.000000
ITER: 1 | i: 0 | j: 3 Result(i: 0.000000 | j: 1.732051 | ij:
0.000000): 0.000000
ITER: 1 | i: 0 | j: 4 Result(i: 0.000000 | j: 2.000000 | ij:
0.000000): 0.000000
ITER: 1 | i: 0 | j: 5 Result(i: 0.000000 | j: 2.236068 | ij:
0.000000): 0.000000
ITER: 1 | i: 0 | j: 6 Result(i: 0.000000 | j: 2.449490 | ij:
0.000000): 0.000000
ITER: 1 | i: 0 | j: 7 Result(i: 0.000000 | j: 2.645751 | ij:
0.000000): 0.000000
ITER: 1 | i: 0 | j: 8 Result(i: 0.000000 | j: 2.828427 | ij:
0.000000): 0.000000
ITER: 1 | i: 0 | j: 9 Result(i: 0.000000 | j: 3.000000 | ij:
0.000000): 0.000000
ITER: 1 | i: 1 | j: 0 Result(i: 1.000000 | j: 0.000000 | ij:
0.000000): 0.000000
ITER: 1 | i: 1 | j: 1 Result(i: 1.000000 | j: 1.000000 | ij:
1.000000): 1.000000
ITER: 1 | i: 1 | j: 2 Result(i: 1.000000 | j: 1.414214 | ij:
1.414214): 2.414214
ITER: 1 | i: 1 | j: 3 Result(i: 1.000000 | j: 1.732051 | ij:
1.732051): 4.146264
ITER: 1 | i: 1 | j: 4 Result(i: 1.000000 | j: 2.000000 | ij:
2.000000): 6.146264
ITER: 1 | i: 1 | j: 5 Result(i: 1.000000 | j: 2.236068 | ij:
2.236068): 8.382332
ITER: 1 | i: 1 | j: 6 Result(i: 1.000000 | j: 2.449490 | ij:
2.449490): 10.831822
ITER: 1 | i: 1 | j: 7 Result(i: 1.000000 | j: 2.645751 | ij:
2.645751): 13.477573
ITER: 1 | i: 1 | j: 8 Result(i: 1.000000 | j: 2.828427 | ij:
2.828427): 16.306001
ITER: 1 | i: 1 | j: 9 Result(i: 1.000000 | j: 3.000000 | ij:
3.000000): 19.306001
ITER: 1 | i: 2 | j: 0 Result(i: 1.414214 | j: 0.000000 | ij:
0.000000): 19.306001
ITER: 1 | i: 2 | j: 1 Result(i: 1.414214 | j: 1.000000 | ij:
1.414214): 20.720214
ITER: 1 | i: 2 | j: 2 Result(i: 1.414214 | j: 1.414214 | ij:
2.000000): 22.720214
ITER: 1 | i: 2 | j: 3 Result(i: 1.414214 | j: 1.732051 | ij:
2.449490): 25.169704
ITER: 1 | i: 2 | j: 4 Result(i: 1.414214 | j: 2.000000 | ij:
2.828427): 27.998131
ITER: 1 | i: 2 | j: 5 Result(i: 1.414214 | j: 2.236068 | ij:
3.162278): 31.160409
ITER: 1 | i: 2 | j: 6 Result(i: 1.414214 | j: 2.449490 | ij:
3.464102): 34.624510
ITER: 1 | i: 2 | j: 7 Result(i: 1.414214 | j: 2.645751 | ij:
3.741657): 38.366168
ITER: 1 | i: 2 | j: 8 Result(i: 1.414214 | j: 2.828427 | ij:
4.000000): 42.366168
ITER: 1 | i: 2 | j: 9 Result(i: 1.414214 | j: 3.000000 | ij:
4.242641): 46.608808
ITER: 1 | i: 3 | j: 0 Result(i: 1.732051 | j: 0.000000 | ij:
0.000000): 46.608808
ITER: 1 | i: 3 | j: 1 Result(i: 1.732051 | j: 1.000000 | ij:
1.732051): 48.340859
ITER: 1 | i: 3 | j: 2 Result(i: 1.732051 | j: 1.414214 | ij:
2.449490): 50.790349
ITER: 1 | i: 3 | j: 3 Result(i: 1.732051 | j: 1.732051 | ij:
3.000000): 53.790349
ITER: 1 | i: 3 | j: 4 Result(i: 1.732051 | j: 2.000000 | ij:
3.464102): 57.254450
ITER: 1 | i: 3 | j: 5 Result(i: 1.732051 | j: 2.236068 | ij:
3.872983): 61.127434
ITER: 1 | i: 3 | j: 6 Result(i: 1.732051 | j: 2.449490 | ij:
4.242641): 65.370075
ITER: 1 | i: 3 | j: 7 Result(i: 1.732051 | j: 2.645751 | ij:
4.582576): 69.952650
ITER: 1 | i: 3 | j: 8 Result(i: 1.732051 | j: 2.828427 | ij:
4.898979): 74.851630
ITER: 1 | i: 3 | j: 9 Result(i: 1.732051 | j: 3.000000 | ij:
5.196152): 80.047782
ITER: 1 | i: 4 | j: 0 Result(i: 2.000000 | j: 0.000000 | ij:
0.000000): 80.047782
ITER: 1 | i: 4 | j: 1 Result(i: 2.000000 | j: 1.000000 | ij:
2.000000): 82.047782
ITER: 1 | i: 4 | j: 2 Result(i: 2.000000 | j: 1.414214 | ij:
2.828427): 84.876209
ITER: 1 | i: 4 | j: 3 Result(i: 2.000000 | j: 1.732051 | ij:
3.464102): 88.340311
ITER: 1 | i: 4 | j: 4 Result(i: 2.000000 | j: 2.000000 | ij:
4.000000): 92.340311
ITER: 1 | i: 4 | j: 5 Result(i: 2.000000 | j: 2.236068 | ij:
4.472136): 96.812447
ITER: 1 | i: 4 | j: 6 Result(i: 2.000000 | j: 2.449490 | ij:
4.898979): 101.711426
ITER: 1 | i: 4 | j: 7 Result(i: 2.000000 | j: 2.645751 | ij:
5.291503): 107.002929
ITER: 1 | i: 4 | j: 8 Result(i: 2.000000 | j: 2.828427 | ij:
5.656854): 112.659783
ITER: 1 | i: 4 | j: 9 Result(i: 2.000000 | j: 3.000000 | ij:
6.000000): 118.659783
ITER: 1 | i: 5 | j: 0 Result(i: 2.236068 | j: 0.000000 | ij:
0.000000): 118.659783
ITER: 1 | i: 5 | j: 1 Result(i: 2.236068 | j: 1.000000 | ij:
2.236068): 120.895851
ITER: 1 | i: 5 | j: 2 Result(i: 2.236068 | j: 1.414214 | ij:
3.162278): 124.058129
ITER: 1 | i: 5 | j: 3 Result(i: 2.236068 | j: 1.732051 | ij:
3.872983): 127.931112
ITER: 1 | i: 5 | j: 4 Result(i: 2.236068 | j: 2.000000 | ij:
4.472136): 132.403248
ITER: 1 | i: 5 | j: 5 Result(i: 2.236068 | j: 2.236068 | ij:
5.000000): 137.403248
ITER: 1 | i: 5 | j: 6 Result(i: 2.236068 | j: 2.449490 | ij:
5.477226): 142.880474
ITER: 1 | i: 5 | j: 7 Result(i: 2.236068 | j: 2.645751 | ij:
5.916080): 148.796553
ITER: 1 | i: 5 | j: 8 Result(i: 2.236068 | j: 2.828427 | ij:
6.324555): 155.121109
ITER: 1 | i: 5 | j: 9 Result(i: 2.236068 | j: 3.000000 | ij:
6.708204): 161.829313
ITER: 1 | i: 6 | j: 0 Result(i: 2.449490 | j: 0.000000 | ij:
0.000000): 161.829313
ITER: 1 | i: 6 | j: 1 Result(i: 2.449490 | j: 1.000000 | ij:
2.449490): 164.278802
ITER: 1 | i: 6 | j: 2 Result(i: 2.449490 | j: 1.414214 | ij:
3.464102): 167.742904
ITER: 1 | i: 6 | j: 3 Result(i: 2.449490 | j: 1.732051 | ij:
4.242641): 171.985545
ITER: 1 | i: 6 | j: 4 Result(i: 2.449490 | j: 2.000000 | ij:
4.898979): 176.884524
ITER: 1 | i: 6 | j: 5 Result(i: 2.449490 | j: 2.236068 | ij:
5.477226): 182.361750
ITER: 1 | i: 6 | j: 6 Result(i: 2.449490 | j: 2.449490 | ij:
6.000000): 188.361750
ITER: 1 | i: 6 | j: 7 Result(i: 2.449490 | j: 2.645751 | ij:
6.480741): 194.842491
ITER: 1 | i: 6 | j: 8 Result(i: 2.449490 | j: 2.828427 | ij:
6.928203): 201.770694
ITER: 1 | i: 6 | j: 9 Result(i: 2.449490 | j: 3.000000 | ij:
7.348469): 209.119163
ITER: 1 | i: 7 | j: 0 Result(i: 2.645751 | j: 0.000000 | ij:
0.000000): 209.119163
ITER: 1 | i: 7 | j: 1 Result(i: 2.645751 | j: 1.000000 | ij:
2.645751): 211.764914
ITER: 1 | i: 7 | j: 2 Result(i: 2.645751 | j: 1.414214 | ij:
3.741657): 215.506572
ITER: 1 | i: 7 | j: 3 Result(i: 2.645751 | j: 1.732051 | ij:
4.582576): 220.089147
ITER: 1 | i: 7 | j: 4 Result(i: 2.645751 | j: 2.000000 | ij:
5.291503): 225.380650
ITER: 1 | i: 7 | j: 5 Result(i: 2.645751 | j: 2.236068 | ij:
5.916080): 231.296730
ITER: 1 | i: 7 | j: 6 Result(i: 2.645751 | j: 2.449490 | ij:
6.480741): 237.777470
ITER: 1 | i: 7 | j: 7 Result(i: 2.645751 | j: 2.645751 | ij:
7.000000): 244.777470
ITER: 1 | i: 7 | j: 8 Result(i: 2.645751 | j: 2.828427 | ij:
7.483315): 252.260785
ITER: 1 | i: 7 | j: 9 Result(i: 2.645751 | j: 3.000000 | ij:
7.937254): 260.198039
ITER: 1 | i: 8 | j: 0 Result(i: 2.828427 | j: 0.000000 | ij:
0.000000): 260.198039
ITER: 1 | i: 8 | j: 1 Result(i: 2.828427 | j: 1.000000 | ij:
2.828427): 263.026466
ITER: 1 | i: 8 | j: 2 Result(i: 2.828427 | j: 1.414214 | ij:
4.000000): 267.026466
ITER: 1 | i: 8 | j: 3 Result(i: 2.828427 | j: 1.732051 | ij:
4.898979): 271.925446
ITER: 1 | i: 8 | j: 4 Result(i: 2.828427 | j: 2.000000 | ij:
5.656854): 277.582300
ITER: 1 | i: 8 | j: 5 Result(i: 2.828427 | j: 2.236068 | ij:
6.324555): 283.906855
ITER: 1 | i: 8 | j: 6 Result(i: 2.828427 | j: 2.449490 | ij:
6.928203): 290.835059
ITER: 1 | i: 8 | j: 7 Result(i: 2.828427 | j: 2.645751 | ij:
7.483315): 298.318373
ITER: 1 | i: 8 | j: 8 Result(i: 2.828427 | j: 2.828427 | ij:
8.000000): 306.318373
ITER: 1 | i: 8 | j: 9 Result(i: 2.828427 | j: 3.000000 | ij:
8.485281): 314.803655
ITER: 1 | i: 9 | j: 0 Result(i: 3.000000 | j: 0.000000 | ij:
0.000000): 314.803655
ITER: 1 | i: 9 | j: 1 Result(i: 3.000000 | j: 1.000000 | ij:
3.000000): 317.803655
ITER: 1 | i: 9 | j: 2 Result(i: 3.000000 | j: 1.414214 | ij:
4.242641): 322.046295
ITER: 1 | i: 9 | j: 3 Result(i: 3.000000 | j: 1.732051 | ij:
5.196152): 327.242448
ITER: 1 | i: 9 | j: 4 Result(i: 3.000000 | j: 2.000000 | ij:
6.000000): 333.242448
ITER: 1 | i: 9 | j: 5 Result(i: 3.000000 | j: 2.236068 | ij:
6.708204): 339.950652
ITER: 1 | i: 9 | j: 6 Result(i: 3.000000 | j: 2.449490 | ij:
7.348469): 347.299121
ITER: 1 | i: 9 | j: 7 Result(i: 3.000000 | j: 2.645751 | ij:
7.937254): 355.236375
ITER: 1 | i: 9 | j: 8 Result(i: 3.000000 | j: 2.828427 | ij:
8.485281): 363.721656
ITER: 1 | i: 9 | j: 9 Result(i: 3.000000 | j: 3.000000 | ij:
9.000000): 372.721656
Final Result: 372.721656
As we can see in the following iterations the sqrt(1) as well as the
result is set to zero for some reason.
ITER: 0 | i: 1 | j: 4 Result(i: 0.000000 | j: 2.000000 | ij:
0.000000): 0.000000
ITER: 0 | i: 1 | j: 5 Result(i: 0.000000 | j: 2.236068 | ij:
0.000000): 0.000000
ITER: 0 | i: 1 | j: 6 Result(i: 0.000000 | j: 2.449490 | ij:
0.000000): 0.000000
ITER: 0 | i: 1 | j: 7 Result(i: 0.000000 | j: 2.645751 | ij:
0.000000): 0.000000
ITER: 0 | i: 1 | j: 8 Result(i: 0.000000 | j: 2.828427 | ij:
0.000000): 0.000000
ITER: 0 | i: 1 | j: 9 Result(i: 0.000000 | j: 3.000000 | ij:
0.000000): 0.000000
Please help me to resolve the accuracy issue! I think that it will be
very useful for gem5 community.
To be noticed, I find the correct simulated tick in which the
application started in FS (using m5 dumpstats), and I start the
--debug-start, but the trace file which is generated is 10x larger
than SE mode for the same application. How can I compare them?
Thank you in advance!
Best regards,
Nikos
Quoting Νικόλαος Ταμπουρατζής ntampouratzis@ece.auth.gr:
Dear Jason,
I am trying to use --debug-start but in FS mode it is very difficult
to find the tick on which the application is started!
However, I am writing the following very simple c++ program:
#include <cmath>
#include <stdio.h>
int main(){
int dim = 4096;
double result;
for (int iter = 0; iter < 2; iter++){
result = 0;
for (int i = 0; i < dim; i++){
for (int j = 0; j < dim; j++){
result += sqrt(i) * sqrt(j);
}
}
printf("Result: %lf\n", result); //Result: 30530733453.127449
}
}
I cross-compile it using: riscv64-linux-gnu-g++ -static -O3 -o
test_riscv test_riscv.cpp
While in X86 (without cross-compilation of course), QEMU-RISCV,
GEM5-SE the result is the same (30530733453.127449), in GEM5-FS the
result is different! In addition, the result is also different
between the 2 iterations.
Please reproduce the error if you want in order to verify my result.
Ηow can the issue be resolved?
Thank you in advance!
Best regards,
Nikos
Quoting Jason Lowe-Power jason@lowepower.com:
Hi Nikos,
You can use --debug-start to start the debugging after some number of
ticks. Also, I would expect that the difference should come up quickly,
no need to run the program to the end.
For the FS mode one, you will want to just start the trace as the
application starts. This could be a bit of a pain.
I'm not really sure what fundamentally could be different. FS and SE
use the exact same code for executing instructions, so I don't think
the problem. Have you tried running for smaller inputs or just one
iteration?
Jason
On Wed, Sep 21, 2022 at 9:04 AM Νικόλαος Ταμπουρατζής <
ntampouratzis@ece.auth.gr> wrote:
Dear Bobby,
Iam trying to add --debug-flags=Exec (building the gem5 for gem5.opt
not for gem5.fast which I had) but the debug traces exceed the 20GB
(and it is not finished yet) for less than 1 simulated second. How can
I reduce the size of the debug-flags (or set something more specific)?
In contrast I build the HPCG benchmark with DHPCG_DEBUG flag. If you
want, you can compare these two output files
(hpcg20010909T014640_SE_Mode & HPCG-Benchmark_3.1_FS_Mode). As you can
see, something goes wrong with the accuracy of calculations in FS mode
(benchmark uses double precission). You can find the files here:
http://kition.mhl.tuc.gr:8000/d/68d82f3533/
Best regards,
Nikos
Quoting Jason Lowe-Power jason@lowepower.com:
That's quite odd that it works in SE mode but not FS mode!
I would suggest running with --debug-flags=Exec for both and then
diff to see how they differ.
Cheers,
Jason
On Tue, Sep 20, 2022 at 2:45 PM Νικόλαος Ταμπουρατζής <
ntampouratzis@ece.auth.gr> wrote:
Dear Bobby,
In QEMU I get the same (correct) results that I get in SE mode
simulation. I get invalid results in FS simulation (in both
riscv-fs.py and riscv-ubuntu-run.py). I cannot access real RISCV
hardware at this moment, however, if you want you may execute my
Hi Nikos,
I notice you said the following in your original email:
In addition, I used the RISCV Ubuntu image
I installed the gcc compiler, compile it (through qemu) and I get
wrong results too.
Is this saying you get the wrong results is QEMU? If so, the bug
or the HPCG workload, not in gem5. If not, I would test in QEMU to
sure the binary works there. Another way you could test to see if
problem is your binary or gem5 would be to run it on real
access to some RISC-V hardware here at UC Davis, if you don't have
Dear Bobby,
- I use the original riscv-fs.py which is provided in the latest
release.
I run the gem5 once (./build/RISCV/gem5.fast -d ./HPCG_FS_results
./configs/example/gem5_library/riscv-fs.py) in order to download
riscv-bootloader-vmlinux-5.10 and riscv-disk-img.
After this I mount the riscv-disk-img (sudo mount -o loop
riscv-disk-img /mnt), put the xhpcg executable and I do the
changes in riscv-fs.py to boot the riscv-disk-img with executable:
image = CustomDiskImageResource(
local_path = "/home/cossim/.cache/gem5/riscv-disk-img",
)
Set the Full System workload.
board.set_kernel_disk_workload(
kernel=Resource("riscv-bootloader-vmlinux-5.10"),
disk_image=image,
)
Finally, in the
gem5/src/python/gem5/components/boards/riscv_board.py
I change the last line to "return ["console=ttyS0",
"root={root_value}", "rw"]" in order to allow the write
the image.
- The HPCG benchmark after some iterations calculates if the
are valid or not valid. In the case of FS it gives invalid
I see from the results, one (at least) problem is that produces
different results in each HPCG execution (with the same
I'm going to need a bit more information to help:
- In what way have you modified
./configs/example/gem5_library/riscv-fs.py? Can you attach the
- What error are you getting or in what way are the results
Dear gem5 community,
I have successfully cross-compile the HPCG benchmark for RISCV
version, without MPI and OpenMP). While it working properly in
mode (./build/RISCV/gem5.fast -d ./HPCG_SE_results
./configs/example/se.py -c xhpcg --options '--nx=16 --ny=16
--npx=1 --npy=1 --npz=1 --rt=0.1'), I get invalid results in FS
simulation using "./build/RISCV/gem5.fast -d ./HPCG_FS_results
./configs/example/gem5_library/riscv-fs.py" (I mount the riscv
and put it).
Can you help me please?
In addition, I used the RISCV Ubuntu image
(
I installed the gcc compiler, compile it (through qemu) and I
Hi,
It's quite odd that both sqrt_i and result were zeroed out at the same
time. Does the problem appear in other ISA FS mode, e.g. x86 FS mode? Can
you show the objdump of the loop as well?
Regards,
Hoa Nguyen
On Thu, Oct 6, 2022, 04:06 Νικόλαος Ταμπουρατζής <ntampouratzis@ece.auth.gr>
wrote:
> Dear Jason, all,
>
> I am trying to find the accuracy problem with RISCV-FS and I observe
> that the problem is created (at least in my dummy example) because the
> variables (double) are set to zero in random simulated time (for this
> reason I get different results among executions of the same code).
> Specifically for the following dummy code:
>
>
> #include <cmath>
> #include <stdio.h>
>
> int main(){
>
> int dim = 10;
>
> float result;
>
> for (int iter = 0; iter < 2; iter++){
> result = 0;
> for (int i = 0; i < dim; i++){
> for (int j = 0; j < dim; j++){
> float sq_i = sqrt(i);
> float sq_j = sqrt(j);
> result += sq_i * sq_j;
> printf("ITER: %d | i: %d | j: %d Result(i: %f | j: %f
> | i*j: %f): %f\n", iter, i , j, sq_i, sq_j, sq_i * sq_j, result);
> }
> }
> printf("Final Result: %lf\n", result);
> }
> }
>
>
> The correct Final Result in both iterations is 372.721656. However, I
> get the following results in FS:
>
> ITER: 0 | i: 0 | j: 0 Result(i: 0.000000 | j: 0.000000 | i*j:
> 0.000000): 0.000000
> ITER: 0 | i: 0 | j: 1 Result(i: 0.000000 | j: 1.000000 | i*j:
> 0.000000): 0.000000
> ITER: 0 | i: 0 | j: 2 Result(i: 0.000000 | j: 1.414214 | i*j:
> 0.000000): 0.000000
> ITER: 0 | i: 0 | j: 3 Result(i: 0.000000 | j: 1.732051 | i*j:
> 0.000000): 0.000000
> ITER: 0 | i: 0 | j: 4 Result(i: 0.000000 | j: 2.000000 | i*j:
> 0.000000): 0.000000
> ITER: 0 | i: 0 | j: 5 Result(i: 0.000000 | j: 2.236068 | i*j:
> 0.000000): 0.000000
> ITER: 0 | i: 0 | j: 6 Result(i: 0.000000 | j: 2.449490 | i*j:
> 0.000000): 0.000000
> ITER: 0 | i: 0 | j: 7 Result(i: 0.000000 | j: 2.645751 | i*j:
> 0.000000): 0.000000
> ITER: 0 | i: 0 | j: 8 Result(i: 0.000000 | j: 2.828427 | i*j:
> 0.000000): 0.000000
> ITER: 0 | i: 0 | j: 9 Result(i: 0.000000 | j: 3.000000 | i*j:
> 0.000000): 0.000000
> ITER: 0 | i: 1 | j: 0 Result(i: 1.000000 | j: 0.000000 | i*j:
> 0.000000): 0.000000
> ITER: 0 | i: 1 | j: 1 Result(i: 1.000000 | j: 1.000000 | i*j:
> 1.000000): 1.000000
> ITER: 0 | i: 1 | j: 2 Result(i: 1.000000 | j: 1.414214 | i*j:
> 1.414214): 2.414214
> ITER: 0 | i: 1 | j: 3 Result(i: 1.000000 | j: 1.732051 | i*j:
> 1.732051): 4.146264
> ITER: 0 | i: 1 | j: 4 Result(i: 0.000000 | j: 2.000000 | i*j:
> 0.000000): 0.000000
> ITER: 0 | i: 1 | j: 5 Result(i: 0.000000 | j: 2.236068 | i*j:
> 0.000000): 0.000000
> ITER: 0 | i: 1 | j: 6 Result(i: 0.000000 | j: 2.449490 | i*j:
> 0.000000): 0.000000
> ITER: 0 | i: 1 | j: 7 Result(i: 0.000000 | j: 2.645751 | i*j:
> 0.000000): 0.000000
> ITER: 0 | i: 1 | j: 8 Result(i: 0.000000 | j: 2.828427 | i*j:
> 0.000000): 0.000000
> ITER: 0 | i: 1 | j: 9 Result(i: 0.000000 | j: 3.000000 | i*j:
> 0.000000): 0.000000
> ITER: 0 | i: 2 | j: 0 Result(i: 1.414214 | j: 0.000000 | i*j:
> 0.000000): 0.000000
> ITER: 0 | i: 2 | j: 1 Result(i: 1.414214 | j: 1.000000 | i*j:
> 1.414214): 1.414214
> ITER: 0 | i: 2 | j: 2 Result(i: 1.414214 | j: 1.414214 | i*j:
> 2.000000): 3.414214
> ITER: 0 | i: 2 | j: 3 Result(i: 1.414214 | j: 1.732051 | i*j:
> 2.449490): 5.863703
> ITER: 0 | i: 2 | j: 4 Result(i: 1.414214 | j: 2.000000 | i*j:
> 2.828427): 8.692130
> ITER: 0 | i: 2 | j: 5 Result(i: 1.414214 | j: 2.236068 | i*j:
> 3.162278): 11.854408
> ITER: 0 | i: 2 | j: 6 Result(i: 1.414214 | j: 2.449490 | i*j:
> 3.464102): 15.318510
> ITER: 0 | i: 2 | j: 7 Result(i: 1.414214 | j: 2.645751 | i*j:
> 3.741657): 19.060167
> ITER: 0 | i: 2 | j: 8 Result(i: 1.414214 | j: 2.828427 | i*j:
> 4.000000): 23.060167
> ITER: 0 | i: 2 | j: 9 Result(i: 1.414214 | j: 3.000000 | i*j:
> 4.242641): 27.302808
> ITER: 0 | i: 3 | j: 0 Result(i: 1.732051 | j: 0.000000 | i*j:
> 0.000000): 27.302808
> ITER: 0 | i: 3 | j: 1 Result(i: 1.732051 | j: 1.000000 | i*j:
> 1.732051): 29.034859
> ITER: 0 | i: 3 | j: 2 Result(i: 1.732051 | j: 1.414214 | i*j:
> 2.449490): 31.484348
> ITER: 0 | i: 3 | j: 3 Result(i: 1.732051 | j: 1.732051 | i*j:
> 3.000000): 34.484348
> ITER: 0 | i: 3 | j: 4 Result(i: 1.732051 | j: 2.000000 | i*j:
> 3.464102): 37.948450
> ITER: 0 | i: 3 | j: 5 Result(i: 1.732051 | j: 2.236068 | i*j:
> 3.872983): 41.821433
> ITER: 0 | i: 3 | j: 6 Result(i: 1.732051 | j: 2.449490 | i*j:
> 4.242641): 46.064074
> ITER: 0 | i: 3 | j: 7 Result(i: 1.732051 | j: 2.645751 | i*j:
> 4.582576): 50.646650
> ITER: 0 | i: 3 | j: 8 Result(i: 1.732051 | j: 2.828427 | i*j:
> 4.898979): 55.545629
> ITER: 0 | i: 3 | j: 9 Result(i: 1.732051 | j: 3.000000 | i*j:
> 5.196152): 60.741782
> ITER: 0 | i: 4 | j: 0 Result(i: 2.000000 | j: 0.000000 | i*j:
> 0.000000): 60.741782
> ITER: 0 | i: 4 | j: 1 Result(i: 2.000000 | j: 1.000000 | i*j:
> 2.000000): 62.741782
> ITER: 0 | i: 4 | j: 2 Result(i: 2.000000 | j: 1.414214 | i*j:
> 2.828427): 65.570209
> ITER: 0 | i: 4 | j: 3 Result(i: 2.000000 | j: 1.732051 | i*j:
> 3.464102): 69.034310
> ITER: 0 | i: 4 | j: 4 Result(i: 2.000000 | j: 2.000000 | i*j:
> 4.000000): 73.034310
> ITER: 0 | i: 4 | j: 5 Result(i: 2.000000 | j: 2.236068 | i*j:
> 4.472136): 77.506446
> ITER: 0 | i: 4 | j: 6 Result(i: 2.000000 | j: 2.449490 | i*j:
> 4.898979): 82.405426
> ITER: 0 | i: 4 | j: 7 Result(i: 2.000000 | j: 2.645751 | i*j:
> 5.291503): 87.696928
> ITER: 0 | i: 4 | j: 8 Result(i: 2.000000 | j: 2.828427 | i*j:
> 5.656854): 93.353783
> ITER: 0 | i: 4 | j: 9 Result(i: 2.000000 | j: 3.000000 | i*j:
> 6.000000): 99.353783
> ITER: 0 | i: 5 | j: 0 Result(i: 2.236068 | j: 0.000000 | i*j:
> 0.000000): 99.353783
> ITER: 0 | i: 5 | j: 1 Result(i: 2.236068 | j: 1.000000 | i*j:
> 2.236068): 101.589851
> ITER: 0 | i: 5 | j: 2 Result(i: 2.236068 | j: 1.414214 | i*j:
> 3.162278): 104.752128
> ITER: 0 | i: 5 | j: 3 Result(i: 2.236068 | j: 1.732051 | i*j:
> 3.872983): 108.625112
> ITER: 0 | i: 5 | j: 4 Result(i: 2.236068 | j: 2.000000 | i*j:
> 4.472136): 113.097248
> ITER: 0 | i: 5 | j: 5 Result(i: 2.236068 | j: 2.236068 | i*j:
> 5.000000): 118.097248
> ITER: 0 | i: 5 | j: 6 Result(i: 2.236068 | j: 2.449490 | i*j:
> 5.477226): 123.574473
> ITER: 0 | i: 5 | j: 7 Result(i: 2.236068 | j: 2.645751 | i*j:
> 5.916080): 129.490553
> ITER: 0 | i: 5 | j: 8 Result(i: 2.236068 | j: 2.828427 | i*j:
> 6.324555): 135.815108
> ITER: 0 | i: 5 | j: 9 Result(i: 2.236068 | j: 3.000000 | i*j:
> 6.708204): 142.523312
> ITER: 0 | i: 6 | j: 0 Result(i: 2.449490 | j: 0.000000 | i*j:
> 0.000000): 142.523312
> ITER: 0 | i: 6 | j: 1 Result(i: 2.449490 | j: 1.000000 | i*j:
> 2.449490): 144.972802
> ITER: 0 | i: 6 | j: 2 Result(i: 2.449490 | j: 1.414214 | i*j:
> 3.464102): 148.436904
> ITER: 0 | i: 6 | j: 3 Result(i: 2.449490 | j: 1.732051 | i*j:
> 4.242641): 152.679544
> ITER: 0 | i: 6 | j: 4 Result(i: 2.449490 | j: 2.000000 | i*j:
> 4.898979): 157.578524
> ITER: 0 | i: 6 | j: 5 Result(i: 2.449490 | j: 2.236068 | i*j:
> 5.477226): 163.055749
> ITER: 0 | i: 6 | j: 6 Result(i: 2.449490 | j: 2.449490 | i*j:
> 6.000000): 169.055749
> ITER: 0 | i: 6 | j: 7 Result(i: 2.449490 | j: 2.645751 | i*j:
> 6.480741): 175.536490
> ITER: 0 | i: 6 | j: 8 Result(i: 2.449490 | j: 2.828427 | i*j:
> 6.928203): 182.464693
> ITER: 0 | i: 6 | j: 9 Result(i: 2.449490 | j: 3.000000 | i*j:
> 7.348469): 189.813162
> ITER: 0 | i: 7 | j: 0 Result(i: 2.645751 | j: 0.000000 | i*j:
> 0.000000): 189.813162
> ITER: 0 | i: 7 | j: 1 Result(i: 2.645751 | j: 1.000000 | i*j:
> 2.645751): 192.458914
> ITER: 0 | i: 7 | j: 2 Result(i: 2.645751 | j: 1.414214 | i*j:
> 3.741657): 196.200571
> ITER: 0 | i: 7 | j: 3 Result(i: 2.645751 | j: 1.732051 | i*j:
> 4.582576): 200.783147
> ITER: 0 | i: 7 | j: 4 Result(i: 2.645751 | j: 2.000000 | i*j:
> 5.291503): 206.074649
> ITER: 0 | i: 7 | j: 5 Result(i: 2.645751 | j: 2.236068 | i*j:
> 5.916080): 211.990729
> ITER: 0 | i: 7 | j: 6 Result(i: 2.645751 | j: 2.449490 | i*j:
> 6.480741): 218.471470
> ITER: 0 | i: 7 | j: 7 Result(i: 2.645751 | j: 2.645751 | i*j:
> 7.000000): 225.471470
> ITER: 0 | i: 7 | j: 8 Result(i: 2.645751 | j: 2.828427 | i*j:
> 7.483315): 232.954785
> ITER: 0 | i: 7 | j: 9 Result(i: 2.645751 | j: 3.000000 | i*j:
> 7.937254): 240.892039
> ITER: 0 | i: 8 | j: 0 Result(i: 2.828427 | j: 0.000000 | i*j:
> 0.000000): 240.892039
> ITER: 0 | i: 8 | j: 1 Result(i: 2.828427 | j: 1.000000 | i*j:
> 2.828427): 243.720466
> ITER: 0 | i: 8 | j: 2 Result(i: 2.828427 | j: 1.414214 | i*j:
> 4.000000): 247.720466
> ITER: 0 | i: 8 | j: 3 Result(i: 2.828427 | j: 1.732051 | i*j:
> 4.898979): 252.619445
> ITER: 0 | i: 8 | j: 4 Result(i: 2.828427 | j: 2.000000 | i*j:
> 5.656854): 258.276300
> ITER: 0 | i: 8 | j: 5 Result(i: 2.828427 | j: 2.236068 | i*j:
> 6.324555): 264.600855
> ITER: 0 | i: 8 | j: 6 Result(i: 2.828427 | j: 2.449490 | i*j:
> 6.928203): 271.529058
> ITER: 0 | i: 8 | j: 7 Result(i: 2.828427 | j: 2.645751 | i*j:
> 7.483315): 279.012373
> ITER: 0 | i: 8 | j: 8 Result(i: 2.828427 | j: 2.828427 | i*j:
> 8.000000): 287.012373
> ITER: 0 | i: 8 | j: 9 Result(i: 2.828427 | j: 3.000000 | i*j:
> 8.485281): 295.497654
> ITER: 0 | i: 9 | j: 0 Result(i: 3.000000 | j: 0.000000 | i*j:
> 0.000000): 295.497654
> ITER: 0 | i: 9 | j: 1 Result(i: 3.000000 | j: 1.000000 | i*j:
> 3.000000): 298.497654
> ITER: 0 | i: 9 | j: 2 Result(i: 3.000000 | j: 1.414214 | i*j:
> 4.242641): 302.740295
> ITER: 0 | i: 9 | j: 3 Result(i: 3.000000 | j: 1.732051 | i*j:
> 5.196152): 307.936447
> ITER: 0 | i: 9 | j: 4 Result(i: 3.000000 | j: 2.000000 | i*j:
> 6.000000): 313.936447
> ITER: 0 | i: 9 | j: 5 Result(i: 3.000000 | j: 2.236068 | i*j:
> 6.708204): 320.644651
> ITER: 0 | i: 9 | j: 6 Result(i: 3.000000 | j: 2.449490 | i*j:
> 7.348469): 327.993120
> ITER: 0 | i: 9 | j: 7 Result(i: 3.000000 | j: 2.645751 | i*j:
> 7.937254): 335.930374
> ITER: 0 | i: 9 | j: 8 Result(i: 3.000000 | j: 2.828427 | i*j:
> 8.485281): 344.415656
> ITER: 0 | i: 9 | j: 9 Result(i: 3.000000 | j: 3.000000 | i*j:
> 9.000000): 353.415656
> Final Result: 353.415656
> ITER: 1 | i: 0 | j: 0 Result(i: 0.000000 | j: 0.000000 | i*j:
> 0.000000): 0.000000
> ITER: 1 | i: 0 | j: 1 Result(i: 0.000000 | j: 1.000000 | i*j:
> 0.000000): 0.000000
> ITER: 1 | i: 0 | j: 2 Result(i: 0.000000 | j: 1.414214 | i*j:
> 0.000000): 0.000000
> ITER: 1 | i: 0 | j: 3 Result(i: 0.000000 | j: 1.732051 | i*j:
> 0.000000): 0.000000
> ITER: 1 | i: 0 | j: 4 Result(i: 0.000000 | j: 2.000000 | i*j:
> 0.000000): 0.000000
> ITER: 1 | i: 0 | j: 5 Result(i: 0.000000 | j: 2.236068 | i*j:
> 0.000000): 0.000000
> ITER: 1 | i: 0 | j: 6 Result(i: 0.000000 | j: 2.449490 | i*j:
> 0.000000): 0.000000
> ITER: 1 | i: 0 | j: 7 Result(i: 0.000000 | j: 2.645751 | i*j:
> 0.000000): 0.000000
> ITER: 1 | i: 0 | j: 8 Result(i: 0.000000 | j: 2.828427 | i*j:
> 0.000000): 0.000000
> ITER: 1 | i: 0 | j: 9 Result(i: 0.000000 | j: 3.000000 | i*j:
> 0.000000): 0.000000
> ITER: 1 | i: 1 | j: 0 Result(i: 1.000000 | j: 0.000000 | i*j:
> 0.000000): 0.000000
> ITER: 1 | i: 1 | j: 1 Result(i: 1.000000 | j: 1.000000 | i*j:
> 1.000000): 1.000000
> ITER: 1 | i: 1 | j: 2 Result(i: 1.000000 | j: 1.414214 | i*j:
> 1.414214): 2.414214
> ITER: 1 | i: 1 | j: 3 Result(i: 1.000000 | j: 1.732051 | i*j:
> 1.732051): 4.146264
> ITER: 1 | i: 1 | j: 4 Result(i: 1.000000 | j: 2.000000 | i*j:
> 2.000000): 6.146264
> ITER: 1 | i: 1 | j: 5 Result(i: 1.000000 | j: 2.236068 | i*j:
> 2.236068): 8.382332
> ITER: 1 | i: 1 | j: 6 Result(i: 1.000000 | j: 2.449490 | i*j:
> 2.449490): 10.831822
> ITER: 1 | i: 1 | j: 7 Result(i: 1.000000 | j: 2.645751 | i*j:
> 2.645751): 13.477573
> ITER: 1 | i: 1 | j: 8 Result(i: 1.000000 | j: 2.828427 | i*j:
> 2.828427): 16.306001
> ITER: 1 | i: 1 | j: 9 Result(i: 1.000000 | j: 3.000000 | i*j:
> 3.000000): 19.306001
> ITER: 1 | i: 2 | j: 0 Result(i: 1.414214 | j: 0.000000 | i*j:
> 0.000000): 19.306001
> ITER: 1 | i: 2 | j: 1 Result(i: 1.414214 | j: 1.000000 | i*j:
> 1.414214): 20.720214
> ITER: 1 | i: 2 | j: 2 Result(i: 1.414214 | j: 1.414214 | i*j:
> 2.000000): 22.720214
> ITER: 1 | i: 2 | j: 3 Result(i: 1.414214 | j: 1.732051 | i*j:
> 2.449490): 25.169704
> ITER: 1 | i: 2 | j: 4 Result(i: 1.414214 | j: 2.000000 | i*j:
> 2.828427): 27.998131
> ITER: 1 | i: 2 | j: 5 Result(i: 1.414214 | j: 2.236068 | i*j:
> 3.162278): 31.160409
> ITER: 1 | i: 2 | j: 6 Result(i: 1.414214 | j: 2.449490 | i*j:
> 3.464102): 34.624510
> ITER: 1 | i: 2 | j: 7 Result(i: 1.414214 | j: 2.645751 | i*j:
> 3.741657): 38.366168
> ITER: 1 | i: 2 | j: 8 Result(i: 1.414214 | j: 2.828427 | i*j:
> 4.000000): 42.366168
> ITER: 1 | i: 2 | j: 9 Result(i: 1.414214 | j: 3.000000 | i*j:
> 4.242641): 46.608808
> ITER: 1 | i: 3 | j: 0 Result(i: 1.732051 | j: 0.000000 | i*j:
> 0.000000): 46.608808
> ITER: 1 | i: 3 | j: 1 Result(i: 1.732051 | j: 1.000000 | i*j:
> 1.732051): 48.340859
> ITER: 1 | i: 3 | j: 2 Result(i: 1.732051 | j: 1.414214 | i*j:
> 2.449490): 50.790349
> ITER: 1 | i: 3 | j: 3 Result(i: 1.732051 | j: 1.732051 | i*j:
> 3.000000): 53.790349
> ITER: 1 | i: 3 | j: 4 Result(i: 1.732051 | j: 2.000000 | i*j:
> 3.464102): 57.254450
> ITER: 1 | i: 3 | j: 5 Result(i: 1.732051 | j: 2.236068 | i*j:
> 3.872983): 61.127434
> ITER: 1 | i: 3 | j: 6 Result(i: 1.732051 | j: 2.449490 | i*j:
> 4.242641): 65.370075
> ITER: 1 | i: 3 | j: 7 Result(i: 1.732051 | j: 2.645751 | i*j:
> 4.582576): 69.952650
> ITER: 1 | i: 3 | j: 8 Result(i: 1.732051 | j: 2.828427 | i*j:
> 4.898979): 74.851630
> ITER: 1 | i: 3 | j: 9 Result(i: 1.732051 | j: 3.000000 | i*j:
> 5.196152): 80.047782
> ITER: 1 | i: 4 | j: 0 Result(i: 2.000000 | j: 0.000000 | i*j:
> 0.000000): 80.047782
> ITER: 1 | i: 4 | j: 1 Result(i: 2.000000 | j: 1.000000 | i*j:
> 2.000000): 82.047782
> ITER: 1 | i: 4 | j: 2 Result(i: 2.000000 | j: 1.414214 | i*j:
> 2.828427): 84.876209
> ITER: 1 | i: 4 | j: 3 Result(i: 2.000000 | j: 1.732051 | i*j:
> 3.464102): 88.340311
> ITER: 1 | i: 4 | j: 4 Result(i: 2.000000 | j: 2.000000 | i*j:
> 4.000000): 92.340311
> ITER: 1 | i: 4 | j: 5 Result(i: 2.000000 | j: 2.236068 | i*j:
> 4.472136): 96.812447
> ITER: 1 | i: 4 | j: 6 Result(i: 2.000000 | j: 2.449490 | i*j:
> 4.898979): 101.711426
> ITER: 1 | i: 4 | j: 7 Result(i: 2.000000 | j: 2.645751 | i*j:
> 5.291503): 107.002929
> ITER: 1 | i: 4 | j: 8 Result(i: 2.000000 | j: 2.828427 | i*j:
> 5.656854): 112.659783
> ITER: 1 | i: 4 | j: 9 Result(i: 2.000000 | j: 3.000000 | i*j:
> 6.000000): 118.659783
> ITER: 1 | i: 5 | j: 0 Result(i: 2.236068 | j: 0.000000 | i*j:
> 0.000000): 118.659783
> ITER: 1 | i: 5 | j: 1 Result(i: 2.236068 | j: 1.000000 | i*j:
> 2.236068): 120.895851
> ITER: 1 | i: 5 | j: 2 Result(i: 2.236068 | j: 1.414214 | i*j:
> 3.162278): 124.058129
> ITER: 1 | i: 5 | j: 3 Result(i: 2.236068 | j: 1.732051 | i*j:
> 3.872983): 127.931112
> ITER: 1 | i: 5 | j: 4 Result(i: 2.236068 | j: 2.000000 | i*j:
> 4.472136): 132.403248
> ITER: 1 | i: 5 | j: 5 Result(i: 2.236068 | j: 2.236068 | i*j:
> 5.000000): 137.403248
> ITER: 1 | i: 5 | j: 6 Result(i: 2.236068 | j: 2.449490 | i*j:
> 5.477226): 142.880474
> ITER: 1 | i: 5 | j: 7 Result(i: 2.236068 | j: 2.645751 | i*j:
> 5.916080): 148.796553
> ITER: 1 | i: 5 | j: 8 Result(i: 2.236068 | j: 2.828427 | i*j:
> 6.324555): 155.121109
> ITER: 1 | i: 5 | j: 9 Result(i: 2.236068 | j: 3.000000 | i*j:
> 6.708204): 161.829313
> ITER: 1 | i: 6 | j: 0 Result(i: 2.449490 | j: 0.000000 | i*j:
> 0.000000): 161.829313
> ITER: 1 | i: 6 | j: 1 Result(i: 2.449490 | j: 1.000000 | i*j:
> 2.449490): 164.278802
> ITER: 1 | i: 6 | j: 2 Result(i: 2.449490 | j: 1.414214 | i*j:
> 3.464102): 167.742904
> ITER: 1 | i: 6 | j: 3 Result(i: 2.449490 | j: 1.732051 | i*j:
> 4.242641): 171.985545
> ITER: 1 | i: 6 | j: 4 Result(i: 2.449490 | j: 2.000000 | i*j:
> 4.898979): 176.884524
> ITER: 1 | i: 6 | j: 5 Result(i: 2.449490 | j: 2.236068 | i*j:
> 5.477226): 182.361750
> ITER: 1 | i: 6 | j: 6 Result(i: 2.449490 | j: 2.449490 | i*j:
> 6.000000): 188.361750
> ITER: 1 | i: 6 | j: 7 Result(i: 2.449490 | j: 2.645751 | i*j:
> 6.480741): 194.842491
> ITER: 1 | i: 6 | j: 8 Result(i: 2.449490 | j: 2.828427 | i*j:
> 6.928203): 201.770694
> ITER: 1 | i: 6 | j: 9 Result(i: 2.449490 | j: 3.000000 | i*j:
> 7.348469): 209.119163
> ITER: 1 | i: 7 | j: 0 Result(i: 2.645751 | j: 0.000000 | i*j:
> 0.000000): 209.119163
> ITER: 1 | i: 7 | j: 1 Result(i: 2.645751 | j: 1.000000 | i*j:
> 2.645751): 211.764914
> ITER: 1 | i: 7 | j: 2 Result(i: 2.645751 | j: 1.414214 | i*j:
> 3.741657): 215.506572
> ITER: 1 | i: 7 | j: 3 Result(i: 2.645751 | j: 1.732051 | i*j:
> 4.582576): 220.089147
> ITER: 1 | i: 7 | j: 4 Result(i: 2.645751 | j: 2.000000 | i*j:
> 5.291503): 225.380650
> ITER: 1 | i: 7 | j: 5 Result(i: 2.645751 | j: 2.236068 | i*j:
> 5.916080): 231.296730
> ITER: 1 | i: 7 | j: 6 Result(i: 2.645751 | j: 2.449490 | i*j:
> 6.480741): 237.777470
> ITER: 1 | i: 7 | j: 7 Result(i: 2.645751 | j: 2.645751 | i*j:
> 7.000000): 244.777470
> ITER: 1 | i: 7 | j: 8 Result(i: 2.645751 | j: 2.828427 | i*j:
> 7.483315): 252.260785
> ITER: 1 | i: 7 | j: 9 Result(i: 2.645751 | j: 3.000000 | i*j:
> 7.937254): 260.198039
> ITER: 1 | i: 8 | j: 0 Result(i: 2.828427 | j: 0.000000 | i*j:
> 0.000000): 260.198039
> ITER: 1 | i: 8 | j: 1 Result(i: 2.828427 | j: 1.000000 | i*j:
> 2.828427): 263.026466
> ITER: 1 | i: 8 | j: 2 Result(i: 2.828427 | j: 1.414214 | i*j:
> 4.000000): 267.026466
> ITER: 1 | i: 8 | j: 3 Result(i: 2.828427 | j: 1.732051 | i*j:
> 4.898979): 271.925446
> ITER: 1 | i: 8 | j: 4 Result(i: 2.828427 | j: 2.000000 | i*j:
> 5.656854): 277.582300
> ITER: 1 | i: 8 | j: 5 Result(i: 2.828427 | j: 2.236068 | i*j:
> 6.324555): 283.906855
> ITER: 1 | i: 8 | j: 6 Result(i: 2.828427 | j: 2.449490 | i*j:
> 6.928203): 290.835059
> ITER: 1 | i: 8 | j: 7 Result(i: 2.828427 | j: 2.645751 | i*j:
> 7.483315): 298.318373
> ITER: 1 | i: 8 | j: 8 Result(i: 2.828427 | j: 2.828427 | i*j:
> 8.000000): 306.318373
> ITER: 1 | i: 8 | j: 9 Result(i: 2.828427 | j: 3.000000 | i*j:
> 8.485281): 314.803655
> ITER: 1 | i: 9 | j: 0 Result(i: 3.000000 | j: 0.000000 | i*j:
> 0.000000): 314.803655
> ITER: 1 | i: 9 | j: 1 Result(i: 3.000000 | j: 1.000000 | i*j:
> 3.000000): 317.803655
> ITER: 1 | i: 9 | j: 2 Result(i: 3.000000 | j: 1.414214 | i*j:
> 4.242641): 322.046295
> ITER: 1 | i: 9 | j: 3 Result(i: 3.000000 | j: 1.732051 | i*j:
> 5.196152): 327.242448
> ITER: 1 | i: 9 | j: 4 Result(i: 3.000000 | j: 2.000000 | i*j:
> 6.000000): 333.242448
> ITER: 1 | i: 9 | j: 5 Result(i: 3.000000 | j: 2.236068 | i*j:
> 6.708204): 339.950652
> ITER: 1 | i: 9 | j: 6 Result(i: 3.000000 | j: 2.449490 | i*j:
> 7.348469): 347.299121
> ITER: 1 | i: 9 | j: 7 Result(i: 3.000000 | j: 2.645751 | i*j:
> 7.937254): 355.236375
> ITER: 1 | i: 9 | j: 8 Result(i: 3.000000 | j: 2.828427 | i*j:
> 8.485281): 363.721656
> ITER: 1 | i: 9 | j: 9 Result(i: 3.000000 | j: 3.000000 | i*j:
> 9.000000): 372.721656
> Final Result: 372.721656
>
>
>
> As we can see in the following iterations the sqrt(1) as well as the
> result is set to zero for some reason.
>
> ITER: 0 | i: 1 | j: 4 Result(i: 0.000000 | j: 2.000000 | i*j:
> 0.000000): 0.000000
> ITER: 0 | i: 1 | j: 5 Result(i: 0.000000 | j: 2.236068 | i*j:
> 0.000000): 0.000000
> ITER: 0 | i: 1 | j: 6 Result(i: 0.000000 | j: 2.449490 | i*j:
> 0.000000): 0.000000
> ITER: 0 | i: 1 | j: 7 Result(i: 0.000000 | j: 2.645751 | i*j:
> 0.000000): 0.000000
> ITER: 0 | i: 1 | j: 8 Result(i: 0.000000 | j: 2.828427 | i*j:
> 0.000000): 0.000000
> ITER: 0 | i: 1 | j: 9 Result(i: 0.000000 | j: 3.000000 | i*j:
> 0.000000): 0.000000
>
> Please help me to resolve the accuracy issue! I think that it will be
> very useful for gem5 community.
>
> To be noticed, I find the correct simulated tick in which the
> application started in FS (using m5 dumpstats), and I start the
> --debug-start, but the trace file which is generated is 10x larger
> than SE mode for the same application. How can I compare them?
>
> Thank you in advance!
> Best regards,
> Nikos
>
> Quoting Νικόλαος Ταμπουρατζής <ntampouratzis@ece.auth.gr>:
>
> > Dear Jason,
> >
> > I am trying to use --debug-start but in FS mode it is very difficult
> > to find the tick on which the application is started!
> >
> > However, I am writing the following very simple c++ program:
> >
> > #include <cmath>
> > #include <stdio.h>
> >
> > int main(){
> >
> > int dim = 4096;
> >
> > double result;
> >
> > for (int iter = 0; iter < 2; iter++){
> > result = 0;
> > for (int i = 0; i < dim; i++){
> > for (int j = 0; j < dim; j++){
> > result += sqrt(i) * sqrt(j);
> > }
> > }
> > printf("Result: %lf\n", result); //Result: 30530733453.127449
> > }
> > }
> >
> > I cross-compile it using: riscv64-linux-gnu-g++ -static -O3 -o
> > test_riscv test_riscv.cpp
> >
> >
> > While in X86 (without cross-compilation of course), QEMU-RISCV,
> > GEM5-SE the result is the same (30530733453.127449), in GEM5-FS the
> > result is different! In addition, the result is also different
> > between the 2 iterations.
> >
> > Please reproduce the error if you want in order to verify my result.
> > Ηow can the issue be resolved?
> >
> > Thank you in advance!
> >
> > Best regards,
> > Nikos
> >
> >
> > Quoting Jason Lowe-Power <jason@lowepower.com>:
> >
> >> Hi Nikos,
> >>
> >> You can use --debug-start to start the debugging after some number of
> >> ticks. Also, I would expect that the difference should come up quickly,
> so
> >> no need to run the program to the end.
> >>
> >> For the FS mode one, you will want to just start the trace as the
> >> application starts. This could be a bit of a pain.
> >>
> >> I'm not really sure what fundamentally could be different. FS and SE
> mode
> >> use the exact same code for executing instructions, so I don't think
> that's
> >> the problem. Have you tried running for smaller inputs or just one
> >> iteration?
> >>
> >> Jason
> >>
> >>
> >>
> >> On Wed, Sep 21, 2022 at 9:04 AM Νικόλαος Ταμπουρατζής <
> >> ntampouratzis@ece.auth.gr> wrote:
> >>
> >>> Dear Bobby,
> >>>
> >>> Iam trying to add --debug-flags=Exec (building the gem5 for gem5.opt
> >>> not for gem5.fast which I had) but the debug traces exceed the 20GB
> >>> (and it is not finished yet) for less than 1 simulated second. How can
> >>> I reduce the size of the debug-flags (or set something more specific)?
> >>>
> >>> In contrast I build the HPCG benchmark with DHPCG_DEBUG flag. If you
> >>> want, you can compare these two output files
> >>> (hpcg20010909T014640_SE_Mode & HPCG-Benchmark_3.1_FS_Mode). As you can
> >>> see, something goes wrong with the accuracy of calculations in FS mode
> >>> (benchmark uses double precission). You can find the files here:
> >>> http://kition.mhl.tuc.gr:8000/d/68d82f3533/
> >>>
> >>> Best regards,
> >>> Nikos
> >>>
> >>> Quoting Jason Lowe-Power <jason@lowepower.com>:
> >>>
> >>>> That's quite odd that it works in SE mode but not FS mode!
> >>>>
> >>>> I would suggest running with --debug-flags=Exec for both and then
> >>> perform a
> >>>> diff to see how they differ.
> >>>>
> >>>> Cheers,
> >>>> Jason
> >>>>
> >>>> On Tue, Sep 20, 2022 at 2:45 PM Νικόλαος Ταμπουρατζής <
> >>>> ntampouratzis@ece.auth.gr> wrote:
> >>>>
> >>>>> Dear Bobby,
> >>>>>
> >>>>> In QEMU I get the same (correct) results that I get in SE mode
> >>>>> simulation. I get invalid results in FS simulation (in both
> >>>>> riscv-fs.py and riscv-ubuntu-run.py). I cannot access real RISCV
> >>>>> hardware at this moment, however, if you want you may execute my
> xhpcg
> >>>>> binary (http://kition.mhl.tuc.gr:8000/f/4ca25fdd3c/) with the
> >>>>> following configuration:
> >>>>>
> >>>>> ./xhpcg --nx=16 --ny=16 --nz=16 --npx=1 --npy=1 --npz=1 --rt=0.1
> >>>>>
> >>>>> Please let me know if you have any updates!
> >>>>>
> >>>>> Best regards,
> >>>>> Nikos
> >>>>>
> >>>>>
> >>>>> Quoting Jason Lowe-Power <jason@lowepower.com>:
> >>>>>
> >>>>> > Hi Nikos,
> >>>>> >
> >>>>> > I notice you said the following in your original email:
> >>>>> >
> >>>>> > In addition, I used the RISCV Ubuntu image
> >>>>> >> (
> https://github.com/gem5/gem5-resources/tree/stable/src/riscv-ubuntu
> >>> ),
> >>>>> >> I installed the gcc compiler, compile it (through qemu) and I get
> >>>>> >> wrong results too.
> >>>>> >
> >>>>> >
> >>>>> > Is this saying you get the wrong results is QEMU? If so, the bug
> is in
> >>>>> GCC
> >>>>> > or the HPCG workload, not in gem5. If not, I would test in QEMU to
> >>> make
> >>>>> > sure the binary works there. Another way you could test to see if
> the
> >>>>> > problem is your binary or gem5 would be to run it on real
> hardware. We
> >>>>> have
> >>>>> > access to some RISC-V hardware here at UC Davis, if you don't have
> >>> access
> >>>>> > to it.
> >>>>> >
> >>>>> > Cheers,
> >>>>> > Jason
> >>>>> >
> >>>>> > On Tue, Sep 20, 2022 at 12:58 AM Νικόλαος Ταμπουρατζής <
> >>>>> > ntampouratzis@ece.auth.gr> wrote:
> >>>>> >
> >>>>> >> Dear Bobby,
> >>>>> >>
> >>>>> >> 1) I use the original riscv-fs.py which is provided in the latest
> >>> gem5
> >>>>> >> release.
> >>>>> >> I run the gem5 once (./build/RISCV/gem5.fast -d ./HPCG_FS_results
> >>>>> >> ./configs/example/gem5_library/riscv-fs.py) in order to download
> the
> >>>>> >> riscv-bootloader-vmlinux-5.10 and riscv-disk-img.
> >>>>> >> After this I mount the riscv-disk-img (sudo mount -o loop
> >>>>> >> riscv-disk-img /mnt), put the xhpcg executable and I do the
> following
> >>>>> >> changes in riscv-fs.py to boot the riscv-disk-img with executable:
> >>>>> >>
> >>>>> >> image = CustomDiskImageResource(
> >>>>> >> local_path = "/home/cossim/.cache/gem5/riscv-disk-img",
> >>>>> >> )
> >>>>> >>
> >>>>> >> # Set the Full System workload.
> >>>>> >> board.set_kernel_disk_workload(
> >>>>> >>
> kernel=Resource("riscv-bootloader-vmlinux-5.10"),
> >>>>> >> disk_image=image,
> >>>>> >> )
> >>>>> >>
> >>>>> >> Finally, in the
> gem5/src/python/gem5/components/boards/riscv_board.py
> >>>>> >> I change the last line to "return ["console=ttyS0",
> >>>>> >> "root={root_value}", "rw"]" in order to allow the write
> permissions
> >>> in
> >>>>> >> the image.
> >>>>> >>
> >>>>> >>
> >>>>> >> 2) The HPCG benchmark after some iterations calculates if the
> results
> >>>>> >> are valid or not valid. In the case of FS it gives invalid
> results.
> >>> As
> >>>>> >> I see from the results, one (at least) problem is that produces
> >>>>> >> different results in each HPCG execution (with the same
> >>> configuration).
> >>>>> >>
> >>>>> >> Here is the HPCG output and riscv-fs.py
> >>>>> >> (http://kition.mhl.tuc.gr:8000/d/68d82f3533/). You may reproduce
> the
> >>>>> >> results in the video if you use the xhpcg executable
> >>>>> >> (http://kition.mhl.tuc.gr:8000/f/4ca25fdd3c/)
> >>>>> >>
> >>>>> >> Please help me in order to solve it!
> >>>>> >>
> >>>>> >> Finally, I get invalid results in the HPL benchmark in FS mode
> too.
> >>>>> >>
> >>>>> >> Best regards,
> >>>>> >> Nikos
> >>>>> >>
> >>>>> >>
> >>>>> >> Quoting Bobby Bruce <bbruce@ucdavis.edu>:
> >>>>> >>
> >>>>> >> > I'm going to need a bit more information to help:
> >>>>> >> >
> >>>>> >> > 1. In what way have you modified
> >>>>> >> > ./configs/example/gem5_library/riscv-fs.py? Can you attach the
> >>> script
> >>>>> >> here?
> >>>>> >> > 2. What error are you getting or in what way are the results
> >>> invalid?
> >>>>> >> >
> >>>>> >> > -
> >>>>> >> > Dr. Bobby R. Bruce
> >>>>> >> > Room 3050,
> >>>>> >> > Kemper Hall, UC Davis
> >>>>> >> > Davis,
> >>>>> >> > CA, 95616
> >>>>> >> >
> >>>>> >> > web: https://www.bobbybruce.net
> >>>>> >> >
> >>>>> >> >
> >>>>> >> > On Mon, Sep 19, 2022 at 1:43 PM Νικόλαος Ταμπουρατζής <
> >>>>> >> > ntampouratzis@ece.auth.gr> wrote:
> >>>>> >> >
> >>>>> >> >>
> >>>>> >> >> Dear gem5 community,
> >>>>> >> >>
> >>>>> >> >> I have successfully cross-compile the HPCG benchmark for RISCV
> >>>>> (Serial
> >>>>> >> >> version, without MPI and OpenMP). While it working properly in
> >>> gem5
> >>>>> SE
> >>>>> >> >> mode (./build/RISCV/gem5.fast -d ./HPCG_SE_results
> >>>>> >> >> ./configs/example/se.py -c xhpcg --options '--nx=16 --ny=16
> >>> --nz=16
> >>>>> >> >> --npx=1 --npy=1 --npz=1 --rt=0.1'), I get invalid results in FS
> >>>>> >> >> simulation using "./build/RISCV/gem5.fast -d ./HPCG_FS_results
> >>>>> >> >> ./configs/example/gem5_library/riscv-fs.py" (I mount the riscv
> >>> image
> >>>>> >> >> and put it).
> >>>>> >> >>
> >>>>> >> >> Can you help me please?
> >>>>> >> >>
> >>>>> >> >> In addition, I used the RISCV Ubuntu image
> >>>>> >> >> (
> >>> https://github.com/gem5/gem5-resources/tree/stable/src/riscv-ubuntu
> >>>>> ),
> >>>>> >> >> I installed the gcc compiler, compile it (through qemu) and I
> get
> >>>>> >> >> wrong results too.
> >>>>> >> >>
> >>>>> >> >> Here is the Makefile which I use, the hpcg executable for RISCV
> >>>>> >> >> (xhpcg), and a video that shows the results
> >>>>> >> >> (http://kition.mhl.tuc.gr:8000/f/4ca25fdd3c/).
> >>>>> >> >>
> >>>>> >> >> P.S. I use the latest gem5 version.
> >>>>> >> >>
> >>>>> >> >> Thank you in advance! :)
> >>>>> >> >>
> >>>>> >> >> Best regards,
> >>>>> >> >> Nikos
> >>>>> >> >> _______________________________________________
> >>>>> >> >> gem5-users mailing list -- gem5-users@gem5.org
> >>>>> >> >> To unsubscribe send an email to gem5-users-leave@gem5.org
> >>>>> >> >>
> >>>>> >>
> >>>>> >>
> >>>>> >> _______________________________________________
> >>>>> >> gem5-users mailing list -- gem5-users@gem5.org
> >>>>> >> To unsubscribe send an email to gem5-users-leave@gem5.org
> >>>>> >>
> >>>>>
> >>>>>
> >>>>> _______________________________________________
> >>>>> gem5-users mailing list -- gem5-users@gem5.org
> >>>>> To unsubscribe send an email to gem5-users-leave@gem5.org
> >>>>>
> >>>
> >>>
> >>> _______________________________________________
> >>> gem5-users mailing list -- gem5-users@gem5.org
> >>> To unsubscribe send an email to gem5-users-leave@gem5.org
> >>>
> >
> >
> > _______________________________________________
> > gem5-users mailing list -- gem5-users@gem5.org
> > To unsubscribe send an email to gem5-users-leave@gem5.org
>
>
> _______________________________________________
> gem5-users mailing list -- gem5-users@gem5.org
> To unsubscribe send an email to gem5-users-leave@gem5.org
>
Sat, Oct 8, 2022 10:20 AM
Dear Hoa, all
I have ported successfully HPCG and many simple examples using gem5
ARM-FS and they are working properly. The problem is only in RISCV-FS
using double and float variables. Which option of objdump to use?
Best regards,
Nikos
Quoting Hoa Nguyen hoanguyen@ucdavis.edu:
Hi,
It's quite odd that both sqrt_i and result were zeroed out at the same
time. Does the problem appear in other ISA FS mode, e.g. x86 FS mode? Can
you show the objdump of the loop as well?
Regards,
Hoa Nguyen
On Thu, Oct 6, 2022, 04:06 Νικόλαος Ταμπουρατζής ntampouratzis@ece.auth.gr
wrote:
Dear Jason, all,
I am trying to find the accuracy problem with RISCV-FS and I observe
that the problem is created (at least in my dummy example) because the
variables (double) are set to zero in random simulated time (for this
reason I get different results among executions of the same code).
Specifically for the following dummy code:
#include <cmath>
#include <stdio.h>
int main(){
int dim = 10;
float result;
for (int iter = 0; iter < 2; iter++){
result = 0;
for (int i = 0; i < dim; i++){
for (int j = 0; j < dim; j++){
float sq_i = sqrt(i);
float sq_j = sqrt(j);
result += sq_i * sq_j;
printf("ITER: %d | i: %d | j: %d Result(i: %f | j: %f
| i*j: %f): %f\n", iter, i , j, sq_i, sq_j, sq_i * sq_j, result);
}
}
printf("Final Result: %lf\n", result);
}
}
The correct Final Result in both iterations is 372.721656. However, I
get the following results in FS:
ITER: 0 | i: 0 | j: 0 Result(i: 0.000000 | j: 0.000000 | ij:
0.000000): 0.000000
ITER: 0 | i: 0 | j: 1 Result(i: 0.000000 | j: 1.000000 | ij:
0.000000): 0.000000
ITER: 0 | i: 0 | j: 2 Result(i: 0.000000 | j: 1.414214 | ij:
0.000000): 0.000000
ITER: 0 | i: 0 | j: 3 Result(i: 0.000000 | j: 1.732051 | ij:
0.000000): 0.000000
ITER: 0 | i: 0 | j: 4 Result(i: 0.000000 | j: 2.000000 | ij:
0.000000): 0.000000
ITER: 0 | i: 0 | j: 5 Result(i: 0.000000 | j: 2.236068 | ij:
0.000000): 0.000000
ITER: 0 | i: 0 | j: 6 Result(i: 0.000000 | j: 2.449490 | ij:
0.000000): 0.000000
ITER: 0 | i: 0 | j: 7 Result(i: 0.000000 | j: 2.645751 | ij:
0.000000): 0.000000
ITER: 0 | i: 0 | j: 8 Result(i: 0.000000 | j: 2.828427 | ij:
0.000000): 0.000000
ITER: 0 | i: 0 | j: 9 Result(i: 0.000000 | j: 3.000000 | ij:
0.000000): 0.000000
ITER: 0 | i: 1 | j: 0 Result(i: 1.000000 | j: 0.000000 | ij:
0.000000): 0.000000
ITER: 0 | i: 1 | j: 1 Result(i: 1.000000 | j: 1.000000 | ij:
1.000000): 1.000000
ITER: 0 | i: 1 | j: 2 Result(i: 1.000000 | j: 1.414214 | ij:
1.414214): 2.414214
ITER: 0 | i: 1 | j: 3 Result(i: 1.000000 | j: 1.732051 | ij:
1.732051): 4.146264
ITER: 0 | i: 1 | j: 4 Result(i: 0.000000 | j: 2.000000 | ij:
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ITER: 0 | i: 1 | j: 7 Result(i: 0.000000 | j: 2.645751 | ij:
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ITER: 0 | i: 1 | j: 8 Result(i: 0.000000 | j: 2.828427 | ij:
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ITER: 0 | i: 1 | j: 9 Result(i: 0.000000 | j: 3.000000 | ij:
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ITER: 0 | i: 2 | j: 1 Result(i: 1.414214 | j: 1.000000 | ij:
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ITER: 0 | i: 2 | j: 2 Result(i: 1.414214 | j: 1.414214 | ij:
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ITER: 0 | i: 2 | j: 3 Result(i: 1.414214 | j: 1.732051 | ij:
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ITER: 0 | i: 2 | j: 4 Result(i: 1.414214 | j: 2.000000 | ij:
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ITER: 0 | i: 2 | j: 5 Result(i: 1.414214 | j: 2.236068 | ij:
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ITER: 0 | i: 2 | j: 6 Result(i: 1.414214 | j: 2.449490 | ij:
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ITER: 0 | i: 2 | j: 7 Result(i: 1.414214 | j: 2.645751 | ij:
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ITER: 0 | i: 2 | j: 8 Result(i: 1.414214 | j: 2.828427 | ij:
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ITER: 0 | i: 2 | j: 9 Result(i: 1.414214 | j: 3.000000 | ij:
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ITER: 0 | i: 3 | j: 0 Result(i: 1.732051 | j: 0.000000 | ij:
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ITER: 0 | i: 3 | j: 1 Result(i: 1.732051 | j: 1.000000 | ij:
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ITER: 0 | i: 3 | j: 2 Result(i: 1.732051 | j: 1.414214 | ij:
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ITER: 0 | i: 3 | j: 3 Result(i: 1.732051 | j: 1.732051 | ij:
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ITER: 0 | i: 3 | j: 4 Result(i: 1.732051 | j: 2.000000 | ij:
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ITER: 0 | i: 3 | j: 5 Result(i: 1.732051 | j: 2.236068 | ij:
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ITER: 0 | i: 3 | j: 6 Result(i: 1.732051 | j: 2.449490 | ij:
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ITER: 0 | i: 3 | j: 7 Result(i: 1.732051 | j: 2.645751 | ij:
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ITER: 0 | i: 3 | j: 8 Result(i: 1.732051 | j: 2.828427 | ij:
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ITER: 0 | i: 3 | j: 9 Result(i: 1.732051 | j: 3.000000 | ij:
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ITER: 0 | i: 4 | j: 0 Result(i: 2.000000 | j: 0.000000 | ij:
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ITER: 0 | i: 4 | j: 1 Result(i: 2.000000 | j: 1.000000 | ij:
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ITER: 0 | i: 4 | j: 2 Result(i: 2.000000 | j: 1.414214 | ij:
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ITER: 0 | i: 4 | j: 3 Result(i: 2.000000 | j: 1.732051 | ij:
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ITER: 0 | i: 4 | j: 4 Result(i: 2.000000 | j: 2.000000 | ij:
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ITER: 0 | i: 4 | j: 5 Result(i: 2.000000 | j: 2.236068 | ij:
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ITER: 0 | i: 4 | j: 6 Result(i: 2.000000 | j: 2.449490 | ij:
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ITER: 0 | i: 4 | j: 7 Result(i: 2.000000 | j: 2.645751 | ij:
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ITER: 0 | i: 4 | j: 8 Result(i: 2.000000 | j: 2.828427 | ij:
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ITER: 0 | i: 4 | j: 9 Result(i: 2.000000 | j: 3.000000 | ij:
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ITER: 0 | i: 5 | j: 0 Result(i: 2.236068 | j: 0.000000 | ij:
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ITER: 0 | i: 5 | j: 1 Result(i: 2.236068 | j: 1.000000 | ij:
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ITER: 0 | i: 5 | j: 2 Result(i: 2.236068 | j: 1.414214 | ij:
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ITER: 0 | i: 5 | j: 3 Result(i: 2.236068 | j: 1.732051 | ij:
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ITER: 0 | i: 5 | j: 4 Result(i: 2.236068 | j: 2.000000 | ij:
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ITER: 0 | i: 5 | j: 5 Result(i: 2.236068 | j: 2.236068 | ij:
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ITER: 0 | i: 5 | j: 6 Result(i: 2.236068 | j: 2.449490 | ij:
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ITER: 0 | i: 5 | j: 7 Result(i: 2.236068 | j: 2.645751 | ij:
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ITER: 0 | i: 5 | j: 8 Result(i: 2.236068 | j: 2.828427 | ij:
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ITER: 0 | i: 5 | j: 9 Result(i: 2.236068 | j: 3.000000 | ij:
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ITER: 0 | i: 6 | j: 0 Result(i: 2.449490 | j: 0.000000 | ij:
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ITER: 0 | i: 6 | j: 1 Result(i: 2.449490 | j: 1.000000 | ij:
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ITER: 0 | i: 6 | j: 2 Result(i: 2.449490 | j: 1.414214 | ij:
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ITER: 0 | i: 6 | j: 3 Result(i: 2.449490 | j: 1.732051 | ij:
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ITER: 0 | i: 6 | j: 4 Result(i: 2.449490 | j: 2.000000 | ij:
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ITER: 0 | i: 6 | j: 5 Result(i: 2.449490 | j: 2.236068 | ij:
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ITER: 0 | i: 6 | j: 6 Result(i: 2.449490 | j: 2.449490 | ij:
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ITER: 0 | i: 6 | j: 7 Result(i: 2.449490 | j: 2.645751 | ij:
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ITER: 0 | i: 6 | j: 8 Result(i: 2.449490 | j: 2.828427 | ij:
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ITER: 0 | i: 6 | j: 9 Result(i: 2.449490 | j: 3.000000 | ij:
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ITER: 0 | i: 7 | j: 0 Result(i: 2.645751 | j: 0.000000 | ij:
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ITER: 0 | i: 7 | j: 1 Result(i: 2.645751 | j: 1.000000 | ij:
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ITER: 0 | i: 7 | j: 2 Result(i: 2.645751 | j: 1.414214 | ij:
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ITER: 0 | i: 7 | j: 3 Result(i: 2.645751 | j: 1.732051 | ij:
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ITER: 0 | i: 7 | j: 4 Result(i: 2.645751 | j: 2.000000 | ij:
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ITER: 0 | i: 7 | j: 5 Result(i: 2.645751 | j: 2.236068 | ij:
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ITER: 0 | i: 7 | j: 6 Result(i: 2.645751 | j: 2.449490 | ij:
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ITER: 0 | i: 7 | j: 7 Result(i: 2.645751 | j: 2.645751 | ij:
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ITER: 0 | i: 7 | j: 8 Result(i: 2.645751 | j: 2.828427 | ij:
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ITER: 0 | i: 7 | j: 9 Result(i: 2.645751 | j: 3.000000 | ij:
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ITER: 0 | i: 8 | j: 0 Result(i: 2.828427 | j: 0.000000 | ij:
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ITER: 0 | i: 8 | j: 1 Result(i: 2.828427 | j: 1.000000 | ij:
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ITER: 0 | i: 8 | j: 2 Result(i: 2.828427 | j: 1.414214 | ij:
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ITER: 0 | i: 8 | j: 3 Result(i: 2.828427 | j: 1.732051 | ij:
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ITER: 0 | i: 8 | j: 4 Result(i: 2.828427 | j: 2.000000 | ij:
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ITER: 0 | i: 8 | j: 5 Result(i: 2.828427 | j: 2.236068 | ij:
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ITER: 0 | i: 8 | j: 6 Result(i: 2.828427 | j: 2.449490 | ij:
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ITER: 0 | i: 8 | j: 7 Result(i: 2.828427 | j: 2.645751 | ij:
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ITER: 0 | i: 8 | j: 8 Result(i: 2.828427 | j: 2.828427 | ij:
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ITER: 0 | i: 8 | j: 9 Result(i: 2.828427 | j: 3.000000 | ij:
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ITER: 0 | i: 9 | j: 0 Result(i: 3.000000 | j: 0.000000 | ij:
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ITER: 0 | i: 9 | j: 1 Result(i: 3.000000 | j: 1.000000 | ij:
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ITER: 0 | i: 9 | j: 2 Result(i: 3.000000 | j: 1.414214 | ij:
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ITER: 0 | i: 9 | j: 3 Result(i: 3.000000 | j: 1.732051 | ij:
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ITER: 0 | i: 9 | j: 4 Result(i: 3.000000 | j: 2.000000 | ij:
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ITER: 0 | i: 9 | j: 5 Result(i: 3.000000 | j: 2.236068 | ij:
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ITER: 0 | i: 9 | j: 6 Result(i: 3.000000 | j: 2.449490 | ij:
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ITER: 0 | i: 9 | j: 7 Result(i: 3.000000 | j: 2.645751 | ij:
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ITER: 0 | i: 9 | j: 8 Result(i: 3.000000 | j: 2.828427 | ij:
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ITER: 0 | i: 9 | j: 9 Result(i: 3.000000 | j: 3.000000 | ij:
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ITER: 1 | i: 1 | j: 4 Result(i: 1.000000 | j: 2.000000 | ij:
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ITER: 1 | i: 1 | j: 5 Result(i: 1.000000 | j: 2.236068 | ij:
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ITER: 1 | i: 1 | j: 6 Result(i: 1.000000 | j: 2.449490 | ij:
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ITER: 1 | i: 1 | j: 7 Result(i: 1.000000 | j: 2.645751 | ij:
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ITER: 1 | i: 1 | j: 8 Result(i: 1.000000 | j: 2.828427 | ij:
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ITER: 1 | i: 1 | j: 9 Result(i: 1.000000 | j: 3.000000 | ij:
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ITER: 1 | i: 2 | j: 0 Result(i: 1.414214 | j: 0.000000 | ij:
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ITER: 1 | i: 2 | j: 1 Result(i: 1.414214 | j: 1.000000 | ij:
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ITER: 1 | i: 2 | j: 2 Result(i: 1.414214 | j: 1.414214 | ij:
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ITER: 1 | i: 2 | j: 3 Result(i: 1.414214 | j: 1.732051 | ij:
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ITER: 1 | i: 2 | j: 4 Result(i: 1.414214 | j: 2.000000 | ij:
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ITER: 1 | i: 2 | j: 5 Result(i: 1.414214 | j: 2.236068 | ij:
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ITER: 1 | i: 2 | j: 6 Result(i: 1.414214 | j: 2.449490 | ij:
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ITER: 1 | i: 2 | j: 7 Result(i: 1.414214 | j: 2.645751 | ij:
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ITER: 1 | i: 2 | j: 8 Result(i: 1.414214 | j: 2.828427 | ij:
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ITER: 1 | i: 2 | j: 9 Result(i: 1.414214 | j: 3.000000 | ij:
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ITER: 1 | i: 3 | j: 0 Result(i: 1.732051 | j: 0.000000 | ij:
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ITER: 1 | i: 3 | j: 1 Result(i: 1.732051 | j: 1.000000 | ij:
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ITER: 1 | i: 3 | j: 2 Result(i: 1.732051 | j: 1.414214 | ij:
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ITER: 1 | i: 3 | j: 3 Result(i: 1.732051 | j: 1.732051 | ij:
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ITER: 1 | i: 3 | j: 4 Result(i: 1.732051 | j: 2.000000 | ij:
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ITER: 1 | i: 3 | j: 5 Result(i: 1.732051 | j: 2.236068 | ij:
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ITER: 1 | i: 3 | j: 6 Result(i: 1.732051 | j: 2.449490 | ij:
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ITER: 1 | i: 3 | j: 7 Result(i: 1.732051 | j: 2.645751 | ij:
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ITER: 1 | i: 3 | j: 8 Result(i: 1.732051 | j: 2.828427 | ij:
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ITER: 1 | i: 3 | j: 9 Result(i: 1.732051 | j: 3.000000 | ij:
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ITER: 1 | i: 4 | j: 1 Result(i: 2.000000 | j: 1.000000 | ij:
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ITER: 1 | i: 4 | j: 2 Result(i: 2.000000 | j: 1.414214 | ij:
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ITER: 1 | i: 4 | j: 3 Result(i: 2.000000 | j: 1.732051 | ij:
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ITER: 1 | i: 4 | j: 4 Result(i: 2.000000 | j: 2.000000 | ij:
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ITER: 1 | i: 4 | j: 5 Result(i: 2.000000 | j: 2.236068 | ij:
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ITER: 1 | i: 4 | j: 6 Result(i: 2.000000 | j: 2.449490 | ij:
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ITER: 1 | i: 4 | j: 7 Result(i: 2.000000 | j: 2.645751 | ij:
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ITER: 1 | i: 4 | j: 8 Result(i: 2.000000 | j: 2.828427 | ij:
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ITER: 1 | i: 4 | j: 9 Result(i: 2.000000 | j: 3.000000 | ij:
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ITER: 1 | i: 5 | j: 0 Result(i: 2.236068 | j: 0.000000 | ij:
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ITER: 1 | i: 5 | j: 1 Result(i: 2.236068 | j: 1.000000 | ij:
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ITER: 1 | i: 5 | j: 2 Result(i: 2.236068 | j: 1.414214 | ij:
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ITER: 1 | i: 5 | j: 3 Result(i: 2.236068 | j: 1.732051 | ij:
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ITER: 1 | i: 5 | j: 4 Result(i: 2.236068 | j: 2.000000 | ij:
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ITER: 1 | i: 5 | j: 5 Result(i: 2.236068 | j: 2.236068 | ij:
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ITER: 1 | i: 5 | j: 6 Result(i: 2.236068 | j: 2.449490 | ij:
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ITER: 1 | i: 5 | j: 7 Result(i: 2.236068 | j: 2.645751 | ij:
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ITER: 1 | i: 5 | j: 8 Result(i: 2.236068 | j: 2.828427 | ij:
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ITER: 1 | i: 5 | j: 9 Result(i: 2.236068 | j: 3.000000 | ij:
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ITER: 1 | i: 6 | j: 0 Result(i: 2.449490 | j: 0.000000 | ij:
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ITER: 1 | i: 6 | j: 1 Result(i: 2.449490 | j: 1.000000 | ij:
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ITER: 1 | i: 6 | j: 2 Result(i: 2.449490 | j: 1.414214 | ij:
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ITER: 1 | i: 6 | j: 3 Result(i: 2.449490 | j: 1.732051 | ij:
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ITER: 1 | i: 6 | j: 4 Result(i: 2.449490 | j: 2.000000 | ij:
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ITER: 1 | i: 6 | j: 5 Result(i: 2.449490 | j: 2.236068 | ij:
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ITER: 1 | i: 6 | j: 6 Result(i: 2.449490 | j: 2.449490 | ij:
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ITER: 1 | i: 6 | j: 7 Result(i: 2.449490 | j: 2.645751 | ij:
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ITER: 1 | i: 6 | j: 8 Result(i: 2.449490 | j: 2.828427 | ij:
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ITER: 1 | i: 6 | j: 9 Result(i: 2.449490 | j: 3.000000 | ij:
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ITER: 1 | i: 7 | j: 0 Result(i: 2.645751 | j: 0.000000 | ij:
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ITER: 1 | i: 7 | j: 1 Result(i: 2.645751 | j: 1.000000 | ij:
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ITER: 1 | i: 7 | j: 2 Result(i: 2.645751 | j: 1.414214 | ij:
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ITER: 1 | i: 7 | j: 3 Result(i: 2.645751 | j: 1.732051 | ij:
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ITER: 1 | i: 7 | j: 4 Result(i: 2.645751 | j: 2.000000 | ij:
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ITER: 1 | i: 7 | j: 5 Result(i: 2.645751 | j: 2.236068 | ij:
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ITER: 1 | i: 7 | j: 6 Result(i: 2.645751 | j: 2.449490 | ij:
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ITER: 1 | i: 7 | j: 7 Result(i: 2.645751 | j: 2.645751 | ij:
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ITER: 1 | i: 7 | j: 8 Result(i: 2.645751 | j: 2.828427 | ij:
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ITER: 1 | i: 7 | j: 9 Result(i: 2.645751 | j: 3.000000 | ij:
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ITER: 1 | i: 8 | j: 0 Result(i: 2.828427 | j: 0.000000 | ij:
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ITER: 1 | i: 8 | j: 1 Result(i: 2.828427 | j: 1.000000 | ij:
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ITER: 1 | i: 8 | j: 2 Result(i: 2.828427 | j: 1.414214 | ij:
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ITER: 1 | i: 8 | j: 3 Result(i: 2.828427 | j: 1.732051 | ij:
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ITER: 1 | i: 8 | j: 4 Result(i: 2.828427 | j: 2.000000 | ij:
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ITER: 1 | i: 8 | j: 5 Result(i: 2.828427 | j: 2.236068 | ij:
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ITER: 1 | i: 8 | j: 6 Result(i: 2.828427 | j: 2.449490 | ij:
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ITER: 1 | i: 8 | j: 7 Result(i: 2.828427 | j: 2.645751 | ij:
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ITER: 1 | i: 8 | j: 8 Result(i: 2.828427 | j: 2.828427 | ij:
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ITER: 1 | i: 8 | j: 9 Result(i: 2.828427 | j: 3.000000 | ij:
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ITER: 1 | i: 9 | j: 0 Result(i: 3.000000 | j: 0.000000 | ij:
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ITER: 1 | i: 9 | j: 1 Result(i: 3.000000 | j: 1.000000 | ij:
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ITER: 1 | i: 9 | j: 2 Result(i: 3.000000 | j: 1.414214 | ij:
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ITER: 1 | i: 9 | j: 3 Result(i: 3.000000 | j: 1.732051 | ij:
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ITER: 1 | i: 9 | j: 4 Result(i: 3.000000 | j: 2.000000 | ij:
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ITER: 1 | i: 9 | j: 5 Result(i: 3.000000 | j: 2.236068 | ij:
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ITER: 1 | i: 9 | j: 6 Result(i: 3.000000 | j: 2.449490 | ij:
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ITER: 1 | i: 9 | j: 7 Result(i: 3.000000 | j: 2.645751 | ij:
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ITER: 1 | i: 9 | j: 8 Result(i: 3.000000 | j: 2.828427 | ij:
8.485281): 363.721656
ITER: 1 | i: 9 | j: 9 Result(i: 3.000000 | j: 3.000000 | ij:
9.000000): 372.721656
Final Result: 372.721656
As we can see in the following iterations the sqrt(1) as well as the
result is set to zero for some reason.
ITER: 0 | i: 1 | j: 4 Result(i: 0.000000 | j: 2.000000 | ij:
0.000000): 0.000000
ITER: 0 | i: 1 | j: 5 Result(i: 0.000000 | j: 2.236068 | ij:
0.000000): 0.000000
ITER: 0 | i: 1 | j: 6 Result(i: 0.000000 | j: 2.449490 | ij:
0.000000): 0.000000
ITER: 0 | i: 1 | j: 7 Result(i: 0.000000 | j: 2.645751 | ij:
0.000000): 0.000000
ITER: 0 | i: 1 | j: 8 Result(i: 0.000000 | j: 2.828427 | ij:
0.000000): 0.000000
ITER: 0 | i: 1 | j: 9 Result(i: 0.000000 | j: 3.000000 | ij:
0.000000): 0.000000
Please help me to resolve the accuracy issue! I think that it will be
very useful for gem5 community.
To be noticed, I find the correct simulated tick in which the
application started in FS (using m5 dumpstats), and I start the
--debug-start, but the trace file which is generated is 10x larger
than SE mode for the same application. How can I compare them?
Thank you in advance!
Best regards,
Nikos
Quoting Νικόλαος Ταμπουρατζής ntampouratzis@ece.auth.gr:
Dear Jason,
I am trying to use --debug-start but in FS mode it is very difficult
to find the tick on which the application is started!
However, I am writing the following very simple c++ program:
#include <cmath>
#include <stdio.h>
int main(){
int dim = 4096;
double result;
for (int iter = 0; iter < 2; iter++){
result = 0;
for (int i = 0; i < dim; i++){
for (int j = 0; j < dim; j++){
result += sqrt(i) * sqrt(j);
}
}
printf("Result: %lf\n", result); //Result: 30530733453.127449
}
}
I cross-compile it using: riscv64-linux-gnu-g++ -static -O3 -o
test_riscv test_riscv.cpp
While in X86 (without cross-compilation of course), QEMU-RISCV,
GEM5-SE the result is the same (30530733453.127449), in GEM5-FS the
result is different! In addition, the result is also different
between the 2 iterations.
Please reproduce the error if you want in order to verify my result.
Ηow can the issue be resolved?
Thank you in advance!
Best regards,
Nikos
Quoting Jason Lowe-Power jason@lowepower.com:
Hi Nikos,
You can use --debug-start to start the debugging after some number of
ticks. Also, I would expect that the difference should come up quickly,
no need to run the program to the end.
For the FS mode one, you will want to just start the trace as the
application starts. This could be a bit of a pain.
I'm not really sure what fundamentally could be different. FS and SE
use the exact same code for executing instructions, so I don't think
the problem. Have you tried running for smaller inputs or just one
iteration?
Jason
On Wed, Sep 21, 2022 at 9:04 AM Νικόλαος Ταμπουρατζής <
ntampouratzis@ece.auth.gr> wrote:
Dear Bobby,
Iam trying to add --debug-flags=Exec (building the gem5 for gem5.opt
not for gem5.fast which I had) but the debug traces exceed the 20GB
(and it is not finished yet) for less than 1 simulated second. How can
I reduce the size of the debug-flags (or set something more specific)?
In contrast I build the HPCG benchmark with DHPCG_DEBUG flag. If you
want, you can compare these two output files
(hpcg20010909T014640_SE_Mode & HPCG-Benchmark_3.1_FS_Mode). As you can
see, something goes wrong with the accuracy of calculations in FS mode
(benchmark uses double precission). You can find the files here:
http://kition.mhl.tuc.gr:8000/d/68d82f3533/
Best regards,
Nikos
Quoting Jason Lowe-Power jason@lowepower.com:
That's quite odd that it works in SE mode but not FS mode!
I would suggest running with --debug-flags=Exec for both and then
diff to see how they differ.
Cheers,
Jason
On Tue, Sep 20, 2022 at 2:45 PM Νικόλαος Ταμπουρατζής <
ntampouratzis@ece.auth.gr> wrote:
Dear Bobby,
In QEMU I get the same (correct) results that I get in SE mode
simulation. I get invalid results in FS simulation (in both
riscv-fs.py and riscv-ubuntu-run.py). I cannot access real RISCV
hardware at this moment, however, if you want you may execute my
Hi Nikos,
I notice you said the following in your original email:
In addition, I used the RISCV Ubuntu image
I installed the gcc compiler, compile it (through qemu) and I get
wrong results too.
Is this saying you get the wrong results is QEMU? If so, the bug
or the HPCG workload, not in gem5. If not, I would test in QEMU to
sure the binary works there. Another way you could test to see if
problem is your binary or gem5 would be to run it on real
access to some RISC-V hardware here at UC Davis, if you don't have
Dear Bobby,
- I use the original riscv-fs.py which is provided in the latest
release.
I run the gem5 once (./build/RISCV/gem5.fast -d ./HPCG_FS_results
./configs/example/gem5_library/riscv-fs.py) in order to download
riscv-bootloader-vmlinux-5.10 and riscv-disk-img.
After this I mount the riscv-disk-img (sudo mount -o loop
riscv-disk-img /mnt), put the xhpcg executable and I do the
changes in riscv-fs.py to boot the riscv-disk-img with executable:
image = CustomDiskImageResource(
local_path = "/home/cossim/.cache/gem5/riscv-disk-img",
)
Set the Full System workload.
board.set_kernel_disk_workload(
kernel=Resource("riscv-bootloader-vmlinux-5.10"),
disk_image=image,
)
Finally, in the
gem5/src/python/gem5/components/boards/riscv_board.py
I change the last line to "return ["console=ttyS0",
"root={root_value}", "rw"]" in order to allow the write
the image.
- The HPCG benchmark after some iterations calculates if the
are valid or not valid. In the case of FS it gives invalid
I see from the results, one (at least) problem is that produces
different results in each HPCG execution (with the same
I'm going to need a bit more information to help:
- In what way have you modified
./configs/example/gem5_library/riscv-fs.py? Can you attach the
- What error are you getting or in what way are the results
Dear gem5 community,
I have successfully cross-compile the HPCG benchmark for RISCV
version, without MPI and OpenMP). While it working properly in
mode (./build/RISCV/gem5.fast -d ./HPCG_SE_results
./configs/example/se.py -c xhpcg --options '--nx=16 --ny=16
--npx=1 --npy=1 --npz=1 --rt=0.1'), I get invalid results in FS
simulation using "./build/RISCV/gem5.fast -d ./HPCG_FS_results
./configs/example/gem5_library/riscv-fs.py" (I mount the riscv
and put it).
Can you help me please?
In addition, I used the RISCV Ubuntu image
(
I installed the gcc compiler, compile it (through qemu) and I
Dear Hoa, all
I have ported successfully HPCG and many simple examples using gem5
ARM-FS and they are working properly. The problem is only in RISCV-FS
using double and float variables. Which option of objdump to use?
Best regards,
Nikos
Quoting Hoa Nguyen <hoanguyen@ucdavis.edu>:
> Hi,
>
> It's quite odd that both sqrt_i and result were zeroed out at the same
> time. Does the problem appear in other ISA FS mode, e.g. x86 FS mode? Can
> you show the objdump of the loop as well?
>
> Regards,
> Hoa Nguyen
>
> On Thu, Oct 6, 2022, 04:06 Νικόλαος Ταμπουρατζής <ntampouratzis@ece.auth.gr>
> wrote:
>
>> Dear Jason, all,
>>
>> I am trying to find the accuracy problem with RISCV-FS and I observe
>> that the problem is created (at least in my dummy example) because the
>> variables (double) are set to zero in random simulated time (for this
>> reason I get different results among executions of the same code).
>> Specifically for the following dummy code:
>>
>>
>> #include <cmath>
>> #include <stdio.h>
>>
>> int main(){
>>
>> int dim = 10;
>>
>> float result;
>>
>> for (int iter = 0; iter < 2; iter++){
>> result = 0;
>> for (int i = 0; i < dim; i++){
>> for (int j = 0; j < dim; j++){
>> float sq_i = sqrt(i);
>> float sq_j = sqrt(j);
>> result += sq_i * sq_j;
>> printf("ITER: %d | i: %d | j: %d Result(i: %f | j: %f
>> | i*j: %f): %f\n", iter, i , j, sq_i, sq_j, sq_i * sq_j, result);
>> }
>> }
>> printf("Final Result: %lf\n", result);
>> }
>> }
>>
>>
>> The correct Final Result in both iterations is 372.721656. However, I
>> get the following results in FS:
>>
>> ITER: 0 | i: 0 | j: 0 Result(i: 0.000000 | j: 0.000000 | i*j:
>> 0.000000): 0.000000
>> ITER: 0 | i: 0 | j: 1 Result(i: 0.000000 | j: 1.000000 | i*j:
>> 0.000000): 0.000000
>> ITER: 0 | i: 0 | j: 2 Result(i: 0.000000 | j: 1.414214 | i*j:
>> 0.000000): 0.000000
>> ITER: 0 | i: 0 | j: 3 Result(i: 0.000000 | j: 1.732051 | i*j:
>> 0.000000): 0.000000
>> ITER: 0 | i: 0 | j: 4 Result(i: 0.000000 | j: 2.000000 | i*j:
>> 0.000000): 0.000000
>> ITER: 0 | i: 0 | j: 5 Result(i: 0.000000 | j: 2.236068 | i*j:
>> 0.000000): 0.000000
>> ITER: 0 | i: 0 | j: 6 Result(i: 0.000000 | j: 2.449490 | i*j:
>> 0.000000): 0.000000
>> ITER: 0 | i: 0 | j: 7 Result(i: 0.000000 | j: 2.645751 | i*j:
>> 0.000000): 0.000000
>> ITER: 0 | i: 0 | j: 8 Result(i: 0.000000 | j: 2.828427 | i*j:
>> 0.000000): 0.000000
>> ITER: 0 | i: 0 | j: 9 Result(i: 0.000000 | j: 3.000000 | i*j:
>> 0.000000): 0.000000
>> ITER: 0 | i: 1 | j: 0 Result(i: 1.000000 | j: 0.000000 | i*j:
>> 0.000000): 0.000000
>> ITER: 0 | i: 1 | j: 1 Result(i: 1.000000 | j: 1.000000 | i*j:
>> 1.000000): 1.000000
>> ITER: 0 | i: 1 | j: 2 Result(i: 1.000000 | j: 1.414214 | i*j:
>> 1.414214): 2.414214
>> ITER: 0 | i: 1 | j: 3 Result(i: 1.000000 | j: 1.732051 | i*j:
>> 1.732051): 4.146264
>> ITER: 0 | i: 1 | j: 4 Result(i: 0.000000 | j: 2.000000 | i*j:
>> 0.000000): 0.000000
>> ITER: 0 | i: 1 | j: 5 Result(i: 0.000000 | j: 2.236068 | i*j:
>> 0.000000): 0.000000
>> ITER: 0 | i: 1 | j: 6 Result(i: 0.000000 | j: 2.449490 | i*j:
>> 0.000000): 0.000000
>> ITER: 0 | i: 1 | j: 7 Result(i: 0.000000 | j: 2.645751 | i*j:
>> 0.000000): 0.000000
>> ITER: 0 | i: 1 | j: 8 Result(i: 0.000000 | j: 2.828427 | i*j:
>> 0.000000): 0.000000
>> ITER: 0 | i: 1 | j: 9 Result(i: 0.000000 | j: 3.000000 | i*j:
>> 0.000000): 0.000000
>> ITER: 0 | i: 2 | j: 0 Result(i: 1.414214 | j: 0.000000 | i*j:
>> 0.000000): 0.000000
>> ITER: 0 | i: 2 | j: 1 Result(i: 1.414214 | j: 1.000000 | i*j:
>> 1.414214): 1.414214
>> ITER: 0 | i: 2 | j: 2 Result(i: 1.414214 | j: 1.414214 | i*j:
>> 2.000000): 3.414214
>> ITER: 0 | i: 2 | j: 3 Result(i: 1.414214 | j: 1.732051 | i*j:
>> 2.449490): 5.863703
>> ITER: 0 | i: 2 | j: 4 Result(i: 1.414214 | j: 2.000000 | i*j:
>> 2.828427): 8.692130
>> ITER: 0 | i: 2 | j: 5 Result(i: 1.414214 | j: 2.236068 | i*j:
>> 3.162278): 11.854408
>> ITER: 0 | i: 2 | j: 6 Result(i: 1.414214 | j: 2.449490 | i*j:
>> 3.464102): 15.318510
>> ITER: 0 | i: 2 | j: 7 Result(i: 1.414214 | j: 2.645751 | i*j:
>> 3.741657): 19.060167
>> ITER: 0 | i: 2 | j: 8 Result(i: 1.414214 | j: 2.828427 | i*j:
>> 4.000000): 23.060167
>> ITER: 0 | i: 2 | j: 9 Result(i: 1.414214 | j: 3.000000 | i*j:
>> 4.242641): 27.302808
>> ITER: 0 | i: 3 | j: 0 Result(i: 1.732051 | j: 0.000000 | i*j:
>> 0.000000): 27.302808
>> ITER: 0 | i: 3 | j: 1 Result(i: 1.732051 | j: 1.000000 | i*j:
>> 1.732051): 29.034859
>> ITER: 0 | i: 3 | j: 2 Result(i: 1.732051 | j: 1.414214 | i*j:
>> 2.449490): 31.484348
>> ITER: 0 | i: 3 | j: 3 Result(i: 1.732051 | j: 1.732051 | i*j:
>> 3.000000): 34.484348
>> ITER: 0 | i: 3 | j: 4 Result(i: 1.732051 | j: 2.000000 | i*j:
>> 3.464102): 37.948450
>> ITER: 0 | i: 3 | j: 5 Result(i: 1.732051 | j: 2.236068 | i*j:
>> 3.872983): 41.821433
>> ITER: 0 | i: 3 | j: 6 Result(i: 1.732051 | j: 2.449490 | i*j:
>> 4.242641): 46.064074
>> ITER: 0 | i: 3 | j: 7 Result(i: 1.732051 | j: 2.645751 | i*j:
>> 4.582576): 50.646650
>> ITER: 0 | i: 3 | j: 8 Result(i: 1.732051 | j: 2.828427 | i*j:
>> 4.898979): 55.545629
>> ITER: 0 | i: 3 | j: 9 Result(i: 1.732051 | j: 3.000000 | i*j:
>> 5.196152): 60.741782
>> ITER: 0 | i: 4 | j: 0 Result(i: 2.000000 | j: 0.000000 | i*j:
>> 0.000000): 60.741782
>> ITER: 0 | i: 4 | j: 1 Result(i: 2.000000 | j: 1.000000 | i*j:
>> 2.000000): 62.741782
>> ITER: 0 | i: 4 | j: 2 Result(i: 2.000000 | j: 1.414214 | i*j:
>> 2.828427): 65.570209
>> ITER: 0 | i: 4 | j: 3 Result(i: 2.000000 | j: 1.732051 | i*j:
>> 3.464102): 69.034310
>> ITER: 0 | i: 4 | j: 4 Result(i: 2.000000 | j: 2.000000 | i*j:
>> 4.000000): 73.034310
>> ITER: 0 | i: 4 | j: 5 Result(i: 2.000000 | j: 2.236068 | i*j:
>> 4.472136): 77.506446
>> ITER: 0 | i: 4 | j: 6 Result(i: 2.000000 | j: 2.449490 | i*j:
>> 4.898979): 82.405426
>> ITER: 0 | i: 4 | j: 7 Result(i: 2.000000 | j: 2.645751 | i*j:
>> 5.291503): 87.696928
>> ITER: 0 | i: 4 | j: 8 Result(i: 2.000000 | j: 2.828427 | i*j:
>> 5.656854): 93.353783
>> ITER: 0 | i: 4 | j: 9 Result(i: 2.000000 | j: 3.000000 | i*j:
>> 6.000000): 99.353783
>> ITER: 0 | i: 5 | j: 0 Result(i: 2.236068 | j: 0.000000 | i*j:
>> 0.000000): 99.353783
>> ITER: 0 | i: 5 | j: 1 Result(i: 2.236068 | j: 1.000000 | i*j:
>> 2.236068): 101.589851
>> ITER: 0 | i: 5 | j: 2 Result(i: 2.236068 | j: 1.414214 | i*j:
>> 3.162278): 104.752128
>> ITER: 0 | i: 5 | j: 3 Result(i: 2.236068 | j: 1.732051 | i*j:
>> 3.872983): 108.625112
>> ITER: 0 | i: 5 | j: 4 Result(i: 2.236068 | j: 2.000000 | i*j:
>> 4.472136): 113.097248
>> ITER: 0 | i: 5 | j: 5 Result(i: 2.236068 | j: 2.236068 | i*j:
>> 5.000000): 118.097248
>> ITER: 0 | i: 5 | j: 6 Result(i: 2.236068 | j: 2.449490 | i*j:
>> 5.477226): 123.574473
>> ITER: 0 | i: 5 | j: 7 Result(i: 2.236068 | j: 2.645751 | i*j:
>> 5.916080): 129.490553
>> ITER: 0 | i: 5 | j: 8 Result(i: 2.236068 | j: 2.828427 | i*j:
>> 6.324555): 135.815108
>> ITER: 0 | i: 5 | j: 9 Result(i: 2.236068 | j: 3.000000 | i*j:
>> 6.708204): 142.523312
>> ITER: 0 | i: 6 | j: 0 Result(i: 2.449490 | j: 0.000000 | i*j:
>> 0.000000): 142.523312
>> ITER: 0 | i: 6 | j: 1 Result(i: 2.449490 | j: 1.000000 | i*j:
>> 2.449490): 144.972802
>> ITER: 0 | i: 6 | j: 2 Result(i: 2.449490 | j: 1.414214 | i*j:
>> 3.464102): 148.436904
>> ITER: 0 | i: 6 | j: 3 Result(i: 2.449490 | j: 1.732051 | i*j:
>> 4.242641): 152.679544
>> ITER: 0 | i: 6 | j: 4 Result(i: 2.449490 | j: 2.000000 | i*j:
>> 4.898979): 157.578524
>> ITER: 0 | i: 6 | j: 5 Result(i: 2.449490 | j: 2.236068 | i*j:
>> 5.477226): 163.055749
>> ITER: 0 | i: 6 | j: 6 Result(i: 2.449490 | j: 2.449490 | i*j:
>> 6.000000): 169.055749
>> ITER: 0 | i: 6 | j: 7 Result(i: 2.449490 | j: 2.645751 | i*j:
>> 6.480741): 175.536490
>> ITER: 0 | i: 6 | j: 8 Result(i: 2.449490 | j: 2.828427 | i*j:
>> 6.928203): 182.464693
>> ITER: 0 | i: 6 | j: 9 Result(i: 2.449490 | j: 3.000000 | i*j:
>> 7.348469): 189.813162
>> ITER: 0 | i: 7 | j: 0 Result(i: 2.645751 | j: 0.000000 | i*j:
>> 0.000000): 189.813162
>> ITER: 0 | i: 7 | j: 1 Result(i: 2.645751 | j: 1.000000 | i*j:
>> 2.645751): 192.458914
>> ITER: 0 | i: 7 | j: 2 Result(i: 2.645751 | j: 1.414214 | i*j:
>> 3.741657): 196.200571
>> ITER: 0 | i: 7 | j: 3 Result(i: 2.645751 | j: 1.732051 | i*j:
>> 4.582576): 200.783147
>> ITER: 0 | i: 7 | j: 4 Result(i: 2.645751 | j: 2.000000 | i*j:
>> 5.291503): 206.074649
>> ITER: 0 | i: 7 | j: 5 Result(i: 2.645751 | j: 2.236068 | i*j:
>> 5.916080): 211.990729
>> ITER: 0 | i: 7 | j: 6 Result(i: 2.645751 | j: 2.449490 | i*j:
>> 6.480741): 218.471470
>> ITER: 0 | i: 7 | j: 7 Result(i: 2.645751 | j: 2.645751 | i*j:
>> 7.000000): 225.471470
>> ITER: 0 | i: 7 | j: 8 Result(i: 2.645751 | j: 2.828427 | i*j:
>> 7.483315): 232.954785
>> ITER: 0 | i: 7 | j: 9 Result(i: 2.645751 | j: 3.000000 | i*j:
>> 7.937254): 240.892039
>> ITER: 0 | i: 8 | j: 0 Result(i: 2.828427 | j: 0.000000 | i*j:
>> 0.000000): 240.892039
>> ITER: 0 | i: 8 | j: 1 Result(i: 2.828427 | j: 1.000000 | i*j:
>> 2.828427): 243.720466
>> ITER: 0 | i: 8 | j: 2 Result(i: 2.828427 | j: 1.414214 | i*j:
>> 4.000000): 247.720466
>> ITER: 0 | i: 8 | j: 3 Result(i: 2.828427 | j: 1.732051 | i*j:
>> 4.898979): 252.619445
>> ITER: 0 | i: 8 | j: 4 Result(i: 2.828427 | j: 2.000000 | i*j:
>> 5.656854): 258.276300
>> ITER: 0 | i: 8 | j: 5 Result(i: 2.828427 | j: 2.236068 | i*j:
>> 6.324555): 264.600855
>> ITER: 0 | i: 8 | j: 6 Result(i: 2.828427 | j: 2.449490 | i*j:
>> 6.928203): 271.529058
>> ITER: 0 | i: 8 | j: 7 Result(i: 2.828427 | j: 2.645751 | i*j:
>> 7.483315): 279.012373
>> ITER: 0 | i: 8 | j: 8 Result(i: 2.828427 | j: 2.828427 | i*j:
>> 8.000000): 287.012373
>> ITER: 0 | i: 8 | j: 9 Result(i: 2.828427 | j: 3.000000 | i*j:
>> 8.485281): 295.497654
>> ITER: 0 | i: 9 | j: 0 Result(i: 3.000000 | j: 0.000000 | i*j:
>> 0.000000): 295.497654
>> ITER: 0 | i: 9 | j: 1 Result(i: 3.000000 | j: 1.000000 | i*j:
>> 3.000000): 298.497654
>> ITER: 0 | i: 9 | j: 2 Result(i: 3.000000 | j: 1.414214 | i*j:
>> 4.242641): 302.740295
>> ITER: 0 | i: 9 | j: 3 Result(i: 3.000000 | j: 1.732051 | i*j:
>> 5.196152): 307.936447
>> ITER: 0 | i: 9 | j: 4 Result(i: 3.000000 | j: 2.000000 | i*j:
>> 6.000000): 313.936447
>> ITER: 0 | i: 9 | j: 5 Result(i: 3.000000 | j: 2.236068 | i*j:
>> 6.708204): 320.644651
>> ITER: 0 | i: 9 | j: 6 Result(i: 3.000000 | j: 2.449490 | i*j:
>> 7.348469): 327.993120
>> ITER: 0 | i: 9 | j: 7 Result(i: 3.000000 | j: 2.645751 | i*j:
>> 7.937254): 335.930374
>> ITER: 0 | i: 9 | j: 8 Result(i: 3.000000 | j: 2.828427 | i*j:
>> 8.485281): 344.415656
>> ITER: 0 | i: 9 | j: 9 Result(i: 3.000000 | j: 3.000000 | i*j:
>> 9.000000): 353.415656
>> Final Result: 353.415656
>> ITER: 1 | i: 0 | j: 0 Result(i: 0.000000 | j: 0.000000 | i*j:
>> 0.000000): 0.000000
>> ITER: 1 | i: 0 | j: 1 Result(i: 0.000000 | j: 1.000000 | i*j:
>> 0.000000): 0.000000
>> ITER: 1 | i: 0 | j: 2 Result(i: 0.000000 | j: 1.414214 | i*j:
>> 0.000000): 0.000000
>> ITER: 1 | i: 0 | j: 3 Result(i: 0.000000 | j: 1.732051 | i*j:
>> 0.000000): 0.000000
>> ITER: 1 | i: 0 | j: 4 Result(i: 0.000000 | j: 2.000000 | i*j:
>> 0.000000): 0.000000
>> ITER: 1 | i: 0 | j: 5 Result(i: 0.000000 | j: 2.236068 | i*j:
>> 0.000000): 0.000000
>> ITER: 1 | i: 0 | j: 6 Result(i: 0.000000 | j: 2.449490 | i*j:
>> 0.000000): 0.000000
>> ITER: 1 | i: 0 | j: 7 Result(i: 0.000000 | j: 2.645751 | i*j:
>> 0.000000): 0.000000
>> ITER: 1 | i: 0 | j: 8 Result(i: 0.000000 | j: 2.828427 | i*j:
>> 0.000000): 0.000000
>> ITER: 1 | i: 0 | j: 9 Result(i: 0.000000 | j: 3.000000 | i*j:
>> 0.000000): 0.000000
>> ITER: 1 | i: 1 | j: 0 Result(i: 1.000000 | j: 0.000000 | i*j:
>> 0.000000): 0.000000
>> ITER: 1 | i: 1 | j: 1 Result(i: 1.000000 | j: 1.000000 | i*j:
>> 1.000000): 1.000000
>> ITER: 1 | i: 1 | j: 2 Result(i: 1.000000 | j: 1.414214 | i*j:
>> 1.414214): 2.414214
>> ITER: 1 | i: 1 | j: 3 Result(i: 1.000000 | j: 1.732051 | i*j:
>> 1.732051): 4.146264
>> ITER: 1 | i: 1 | j: 4 Result(i: 1.000000 | j: 2.000000 | i*j:
>> 2.000000): 6.146264
>> ITER: 1 | i: 1 | j: 5 Result(i: 1.000000 | j: 2.236068 | i*j:
>> 2.236068): 8.382332
>> ITER: 1 | i: 1 | j: 6 Result(i: 1.000000 | j: 2.449490 | i*j:
>> 2.449490): 10.831822
>> ITER: 1 | i: 1 | j: 7 Result(i: 1.000000 | j: 2.645751 | i*j:
>> 2.645751): 13.477573
>> ITER: 1 | i: 1 | j: 8 Result(i: 1.000000 | j: 2.828427 | i*j:
>> 2.828427): 16.306001
>> ITER: 1 | i: 1 | j: 9 Result(i: 1.000000 | j: 3.000000 | i*j:
>> 3.000000): 19.306001
>> ITER: 1 | i: 2 | j: 0 Result(i: 1.414214 | j: 0.000000 | i*j:
>> 0.000000): 19.306001
>> ITER: 1 | i: 2 | j: 1 Result(i: 1.414214 | j: 1.000000 | i*j:
>> 1.414214): 20.720214
>> ITER: 1 | i: 2 | j: 2 Result(i: 1.414214 | j: 1.414214 | i*j:
>> 2.000000): 22.720214
>> ITER: 1 | i: 2 | j: 3 Result(i: 1.414214 | j: 1.732051 | i*j:
>> 2.449490): 25.169704
>> ITER: 1 | i: 2 | j: 4 Result(i: 1.414214 | j: 2.000000 | i*j:
>> 2.828427): 27.998131
>> ITER: 1 | i: 2 | j: 5 Result(i: 1.414214 | j: 2.236068 | i*j:
>> 3.162278): 31.160409
>> ITER: 1 | i: 2 | j: 6 Result(i: 1.414214 | j: 2.449490 | i*j:
>> 3.464102): 34.624510
>> ITER: 1 | i: 2 | j: 7 Result(i: 1.414214 | j: 2.645751 | i*j:
>> 3.741657): 38.366168
>> ITER: 1 | i: 2 | j: 8 Result(i: 1.414214 | j: 2.828427 | i*j:
>> 4.000000): 42.366168
>> ITER: 1 | i: 2 | j: 9 Result(i: 1.414214 | j: 3.000000 | i*j:
>> 4.242641): 46.608808
>> ITER: 1 | i: 3 | j: 0 Result(i: 1.732051 | j: 0.000000 | i*j:
>> 0.000000): 46.608808
>> ITER: 1 | i: 3 | j: 1 Result(i: 1.732051 | j: 1.000000 | i*j:
>> 1.732051): 48.340859
>> ITER: 1 | i: 3 | j: 2 Result(i: 1.732051 | j: 1.414214 | i*j:
>> 2.449490): 50.790349
>> ITER: 1 | i: 3 | j: 3 Result(i: 1.732051 | j: 1.732051 | i*j:
>> 3.000000): 53.790349
>> ITER: 1 | i: 3 | j: 4 Result(i: 1.732051 | j: 2.000000 | i*j:
>> 3.464102): 57.254450
>> ITER: 1 | i: 3 | j: 5 Result(i: 1.732051 | j: 2.236068 | i*j:
>> 3.872983): 61.127434
>> ITER: 1 | i: 3 | j: 6 Result(i: 1.732051 | j: 2.449490 | i*j:
>> 4.242641): 65.370075
>> ITER: 1 | i: 3 | j: 7 Result(i: 1.732051 | j: 2.645751 | i*j:
>> 4.582576): 69.952650
>> ITER: 1 | i: 3 | j: 8 Result(i: 1.732051 | j: 2.828427 | i*j:
>> 4.898979): 74.851630
>> ITER: 1 | i: 3 | j: 9 Result(i: 1.732051 | j: 3.000000 | i*j:
>> 5.196152): 80.047782
>> ITER: 1 | i: 4 | j: 0 Result(i: 2.000000 | j: 0.000000 | i*j:
>> 0.000000): 80.047782
>> ITER: 1 | i: 4 | j: 1 Result(i: 2.000000 | j: 1.000000 | i*j:
>> 2.000000): 82.047782
>> ITER: 1 | i: 4 | j: 2 Result(i: 2.000000 | j: 1.414214 | i*j:
>> 2.828427): 84.876209
>> ITER: 1 | i: 4 | j: 3 Result(i: 2.000000 | j: 1.732051 | i*j:
>> 3.464102): 88.340311
>> ITER: 1 | i: 4 | j: 4 Result(i: 2.000000 | j: 2.000000 | i*j:
>> 4.000000): 92.340311
>> ITER: 1 | i: 4 | j: 5 Result(i: 2.000000 | j: 2.236068 | i*j:
>> 4.472136): 96.812447
>> ITER: 1 | i: 4 | j: 6 Result(i: 2.000000 | j: 2.449490 | i*j:
>> 4.898979): 101.711426
>> ITER: 1 | i: 4 | j: 7 Result(i: 2.000000 | j: 2.645751 | i*j:
>> 5.291503): 107.002929
>> ITER: 1 | i: 4 | j: 8 Result(i: 2.000000 | j: 2.828427 | i*j:
>> 5.656854): 112.659783
>> ITER: 1 | i: 4 | j: 9 Result(i: 2.000000 | j: 3.000000 | i*j:
>> 6.000000): 118.659783
>> ITER: 1 | i: 5 | j: 0 Result(i: 2.236068 | j: 0.000000 | i*j:
>> 0.000000): 118.659783
>> ITER: 1 | i: 5 | j: 1 Result(i: 2.236068 | j: 1.000000 | i*j:
>> 2.236068): 120.895851
>> ITER: 1 | i: 5 | j: 2 Result(i: 2.236068 | j: 1.414214 | i*j:
>> 3.162278): 124.058129
>> ITER: 1 | i: 5 | j: 3 Result(i: 2.236068 | j: 1.732051 | i*j:
>> 3.872983): 127.931112
>> ITER: 1 | i: 5 | j: 4 Result(i: 2.236068 | j: 2.000000 | i*j:
>> 4.472136): 132.403248
>> ITER: 1 | i: 5 | j: 5 Result(i: 2.236068 | j: 2.236068 | i*j:
>> 5.000000): 137.403248
>> ITER: 1 | i: 5 | j: 6 Result(i: 2.236068 | j: 2.449490 | i*j:
>> 5.477226): 142.880474
>> ITER: 1 | i: 5 | j: 7 Result(i: 2.236068 | j: 2.645751 | i*j:
>> 5.916080): 148.796553
>> ITER: 1 | i: 5 | j: 8 Result(i: 2.236068 | j: 2.828427 | i*j:
>> 6.324555): 155.121109
>> ITER: 1 | i: 5 | j: 9 Result(i: 2.236068 | j: 3.000000 | i*j:
>> 6.708204): 161.829313
>> ITER: 1 | i: 6 | j: 0 Result(i: 2.449490 | j: 0.000000 | i*j:
>> 0.000000): 161.829313
>> ITER: 1 | i: 6 | j: 1 Result(i: 2.449490 | j: 1.000000 | i*j:
>> 2.449490): 164.278802
>> ITER: 1 | i: 6 | j: 2 Result(i: 2.449490 | j: 1.414214 | i*j:
>> 3.464102): 167.742904
>> ITER: 1 | i: 6 | j: 3 Result(i: 2.449490 | j: 1.732051 | i*j:
>> 4.242641): 171.985545
>> ITER: 1 | i: 6 | j: 4 Result(i: 2.449490 | j: 2.000000 | i*j:
>> 4.898979): 176.884524
>> ITER: 1 | i: 6 | j: 5 Result(i: 2.449490 | j: 2.236068 | i*j:
>> 5.477226): 182.361750
>> ITER: 1 | i: 6 | j: 6 Result(i: 2.449490 | j: 2.449490 | i*j:
>> 6.000000): 188.361750
>> ITER: 1 | i: 6 | j: 7 Result(i: 2.449490 | j: 2.645751 | i*j:
>> 6.480741): 194.842491
>> ITER: 1 | i: 6 | j: 8 Result(i: 2.449490 | j: 2.828427 | i*j:
>> 6.928203): 201.770694
>> ITER: 1 | i: 6 | j: 9 Result(i: 2.449490 | j: 3.000000 | i*j:
>> 7.348469): 209.119163
>> ITER: 1 | i: 7 | j: 0 Result(i: 2.645751 | j: 0.000000 | i*j:
>> 0.000000): 209.119163
>> ITER: 1 | i: 7 | j: 1 Result(i: 2.645751 | j: 1.000000 | i*j:
>> 2.645751): 211.764914
>> ITER: 1 | i: 7 | j: 2 Result(i: 2.645751 | j: 1.414214 | i*j:
>> 3.741657): 215.506572
>> ITER: 1 | i: 7 | j: 3 Result(i: 2.645751 | j: 1.732051 | i*j:
>> 4.582576): 220.089147
>> ITER: 1 | i: 7 | j: 4 Result(i: 2.645751 | j: 2.000000 | i*j:
>> 5.291503): 225.380650
>> ITER: 1 | i: 7 | j: 5 Result(i: 2.645751 | j: 2.236068 | i*j:
>> 5.916080): 231.296730
>> ITER: 1 | i: 7 | j: 6 Result(i: 2.645751 | j: 2.449490 | i*j:
>> 6.480741): 237.777470
>> ITER: 1 | i: 7 | j: 7 Result(i: 2.645751 | j: 2.645751 | i*j:
>> 7.000000): 244.777470
>> ITER: 1 | i: 7 | j: 8 Result(i: 2.645751 | j: 2.828427 | i*j:
>> 7.483315): 252.260785
>> ITER: 1 | i: 7 | j: 9 Result(i: 2.645751 | j: 3.000000 | i*j:
>> 7.937254): 260.198039
>> ITER: 1 | i: 8 | j: 0 Result(i: 2.828427 | j: 0.000000 | i*j:
>> 0.000000): 260.198039
>> ITER: 1 | i: 8 | j: 1 Result(i: 2.828427 | j: 1.000000 | i*j:
>> 2.828427): 263.026466
>> ITER: 1 | i: 8 | j: 2 Result(i: 2.828427 | j: 1.414214 | i*j:
>> 4.000000): 267.026466
>> ITER: 1 | i: 8 | j: 3 Result(i: 2.828427 | j: 1.732051 | i*j:
>> 4.898979): 271.925446
>> ITER: 1 | i: 8 | j: 4 Result(i: 2.828427 | j: 2.000000 | i*j:
>> 5.656854): 277.582300
>> ITER: 1 | i: 8 | j: 5 Result(i: 2.828427 | j: 2.236068 | i*j:
>> 6.324555): 283.906855
>> ITER: 1 | i: 8 | j: 6 Result(i: 2.828427 | j: 2.449490 | i*j:
>> 6.928203): 290.835059
>> ITER: 1 | i: 8 | j: 7 Result(i: 2.828427 | j: 2.645751 | i*j:
>> 7.483315): 298.318373
>> ITER: 1 | i: 8 | j: 8 Result(i: 2.828427 | j: 2.828427 | i*j:
>> 8.000000): 306.318373
>> ITER: 1 | i: 8 | j: 9 Result(i: 2.828427 | j: 3.000000 | i*j:
>> 8.485281): 314.803655
>> ITER: 1 | i: 9 | j: 0 Result(i: 3.000000 | j: 0.000000 | i*j:
>> 0.000000): 314.803655
>> ITER: 1 | i: 9 | j: 1 Result(i: 3.000000 | j: 1.000000 | i*j:
>> 3.000000): 317.803655
>> ITER: 1 | i: 9 | j: 2 Result(i: 3.000000 | j: 1.414214 | i*j:
>> 4.242641): 322.046295
>> ITER: 1 | i: 9 | j: 3 Result(i: 3.000000 | j: 1.732051 | i*j:
>> 5.196152): 327.242448
>> ITER: 1 | i: 9 | j: 4 Result(i: 3.000000 | j: 2.000000 | i*j:
>> 6.000000): 333.242448
>> ITER: 1 | i: 9 | j: 5 Result(i: 3.000000 | j: 2.236068 | i*j:
>> 6.708204): 339.950652
>> ITER: 1 | i: 9 | j: 6 Result(i: 3.000000 | j: 2.449490 | i*j:
>> 7.348469): 347.299121
>> ITER: 1 | i: 9 | j: 7 Result(i: 3.000000 | j: 2.645751 | i*j:
>> 7.937254): 355.236375
>> ITER: 1 | i: 9 | j: 8 Result(i: 3.000000 | j: 2.828427 | i*j:
>> 8.485281): 363.721656
>> ITER: 1 | i: 9 | j: 9 Result(i: 3.000000 | j: 3.000000 | i*j:
>> 9.000000): 372.721656
>> Final Result: 372.721656
>>
>>
>>
>> As we can see in the following iterations the sqrt(1) as well as the
>> result is set to zero for some reason.
>>
>> ITER: 0 | i: 1 | j: 4 Result(i: 0.000000 | j: 2.000000 | i*j:
>> 0.000000): 0.000000
>> ITER: 0 | i: 1 | j: 5 Result(i: 0.000000 | j: 2.236068 | i*j:
>> 0.000000): 0.000000
>> ITER: 0 | i: 1 | j: 6 Result(i: 0.000000 | j: 2.449490 | i*j:
>> 0.000000): 0.000000
>> ITER: 0 | i: 1 | j: 7 Result(i: 0.000000 | j: 2.645751 | i*j:
>> 0.000000): 0.000000
>> ITER: 0 | i: 1 | j: 8 Result(i: 0.000000 | j: 2.828427 | i*j:
>> 0.000000): 0.000000
>> ITER: 0 | i: 1 | j: 9 Result(i: 0.000000 | j: 3.000000 | i*j:
>> 0.000000): 0.000000
>>
>> Please help me to resolve the accuracy issue! I think that it will be
>> very useful for gem5 community.
>>
>> To be noticed, I find the correct simulated tick in which the
>> application started in FS (using m5 dumpstats), and I start the
>> --debug-start, but the trace file which is generated is 10x larger
>> than SE mode for the same application. How can I compare them?
>>
>> Thank you in advance!
>> Best regards,
>> Nikos
>>
>> Quoting Νικόλαος Ταμπουρατζής <ntampouratzis@ece.auth.gr>:
>>
>> > Dear Jason,
>> >
>> > I am trying to use --debug-start but in FS mode it is very difficult
>> > to find the tick on which the application is started!
>> >
>> > However, I am writing the following very simple c++ program:
>> >
>> > #include <cmath>
>> > #include <stdio.h>
>> >
>> > int main(){
>> >
>> > int dim = 4096;
>> >
>> > double result;
>> >
>> > for (int iter = 0; iter < 2; iter++){
>> > result = 0;
>> > for (int i = 0; i < dim; i++){
>> > for (int j = 0; j < dim; j++){
>> > result += sqrt(i) * sqrt(j);
>> > }
>> > }
>> > printf("Result: %lf\n", result); //Result: 30530733453.127449
>> > }
>> > }
>> >
>> > I cross-compile it using: riscv64-linux-gnu-g++ -static -O3 -o
>> > test_riscv test_riscv.cpp
>> >
>> >
>> > While in X86 (without cross-compilation of course), QEMU-RISCV,
>> > GEM5-SE the result is the same (30530733453.127449), in GEM5-FS the
>> > result is different! In addition, the result is also different
>> > between the 2 iterations.
>> >
>> > Please reproduce the error if you want in order to verify my result.
>> > Ηow can the issue be resolved?
>> >
>> > Thank you in advance!
>> >
>> > Best regards,
>> > Nikos
>> >
>> >
>> > Quoting Jason Lowe-Power <jason@lowepower.com>:
>> >
>> >> Hi Nikos,
>> >>
>> >> You can use --debug-start to start the debugging after some number of
>> >> ticks. Also, I would expect that the difference should come up quickly,
>> so
>> >> no need to run the program to the end.
>> >>
>> >> For the FS mode one, you will want to just start the trace as the
>> >> application starts. This could be a bit of a pain.
>> >>
>> >> I'm not really sure what fundamentally could be different. FS and SE
>> mode
>> >> use the exact same code for executing instructions, so I don't think
>> that's
>> >> the problem. Have you tried running for smaller inputs or just one
>> >> iteration?
>> >>
>> >> Jason
>> >>
>> >>
>> >>
>> >> On Wed, Sep 21, 2022 at 9:04 AM Νικόλαος Ταμπουρατζής <
>> >> ntampouratzis@ece.auth.gr> wrote:
>> >>
>> >>> Dear Bobby,
>> >>>
>> >>> Iam trying to add --debug-flags=Exec (building the gem5 for gem5.opt
>> >>> not for gem5.fast which I had) but the debug traces exceed the 20GB
>> >>> (and it is not finished yet) for less than 1 simulated second. How can
>> >>> I reduce the size of the debug-flags (or set something more specific)?
>> >>>
>> >>> In contrast I build the HPCG benchmark with DHPCG_DEBUG flag. If you
>> >>> want, you can compare these two output files
>> >>> (hpcg20010909T014640_SE_Mode & HPCG-Benchmark_3.1_FS_Mode). As you can
>> >>> see, something goes wrong with the accuracy of calculations in FS mode
>> >>> (benchmark uses double precission). You can find the files here:
>> >>> http://kition.mhl.tuc.gr:8000/d/68d82f3533/
>> >>>
>> >>> Best regards,
>> >>> Nikos
>> >>>
>> >>> Quoting Jason Lowe-Power <jason@lowepower.com>:
>> >>>
>> >>>> That's quite odd that it works in SE mode but not FS mode!
>> >>>>
>> >>>> I would suggest running with --debug-flags=Exec for both and then
>> >>> perform a
>> >>>> diff to see how they differ.
>> >>>>
>> >>>> Cheers,
>> >>>> Jason
>> >>>>
>> >>>> On Tue, Sep 20, 2022 at 2:45 PM Νικόλαος Ταμπουρατζής <
>> >>>> ntampouratzis@ece.auth.gr> wrote:
>> >>>>
>> >>>>> Dear Bobby,
>> >>>>>
>> >>>>> In QEMU I get the same (correct) results that I get in SE mode
>> >>>>> simulation. I get invalid results in FS simulation (in both
>> >>>>> riscv-fs.py and riscv-ubuntu-run.py). I cannot access real RISCV
>> >>>>> hardware at this moment, however, if you want you may execute my
>> xhpcg
>> >>>>> binary (http://kition.mhl.tuc.gr:8000/f/4ca25fdd3c/) with the
>> >>>>> following configuration:
>> >>>>>
>> >>>>> ./xhpcg --nx=16 --ny=16 --nz=16 --npx=1 --npy=1 --npz=1 --rt=0.1
>> >>>>>
>> >>>>> Please let me know if you have any updates!
>> >>>>>
>> >>>>> Best regards,
>> >>>>> Nikos
>> >>>>>
>> >>>>>
>> >>>>> Quoting Jason Lowe-Power <jason@lowepower.com>:
>> >>>>>
>> >>>>> > Hi Nikos,
>> >>>>> >
>> >>>>> > I notice you said the following in your original email:
>> >>>>> >
>> >>>>> > In addition, I used the RISCV Ubuntu image
>> >>>>> >> (
>> https://github.com/gem5/gem5-resources/tree/stable/src/riscv-ubuntu
>> >>> ),
>> >>>>> >> I installed the gcc compiler, compile it (through qemu) and I get
>> >>>>> >> wrong results too.
>> >>>>> >
>> >>>>> >
>> >>>>> > Is this saying you get the wrong results is QEMU? If so, the bug
>> is in
>> >>>>> GCC
>> >>>>> > or the HPCG workload, not in gem5. If not, I would test in QEMU to
>> >>> make
>> >>>>> > sure the binary works there. Another way you could test to see if
>> the
>> >>>>> > problem is your binary or gem5 would be to run it on real
>> hardware. We
>> >>>>> have
>> >>>>> > access to some RISC-V hardware here at UC Davis, if you don't have
>> >>> access
>> >>>>> > to it.
>> >>>>> >
>> >>>>> > Cheers,
>> >>>>> > Jason
>> >>>>> >
>> >>>>> > On Tue, Sep 20, 2022 at 12:58 AM Νικόλαος Ταμπουρατζής <
>> >>>>> > ntampouratzis@ece.auth.gr> wrote:
>> >>>>> >
>> >>>>> >> Dear Bobby,
>> >>>>> >>
>> >>>>> >> 1) I use the original riscv-fs.py which is provided in the latest
>> >>> gem5
>> >>>>> >> release.
>> >>>>> >> I run the gem5 once (./build/RISCV/gem5.fast -d ./HPCG_FS_results
>> >>>>> >> ./configs/example/gem5_library/riscv-fs.py) in order to download
>> the
>> >>>>> >> riscv-bootloader-vmlinux-5.10 and riscv-disk-img.
>> >>>>> >> After this I mount the riscv-disk-img (sudo mount -o loop
>> >>>>> >> riscv-disk-img /mnt), put the xhpcg executable and I do the
>> following
>> >>>>> >> changes in riscv-fs.py to boot the riscv-disk-img with executable:
>> >>>>> >>
>> >>>>> >> image = CustomDiskImageResource(
>> >>>>> >> local_path = "/home/cossim/.cache/gem5/riscv-disk-img",
>> >>>>> >> )
>> >>>>> >>
>> >>>>> >> # Set the Full System workload.
>> >>>>> >> board.set_kernel_disk_workload(
>> >>>>> >>
>> kernel=Resource("riscv-bootloader-vmlinux-5.10"),
>> >>>>> >> disk_image=image,
>> >>>>> >> )
>> >>>>> >>
>> >>>>> >> Finally, in the
>> gem5/src/python/gem5/components/boards/riscv_board.py
>> >>>>> >> I change the last line to "return ["console=ttyS0",
>> >>>>> >> "root={root_value}", "rw"]" in order to allow the write
>> permissions
>> >>> in
>> >>>>> >> the image.
>> >>>>> >>
>> >>>>> >>
>> >>>>> >> 2) The HPCG benchmark after some iterations calculates if the
>> results
>> >>>>> >> are valid or not valid. In the case of FS it gives invalid
>> results.
>> >>> As
>> >>>>> >> I see from the results, one (at least) problem is that produces
>> >>>>> >> different results in each HPCG execution (with the same
>> >>> configuration).
>> >>>>> >>
>> >>>>> >> Here is the HPCG output and riscv-fs.py
>> >>>>> >> (http://kition.mhl.tuc.gr:8000/d/68d82f3533/). You may reproduce
>> the
>> >>>>> >> results in the video if you use the xhpcg executable
>> >>>>> >> (http://kition.mhl.tuc.gr:8000/f/4ca25fdd3c/)
>> >>>>> >>
>> >>>>> >> Please help me in order to solve it!
>> >>>>> >>
>> >>>>> >> Finally, I get invalid results in the HPL benchmark in FS mode
>> too.
>> >>>>> >>
>> >>>>> >> Best regards,
>> >>>>> >> Nikos
>> >>>>> >>
>> >>>>> >>
>> >>>>> >> Quoting Bobby Bruce <bbruce@ucdavis.edu>:
>> >>>>> >>
>> >>>>> >> > I'm going to need a bit more information to help:
>> >>>>> >> >
>> >>>>> >> > 1. In what way have you modified
>> >>>>> >> > ./configs/example/gem5_library/riscv-fs.py? Can you attach the
>> >>> script
>> >>>>> >> here?
>> >>>>> >> > 2. What error are you getting or in what way are the results
>> >>> invalid?
>> >>>>> >> >
>> >>>>> >> > -
>> >>>>> >> > Dr. Bobby R. Bruce
>> >>>>> >> > Room 3050,
>> >>>>> >> > Kemper Hall, UC Davis
>> >>>>> >> > Davis,
>> >>>>> >> > CA, 95616
>> >>>>> >> >
>> >>>>> >> > web: https://www.bobbybruce.net
>> >>>>> >> >
>> >>>>> >> >
>> >>>>> >> > On Mon, Sep 19, 2022 at 1:43 PM Νικόλαος Ταμπουρατζής <
>> >>>>> >> > ntampouratzis@ece.auth.gr> wrote:
>> >>>>> >> >
>> >>>>> >> >>
>> >>>>> >> >> Dear gem5 community,
>> >>>>> >> >>
>> >>>>> >> >> I have successfully cross-compile the HPCG benchmark for RISCV
>> >>>>> (Serial
>> >>>>> >> >> version, without MPI and OpenMP). While it working properly in
>> >>> gem5
>> >>>>> SE
>> >>>>> >> >> mode (./build/RISCV/gem5.fast -d ./HPCG_SE_results
>> >>>>> >> >> ./configs/example/se.py -c xhpcg --options '--nx=16 --ny=16
>> >>> --nz=16
>> >>>>> >> >> --npx=1 --npy=1 --npz=1 --rt=0.1'), I get invalid results in FS
>> >>>>> >> >> simulation using "./build/RISCV/gem5.fast -d ./HPCG_FS_results
>> >>>>> >> >> ./configs/example/gem5_library/riscv-fs.py" (I mount the riscv
>> >>> image
>> >>>>> >> >> and put it).
>> >>>>> >> >>
>> >>>>> >> >> Can you help me please?
>> >>>>> >> >>
>> >>>>> >> >> In addition, I used the RISCV Ubuntu image
>> >>>>> >> >> (
>> >>> https://github.com/gem5/gem5-resources/tree/stable/src/riscv-ubuntu
>> >>>>> ),
>> >>>>> >> >> I installed the gcc compiler, compile it (through qemu) and I
>> get
>> >>>>> >> >> wrong results too.
>> >>>>> >> >>
>> >>>>> >> >> Here is the Makefile which I use, the hpcg executable for RISCV
>> >>>>> >> >> (xhpcg), and a video that shows the results
>> >>>>> >> >> (http://kition.mhl.tuc.gr:8000/f/4ca25fdd3c/).
>> >>>>> >> >>
>> >>>>> >> >> P.S. I use the latest gem5 version.
>> >>>>> >> >>
>> >>>>> >> >> Thank you in advance! :)
>> >>>>> >> >>
>> >>>>> >> >> Best regards,
>> >>>>> >> >> Nikos
>> >>>>> >> >> _______________________________________________
>> >>>>> >> >> gem5-users mailing list -- gem5-users@gem5.org
>> >>>>> >> >> To unsubscribe send an email to gem5-users-leave@gem5.org
>> >>>>> >> >>
>> >>>>> >>
>> >>>>> >>
>> >>>>> >> _______________________________________________
>> >>>>> >> gem5-users mailing list -- gem5-users@gem5.org
>> >>>>> >> To unsubscribe send an email to gem5-users-leave@gem5.org
>> >>>>> >>
>> >>>>>
>> >>>>>
>> >>>>> _______________________________________________
>> >>>>> gem5-users mailing list -- gem5-users@gem5.org
>> >>>>> To unsubscribe send an email to gem5-users-leave@gem5.org
>> >>>>>
>> >>>
>> >>>
>> >>> _______________________________________________
>> >>> gem5-users mailing list -- gem5-users@gem5.org
>> >>> To unsubscribe send an email to gem5-users-leave@gem5.org
>> >>>
>> >
>> >
>> > _______________________________________________
>> > gem5-users mailing list -- gem5-users@gem5.org
>> > To unsubscribe send an email to gem5-users-leave@gem5.org
>>
>>
>> _______________________________________________
>> gem5-users mailing list -- gem5-users@gem5.org
>> To unsubscribe send an email to gem5-users-leave@gem5.org
>>
BB
Bobby Bruce
Wed, Oct 12, 2022 6:33 PM
Jason and I had a theory that this may be due to the "Rounding Mode" for
floating pointing being set incorrectly in FS mode. That's set via a macro
here:
https://gem5.googlesource.com/public/gem5/+/refs/tags/v22.0.0.2/src/arch/riscv/fp_inst.hh#36
I manually expanded the macro here:
https://gem5.googlesource.com/public/gem5/+/refs/tags/v22.0.0.2/src/arch/riscv/isa/decoder.isa#1495,
inside the "fsqrt_d" definition then compiled "build/ALL/gem5.debug". Then
used gdb to add a breakpoint in the "Fsqrt_d::execute" function (in the
generated "build/ALL/arch/riscv/generated/exec-ns.cc.inc" file).
gdb build/ALL/gem5.opt
break Fsqrt_d::execute
run bug-recreation/se-mode-run.py # or `run bug-recreation/fs-mode-run.py`
Stepping through with gdb I the rounding mode is 0 for SE mode and 0
for FS mode as well. So, no luck with that theory.
My new theory is that this bug has something to do with thread context
switching being implemented incorrectly in RISC-V somehow. I find it
strange that the sqrt(1) works fine for a while (i.e. returns 1) then
suddenly starts returning zero after a certain point in the execution. In
addition, it's odd that the loop is not returning the same value each time
despite executing the same code. It'd make sense to me that the thread is
being stored and then resumed with some corruption of the floating point
data. This would also explain why this bug only occurs in FS mode.
I'll try to find time to figure out a good test for this. If anyone has any
other theories or ideas then let me know.
--
Dr. Bobby R. Bruce
Room 3050,
Kemper Hall, UC Davis
Davis,
CA, 95616
web: https://www.bobbybruce.net
On Fri, Oct 7, 2022 at 12:50 PM Νικόλαος Ταμπουρατζής <
ntampouratzis@ece.auth.gr> wrote:
Dear Jason & Boddy,
Unfortunately, I have tried my simple example without the sqrt
function and the problem remains. Specifically, I have the following
simple code:
#include <cmath>
#include <stdio.h>
int main(){
int dim = 1024;
double result;
for (int iter = 0; iter < 2; iter++){
result = 0;
for (int i = 0; i < dim; i++){
for (int j = 0; j < dim; j++){
result += i * j;
}
}
printf("Final Result: %lf\n", result);
}
}
In the above code, the correct result is 274341298176.000000 (from
RISCV-SE mode and x86), while in FS mode I get sometimes the correct
result and other times a different number.
Best regards,
Nikos
Quoting Jason Lowe-Power jason@lowepower.com:
I have an idea...
Have you put a breakpoint in the implementation of the fsqrt_d
would like to know if when running in SE mode and running in FS mode we
using the same rounding mode. My hypothesis is that in FS mode the
mode is set differently.
Cheers,
Jason
On Fri, Oct 7, 2022 at 12:15 AM Νικόλαος Ταμπουρατζής <
ntampouratzis@ece.auth.gr> wrote:
Dear Boddy,
Thanks a lot for the effort! I looked in detail and I observe that the
problem is created only using float and double variables (in the case
of int it is working properly in FS mode). Specifically, in the case
of float the variables are set to "nan", while in the case of double
the variables are set to 0.000000 (in random time - probably from some
instruction of simulated OS?). You may use a simple c/c++ example in
order to get some traces before going to HPCG...
Thank you in advance!!
Best regards,
Nikos
Quoting Bobby Bruce bbruce@ucdavis.edu:
Hey Niko,
Thanks for this analysis. I jumped a little into this today but
as far as you did. I wanted to find a quick way to recreate the
free to use this, if it helps any.
It's very strange to me that this bug hasn't manifested itself
it's undeniably there. I'll try to spend more time looking at this
with some traces and debug flags and see if I can narrow down the
In my previous results, I had used double (not float) for the
following variables: result, sq_i and sq_j. In the case of float
instead of double I get "nan" and not 0.000000.
Quoting Νικόλαος Ταμπουρατζής ntampouratzis@ece.auth.gr:
Dear Jason, all,
I am trying to find the accuracy problem with RISCV-FS and I
that the problem is created (at least in my dummy example) because
the variables (double) are set to zero in random simulated time
this reason I get different results among executions of the same
code). Specifically for the following dummy code:
#include <cmath>
#include <stdio.h>
int main(){
int dim = 10;
float result;
for (int iter = 0; iter < 2; iter++){
result = 0;
for (int i = 0; i < dim; i++){
for (int j = 0; j < dim; j++){
float sq_i = sqrt(i);
float sq_j = sqrt(j);
result += sq_i * sq_j;
printf("ITER: %d | i: %d | j: %d Result(i: %f | j:
%f | i*j: %f): %f\n", iter, i , j, sq_i, sq_j, sq_i * sq_j,
}
}
printf("Final Result: %lf\n", result);
}
}
The correct Final Result in both iterations is 372.721656.
I get the following results in FS:
ITER: 0 | i: 0 | j: 0 Result(i: 0.000000 | j: 0.000000 | ij:
0.000000): 0.000000
ITER: 0 | i: 0 | j: 1 Result(i: 0.000000 | j: 1.000000 | ij:
0.000000): 0.000000
ITER: 0 | i: 0 | j: 2 Result(i: 0.000000 | j: 1.414214 | ij:
0.000000): 0.000000
ITER: 0 | i: 0 | j: 3 Result(i: 0.000000 | j: 1.732051 | ij:
0.000000): 0.000000
ITER: 0 | i: 0 | j: 4 Result(i: 0.000000 | j: 2.000000 | ij:
0.000000): 0.000000
ITER: 0 | i: 0 | j: 5 Result(i: 0.000000 | j: 2.236068 | ij:
0.000000): 0.000000
ITER: 0 | i: 0 | j: 6 Result(i: 0.000000 | j: 2.449490 | ij:
0.000000): 0.000000
ITER: 0 | i: 0 | j: 7 Result(i: 0.000000 | j: 2.645751 | ij:
0.000000): 0.000000
ITER: 0 | i: 0 | j: 8 Result(i: 0.000000 | j: 2.828427 | ij:
0.000000): 0.000000
ITER: 0 | i: 0 | j: 9 Result(i: 0.000000 | j: 3.000000 | ij:
0.000000): 0.000000
ITER: 0 | i: 1 | j: 0 Result(i: 1.000000 | j: 0.000000 | ij:
0.000000): 0.000000
ITER: 0 | i: 1 | j: 1 Result(i: 1.000000 | j: 1.000000 | ij:
1.000000): 1.000000
ITER: 0 | i: 1 | j: 2 Result(i: 1.000000 | j: 1.414214 | ij:
1.414214): 2.414214
ITER: 0 | i: 1 | j: 3 Result(i: 1.000000 | j: 1.732051 | ij:
1.732051): 4.146264
ITER: 0 | i: 1 | j: 4 Result(i: 0.000000 | j: 2.000000 | ij:
0.000000): 0.000000
ITER: 0 | i: 1 | j: 5 Result(i: 0.000000 | j: 2.236068 | ij:
0.000000): 0.000000
ITER: 0 | i: 1 | j: 6 Result(i: 0.000000 | j: 2.449490 | ij:
0.000000): 0.000000
ITER: 0 | i: 1 | j: 7 Result(i: 0.000000 | j: 2.645751 | ij:
0.000000): 0.000000
ITER: 0 | i: 1 | j: 8 Result(i: 0.000000 | j: 2.828427 | ij:
0.000000): 0.000000
ITER: 0 | i: 1 | j: 9 Result(i: 0.000000 | j: 3.000000 | ij:
0.000000): 0.000000
ITER: 0 | i: 2 | j: 0 Result(i: 1.414214 | j: 0.000000 | ij:
0.000000): 0.000000
ITER: 0 | i: 2 | j: 1 Result(i: 1.414214 | j: 1.000000 | ij:
1.414214): 1.414214
ITER: 0 | i: 2 | j: 2 Result(i: 1.414214 | j: 1.414214 | ij:
2.000000): 3.414214
ITER: 0 | i: 2 | j: 3 Result(i: 1.414214 | j: 1.732051 | ij:
2.449490): 5.863703
ITER: 0 | i: 2 | j: 4 Result(i: 1.414214 | j: 2.000000 | ij:
2.828427): 8.692130
ITER: 0 | i: 2 | j: 5 Result(i: 1.414214 | j: 2.236068 | ij:
3.162278): 11.854408
ITER: 0 | i: 2 | j: 6 Result(i: 1.414214 | j: 2.449490 | ij:
3.464102): 15.318510
ITER: 0 | i: 2 | j: 7 Result(i: 1.414214 | j: 2.645751 | ij:
3.741657): 19.060167
ITER: 0 | i: 2 | j: 8 Result(i: 1.414214 | j: 2.828427 | ij:
4.000000): 23.060167
ITER: 0 | i: 2 | j: 9 Result(i: 1.414214 | j: 3.000000 | ij:
4.242641): 27.302808
ITER: 0 | i: 3 | j: 0 Result(i: 1.732051 | j: 0.000000 | ij:
0.000000): 27.302808
ITER: 0 | i: 3 | j: 1 Result(i: 1.732051 | j: 1.000000 | ij:
1.732051): 29.034859
ITER: 0 | i: 3 | j: 2 Result(i: 1.732051 | j: 1.414214 | ij:
2.449490): 31.484348
ITER: 0 | i: 3 | j: 3 Result(i: 1.732051 | j: 1.732051 | ij:
3.000000): 34.484348
ITER: 0 | i: 3 | j: 4 Result(i: 1.732051 | j: 2.000000 | ij:
3.464102): 37.948450
ITER: 0 | i: 3 | j: 5 Result(i: 1.732051 | j: 2.236068 | ij:
3.872983): 41.821433
ITER: 0 | i: 3 | j: 6 Result(i: 1.732051 | j: 2.449490 | ij:
4.242641): 46.064074
ITER: 0 | i: 3 | j: 7 Result(i: 1.732051 | j: 2.645751 | ij:
4.582576): 50.646650
ITER: 0 | i: 3 | j: 8 Result(i: 1.732051 | j: 2.828427 | ij:
4.898979): 55.545629
ITER: 0 | i: 3 | j: 9 Result(i: 1.732051 | j: 3.000000 | ij:
5.196152): 60.741782
ITER: 0 | i: 4 | j: 0 Result(i: 2.000000 | j: 0.000000 | ij:
0.000000): 60.741782
ITER: 0 | i: 4 | j: 1 Result(i: 2.000000 | j: 1.000000 | ij:
2.000000): 62.741782
ITER: 0 | i: 4 | j: 2 Result(i: 2.000000 | j: 1.414214 | ij:
2.828427): 65.570209
ITER: 0 | i: 4 | j: 3 Result(i: 2.000000 | j: 1.732051 | ij:
3.464102): 69.034310
ITER: 0 | i: 4 | j: 4 Result(i: 2.000000 | j: 2.000000 | ij:
4.000000): 73.034310
ITER: 0 | i: 4 | j: 5 Result(i: 2.000000 | j: 2.236068 | ij:
4.472136): 77.506446
ITER: 0 | i: 4 | j: 6 Result(i: 2.000000 | j: 2.449490 | ij:
4.898979): 82.405426
ITER: 0 | i: 4 | j: 7 Result(i: 2.000000 | j: 2.645751 | ij:
5.291503): 87.696928
ITER: 0 | i: 4 | j: 8 Result(i: 2.000000 | j: 2.828427 | ij:
5.656854): 93.353783
ITER: 0 | i: 4 | j: 9 Result(i: 2.000000 | j: 3.000000 | ij:
6.000000): 99.353783
ITER: 0 | i: 5 | j: 0 Result(i: 2.236068 | j: 0.000000 | ij:
0.000000): 99.353783
ITER: 0 | i: 5 | j: 1 Result(i: 2.236068 | j: 1.000000 | ij:
2.236068): 101.589851
ITER: 0 | i: 5 | j: 2 Result(i: 2.236068 | j: 1.414214 | ij:
3.162278): 104.752128
ITER: 0 | i: 5 | j: 3 Result(i: 2.236068 | j: 1.732051 | ij:
3.872983): 108.625112
ITER: 0 | i: 5 | j: 4 Result(i: 2.236068 | j: 2.000000 | ij:
4.472136): 113.097248
ITER: 0 | i: 5 | j: 5 Result(i: 2.236068 | j: 2.236068 | ij:
5.000000): 118.097248
ITER: 0 | i: 5 | j: 6 Result(i: 2.236068 | j: 2.449490 | ij:
5.477226): 123.574473
ITER: 0 | i: 5 | j: 7 Result(i: 2.236068 | j: 2.645751 | ij:
5.916080): 129.490553
ITER: 0 | i: 5 | j: 8 Result(i: 2.236068 | j: 2.828427 | ij:
6.324555): 135.815108
ITER: 0 | i: 5 | j: 9 Result(i: 2.236068 | j: 3.000000 | ij:
6.708204): 142.523312
ITER: 0 | i: 6 | j: 0 Result(i: 2.449490 | j: 0.000000 | ij:
0.000000): 142.523312
ITER: 0 | i: 6 | j: 1 Result(i: 2.449490 | j: 1.000000 | ij:
2.449490): 144.972802
ITER: 0 | i: 6 | j: 2 Result(i: 2.449490 | j: 1.414214 | ij:
3.464102): 148.436904
ITER: 0 | i: 6 | j: 3 Result(i: 2.449490 | j: 1.732051 | ij:
4.242641): 152.679544
ITER: 0 | i: 6 | j: 4 Result(i: 2.449490 | j: 2.000000 | ij:
4.898979): 157.578524
ITER: 0 | i: 6 | j: 5 Result(i: 2.449490 | j: 2.236068 | ij:
5.477226): 163.055749
ITER: 0 | i: 6 | j: 6 Result(i: 2.449490 | j: 2.449490 | ij:
6.000000): 169.055749
ITER: 0 | i: 6 | j: 7 Result(i: 2.449490 | j: 2.645751 | ij:
6.480741): 175.536490
ITER: 0 | i: 6 | j: 8 Result(i: 2.449490 | j: 2.828427 | ij:
6.928203): 182.464693
ITER: 0 | i: 6 | j: 9 Result(i: 2.449490 | j: 3.000000 | ij:
7.348469): 189.813162
ITER: 0 | i: 7 | j: 0 Result(i: 2.645751 | j: 0.000000 | ij:
0.000000): 189.813162
ITER: 0 | i: 7 | j: 1 Result(i: 2.645751 | j: 1.000000 | ij:
2.645751): 192.458914
ITER: 0 | i: 7 | j: 2 Result(i: 2.645751 | j: 1.414214 | ij:
3.741657): 196.200571
ITER: 0 | i: 7 | j: 3 Result(i: 2.645751 | j: 1.732051 | ij:
4.582576): 200.783147
ITER: 0 | i: 7 | j: 4 Result(i: 2.645751 | j: 2.000000 | ij:
5.291503): 206.074649
ITER: 0 | i: 7 | j: 5 Result(i: 2.645751 | j: 2.236068 | ij:
5.916080): 211.990729
ITER: 0 | i: 7 | j: 6 Result(i: 2.645751 | j: 2.449490 | ij:
6.480741): 218.471470
ITER: 0 | i: 7 | j: 7 Result(i: 2.645751 | j: 2.645751 | ij:
7.000000): 225.471470
ITER: 0 | i: 7 | j: 8 Result(i: 2.645751 | j: 2.828427 | ij:
7.483315): 232.954785
ITER: 0 | i: 7 | j: 9 Result(i: 2.645751 | j: 3.000000 | ij:
7.937254): 240.892039
ITER: 0 | i: 8 | j: 0 Result(i: 2.828427 | j: 0.000000 | ij:
0.000000): 240.892039
ITER: 0 | i: 8 | j: 1 Result(i: 2.828427 | j: 1.000000 | ij:
2.828427): 243.720466
ITER: 0 | i: 8 | j: 2 Result(i: 2.828427 | j: 1.414214 | ij:
4.000000): 247.720466
ITER: 0 | i: 8 | j: 3 Result(i: 2.828427 | j: 1.732051 | ij:
4.898979): 252.619445
ITER: 0 | i: 8 | j: 4 Result(i: 2.828427 | j: 2.000000 | ij:
5.656854): 258.276300
ITER: 0 | i: 8 | j: 5 Result(i: 2.828427 | j: 2.236068 | ij:
6.324555): 264.600855
ITER: 0 | i: 8 | j: 6 Result(i: 2.828427 | j: 2.449490 | ij:
6.928203): 271.529058
ITER: 0 | i: 8 | j: 7 Result(i: 2.828427 | j: 2.645751 | ij:
7.483315): 279.012373
ITER: 0 | i: 8 | j: 8 Result(i: 2.828427 | j: 2.828427 | ij:
8.000000): 287.012373
ITER: 0 | i: 8 | j: 9 Result(i: 2.828427 | j: 3.000000 | ij:
8.485281): 295.497654
ITER: 0 | i: 9 | j: 0 Result(i: 3.000000 | j: 0.000000 | ij:
0.000000): 295.497654
ITER: 0 | i: 9 | j: 1 Result(i: 3.000000 | j: 1.000000 | ij:
3.000000): 298.497654
ITER: 0 | i: 9 | j: 2 Result(i: 3.000000 | j: 1.414214 | ij:
4.242641): 302.740295
ITER: 0 | i: 9 | j: 3 Result(i: 3.000000 | j: 1.732051 | ij:
5.196152): 307.936447
ITER: 0 | i: 9 | j: 4 Result(i: 3.000000 | j: 2.000000 | ij:
6.000000): 313.936447
ITER: 0 | i: 9 | j: 5 Result(i: 3.000000 | j: 2.236068 | ij:
6.708204): 320.644651
ITER: 0 | i: 9 | j: 6 Result(i: 3.000000 | j: 2.449490 | ij:
7.348469): 327.993120
ITER: 0 | i: 9 | j: 7 Result(i: 3.000000 | j: 2.645751 | ij:
7.937254): 335.930374
ITER: 0 | i: 9 | j: 8 Result(i: 3.000000 | j: 2.828427 | ij:
8.485281): 344.415656
ITER: 0 | i: 9 | j: 9 Result(i: 3.000000 | j: 3.000000 | ij:
9.000000): 353.415656
Final Result: 353.415656
ITER: 1 | i: 0 | j: 0 Result(i: 0.000000 | j: 0.000000 | ij:
0.000000): 0.000000
ITER: 1 | i: 0 | j: 1 Result(i: 0.000000 | j: 1.000000 | ij:
0.000000): 0.000000
ITER: 1 | i: 0 | j: 2 Result(i: 0.000000 | j: 1.414214 | ij:
0.000000): 0.000000
ITER: 1 | i: 0 | j: 3 Result(i: 0.000000 | j: 1.732051 | ij:
0.000000): 0.000000
ITER: 1 | i: 0 | j: 4 Result(i: 0.000000 | j: 2.000000 | ij:
0.000000): 0.000000
ITER: 1 | i: 0 | j: 5 Result(i: 0.000000 | j: 2.236068 | ij:
0.000000): 0.000000
ITER: 1 | i: 0 | j: 6 Result(i: 0.000000 | j: 2.449490 | ij:
0.000000): 0.000000
ITER: 1 | i: 0 | j: 7 Result(i: 0.000000 | j: 2.645751 | ij:
0.000000): 0.000000
ITER: 1 | i: 0 | j: 8 Result(i: 0.000000 | j: 2.828427 | ij:
0.000000): 0.000000
ITER: 1 | i: 0 | j: 9 Result(i: 0.000000 | j: 3.000000 | ij:
0.000000): 0.000000
ITER: 1 | i: 1 | j: 0 Result(i: 1.000000 | j: 0.000000 | ij:
0.000000): 0.000000
ITER: 1 | i: 1 | j: 1 Result(i: 1.000000 | j: 1.000000 | ij:
1.000000): 1.000000
ITER: 1 | i: 1 | j: 2 Result(i: 1.000000 | j: 1.414214 | ij:
1.414214): 2.414214
ITER: 1 | i: 1 | j: 3 Result(i: 1.000000 | j: 1.732051 | ij:
1.732051): 4.146264
ITER: 1 | i: 1 | j: 4 Result(i: 1.000000 | j: 2.000000 | ij:
2.000000): 6.146264
ITER: 1 | i: 1 | j: 5 Result(i: 1.000000 | j: 2.236068 | ij:
2.236068): 8.382332
ITER: 1 | i: 1 | j: 6 Result(i: 1.000000 | j: 2.449490 | ij:
2.449490): 10.831822
ITER: 1 | i: 1 | j: 7 Result(i: 1.000000 | j: 2.645751 | ij:
2.645751): 13.477573
ITER: 1 | i: 1 | j: 8 Result(i: 1.000000 | j: 2.828427 | ij:
2.828427): 16.306001
ITER: 1 | i: 1 | j: 9 Result(i: 1.000000 | j: 3.000000 | ij:
3.000000): 19.306001
ITER: 1 | i: 2 | j: 0 Result(i: 1.414214 | j: 0.000000 | ij:
0.000000): 19.306001
ITER: 1 | i: 2 | j: 1 Result(i: 1.414214 | j: 1.000000 | ij:
1.414214): 20.720214
ITER: 1 | i: 2 | j: 2 Result(i: 1.414214 | j: 1.414214 | ij:
2.000000): 22.720214
ITER: 1 | i: 2 | j: 3 Result(i: 1.414214 | j: 1.732051 | ij:
2.449490): 25.169704
ITER: 1 | i: 2 | j: 4 Result(i: 1.414214 | j: 2.000000 | ij:
2.828427): 27.998131
ITER: 1 | i: 2 | j: 5 Result(i: 1.414214 | j: 2.236068 | ij:
3.162278): 31.160409
ITER: 1 | i: 2 | j: 6 Result(i: 1.414214 | j: 2.449490 | ij:
3.464102): 34.624510
ITER: 1 | i: 2 | j: 7 Result(i: 1.414214 | j: 2.645751 | ij:
3.741657): 38.366168
ITER: 1 | i: 2 | j: 8 Result(i: 1.414214 | j: 2.828427 | ij:
4.000000): 42.366168
ITER: 1 | i: 2 | j: 9 Result(i: 1.414214 | j: 3.000000 | ij:
4.242641): 46.608808
ITER: 1 | i: 3 | j: 0 Result(i: 1.732051 | j: 0.000000 | ij:
0.000000): 46.608808
ITER: 1 | i: 3 | j: 1 Result(i: 1.732051 | j: 1.000000 | ij:
1.732051): 48.340859
ITER: 1 | i: 3 | j: 2 Result(i: 1.732051 | j: 1.414214 | ij:
2.449490): 50.790349
ITER: 1 | i: 3 | j: 3 Result(i: 1.732051 | j: 1.732051 | ij:
3.000000): 53.790349
ITER: 1 | i: 3 | j: 4 Result(i: 1.732051 | j: 2.000000 | ij:
3.464102): 57.254450
ITER: 1 | i: 3 | j: 5 Result(i: 1.732051 | j: 2.236068 | ij:
3.872983): 61.127434
ITER: 1 | i: 3 | j: 6 Result(i: 1.732051 | j: 2.449490 | ij:
4.242641): 65.370075
ITER: 1 | i: 3 | j: 7 Result(i: 1.732051 | j: 2.645751 | ij:
4.582576): 69.952650
ITER: 1 | i: 3 | j: 8 Result(i: 1.732051 | j: 2.828427 | ij:
4.898979): 74.851630
ITER: 1 | i: 3 | j: 9 Result(i: 1.732051 | j: 3.000000 | ij:
5.196152): 80.047782
ITER: 1 | i: 4 | j: 0 Result(i: 2.000000 | j: 0.000000 | ij:
0.000000): 80.047782
ITER: 1 | i: 4 | j: 1 Result(i: 2.000000 | j: 1.000000 | ij:
2.000000): 82.047782
ITER: 1 | i: 4 | j: 2 Result(i: 2.000000 | j: 1.414214 | ij:
2.828427): 84.876209
ITER: 1 | i: 4 | j: 3 Result(i: 2.000000 | j: 1.732051 | ij:
3.464102): 88.340311
ITER: 1 | i: 4 | j: 4 Result(i: 2.000000 | j: 2.000000 | ij:
4.000000): 92.340311
ITER: 1 | i: 4 | j: 5 Result(i: 2.000000 | j: 2.236068 | ij:
4.472136): 96.812447
ITER: 1 | i: 4 | j: 6 Result(i: 2.000000 | j: 2.449490 | ij:
4.898979): 101.711426
ITER: 1 | i: 4 | j: 7 Result(i: 2.000000 | j: 2.645751 | ij:
5.291503): 107.002929
ITER: 1 | i: 4 | j: 8 Result(i: 2.000000 | j: 2.828427 | ij:
5.656854): 112.659783
ITER: 1 | i: 4 | j: 9 Result(i: 2.000000 | j: 3.000000 | ij:
6.000000): 118.659783
ITER: 1 | i: 5 | j: 0 Result(i: 2.236068 | j: 0.000000 | ij:
0.000000): 118.659783
ITER: 1 | i: 5 | j: 1 Result(i: 2.236068 | j: 1.000000 | ij:
2.236068): 120.895851
ITER: 1 | i: 5 | j: 2 Result(i: 2.236068 | j: 1.414214 | ij:
3.162278): 124.058129
ITER: 1 | i: 5 | j: 3 Result(i: 2.236068 | j: 1.732051 | ij:
3.872983): 127.931112
ITER: 1 | i: 5 | j: 4 Result(i: 2.236068 | j: 2.000000 | ij:
4.472136): 132.403248
ITER: 1 | i: 5 | j: 5 Result(i: 2.236068 | j: 2.236068 | ij:
5.000000): 137.403248
ITER: 1 | i: 5 | j: 6 Result(i: 2.236068 | j: 2.449490 | ij:
5.477226): 142.880474
ITER: 1 | i: 5 | j: 7 Result(i: 2.236068 | j: 2.645751 | ij:
5.916080): 148.796553
ITER: 1 | i: 5 | j: 8 Result(i: 2.236068 | j: 2.828427 | ij:
6.324555): 155.121109
ITER: 1 | i: 5 | j: 9 Result(i: 2.236068 | j: 3.000000 | ij:
6.708204): 161.829313
ITER: 1 | i: 6 | j: 0 Result(i: 2.449490 | j: 0.000000 | ij:
0.000000): 161.829313
ITER: 1 | i: 6 | j: 1 Result(i: 2.449490 | j: 1.000000 | ij:
2.449490): 164.278802
ITER: 1 | i: 6 | j: 2 Result(i: 2.449490 | j: 1.414214 | ij:
3.464102): 167.742904
ITER: 1 | i: 6 | j: 3 Result(i: 2.449490 | j: 1.732051 | ij:
4.242641): 171.985545
ITER: 1 | i: 6 | j: 4 Result(i: 2.449490 | j: 2.000000 | ij:
4.898979): 176.884524
ITER: 1 | i: 6 | j: 5 Result(i: 2.449490 | j: 2.236068 | ij:
5.477226): 182.361750
ITER: 1 | i: 6 | j: 6 Result(i: 2.449490 | j: 2.449490 | ij:
6.000000): 188.361750
ITER: 1 | i: 6 | j: 7 Result(i: 2.449490 | j: 2.645751 | ij:
6.480741): 194.842491
ITER: 1 | i: 6 | j: 8 Result(i: 2.449490 | j: 2.828427 | ij:
6.928203): 201.770694
ITER: 1 | i: 6 | j: 9 Result(i: 2.449490 | j: 3.000000 | ij:
7.348469): 209.119163
ITER: 1 | i: 7 | j: 0 Result(i: 2.645751 | j: 0.000000 | ij:
0.000000): 209.119163
ITER: 1 | i: 7 | j: 1 Result(i: 2.645751 | j: 1.000000 | ij:
2.645751): 211.764914
ITER: 1 | i: 7 | j: 2 Result(i: 2.645751 | j: 1.414214 | ij:
3.741657): 215.506572
ITER: 1 | i: 7 | j: 3 Result(i: 2.645751 | j: 1.732051 | ij:
4.582576): 220.089147
ITER: 1 | i: 7 | j: 4 Result(i: 2.645751 | j: 2.000000 | ij:
5.291503): 225.380650
ITER: 1 | i: 7 | j: 5 Result(i: 2.645751 | j: 2.236068 | ij:
5.916080): 231.296730
ITER: 1 | i: 7 | j: 6 Result(i: 2.645751 | j: 2.449490 | ij:
6.480741): 237.777470
ITER: 1 | i: 7 | j: 7 Result(i: 2.645751 | j: 2.645751 | ij:
7.000000): 244.777470
ITER: 1 | i: 7 | j: 8 Result(i: 2.645751 | j: 2.828427 | ij:
7.483315): 252.260785
ITER: 1 | i: 7 | j: 9 Result(i: 2.645751 | j: 3.000000 | ij:
7.937254): 260.198039
ITER: 1 | i: 8 | j: 0 Result(i: 2.828427 | j: 0.000000 | ij:
0.000000): 260.198039
ITER: 1 | i: 8 | j: 1 Result(i: 2.828427 | j: 1.000000 | ij:
2.828427): 263.026466
ITER: 1 | i: 8 | j: 2 Result(i: 2.828427 | j: 1.414214 | ij:
4.000000): 267.026466
ITER: 1 | i: 8 | j: 3 Result(i: 2.828427 | j: 1.732051 | ij:
4.898979): 271.925446
ITER: 1 | i: 8 | j: 4 Result(i: 2.828427 | j: 2.000000 | ij:
5.656854): 277.582300
ITER: 1 | i: 8 | j: 5 Result(i: 2.828427 | j: 2.236068 | ij:
6.324555): 283.906855
ITER: 1 | i: 8 | j: 6 Result(i: 2.828427 | j: 2.449490 | ij:
6.928203): 290.835059
ITER: 1 | i: 8 | j: 7 Result(i: 2.828427 | j: 2.645751 | ij:
7.483315): 298.318373
ITER: 1 | i: 8 | j: 8 Result(i: 2.828427 | j: 2.828427 | ij:
8.000000): 306.318373
ITER: 1 | i: 8 | j: 9 Result(i: 2.828427 | j: 3.000000 | ij:
8.485281): 314.803655
ITER: 1 | i: 9 | j: 0 Result(i: 3.000000 | j: 0.000000 | ij:
0.000000): 314.803655
ITER: 1 | i: 9 | j: 1 Result(i: 3.000000 | j: 1.000000 | ij:
3.000000): 317.803655
ITER: 1 | i: 9 | j: 2 Result(i: 3.000000 | j: 1.414214 | ij:
4.242641): 322.046295
ITER: 1 | i: 9 | j: 3 Result(i: 3.000000 | j: 1.732051 | ij:
5.196152): 327.242448
ITER: 1 | i: 9 | j: 4 Result(i: 3.000000 | j: 2.000000 | ij:
6.000000): 333.242448
ITER: 1 | i: 9 | j: 5 Result(i: 3.000000 | j: 2.236068 | ij:
6.708204): 339.950652
ITER: 1 | i: 9 | j: 6 Result(i: 3.000000 | j: 2.449490 | ij:
7.348469): 347.299121
ITER: 1 | i: 9 | j: 7 Result(i: 3.000000 | j: 2.645751 | ij:
7.937254): 355.236375
ITER: 1 | i: 9 | j: 8 Result(i: 3.000000 | j: 2.828427 | ij:
8.485281): 363.721656
ITER: 1 | i: 9 | j: 9 Result(i: 3.000000 | j: 3.000000 | ij:
9.000000): 372.721656
Final Result: 372.721656
As we can see in the following iterations the sqrt(1) as well as
result is set to zero for some reason.
ITER: 0 | i: 1 | j: 4 Result(i: 0.000000 | j: 2.000000 | ij:
0.000000): 0.000000
ITER: 0 | i: 1 | j: 5 Result(i: 0.000000 | j: 2.236068 | ij:
0.000000): 0.000000
ITER: 0 | i: 1 | j: 6 Result(i: 0.000000 | j: 2.449490 | ij:
0.000000): 0.000000
ITER: 0 | i: 1 | j: 7 Result(i: 0.000000 | j: 2.645751 | ij:
0.000000): 0.000000
ITER: 0 | i: 1 | j: 8 Result(i: 0.000000 | j: 2.828427 | ij:
0.000000): 0.000000
ITER: 0 | i: 1 | j: 9 Result(i: 0.000000 | j: 3.000000 | ij:
0.000000): 0.000000
Please help me to resolve the accuracy issue! I think that it will
be very useful for gem5 community.
To be noticed, I find the correct simulated tick in which the
application started in FS (using m5 dumpstats), and I start the
--debug-start, but the trace file which is generated is 10x larger
than SE mode for the same application. How can I compare them?
Thank you in advance!
Best regards,
Nikos
Quoting Νικόλαος Ταμπουρατζής ntampouratzis@ece.auth.gr:
Dear Jason,
I am trying to use --debug-start but in FS mode it is very
difficult to find the tick on which the application is started!
However, I am writing the following very simple c++ program:
#include <cmath>
#include <stdio.h>
int main(){
int dim = 4096;
double result;
for (int iter = 0; iter < 2; iter++){
result = 0;
for (int i = 0; i < dim; i++){
for (int j = 0; j < dim; j++){
result += sqrt(i) * sqrt(j);
}
}
printf("Result: %lf\n", result); //Result:
}
}
I cross-compile it using: riscv64-linux-gnu-g++ -static -O3 -o
test_riscv test_riscv.cpp
While in X86 (without cross-compilation of course), QEMU-RISCV,
GEM5-SE the result is the same (30530733453.127449), in GEM5-FS
result is different! In addition, the result is also different
between the 2 iterations.
Please reproduce the error if you want in order to verify my
Ηow can the issue be resolved?
Thank you in advance!
Best regards,
Nikos
Quoting Jason Lowe-Power jason@lowepower.com:
Hi Nikos,
You can use --debug-start to start the debugging after some
ticks. Also, I would expect that the difference should come up
no need to run the program to the end.
For the FS mode one, you will want to just start the trace as
application starts. This could be a bit of a pain.
I'm not really sure what fundamentally could be different. FS
use the exact same code for executing instructions, so I don't
the problem. Have you tried running for smaller inputs or just
Dear Bobby,
Iam trying to add --debug-flags=Exec (building the gem5 for
not for gem5.fast which I had) but the debug traces exceed the
(and it is not finished yet) for less than 1 simulated second.
I reduce the size of the debug-flags (or set something more
In contrast I build the HPCG benchmark with DHPCG_DEBUG flag.
want, you can compare these two output files
(hpcg20010909T014640_SE_Mode & HPCG-Benchmark_3.1_FS_Mode). As
see, something goes wrong with the accuracy of calculations in
(benchmark uses double precission). You can find the files
That's quite odd that it works in SE mode but not FS mode!
I would suggest running with --debug-flags=Exec for both and
diff to see how they differ.
Cheers,
Jason
On Tue, Sep 20, 2022 at 2:45 PM Νικόλαος Ταμπουρατζής <
ntampouratzis@ece.auth.gr> wrote:
Dear Bobby,
In QEMU I get the same (correct) results that I get in SE
simulation. I get invalid results in FS simulation (in both
riscv-fs.py and riscv-ubuntu-run.py). I cannot access real
hardware at this moment, however, if you want you may
Please let me know if you have any updates!
Best regards,
Nikos
Quoting Jason Lowe-Power jason@lowepower.com:
Hi Nikos,
I notice you said the following in your original email:
In addition, I used the RISCV Ubuntu image
I installed the gcc compiler, compile it (through qemu)
Is this saying you get the wrong results is QEMU? If so,
or the HPCG workload, not in gem5. If not, I would test in
sure the binary works there. Another way you could test to
problem is your binary or gem5 would be to run it on real
access to some RISC-V hardware here at UC Davis, if you
Dear Bobby,
- I use the original riscv-fs.py which is provided in the
release.
I run the gem5 once (./build/RISCV/gem5.fast -d
./configs/example/gem5_library/riscv-fs.py) in order to
riscv-bootloader-vmlinux-5.10 and riscv-disk-img.
After this I mount the riscv-disk-img (sudo mount -o loop
riscv-disk-img /mnt), put the xhpcg executable and I do the
changes in riscv-fs.py to boot the riscv-disk-img with
image = CustomDiskImageResource(
local_path =
"/home/cossim/.cache/gem5/riscv-disk-img",
)
Set the Full System workload.
board.set_kernel_disk_workload(
kernel=Resource("riscv-bootloader-vmlinux-5.10"),
disk_image=image,
)
Finally, in the
gem5/src/python/gem5/components/boards/riscv_board.py
I change the last line to "return ["console=ttyS0",
"root={root_value}", "rw"]" in order to allow the write
the image.
- The HPCG benchmark after some iterations calculates if
are valid or not valid. In the case of FS it gives invalid
I see from the results, one (at least) problem is that
different results in each HPCG execution (with the same
I'm going to need a bit more information to help:
- In what way have you modified
./configs/example/gem5_library/riscv-fs.py? Can you
- What error are you getting or in what way are the
Dear gem5 community,
I have successfully cross-compile the HPCG benchmark for
version, without MPI and OpenMP). While it working
mode (./build/RISCV/gem5.fast -d ./HPCG_SE_results
./configs/example/se.py -c xhpcg --options '--nx=16
--npx=1 --npy=1 --npz=1 --rt=0.1'), I get invalid
simulation using "./build/RISCV/gem5.fast -d
./configs/example/gem5_library/riscv-fs.py" (I mount the
and put it).
Can you help me please?
In addition, I used the RISCV Ubuntu image
(
I installed the gcc compiler, compile it (through qemu)
wrong results too.
Here is the Makefile which I use, the hpcg executable
Jason and I had a theory that this may be due to the "Rounding Mode" for
floating pointing being set incorrectly in FS mode. That's set via a macro
here:
https://gem5.googlesource.com/public/gem5/+/refs/tags/v22.0.0.2/src/arch/riscv/fp_inst.hh#36
I manually expanded the macro here:
https://gem5.googlesource.com/public/gem5/+/refs/tags/v22.0.0.2/src/arch/riscv/isa/decoder.isa#1495,
inside the "fsqrt_d" definition then compiled "build/ALL/gem5.debug". Then
used gdb to add a breakpoint in the "Fsqrt_d::execute" function (in the
generated "build/ALL/arch/riscv/generated/exec-ns.cc.inc" file).
```
gdb build/ALL/gem5.opt
break Fsqrt_d::execute
run bug-recreation/se-mode-run.py # or `run bug-recreation/fs-mode-run.py`
```
Stepping through with gdb I the rounding mode is `0` for SE mode and `0`
for FS mode as well. So, no luck with that theory.
My new theory is that this bug has something to do with thread context
switching being implemented incorrectly in RISC-V somehow. I find it
strange that the sqrt(1) works fine for a while (i.e. returns `1`) then
suddenly starts returning zero after a certain point in the execution. In
addition, it's odd that the loop is not returning the same value each time
despite executing the same code. It'd make sense to me that the thread is
being stored and then resumed with some corruption of the floating point
data. This would also explain why this bug only occurs in FS mode.
I'll try to find time to figure out a good test for this. If anyone has any
other theories or ideas then let me know.
--
Dr. Bobby R. Bruce
Room 3050,
Kemper Hall, UC Davis
Davis,
CA, 95616
web: https://www.bobbybruce.net
On Fri, Oct 7, 2022 at 12:50 PM Νικόλαος Ταμπουρατζής <
ntampouratzis@ece.auth.gr> wrote:
>
> Dear Jason & Boddy,
>
> Unfortunately, I have tried my simple example without the sqrt
> function and the problem remains. Specifically, I have the following
> simple code:
>
>
> #include <cmath>
> #include <stdio.h>
>
> int main(){
>
> int dim = 1024;
>
> double result;
>
> for (int iter = 0; iter < 2; iter++){
> result = 0;
> for (int i = 0; i < dim; i++){
> for (int j = 0; j < dim; j++){
> result += i * j;
> }
> }
> printf("Final Result: %lf\n", result);
> }
> }
>
>
> In the above code, the correct result is 274341298176.000000 (from
> RISCV-SE mode and x86), while in FS mode I get sometimes the correct
> result and other times a different number.
>
> Best regards,
> Nikos
>
>
> Quoting Jason Lowe-Power <jason@lowepower.com>:
>
> > I have an idea...
> >
> > Have you put a breakpoint in the implementation of the fsqrt_d
function? I
> > would like to know if when running in SE mode and running in FS mode we
are
> > using the same rounding mode. My hypothesis is that in FS mode the
rounding
> > mode is set differently.
> >
> > Cheers,
> > Jason
> >
> > On Fri, Oct 7, 2022 at 12:15 AM Νικόλαος Ταμπουρατζής <
> > ntampouratzis@ece.auth.gr> wrote:
> >
> >> Dear Boddy,
> >>
> >> Thanks a lot for the effort! I looked in detail and I observe that the
> >> problem is created only using float and double variables (in the case
> >> of int it is working properly in FS mode). Specifically, in the case
> >> of float the variables are set to "nan", while in the case of double
> >> the variables are set to 0.000000 (in random time - probably from some
> >> instruction of simulated OS?). You may use a simple c/c++ example in
> >> order to get some traces before going to HPCG...
> >>
> >> Thank you in advance!!
> >> Best regards,
> >> Nikos
> >>
> >>
> >> Quoting Bobby Bruce <bbruce@ucdavis.edu>:
> >>
> >> > Hey Niko,
> >> >
> >> > Thanks for this analysis. I jumped a little into this today but
didn't
> >> get
> >> > as far as you did. I wanted to find a quick way to recreate the
> >> following:
> >> > https://gem5-review.googlesource.com/c/public/gem5/+/64211. Please
feel
> >> > free to use this, if it helps any.
> >> >
> >> > It's very strange to me that this bug hasn't manifested itself
before but
> >> > it's undeniably there. I'll try to spend more time looking at this
> >> tomorrow
> >> > with some traces and debug flags and see if I can narrow down the
> >> problem.
> >> >
> >> > --
> >> > Dr. Bobby R. Bruce
> >> > Room 3050,
> >> > Kemper Hall, UC Davis
> >> > Davis,
> >> > CA, 95616
> >> >
> >> > web: https://www.bobbybruce.net
> >> >
> >> >
> >> > On Wed, Oct 5, 2022 at 2:26 PM Νικόλαος Ταμπουρατζής <
> >> > ntampouratzis@ece.auth.gr> wrote:
> >> >
> >> >> In my previous results, I had used double (not float) for the
> >> >> following variables: result, sq_i and sq_j. In the case of float
> >> >> instead of double I get "nan" and not 0.000000.
> >> >>
> >> >> Quoting Νικόλαος Ταμπουρατζής <ntampouratzis@ece.auth.gr>:
> >> >>
> >> >> > Dear Jason, all,
> >> >> >
> >> >> > I am trying to find the accuracy problem with RISCV-FS and I
observe
> >> >> > that the problem is created (at least in my dummy example) because
> >> >> > the variables (double) are set to zero in random simulated time
(for
> >> >> > this reason I get different results among executions of the same
> >> >> > code). Specifically for the following dummy code:
> >> >> >
> >> >> >
> >> >> > #include <cmath>
> >> >> > #include <stdio.h>
> >> >> >
> >> >> > int main(){
> >> >> >
> >> >> > int dim = 10;
> >> >> >
> >> >> > float result;
> >> >> >
> >> >> > for (int iter = 0; iter < 2; iter++){
> >> >> > result = 0;
> >> >> > for (int i = 0; i < dim; i++){
> >> >> > for (int j = 0; j < dim; j++){
> >> >> > float sq_i = sqrt(i);
> >> >> > float sq_j = sqrt(j);
> >> >> > result += sq_i * sq_j;
> >> >> > printf("ITER: %d | i: %d | j: %d Result(i: %f | j:
> >> >> > %f | i*j: %f): %f\n", iter, i , j, sq_i, sq_j, sq_i * sq_j,
result);
> >> >> > }
> >> >> > }
> >> >> > printf("Final Result: %lf\n", result);
> >> >> > }
> >> >> > }
> >> >> >
> >> >> >
> >> >> > The correct Final Result in both iterations is 372.721656.
However,
> >> >> > I get the following results in FS:
> >> >> >
> >> >> > ITER: 0 | i: 0 | j: 0 Result(i: 0.000000 | j: 0.000000 | i*j:
> >> >> > 0.000000): 0.000000
> >> >> > ITER: 0 | i: 0 | j: 1 Result(i: 0.000000 | j: 1.000000 | i*j:
> >> >> > 0.000000): 0.000000
> >> >> > ITER: 0 | i: 0 | j: 2 Result(i: 0.000000 | j: 1.414214 | i*j:
> >> >> > 0.000000): 0.000000
> >> >> > ITER: 0 | i: 0 | j: 3 Result(i: 0.000000 | j: 1.732051 | i*j:
> >> >> > 0.000000): 0.000000
> >> >> > ITER: 0 | i: 0 | j: 4 Result(i: 0.000000 | j: 2.000000 | i*j:
> >> >> > 0.000000): 0.000000
> >> >> > ITER: 0 | i: 0 | j: 5 Result(i: 0.000000 | j: 2.236068 | i*j:
> >> >> > 0.000000): 0.000000
> >> >> > ITER: 0 | i: 0 | j: 6 Result(i: 0.000000 | j: 2.449490 | i*j:
> >> >> > 0.000000): 0.000000
> >> >> > ITER: 0 | i: 0 | j: 7 Result(i: 0.000000 | j: 2.645751 | i*j:
> >> >> > 0.000000): 0.000000
> >> >> > ITER: 0 | i: 0 | j: 8 Result(i: 0.000000 | j: 2.828427 | i*j:
> >> >> > 0.000000): 0.000000
> >> >> > ITER: 0 | i: 0 | j: 9 Result(i: 0.000000 | j: 3.000000 | i*j:
> >> >> > 0.000000): 0.000000
> >> >> > ITER: 0 | i: 1 | j: 0 Result(i: 1.000000 | j: 0.000000 | i*j:
> >> >> > 0.000000): 0.000000
> >> >> > ITER: 0 | i: 1 | j: 1 Result(i: 1.000000 | j: 1.000000 | i*j:
> >> >> > 1.000000): 1.000000
> >> >> > ITER: 0 | i: 1 | j: 2 Result(i: 1.000000 | j: 1.414214 | i*j:
> >> >> > 1.414214): 2.414214
> >> >> > ITER: 0 | i: 1 | j: 3 Result(i: 1.000000 | j: 1.732051 | i*j:
> >> >> > 1.732051): 4.146264
> >> >> > ITER: 0 | i: 1 | j: 4 Result(i: 0.000000 | j: 2.000000 | i*j:
> >> >> > 0.000000): 0.000000
> >> >> > ITER: 0 | i: 1 | j: 5 Result(i: 0.000000 | j: 2.236068 | i*j:
> >> >> > 0.000000): 0.000000
> >> >> > ITER: 0 | i: 1 | j: 6 Result(i: 0.000000 | j: 2.449490 | i*j:
> >> >> > 0.000000): 0.000000
> >> >> > ITER: 0 | i: 1 | j: 7 Result(i: 0.000000 | j: 2.645751 | i*j:
> >> >> > 0.000000): 0.000000
> >> >> > ITER: 0 | i: 1 | j: 8 Result(i: 0.000000 | j: 2.828427 | i*j:
> >> >> > 0.000000): 0.000000
> >> >> > ITER: 0 | i: 1 | j: 9 Result(i: 0.000000 | j: 3.000000 | i*j:
> >> >> > 0.000000): 0.000000
> >> >> > ITER: 0 | i: 2 | j: 0 Result(i: 1.414214 | j: 0.000000 | i*j:
> >> >> > 0.000000): 0.000000
> >> >> > ITER: 0 | i: 2 | j: 1 Result(i: 1.414214 | j: 1.000000 | i*j:
> >> >> > 1.414214): 1.414214
> >> >> > ITER: 0 | i: 2 | j: 2 Result(i: 1.414214 | j: 1.414214 | i*j:
> >> >> > 2.000000): 3.414214
> >> >> > ITER: 0 | i: 2 | j: 3 Result(i: 1.414214 | j: 1.732051 | i*j:
> >> >> > 2.449490): 5.863703
> >> >> > ITER: 0 | i: 2 | j: 4 Result(i: 1.414214 | j: 2.000000 | i*j:
> >> >> > 2.828427): 8.692130
> >> >> > ITER: 0 | i: 2 | j: 5 Result(i: 1.414214 | j: 2.236068 | i*j:
> >> >> > 3.162278): 11.854408
> >> >> > ITER: 0 | i: 2 | j: 6 Result(i: 1.414214 | j: 2.449490 | i*j:
> >> >> > 3.464102): 15.318510
> >> >> > ITER: 0 | i: 2 | j: 7 Result(i: 1.414214 | j: 2.645751 | i*j:
> >> >> > 3.741657): 19.060167
> >> >> > ITER: 0 | i: 2 | j: 8 Result(i: 1.414214 | j: 2.828427 | i*j:
> >> >> > 4.000000): 23.060167
> >> >> > ITER: 0 | i: 2 | j: 9 Result(i: 1.414214 | j: 3.000000 | i*j:
> >> >> > 4.242641): 27.302808
> >> >> > ITER: 0 | i: 3 | j: 0 Result(i: 1.732051 | j: 0.000000 | i*j:
> >> >> > 0.000000): 27.302808
> >> >> > ITER: 0 | i: 3 | j: 1 Result(i: 1.732051 | j: 1.000000 | i*j:
> >> >> > 1.732051): 29.034859
> >> >> > ITER: 0 | i: 3 | j: 2 Result(i: 1.732051 | j: 1.414214 | i*j:
> >> >> > 2.449490): 31.484348
> >> >> > ITER: 0 | i: 3 | j: 3 Result(i: 1.732051 | j: 1.732051 | i*j:
> >> >> > 3.000000): 34.484348
> >> >> > ITER: 0 | i: 3 | j: 4 Result(i: 1.732051 | j: 2.000000 | i*j:
> >> >> > 3.464102): 37.948450
> >> >> > ITER: 0 | i: 3 | j: 5 Result(i: 1.732051 | j: 2.236068 | i*j:
> >> >> > 3.872983): 41.821433
> >> >> > ITER: 0 | i: 3 | j: 6 Result(i: 1.732051 | j: 2.449490 | i*j:
> >> >> > 4.242641): 46.064074
> >> >> > ITER: 0 | i: 3 | j: 7 Result(i: 1.732051 | j: 2.645751 | i*j:
> >> >> > 4.582576): 50.646650
> >> >> > ITER: 0 | i: 3 | j: 8 Result(i: 1.732051 | j: 2.828427 | i*j:
> >> >> > 4.898979): 55.545629
> >> >> > ITER: 0 | i: 3 | j: 9 Result(i: 1.732051 | j: 3.000000 | i*j:
> >> >> > 5.196152): 60.741782
> >> >> > ITER: 0 | i: 4 | j: 0 Result(i: 2.000000 | j: 0.000000 | i*j:
> >> >> > 0.000000): 60.741782
> >> >> > ITER: 0 | i: 4 | j: 1 Result(i: 2.000000 | j: 1.000000 | i*j:
> >> >> > 2.000000): 62.741782
> >> >> > ITER: 0 | i: 4 | j: 2 Result(i: 2.000000 | j: 1.414214 | i*j:
> >> >> > 2.828427): 65.570209
> >> >> > ITER: 0 | i: 4 | j: 3 Result(i: 2.000000 | j: 1.732051 | i*j:
> >> >> > 3.464102): 69.034310
> >> >> > ITER: 0 | i: 4 | j: 4 Result(i: 2.000000 | j: 2.000000 | i*j:
> >> >> > 4.000000): 73.034310
> >> >> > ITER: 0 | i: 4 | j: 5 Result(i: 2.000000 | j: 2.236068 | i*j:
> >> >> > 4.472136): 77.506446
> >> >> > ITER: 0 | i: 4 | j: 6 Result(i: 2.000000 | j: 2.449490 | i*j:
> >> >> > 4.898979): 82.405426
> >> >> > ITER: 0 | i: 4 | j: 7 Result(i: 2.000000 | j: 2.645751 | i*j:
> >> >> > 5.291503): 87.696928
> >> >> > ITER: 0 | i: 4 | j: 8 Result(i: 2.000000 | j: 2.828427 | i*j:
> >> >> > 5.656854): 93.353783
> >> >> > ITER: 0 | i: 4 | j: 9 Result(i: 2.000000 | j: 3.000000 | i*j:
> >> >> > 6.000000): 99.353783
> >> >> > ITER: 0 | i: 5 | j: 0 Result(i: 2.236068 | j: 0.000000 | i*j:
> >> >> > 0.000000): 99.353783
> >> >> > ITER: 0 | i: 5 | j: 1 Result(i: 2.236068 | j: 1.000000 | i*j:
> >> >> > 2.236068): 101.589851
> >> >> > ITER: 0 | i: 5 | j: 2 Result(i: 2.236068 | j: 1.414214 | i*j:
> >> >> > 3.162278): 104.752128
> >> >> > ITER: 0 | i: 5 | j: 3 Result(i: 2.236068 | j: 1.732051 | i*j:
> >> >> > 3.872983): 108.625112
> >> >> > ITER: 0 | i: 5 | j: 4 Result(i: 2.236068 | j: 2.000000 | i*j:
> >> >> > 4.472136): 113.097248
> >> >> > ITER: 0 | i: 5 | j: 5 Result(i: 2.236068 | j: 2.236068 | i*j:
> >> >> > 5.000000): 118.097248
> >> >> > ITER: 0 | i: 5 | j: 6 Result(i: 2.236068 | j: 2.449490 | i*j:
> >> >> > 5.477226): 123.574473
> >> >> > ITER: 0 | i: 5 | j: 7 Result(i: 2.236068 | j: 2.645751 | i*j:
> >> >> > 5.916080): 129.490553
> >> >> > ITER: 0 | i: 5 | j: 8 Result(i: 2.236068 | j: 2.828427 | i*j:
> >> >> > 6.324555): 135.815108
> >> >> > ITER: 0 | i: 5 | j: 9 Result(i: 2.236068 | j: 3.000000 | i*j:
> >> >> > 6.708204): 142.523312
> >> >> > ITER: 0 | i: 6 | j: 0 Result(i: 2.449490 | j: 0.000000 | i*j:
> >> >> > 0.000000): 142.523312
> >> >> > ITER: 0 | i: 6 | j: 1 Result(i: 2.449490 | j: 1.000000 | i*j:
> >> >> > 2.449490): 144.972802
> >> >> > ITER: 0 | i: 6 | j: 2 Result(i: 2.449490 | j: 1.414214 | i*j:
> >> >> > 3.464102): 148.436904
> >> >> > ITER: 0 | i: 6 | j: 3 Result(i: 2.449490 | j: 1.732051 | i*j:
> >> >> > 4.242641): 152.679544
> >> >> > ITER: 0 | i: 6 | j: 4 Result(i: 2.449490 | j: 2.000000 | i*j:
> >> >> > 4.898979): 157.578524
> >> >> > ITER: 0 | i: 6 | j: 5 Result(i: 2.449490 | j: 2.236068 | i*j:
> >> >> > 5.477226): 163.055749
> >> >> > ITER: 0 | i: 6 | j: 6 Result(i: 2.449490 | j: 2.449490 | i*j:
> >> >> > 6.000000): 169.055749
> >> >> > ITER: 0 | i: 6 | j: 7 Result(i: 2.449490 | j: 2.645751 | i*j:
> >> >> > 6.480741): 175.536490
> >> >> > ITER: 0 | i: 6 | j: 8 Result(i: 2.449490 | j: 2.828427 | i*j:
> >> >> > 6.928203): 182.464693
> >> >> > ITER: 0 | i: 6 | j: 9 Result(i: 2.449490 | j: 3.000000 | i*j:
> >> >> > 7.348469): 189.813162
> >> >> > ITER: 0 | i: 7 | j: 0 Result(i: 2.645751 | j: 0.000000 | i*j:
> >> >> > 0.000000): 189.813162
> >> >> > ITER: 0 | i: 7 | j: 1 Result(i: 2.645751 | j: 1.000000 | i*j:
> >> >> > 2.645751): 192.458914
> >> >> > ITER: 0 | i: 7 | j: 2 Result(i: 2.645751 | j: 1.414214 | i*j:
> >> >> > 3.741657): 196.200571
> >> >> > ITER: 0 | i: 7 | j: 3 Result(i: 2.645751 | j: 1.732051 | i*j:
> >> >> > 4.582576): 200.783147
> >> >> > ITER: 0 | i: 7 | j: 4 Result(i: 2.645751 | j: 2.000000 | i*j:
> >> >> > 5.291503): 206.074649
> >> >> > ITER: 0 | i: 7 | j: 5 Result(i: 2.645751 | j: 2.236068 | i*j:
> >> >> > 5.916080): 211.990729
> >> >> > ITER: 0 | i: 7 | j: 6 Result(i: 2.645751 | j: 2.449490 | i*j:
> >> >> > 6.480741): 218.471470
> >> >> > ITER: 0 | i: 7 | j: 7 Result(i: 2.645751 | j: 2.645751 | i*j:
> >> >> > 7.000000): 225.471470
> >> >> > ITER: 0 | i: 7 | j: 8 Result(i: 2.645751 | j: 2.828427 | i*j:
> >> >> > 7.483315): 232.954785
> >> >> > ITER: 0 | i: 7 | j: 9 Result(i: 2.645751 | j: 3.000000 | i*j:
> >> >> > 7.937254): 240.892039
> >> >> > ITER: 0 | i: 8 | j: 0 Result(i: 2.828427 | j: 0.000000 | i*j:
> >> >> > 0.000000): 240.892039
> >> >> > ITER: 0 | i: 8 | j: 1 Result(i: 2.828427 | j: 1.000000 | i*j:
> >> >> > 2.828427): 243.720466
> >> >> > ITER: 0 | i: 8 | j: 2 Result(i: 2.828427 | j: 1.414214 | i*j:
> >> >> > 4.000000): 247.720466
> >> >> > ITER: 0 | i: 8 | j: 3 Result(i: 2.828427 | j: 1.732051 | i*j:
> >> >> > 4.898979): 252.619445
> >> >> > ITER: 0 | i: 8 | j: 4 Result(i: 2.828427 | j: 2.000000 | i*j:
> >> >> > 5.656854): 258.276300
> >> >> > ITER: 0 | i: 8 | j: 5 Result(i: 2.828427 | j: 2.236068 | i*j:
> >> >> > 6.324555): 264.600855
> >> >> > ITER: 0 | i: 8 | j: 6 Result(i: 2.828427 | j: 2.449490 | i*j:
> >> >> > 6.928203): 271.529058
> >> >> > ITER: 0 | i: 8 | j: 7 Result(i: 2.828427 | j: 2.645751 | i*j:
> >> >> > 7.483315): 279.012373
> >> >> > ITER: 0 | i: 8 | j: 8 Result(i: 2.828427 | j: 2.828427 | i*j:
> >> >> > 8.000000): 287.012373
> >> >> > ITER: 0 | i: 8 | j: 9 Result(i: 2.828427 | j: 3.000000 | i*j:
> >> >> > 8.485281): 295.497654
> >> >> > ITER: 0 | i: 9 | j: 0 Result(i: 3.000000 | j: 0.000000 | i*j:
> >> >> > 0.000000): 295.497654
> >> >> > ITER: 0 | i: 9 | j: 1 Result(i: 3.000000 | j: 1.000000 | i*j:
> >> >> > 3.000000): 298.497654
> >> >> > ITER: 0 | i: 9 | j: 2 Result(i: 3.000000 | j: 1.414214 | i*j:
> >> >> > 4.242641): 302.740295
> >> >> > ITER: 0 | i: 9 | j: 3 Result(i: 3.000000 | j: 1.732051 | i*j:
> >> >> > 5.196152): 307.936447
> >> >> > ITER: 0 | i: 9 | j: 4 Result(i: 3.000000 | j: 2.000000 | i*j:
> >> >> > 6.000000): 313.936447
> >> >> > ITER: 0 | i: 9 | j: 5 Result(i: 3.000000 | j: 2.236068 | i*j:
> >> >> > 6.708204): 320.644651
> >> >> > ITER: 0 | i: 9 | j: 6 Result(i: 3.000000 | j: 2.449490 | i*j:
> >> >> > 7.348469): 327.993120
> >> >> > ITER: 0 | i: 9 | j: 7 Result(i: 3.000000 | j: 2.645751 | i*j:
> >> >> > 7.937254): 335.930374
> >> >> > ITER: 0 | i: 9 | j: 8 Result(i: 3.000000 | j: 2.828427 | i*j:
> >> >> > 8.485281): 344.415656
> >> >> > ITER: 0 | i: 9 | j: 9 Result(i: 3.000000 | j: 3.000000 | i*j:
> >> >> > 9.000000): 353.415656
> >> >> > Final Result: 353.415656
> >> >> > ITER: 1 | i: 0 | j: 0 Result(i: 0.000000 | j: 0.000000 | i*j:
> >> >> > 0.000000): 0.000000
> >> >> > ITER: 1 | i: 0 | j: 1 Result(i: 0.000000 | j: 1.000000 | i*j:
> >> >> > 0.000000): 0.000000
> >> >> > ITER: 1 | i: 0 | j: 2 Result(i: 0.000000 | j: 1.414214 | i*j:
> >> >> > 0.000000): 0.000000
> >> >> > ITER: 1 | i: 0 | j: 3 Result(i: 0.000000 | j: 1.732051 | i*j:
> >> >> > 0.000000): 0.000000
> >> >> > ITER: 1 | i: 0 | j: 4 Result(i: 0.000000 | j: 2.000000 | i*j:
> >> >> > 0.000000): 0.000000
> >> >> > ITER: 1 | i: 0 | j: 5 Result(i: 0.000000 | j: 2.236068 | i*j:
> >> >> > 0.000000): 0.000000
> >> >> > ITER: 1 | i: 0 | j: 6 Result(i: 0.000000 | j: 2.449490 | i*j:
> >> >> > 0.000000): 0.000000
> >> >> > ITER: 1 | i: 0 | j: 7 Result(i: 0.000000 | j: 2.645751 | i*j:
> >> >> > 0.000000): 0.000000
> >> >> > ITER: 1 | i: 0 | j: 8 Result(i: 0.000000 | j: 2.828427 | i*j:
> >> >> > 0.000000): 0.000000
> >> >> > ITER: 1 | i: 0 | j: 9 Result(i: 0.000000 | j: 3.000000 | i*j:
> >> >> > 0.000000): 0.000000
> >> >> > ITER: 1 | i: 1 | j: 0 Result(i: 1.000000 | j: 0.000000 | i*j:
> >> >> > 0.000000): 0.000000
> >> >> > ITER: 1 | i: 1 | j: 1 Result(i: 1.000000 | j: 1.000000 | i*j:
> >> >> > 1.000000): 1.000000
> >> >> > ITER: 1 | i: 1 | j: 2 Result(i: 1.000000 | j: 1.414214 | i*j:
> >> >> > 1.414214): 2.414214
> >> >> > ITER: 1 | i: 1 | j: 3 Result(i: 1.000000 | j: 1.732051 | i*j:
> >> >> > 1.732051): 4.146264
> >> >> > ITER: 1 | i: 1 | j: 4 Result(i: 1.000000 | j: 2.000000 | i*j:
> >> >> > 2.000000): 6.146264
> >> >> > ITER: 1 | i: 1 | j: 5 Result(i: 1.000000 | j: 2.236068 | i*j:
> >> >> > 2.236068): 8.382332
> >> >> > ITER: 1 | i: 1 | j: 6 Result(i: 1.000000 | j: 2.449490 | i*j:
> >> >> > 2.449490): 10.831822
> >> >> > ITER: 1 | i: 1 | j: 7 Result(i: 1.000000 | j: 2.645751 | i*j:
> >> >> > 2.645751): 13.477573
> >> >> > ITER: 1 | i: 1 | j: 8 Result(i: 1.000000 | j: 2.828427 | i*j:
> >> >> > 2.828427): 16.306001
> >> >> > ITER: 1 | i: 1 | j: 9 Result(i: 1.000000 | j: 3.000000 | i*j:
> >> >> > 3.000000): 19.306001
> >> >> > ITER: 1 | i: 2 | j: 0 Result(i: 1.414214 | j: 0.000000 | i*j:
> >> >> > 0.000000): 19.306001
> >> >> > ITER: 1 | i: 2 | j: 1 Result(i: 1.414214 | j: 1.000000 | i*j:
> >> >> > 1.414214): 20.720214
> >> >> > ITER: 1 | i: 2 | j: 2 Result(i: 1.414214 | j: 1.414214 | i*j:
> >> >> > 2.000000): 22.720214
> >> >> > ITER: 1 | i: 2 | j: 3 Result(i: 1.414214 | j: 1.732051 | i*j:
> >> >> > 2.449490): 25.169704
> >> >> > ITER: 1 | i: 2 | j: 4 Result(i: 1.414214 | j: 2.000000 | i*j:
> >> >> > 2.828427): 27.998131
> >> >> > ITER: 1 | i: 2 | j: 5 Result(i: 1.414214 | j: 2.236068 | i*j:
> >> >> > 3.162278): 31.160409
> >> >> > ITER: 1 | i: 2 | j: 6 Result(i: 1.414214 | j: 2.449490 | i*j:
> >> >> > 3.464102): 34.624510
> >> >> > ITER: 1 | i: 2 | j: 7 Result(i: 1.414214 | j: 2.645751 | i*j:
> >> >> > 3.741657): 38.366168
> >> >> > ITER: 1 | i: 2 | j: 8 Result(i: 1.414214 | j: 2.828427 | i*j:
> >> >> > 4.000000): 42.366168
> >> >> > ITER: 1 | i: 2 | j: 9 Result(i: 1.414214 | j: 3.000000 | i*j:
> >> >> > 4.242641): 46.608808
> >> >> > ITER: 1 | i: 3 | j: 0 Result(i: 1.732051 | j: 0.000000 | i*j:
> >> >> > 0.000000): 46.608808
> >> >> > ITER: 1 | i: 3 | j: 1 Result(i: 1.732051 | j: 1.000000 | i*j:
> >> >> > 1.732051): 48.340859
> >> >> > ITER: 1 | i: 3 | j: 2 Result(i: 1.732051 | j: 1.414214 | i*j:
> >> >> > 2.449490): 50.790349
> >> >> > ITER: 1 | i: 3 | j: 3 Result(i: 1.732051 | j: 1.732051 | i*j:
> >> >> > 3.000000): 53.790349
> >> >> > ITER: 1 | i: 3 | j: 4 Result(i: 1.732051 | j: 2.000000 | i*j:
> >> >> > 3.464102): 57.254450
> >> >> > ITER: 1 | i: 3 | j: 5 Result(i: 1.732051 | j: 2.236068 | i*j:
> >> >> > 3.872983): 61.127434
> >> >> > ITER: 1 | i: 3 | j: 6 Result(i: 1.732051 | j: 2.449490 | i*j:
> >> >> > 4.242641): 65.370075
> >> >> > ITER: 1 | i: 3 | j: 7 Result(i: 1.732051 | j: 2.645751 | i*j:
> >> >> > 4.582576): 69.952650
> >> >> > ITER: 1 | i: 3 | j: 8 Result(i: 1.732051 | j: 2.828427 | i*j:
> >> >> > 4.898979): 74.851630
> >> >> > ITER: 1 | i: 3 | j: 9 Result(i: 1.732051 | j: 3.000000 | i*j:
> >> >> > 5.196152): 80.047782
> >> >> > ITER: 1 | i: 4 | j: 0 Result(i: 2.000000 | j: 0.000000 | i*j:
> >> >> > 0.000000): 80.047782
> >> >> > ITER: 1 | i: 4 | j: 1 Result(i: 2.000000 | j: 1.000000 | i*j:
> >> >> > 2.000000): 82.047782
> >> >> > ITER: 1 | i: 4 | j: 2 Result(i: 2.000000 | j: 1.414214 | i*j:
> >> >> > 2.828427): 84.876209
> >> >> > ITER: 1 | i: 4 | j: 3 Result(i: 2.000000 | j: 1.732051 | i*j:
> >> >> > 3.464102): 88.340311
> >> >> > ITER: 1 | i: 4 | j: 4 Result(i: 2.000000 | j: 2.000000 | i*j:
> >> >> > 4.000000): 92.340311
> >> >> > ITER: 1 | i: 4 | j: 5 Result(i: 2.000000 | j: 2.236068 | i*j:
> >> >> > 4.472136): 96.812447
> >> >> > ITER: 1 | i: 4 | j: 6 Result(i: 2.000000 | j: 2.449490 | i*j:
> >> >> > 4.898979): 101.711426
> >> >> > ITER: 1 | i: 4 | j: 7 Result(i: 2.000000 | j: 2.645751 | i*j:
> >> >> > 5.291503): 107.002929
> >> >> > ITER: 1 | i: 4 | j: 8 Result(i: 2.000000 | j: 2.828427 | i*j:
> >> >> > 5.656854): 112.659783
> >> >> > ITER: 1 | i: 4 | j: 9 Result(i: 2.000000 | j: 3.000000 | i*j:
> >> >> > 6.000000): 118.659783
> >> >> > ITER: 1 | i: 5 | j: 0 Result(i: 2.236068 | j: 0.000000 | i*j:
> >> >> > 0.000000): 118.659783
> >> >> > ITER: 1 | i: 5 | j: 1 Result(i: 2.236068 | j: 1.000000 | i*j:
> >> >> > 2.236068): 120.895851
> >> >> > ITER: 1 | i: 5 | j: 2 Result(i: 2.236068 | j: 1.414214 | i*j:
> >> >> > 3.162278): 124.058129
> >> >> > ITER: 1 | i: 5 | j: 3 Result(i: 2.236068 | j: 1.732051 | i*j:
> >> >> > 3.872983): 127.931112
> >> >> > ITER: 1 | i: 5 | j: 4 Result(i: 2.236068 | j: 2.000000 | i*j:
> >> >> > 4.472136): 132.403248
> >> >> > ITER: 1 | i: 5 | j: 5 Result(i: 2.236068 | j: 2.236068 | i*j:
> >> >> > 5.000000): 137.403248
> >> >> > ITER: 1 | i: 5 | j: 6 Result(i: 2.236068 | j: 2.449490 | i*j:
> >> >> > 5.477226): 142.880474
> >> >> > ITER: 1 | i: 5 | j: 7 Result(i: 2.236068 | j: 2.645751 | i*j:
> >> >> > 5.916080): 148.796553
> >> >> > ITER: 1 | i: 5 | j: 8 Result(i: 2.236068 | j: 2.828427 | i*j:
> >> >> > 6.324555): 155.121109
> >> >> > ITER: 1 | i: 5 | j: 9 Result(i: 2.236068 | j: 3.000000 | i*j:
> >> >> > 6.708204): 161.829313
> >> >> > ITER: 1 | i: 6 | j: 0 Result(i: 2.449490 | j: 0.000000 | i*j:
> >> >> > 0.000000): 161.829313
> >> >> > ITER: 1 | i: 6 | j: 1 Result(i: 2.449490 | j: 1.000000 | i*j:
> >> >> > 2.449490): 164.278802
> >> >> > ITER: 1 | i: 6 | j: 2 Result(i: 2.449490 | j: 1.414214 | i*j:
> >> >> > 3.464102): 167.742904
> >> >> > ITER: 1 | i: 6 | j: 3 Result(i: 2.449490 | j: 1.732051 | i*j:
> >> >> > 4.242641): 171.985545
> >> >> > ITER: 1 | i: 6 | j: 4 Result(i: 2.449490 | j: 2.000000 | i*j:
> >> >> > 4.898979): 176.884524
> >> >> > ITER: 1 | i: 6 | j: 5 Result(i: 2.449490 | j: 2.236068 | i*j:
> >> >> > 5.477226): 182.361750
> >> >> > ITER: 1 | i: 6 | j: 6 Result(i: 2.449490 | j: 2.449490 | i*j:
> >> >> > 6.000000): 188.361750
> >> >> > ITER: 1 | i: 6 | j: 7 Result(i: 2.449490 | j: 2.645751 | i*j:
> >> >> > 6.480741): 194.842491
> >> >> > ITER: 1 | i: 6 | j: 8 Result(i: 2.449490 | j: 2.828427 | i*j:
> >> >> > 6.928203): 201.770694
> >> >> > ITER: 1 | i: 6 | j: 9 Result(i: 2.449490 | j: 3.000000 | i*j:
> >> >> > 7.348469): 209.119163
> >> >> > ITER: 1 | i: 7 | j: 0 Result(i: 2.645751 | j: 0.000000 | i*j:
> >> >> > 0.000000): 209.119163
> >> >> > ITER: 1 | i: 7 | j: 1 Result(i: 2.645751 | j: 1.000000 | i*j:
> >> >> > 2.645751): 211.764914
> >> >> > ITER: 1 | i: 7 | j: 2 Result(i: 2.645751 | j: 1.414214 | i*j:
> >> >> > 3.741657): 215.506572
> >> >> > ITER: 1 | i: 7 | j: 3 Result(i: 2.645751 | j: 1.732051 | i*j:
> >> >> > 4.582576): 220.089147
> >> >> > ITER: 1 | i: 7 | j: 4 Result(i: 2.645751 | j: 2.000000 | i*j:
> >> >> > 5.291503): 225.380650
> >> >> > ITER: 1 | i: 7 | j: 5 Result(i: 2.645751 | j: 2.236068 | i*j:
> >> >> > 5.916080): 231.296730
> >> >> > ITER: 1 | i: 7 | j: 6 Result(i: 2.645751 | j: 2.449490 | i*j:
> >> >> > 6.480741): 237.777470
> >> >> > ITER: 1 | i: 7 | j: 7 Result(i: 2.645751 | j: 2.645751 | i*j:
> >> >> > 7.000000): 244.777470
> >> >> > ITER: 1 | i: 7 | j: 8 Result(i: 2.645751 | j: 2.828427 | i*j:
> >> >> > 7.483315): 252.260785
> >> >> > ITER: 1 | i: 7 | j: 9 Result(i: 2.645751 | j: 3.000000 | i*j:
> >> >> > 7.937254): 260.198039
> >> >> > ITER: 1 | i: 8 | j: 0 Result(i: 2.828427 | j: 0.000000 | i*j:
> >> >> > 0.000000): 260.198039
> >> >> > ITER: 1 | i: 8 | j: 1 Result(i: 2.828427 | j: 1.000000 | i*j:
> >> >> > 2.828427): 263.026466
> >> >> > ITER: 1 | i: 8 | j: 2 Result(i: 2.828427 | j: 1.414214 | i*j:
> >> >> > 4.000000): 267.026466
> >> >> > ITER: 1 | i: 8 | j: 3 Result(i: 2.828427 | j: 1.732051 | i*j:
> >> >> > 4.898979): 271.925446
> >> >> > ITER: 1 | i: 8 | j: 4 Result(i: 2.828427 | j: 2.000000 | i*j:
> >> >> > 5.656854): 277.582300
> >> >> > ITER: 1 | i: 8 | j: 5 Result(i: 2.828427 | j: 2.236068 | i*j:
> >> >> > 6.324555): 283.906855
> >> >> > ITER: 1 | i: 8 | j: 6 Result(i: 2.828427 | j: 2.449490 | i*j:
> >> >> > 6.928203): 290.835059
> >> >> > ITER: 1 | i: 8 | j: 7 Result(i: 2.828427 | j: 2.645751 | i*j:
> >> >> > 7.483315): 298.318373
> >> >> > ITER: 1 | i: 8 | j: 8 Result(i: 2.828427 | j: 2.828427 | i*j:
> >> >> > 8.000000): 306.318373
> >> >> > ITER: 1 | i: 8 | j: 9 Result(i: 2.828427 | j: 3.000000 | i*j:
> >> >> > 8.485281): 314.803655
> >> >> > ITER: 1 | i: 9 | j: 0 Result(i: 3.000000 | j: 0.000000 | i*j:
> >> >> > 0.000000): 314.803655
> >> >> > ITER: 1 | i: 9 | j: 1 Result(i: 3.000000 | j: 1.000000 | i*j:
> >> >> > 3.000000): 317.803655
> >> >> > ITER: 1 | i: 9 | j: 2 Result(i: 3.000000 | j: 1.414214 | i*j:
> >> >> > 4.242641): 322.046295
> >> >> > ITER: 1 | i: 9 | j: 3 Result(i: 3.000000 | j: 1.732051 | i*j:
> >> >> > 5.196152): 327.242448
> >> >> > ITER: 1 | i: 9 | j: 4 Result(i: 3.000000 | j: 2.000000 | i*j:
> >> >> > 6.000000): 333.242448
> >> >> > ITER: 1 | i: 9 | j: 5 Result(i: 3.000000 | j: 2.236068 | i*j:
> >> >> > 6.708204): 339.950652
> >> >> > ITER: 1 | i: 9 | j: 6 Result(i: 3.000000 | j: 2.449490 | i*j:
> >> >> > 7.348469): 347.299121
> >> >> > ITER: 1 | i: 9 | j: 7 Result(i: 3.000000 | j: 2.645751 | i*j:
> >> >> > 7.937254): 355.236375
> >> >> > ITER: 1 | i: 9 | j: 8 Result(i: 3.000000 | j: 2.828427 | i*j:
> >> >> > 8.485281): 363.721656
> >> >> > ITER: 1 | i: 9 | j: 9 Result(i: 3.000000 | j: 3.000000 | i*j:
> >> >> > 9.000000): 372.721656
> >> >> > Final Result: 372.721656
> >> >> >
> >> >> >
> >> >> >
> >> >> > As we can see in the following iterations the sqrt(1) as well as
the
> >> >> > result is set to zero for some reason.
> >> >> >
> >> >> > ITER: 0 | i: 1 | j: 4 Result(i: 0.000000 | j: 2.000000 | i*j:
> >> >> > 0.000000): 0.000000
> >> >> > ITER: 0 | i: 1 | j: 5 Result(i: 0.000000 | j: 2.236068 | i*j:
> >> >> > 0.000000): 0.000000
> >> >> > ITER: 0 | i: 1 | j: 6 Result(i: 0.000000 | j: 2.449490 | i*j:
> >> >> > 0.000000): 0.000000
> >> >> > ITER: 0 | i: 1 | j: 7 Result(i: 0.000000 | j: 2.645751 | i*j:
> >> >> > 0.000000): 0.000000
> >> >> > ITER: 0 | i: 1 | j: 8 Result(i: 0.000000 | j: 2.828427 | i*j:
> >> >> > 0.000000): 0.000000
> >> >> > ITER: 0 | i: 1 | j: 9 Result(i: 0.000000 | j: 3.000000 | i*j:
> >> >> > 0.000000): 0.000000
> >> >> >
> >> >> > Please help me to resolve the accuracy issue! I think that it will
> >> >> > be very useful for gem5 community.
> >> >> >
> >> >> > To be noticed, I find the correct simulated tick in which the
> >> >> > application started in FS (using m5 dumpstats), and I start the
> >> >> > --debug-start, but the trace file which is generated is 10x larger
> >> >> > than SE mode for the same application. How can I compare them?
> >> >> >
> >> >> > Thank you in advance!
> >> >> > Best regards,
> >> >> > Nikos
> >> >> >
> >> >> > Quoting Νικόλαος Ταμπουρατζής <ntampouratzis@ece.auth.gr>:
> >> >> >
> >> >> >> Dear Jason,
> >> >> >>
> >> >> >> I am trying to use --debug-start but in FS mode it is very
> >> >> >> difficult to find the tick on which the application is started!
> >> >> >>
> >> >> >> However, I am writing the following very simple c++ program:
> >> >> >>
> >> >> >> #include <cmath>
> >> >> >> #include <stdio.h>
> >> >> >>
> >> >> >> int main(){
> >> >> >>
> >> >> >> int dim = 4096;
> >> >> >>
> >> >> >> double result;
> >> >> >>
> >> >> >> for (int iter = 0; iter < 2; iter++){
> >> >> >> result = 0;
> >> >> >> for (int i = 0; i < dim; i++){
> >> >> >> for (int j = 0; j < dim; j++){
> >> >> >> result += sqrt(i) * sqrt(j);
> >> >> >> }
> >> >> >> }
> >> >> >> printf("Result: %lf\n", result); //Result:
30530733453.127449
> >> >> >> }
> >> >> >> }
> >> >> >>
> >> >> >> I cross-compile it using: riscv64-linux-gnu-g++ -static -O3 -o
> >> >> >> test_riscv test_riscv.cpp
> >> >> >>
> >> >> >>
> >> >> >> While in X86 (without cross-compilation of course), QEMU-RISCV,
> >> >> >> GEM5-SE the result is the same (30530733453.127449), in GEM5-FS
the
> >> >> >> result is different! In addition, the result is also different
> >> >> >> between the 2 iterations.
> >> >> >>
> >> >> >> Please reproduce the error if you want in order to verify my
result.
> >> >> >> Ηow can the issue be resolved?
> >> >> >>
> >> >> >> Thank you in advance!
> >> >> >>
> >> >> >> Best regards,
> >> >> >> Nikos
> >> >> >>
> >> >> >>
> >> >> >> Quoting Jason Lowe-Power <jason@lowepower.com>:
> >> >> >>
> >> >> >>> Hi Nikos,
> >> >> >>>
> >> >> >>> You can use --debug-start to start the debugging after some
number
> >> of
> >> >> >>> ticks. Also, I would expect that the difference should come up
> >> >> quickly, so
> >> >> >>> no need to run the program to the end.
> >> >> >>>
> >> >> >>> For the FS mode one, you will want to just start the trace as
the
> >> >> >>> application starts. This could be a bit of a pain.
> >> >> >>>
> >> >> >>> I'm not really sure what fundamentally could be different. FS
and SE
> >> >> mode
> >> >> >>> use the exact same code for executing instructions, so I don't
think
> >> >> that's
> >> >> >>> the problem. Have you tried running for smaller inputs or just
one
> >> >> >>> iteration?
> >> >> >>>
> >> >> >>> Jason
> >> >> >>>
> >> >> >>>
> >> >> >>>
> >> >> >>> On Wed, Sep 21, 2022 at 9:04 AM Νικόλαος Ταμπουρατζής <
> >> >> >>> ntampouratzis@ece.auth.gr> wrote:
> >> >> >>>
> >> >> >>>> Dear Bobby,
> >> >> >>>>
> >> >> >>>> Iam trying to add --debug-flags=Exec (building the gem5 for
> >> gem5.opt
> >> >> >>>> not for gem5.fast which I had) but the debug traces exceed the
20GB
> >> >> >>>> (and it is not finished yet) for less than 1 simulated second.
How
> >> can
> >> >> >>>> I reduce the size of the debug-flags (or set something more
> >> specific)?
> >> >> >>>>
> >> >> >>>> In contrast I build the HPCG benchmark with DHPCG_DEBUG flag.
If
> >> you
> >> >> >>>> want, you can compare these two output files
> >> >> >>>> (hpcg20010909T014640_SE_Mode & HPCG-Benchmark_3.1_FS_Mode). As
you
> >> can
> >> >> >>>> see, something goes wrong with the accuracy of calculations in
FS
> >> mode
> >> >> >>>> (benchmark uses double precission). You can find the files
here:
> >> >> >>>> http://kition.mhl.tuc.gr:8000/d/68d82f3533/
> >> >> >>>>
> >> >> >>>> Best regards,
> >> >> >>>> Nikos
> >> >> >>>>
> >> >> >>>> Quoting Jason Lowe-Power <jason@lowepower.com>:
> >> >> >>>>
> >> >> >>>>> That's quite odd that it works in SE mode but not FS mode!
> >> >> >>>>>
> >> >> >>>>> I would suggest running with --debug-flags=Exec for both and
then
> >> >> >>>> perform a
> >> >> >>>>> diff to see how they differ.
> >> >> >>>>>
> >> >> >>>>> Cheers,
> >> >> >>>>> Jason
> >> >> >>>>>
> >> >> >>>>> On Tue, Sep 20, 2022 at 2:45 PM Νικόλαος Ταμπουρατζής <
> >> >> >>>>> ntampouratzis@ece.auth.gr> wrote:
> >> >> >>>>>
> >> >> >>>>>> Dear Bobby,
> >> >> >>>>>>
> >> >> >>>>>> In QEMU I get the same (correct) results that I get in SE
mode
> >> >> >>>>>> simulation. I get invalid results in FS simulation (in both
> >> >> >>>>>> riscv-fs.py and riscv-ubuntu-run.py). I cannot access real
RISCV
> >> >> >>>>>> hardware at this moment, however, if you want you may
execute my
> >> >> xhpcg
> >> >> >>>>>> binary (http://kition.mhl.tuc.gr:8000/f/4ca25fdd3c/) with the
> >> >> >>>>>> following configuration:
> >> >> >>>>>>
> >> >> >>>>>> ./xhpcg --nx=16 --ny=16 --nz=16 --npx=1 --npy=1 --npz=1
--rt=0.1
> >> >> >>>>>>
> >> >> >>>>>> Please let me know if you have any updates!
> >> >> >>>>>>
> >> >> >>>>>> Best regards,
> >> >> >>>>>> Nikos
> >> >> >>>>>>
> >> >> >>>>>>
> >> >> >>>>>> Quoting Jason Lowe-Power <jason@lowepower.com>:
> >> >> >>>>>>
> >> >> >>>>>>> Hi Nikos,
> >> >> >>>>>>>
> >> >> >>>>>>> I notice you said the following in your original email:
> >> >> >>>>>>>
> >> >> >>>>>>> In addition, I used the RISCV Ubuntu image
> >> >> >>>>>>>> (
> >> >> https://github.com/gem5/gem5-resources/tree/stable/src/riscv-ubuntu
> >> >> >>>> ),
> >> >> >>>>>>>> I installed the gcc compiler, compile it (through qemu)
and I
> >> get
> >> >> >>>>>>>> wrong results too.
> >> >> >>>>>>>
> >> >> >>>>>>>
> >> >> >>>>>>> Is this saying you get the wrong results is QEMU? If so,
the bug
> >> >> is in
> >> >> >>>>>> GCC
> >> >> >>>>>>> or the HPCG workload, not in gem5. If not, I would test in
QEMU
> >> to
> >> >> >>>> make
> >> >> >>>>>>> sure the binary works there. Another way you could test to
see
> >> if
> >> >> the
> >> >> >>>>>>> problem is your binary or gem5 would be to run it on real
> >> >> hardware. We
> >> >> >>>>>> have
> >> >> >>>>>>> access to some RISC-V hardware here at UC Davis, if you
don't
> >> have
> >> >> >>>> access
> >> >> >>>>>>> to it.
> >> >> >>>>>>>
> >> >> >>>>>>> Cheers,
> >> >> >>>>>>> Jason
> >> >> >>>>>>>
> >> >> >>>>>>> On Tue, Sep 20, 2022 at 12:58 AM Νικόλαος Ταμπουρατζής <
> >> >> >>>>>>> ntampouratzis@ece.auth.gr> wrote:
> >> >> >>>>>>>
> >> >> >>>>>>>> Dear Bobby,
> >> >> >>>>>>>>
> >> >> >>>>>>>> 1) I use the original riscv-fs.py which is provided in the
> >> latest
> >> >> >>>> gem5
> >> >> >>>>>>>> release.
> >> >> >>>>>>>> I run the gem5 once (./build/RISCV/gem5.fast -d
> >> ./HPCG_FS_results
> >> >> >>>>>>>> ./configs/example/gem5_library/riscv-fs.py) in order to
> >> download
> >> >> the
> >> >> >>>>>>>> riscv-bootloader-vmlinux-5.10 and riscv-disk-img.
> >> >> >>>>>>>> After this I mount the riscv-disk-img (sudo mount -o loop
> >> >> >>>>>>>> riscv-disk-img /mnt), put the xhpcg executable and I do the
> >> >> following
> >> >> >>>>>>>> changes in riscv-fs.py to boot the riscv-disk-img with
> >> executable:
> >> >> >>>>>>>>
> >> >> >>>>>>>> image = CustomDiskImageResource(
> >> >> >>>>>>>> local_path =
"/home/cossim/.cache/gem5/riscv-disk-img",
> >> >> >>>>>>>> )
> >> >> >>>>>>>>
> >> >> >>>>>>>> # Set the Full System workload.
> >> >> >>>>>>>> board.set_kernel_disk_workload(
> >> >> >>>>>>>>
> >> >> kernel=Resource("riscv-bootloader-vmlinux-5.10"),
> >> >> >>>>>>>> disk_image=image,
> >> >> >>>>>>>> )
> >> >> >>>>>>>>
> >> >> >>>>>>>> Finally, in the
> >> >> gem5/src/python/gem5/components/boards/riscv_board.py
> >> >> >>>>>>>> I change the last line to "return ["console=ttyS0",
> >> >> >>>>>>>> "root={root_value}", "rw"]" in order to allow the write
> >> >> permissions
> >> >> >>>> in
> >> >> >>>>>>>> the image.
> >> >> >>>>>>>>
> >> >> >>>>>>>>
> >> >> >>>>>>>> 2) The HPCG benchmark after some iterations calculates if
the
> >> >> results
> >> >> >>>>>>>> are valid or not valid. In the case of FS it gives invalid
> >> >> results.
> >> >> >>>> As
> >> >> >>>>>>>> I see from the results, one (at least) problem is that
produces
> >> >> >>>>>>>> different results in each HPCG execution (with the same
> >> >> >>>> configuration).
> >> >> >>>>>>>>
> >> >> >>>>>>>> Here is the HPCG output and riscv-fs.py
> >> >> >>>>>>>> (http://kition.mhl.tuc.gr:8000/d/68d82f3533/). You may
> >> reproduce
> >> >> the
> >> >> >>>>>>>> results in the video if you use the xhpcg executable
> >> >> >>>>>>>> (http://kition.mhl.tuc.gr:8000/f/4ca25fdd3c/)
> >> >> >>>>>>>>
> >> >> >>>>>>>> Please help me in order to solve it!
> >> >> >>>>>>>>
> >> >> >>>>>>>> Finally, I get invalid results in the HPL benchmark in FS
mode
> >> >> too.
> >> >> >>>>>>>>
> >> >> >>>>>>>> Best regards,
> >> >> >>>>>>>> Nikos
> >> >> >>>>>>>>
> >> >> >>>>>>>>
> >> >> >>>>>>>> Quoting Bobby Bruce <bbruce@ucdavis.edu>:
> >> >> >>>>>>>>
> >> >> >>>>>>>> > I'm going to need a bit more information to help:
> >> >> >>>>>>>> >
> >> >> >>>>>>>> > 1. In what way have you modified
> >> >> >>>>>>>> > ./configs/example/gem5_library/riscv-fs.py? Can you
attach
> >> the
> >> >> >>>> script
> >> >> >>>>>>>> here?
> >> >> >>>>>>>> > 2. What error are you getting or in what way are the
results
> >> >> >>>> invalid?
> >> >> >>>>>>>> >
> >> >> >>>>>>>> > -
> >> >> >>>>>>>> > Dr. Bobby R. Bruce
> >> >> >>>>>>>> > Room 3050,
> >> >> >>>>>>>> > Kemper Hall, UC Davis
> >> >> >>>>>>>> > Davis,
> >> >> >>>>>>>> > CA, 95616
> >> >> >>>>>>>> >
> >> >> >>>>>>>> > web: https://www.bobbybruce.net
> >> >> >>>>>>>> >
> >> >> >>>>>>>> >
> >> >> >>>>>>>> > On Mon, Sep 19, 2022 at 1:43 PM Νικόλαος Ταμπουρατζής <
> >> >> >>>>>>>> > ntampouratzis@ece.auth.gr> wrote:
> >> >> >>>>>>>> >
> >> >> >>>>>>>> >>
> >> >> >>>>>>>> >> Dear gem5 community,
> >> >> >>>>>>>> >>
> >> >> >>>>>>>> >> I have successfully cross-compile the HPCG benchmark for
> >> RISCV
> >> >> >>>>>> (Serial
> >> >> >>>>>>>> >> version, without MPI and OpenMP). While it working
properly
> >> in
> >> >> >>>> gem5
> >> >> >>>>>> SE
> >> >> >>>>>>>> >> mode (./build/RISCV/gem5.fast -d ./HPCG_SE_results
> >> >> >>>>>>>> >> ./configs/example/se.py -c xhpcg --options '--nx=16
--ny=16
> >> >> >>>> --nz=16
> >> >> >>>>>>>> >> --npx=1 --npy=1 --npz=1 --rt=0.1'), I get invalid
results
> >> in FS
> >> >> >>>>>>>> >> simulation using "./build/RISCV/gem5.fast -d
> >> ./HPCG_FS_results
> >> >> >>>>>>>> >> ./configs/example/gem5_library/riscv-fs.py" (I mount the
> >> riscv
> >> >> >>>> image
> >> >> >>>>>>>> >> and put it).
> >> >> >>>>>>>> >>
> >> >> >>>>>>>> >> Can you help me please?
> >> >> >>>>>>>> >>
> >> >> >>>>>>>> >> In addition, I used the RISCV Ubuntu image
> >> >> >>>>>>>> >> (
> >> >> >>>>
> >> https://github.com/gem5/gem5-resources/tree/stable/src/riscv-ubuntu
> >> >> >>>>>> ),
> >> >> >>>>>>>> >> I installed the gcc compiler, compile it (through qemu)
and
> >> I
> >> >> get
> >> >> >>>>>>>> >> wrong results too.
> >> >> >>>>>>>> >>
> >> >> >>>>>>>> >> Here is the Makefile which I use, the hpcg executable
for
> >> RISCV
> >> >> >>>>>>>> >> (xhpcg), and a video that shows the results
> >> >> >>>>>>>> >> (http://kition.mhl.tuc.gr:8000/f/4ca25fdd3c/).
> >> >> >>>>>>>> >>
> >> >> >>>>>>>> >> P.S. I use the latest gem5 version.
> >> >> >>>>>>>> >>
> >> >> >>>>>>>> >> Thank you in advance! :)
> >> >> >>>>>>>> >>
> >> >> >>>>>>>> >> Best regards,
> >> >> >>>>>>>> >> Nikos
> >> >> >>>>>>>> >> _______________________________________________
> >> >> >>>>>>>> >> gem5-users mailing list -- gem5-users@gem5.org
> >> >> >>>>>>>> >> To unsubscribe send an email to
gem5-users-leave@gem5.org
> >> >> >>>>>>>> >>
> >> >> >>>>>>>>
> >> >> >>>>>>>>
> >> >> >>>>>>>> _______________________________________________
> >> >> >>>>>>>> gem5-users mailing list -- gem5-users@gem5.org
> >> >> >>>>>>>> To unsubscribe send an email to gem5-users-leave@gem5.org
> >> >> >>>>>>>>
> >> >> >>>>>>
> >> >> >>>>>>
> >> >> >>>>>> _______________________________________________
> >> >> >>>>>> gem5-users mailing list -- gem5-users@gem5.org
> >> >> >>>>>> To unsubscribe send an email to gem5-users-leave@gem5.org
> >> >> >>>>>>
> >> >> >>>>
> >> >> >>>>
> >> >> >>>> _______________________________________________
> >> >> >>>> gem5-users mailing list -- gem5-users@gem5.org
> >> >> >>>> To unsubscribe send an email to gem5-users-leave@gem5.org
> >> >> >>>>
> >> >> >>
> >> >> >>
> >> >> >> _______________________________________________
> >> >> >> gem5-users mailing list -- gem5-users@gem5.org
> >> >> >> To unsubscribe send an email to gem5-users-leave@gem5.org
> >> >> >
> >> >> >
> >> >> > _______________________________________________
> >> >> > gem5-users mailing list -- gem5-users@gem5.org
> >> >> > To unsubscribe send an email to gem5-users-leave@gem5.org
> >> >>
> >> >>
> >> >> _______________________________________________
> >> >> gem5-users mailing list -- gem5-users@gem5.org
> >> >> To unsubscribe send an email to gem5-users-leave@gem5.org
> >> >>
> >>
> >>
> >> _______________________________________________
> >> gem5-users mailing list -- gem5-users@gem5.org
> >> To unsubscribe send an email to gem5-users-leave@gem5.org
> >>
>
>
> _______________________________________________
> gem5-users mailing list -- gem5-users@gem5.org
> To unsubscribe send an email to gem5-users-leave@gem5.org
Mon, Oct 31, 2022 8:40 AM
Dear Bobby, Jason, all,
Is there any update about the accuracy of RISC-V FS?
Best regards,
Nikos
Quoting Bobby Bruce bbruce@ucdavis.edu:
Jason and I had a theory that this may be due to the "Rounding Mode" for
floating pointing being set incorrectly in FS mode. That's set via a macro
here:
https://gem5.googlesource.com/public/gem5/+/refs/tags/v22.0.0.2/src/arch/riscv/fp_inst.hh#36
I manually expanded the macro here:
https://gem5.googlesource.com/public/gem5/+/refs/tags/v22.0.0.2/src/arch/riscv/isa/decoder.isa#1495,
inside the "fsqrt_d" definition then compiled "build/ALL/gem5.debug". Then
used gdb to add a breakpoint in the "Fsqrt_d::execute" function (in the
generated "build/ALL/arch/riscv/generated/exec-ns.cc.inc" file).
gdb build/ALL/gem5.opt
break Fsqrt_d::execute
run bug-recreation/se-mode-run.py # or `run bug-recreation/fs-mode-run.py`
Stepping through with gdb I the rounding mode is 0 for SE mode and 0
for FS mode as well. So, no luck with that theory.
My new theory is that this bug has something to do with thread context
switching being implemented incorrectly in RISC-V somehow. I find it
strange that the sqrt(1) works fine for a while (i.e. returns 1) then
suddenly starts returning zero after a certain point in the execution. In
addition, it's odd that the loop is not returning the same value each time
despite executing the same code. It'd make sense to me that the thread is
being stored and then resumed with some corruption of the floating point
data. This would also explain why this bug only occurs in FS mode.
I'll try to find time to figure out a good test for this. If anyone has any
other theories or ideas then let me know.
--
Dr. Bobby R. Bruce
Room 3050,
Kemper Hall, UC Davis
Davis,
CA, 95616
web: https://www.bobbybruce.net
On Fri, Oct 7, 2022 at 12:50 PM Νικόλαος Ταμπουρατζής <
ntampouratzis@ece.auth.gr> wrote:
Dear Jason & Boddy,
Unfortunately, I have tried my simple example without the sqrt
function and the problem remains. Specifically, I have the following
simple code:
#include <cmath>
#include <stdio.h>
int main(){
int dim = 1024;
double result;
for (int iter = 0; iter < 2; iter++){
result = 0;
for (int i = 0; i < dim; i++){
for (int j = 0; j < dim; j++){
result += i * j;
}
}
printf("Final Result: %lf\n", result);
}
}
In the above code, the correct result is 274341298176.000000 (from
RISCV-SE mode and x86), while in FS mode I get sometimes the correct
result and other times a different number.
Best regards,
Nikos
Quoting Jason Lowe-Power jason@lowepower.com:
I have an idea...
Have you put a breakpoint in the implementation of the fsqrt_d
would like to know if when running in SE mode and running in FS mode we
using the same rounding mode. My hypothesis is that in FS mode the
mode is set differently.
Cheers,
Jason
On Fri, Oct 7, 2022 at 12:15 AM Νικόλαος Ταμπουρατζής <
ntampouratzis@ece.auth.gr> wrote:
Dear Boddy,
Thanks a lot for the effort! I looked in detail and I observe that the
problem is created only using float and double variables (in the case
of int it is working properly in FS mode). Specifically, in the case
of float the variables are set to "nan", while in the case of double
the variables are set to 0.000000 (in random time - probably from some
instruction of simulated OS?). You may use a simple c/c++ example in
order to get some traces before going to HPCG...
Thank you in advance!!
Best regards,
Nikos
Quoting Bobby Bruce bbruce@ucdavis.edu:
Hey Niko,
Thanks for this analysis. I jumped a little into this today but
as far as you did. I wanted to find a quick way to recreate the
free to use this, if it helps any.
It's very strange to me that this bug hasn't manifested itself
it's undeniably there. I'll try to spend more time looking at this
with some traces and debug flags and see if I can narrow down the
In my previous results, I had used double (not float) for the
following variables: result, sq_i and sq_j. In the case of float
instead of double I get "nan" and not 0.000000.
Quoting Νικόλαος Ταμπουρατζής ntampouratzis@ece.auth.gr:
Dear Jason, all,
I am trying to find the accuracy problem with RISCV-FS and I
that the problem is created (at least in my dummy example) because
the variables (double) are set to zero in random simulated time
this reason I get different results among executions of the same
code). Specifically for the following dummy code:
#include <cmath>
#include <stdio.h>
int main(){
int dim = 10;
float result;
for (int iter = 0; iter < 2; iter++){
result = 0;
for (int i = 0; i < dim; i++){
for (int j = 0; j < dim; j++){
float sq_i = sqrt(i);
float sq_j = sqrt(j);
result += sq_i * sq_j;
printf("ITER: %d | i: %d | j: %d Result(i: %f | j:
%f | i*j: %f): %f\n", iter, i , j, sq_i, sq_j, sq_i * sq_j,
}
}
printf("Final Result: %lf\n", result);
}
}
The correct Final Result in both iterations is 372.721656.
I get the following results in FS:
ITER: 0 | i: 0 | j: 0 Result(i: 0.000000 | j: 0.000000 | ij:
0.000000): 0.000000
ITER: 0 | i: 0 | j: 1 Result(i: 0.000000 | j: 1.000000 | ij:
0.000000): 0.000000
ITER: 0 | i: 0 | j: 2 Result(i: 0.000000 | j: 1.414214 | ij:
0.000000): 0.000000
ITER: 0 | i: 0 | j: 3 Result(i: 0.000000 | j: 1.732051 | ij:
0.000000): 0.000000
ITER: 0 | i: 0 | j: 4 Result(i: 0.000000 | j: 2.000000 | ij:
0.000000): 0.000000
ITER: 0 | i: 0 | j: 5 Result(i: 0.000000 | j: 2.236068 | ij:
0.000000): 0.000000
ITER: 0 | i: 0 | j: 6 Result(i: 0.000000 | j: 2.449490 | ij:
0.000000): 0.000000
ITER: 0 | i: 0 | j: 7 Result(i: 0.000000 | j: 2.645751 | ij:
0.000000): 0.000000
ITER: 0 | i: 0 | j: 8 Result(i: 0.000000 | j: 2.828427 | ij:
0.000000): 0.000000
ITER: 0 | i: 0 | j: 9 Result(i: 0.000000 | j: 3.000000 | ij:
0.000000): 0.000000
ITER: 0 | i: 1 | j: 0 Result(i: 1.000000 | j: 0.000000 | ij:
0.000000): 0.000000
ITER: 0 | i: 1 | j: 1 Result(i: 1.000000 | j: 1.000000 | ij:
1.000000): 1.000000
ITER: 0 | i: 1 | j: 2 Result(i: 1.000000 | j: 1.414214 | ij:
1.414214): 2.414214
ITER: 0 | i: 1 | j: 3 Result(i: 1.000000 | j: 1.732051 | ij:
1.732051): 4.146264
ITER: 0 | i: 1 | j: 4 Result(i: 0.000000 | j: 2.000000 | ij:
0.000000): 0.000000
ITER: 0 | i: 1 | j: 5 Result(i: 0.000000 | j: 2.236068 | ij:
0.000000): 0.000000
ITER: 0 | i: 1 | j: 6 Result(i: 0.000000 | j: 2.449490 | ij:
0.000000): 0.000000
ITER: 0 | i: 1 | j: 7 Result(i: 0.000000 | j: 2.645751 | ij:
0.000000): 0.000000
ITER: 0 | i: 1 | j: 8 Result(i: 0.000000 | j: 2.828427 | ij:
0.000000): 0.000000
ITER: 0 | i: 1 | j: 9 Result(i: 0.000000 | j: 3.000000 | ij:
0.000000): 0.000000
ITER: 0 | i: 2 | j: 0 Result(i: 1.414214 | j: 0.000000 | ij:
0.000000): 0.000000
ITER: 0 | i: 2 | j: 1 Result(i: 1.414214 | j: 1.000000 | ij:
1.414214): 1.414214
ITER: 0 | i: 2 | j: 2 Result(i: 1.414214 | j: 1.414214 | ij:
2.000000): 3.414214
ITER: 0 | i: 2 | j: 3 Result(i: 1.414214 | j: 1.732051 | ij:
2.449490): 5.863703
ITER: 0 | i: 2 | j: 4 Result(i: 1.414214 | j: 2.000000 | ij:
2.828427): 8.692130
ITER: 0 | i: 2 | j: 5 Result(i: 1.414214 | j: 2.236068 | ij:
3.162278): 11.854408
ITER: 0 | i: 2 | j: 6 Result(i: 1.414214 | j: 2.449490 | ij:
3.464102): 15.318510
ITER: 0 | i: 2 | j: 7 Result(i: 1.414214 | j: 2.645751 | ij:
3.741657): 19.060167
ITER: 0 | i: 2 | j: 8 Result(i: 1.414214 | j: 2.828427 | ij:
4.000000): 23.060167
ITER: 0 | i: 2 | j: 9 Result(i: 1.414214 | j: 3.000000 | ij:
4.242641): 27.302808
ITER: 0 | i: 3 | j: 0 Result(i: 1.732051 | j: 0.000000 | ij:
0.000000): 27.302808
ITER: 0 | i: 3 | j: 1 Result(i: 1.732051 | j: 1.000000 | ij:
1.732051): 29.034859
ITER: 0 | i: 3 | j: 2 Result(i: 1.732051 | j: 1.414214 | ij:
2.449490): 31.484348
ITER: 0 | i: 3 | j: 3 Result(i: 1.732051 | j: 1.732051 | ij:
3.000000): 34.484348
ITER: 0 | i: 3 | j: 4 Result(i: 1.732051 | j: 2.000000 | ij:
3.464102): 37.948450
ITER: 0 | i: 3 | j: 5 Result(i: 1.732051 | j: 2.236068 | ij:
3.872983): 41.821433
ITER: 0 | i: 3 | j: 6 Result(i: 1.732051 | j: 2.449490 | ij:
4.242641): 46.064074
ITER: 0 | i: 3 | j: 7 Result(i: 1.732051 | j: 2.645751 | ij:
4.582576): 50.646650
ITER: 0 | i: 3 | j: 8 Result(i: 1.732051 | j: 2.828427 | ij:
4.898979): 55.545629
ITER: 0 | i: 3 | j: 9 Result(i: 1.732051 | j: 3.000000 | ij:
5.196152): 60.741782
ITER: 0 | i: 4 | j: 0 Result(i: 2.000000 | j: 0.000000 | ij:
0.000000): 60.741782
ITER: 0 | i: 4 | j: 1 Result(i: 2.000000 | j: 1.000000 | ij:
2.000000): 62.741782
ITER: 0 | i: 4 | j: 2 Result(i: 2.000000 | j: 1.414214 | ij:
2.828427): 65.570209
ITER: 0 | i: 4 | j: 3 Result(i: 2.000000 | j: 1.732051 | ij:
3.464102): 69.034310
ITER: 0 | i: 4 | j: 4 Result(i: 2.000000 | j: 2.000000 | ij:
4.000000): 73.034310
ITER: 0 | i: 4 | j: 5 Result(i: 2.000000 | j: 2.236068 | ij:
4.472136): 77.506446
ITER: 0 | i: 4 | j: 6 Result(i: 2.000000 | j: 2.449490 | ij:
4.898979): 82.405426
ITER: 0 | i: 4 | j: 7 Result(i: 2.000000 | j: 2.645751 | ij:
5.291503): 87.696928
ITER: 0 | i: 4 | j: 8 Result(i: 2.000000 | j: 2.828427 | ij:
5.656854): 93.353783
ITER: 0 | i: 4 | j: 9 Result(i: 2.000000 | j: 3.000000 | ij:
6.000000): 99.353783
ITER: 0 | i: 5 | j: 0 Result(i: 2.236068 | j: 0.000000 | ij:
0.000000): 99.353783
ITER: 0 | i: 5 | j: 1 Result(i: 2.236068 | j: 1.000000 | ij:
2.236068): 101.589851
ITER: 0 | i: 5 | j: 2 Result(i: 2.236068 | j: 1.414214 | ij:
3.162278): 104.752128
ITER: 0 | i: 5 | j: 3 Result(i: 2.236068 | j: 1.732051 | ij:
3.872983): 108.625112
ITER: 0 | i: 5 | j: 4 Result(i: 2.236068 | j: 2.000000 | ij:
4.472136): 113.097248
ITER: 0 | i: 5 | j: 5 Result(i: 2.236068 | j: 2.236068 | ij:
5.000000): 118.097248
ITER: 0 | i: 5 | j: 6 Result(i: 2.236068 | j: 2.449490 | ij:
5.477226): 123.574473
ITER: 0 | i: 5 | j: 7 Result(i: 2.236068 | j: 2.645751 | ij:
5.916080): 129.490553
ITER: 0 | i: 5 | j: 8 Result(i: 2.236068 | j: 2.828427 | ij:
6.324555): 135.815108
ITER: 0 | i: 5 | j: 9 Result(i: 2.236068 | j: 3.000000 | ij:
6.708204): 142.523312
ITER: 0 | i: 6 | j: 0 Result(i: 2.449490 | j: 0.000000 | ij:
0.000000): 142.523312
ITER: 0 | i: 6 | j: 1 Result(i: 2.449490 | j: 1.000000 | ij:
2.449490): 144.972802
ITER: 0 | i: 6 | j: 2 Result(i: 2.449490 | j: 1.414214 | ij:
3.464102): 148.436904
ITER: 0 | i: 6 | j: 3 Result(i: 2.449490 | j: 1.732051 | ij:
4.242641): 152.679544
ITER: 0 | i: 6 | j: 4 Result(i: 2.449490 | j: 2.000000 | ij:
4.898979): 157.578524
ITER: 0 | i: 6 | j: 5 Result(i: 2.449490 | j: 2.236068 | ij:
5.477226): 163.055749
ITER: 0 | i: 6 | j: 6 Result(i: 2.449490 | j: 2.449490 | ij:
6.000000): 169.055749
ITER: 0 | i: 6 | j: 7 Result(i: 2.449490 | j: 2.645751 | ij:
6.480741): 175.536490
ITER: 0 | i: 6 | j: 8 Result(i: 2.449490 | j: 2.828427 | ij:
6.928203): 182.464693
ITER: 0 | i: 6 | j: 9 Result(i: 2.449490 | j: 3.000000 | ij:
7.348469): 189.813162
ITER: 0 | i: 7 | j: 0 Result(i: 2.645751 | j: 0.000000 | ij:
0.000000): 189.813162
ITER: 0 | i: 7 | j: 1 Result(i: 2.645751 | j: 1.000000 | ij:
2.645751): 192.458914
ITER: 0 | i: 7 | j: 2 Result(i: 2.645751 | j: 1.414214 | ij:
3.741657): 196.200571
ITER: 0 | i: 7 | j: 3 Result(i: 2.645751 | j: 1.732051 | ij:
4.582576): 200.783147
ITER: 0 | i: 7 | j: 4 Result(i: 2.645751 | j: 2.000000 | ij:
5.291503): 206.074649
ITER: 0 | i: 7 | j: 5 Result(i: 2.645751 | j: 2.236068 | ij:
5.916080): 211.990729
ITER: 0 | i: 7 | j: 6 Result(i: 2.645751 | j: 2.449490 | ij:
6.480741): 218.471470
ITER: 0 | i: 7 | j: 7 Result(i: 2.645751 | j: 2.645751 | ij:
7.000000): 225.471470
ITER: 0 | i: 7 | j: 8 Result(i: 2.645751 | j: 2.828427 | ij:
7.483315): 232.954785
ITER: 0 | i: 7 | j: 9 Result(i: 2.645751 | j: 3.000000 | ij:
7.937254): 240.892039
ITER: 0 | i: 8 | j: 0 Result(i: 2.828427 | j: 0.000000 | ij:
0.000000): 240.892039
ITER: 0 | i: 8 | j: 1 Result(i: 2.828427 | j: 1.000000 | ij:
2.828427): 243.720466
ITER: 0 | i: 8 | j: 2 Result(i: 2.828427 | j: 1.414214 | ij:
4.000000): 247.720466
ITER: 0 | i: 8 | j: 3 Result(i: 2.828427 | j: 1.732051 | ij:
4.898979): 252.619445
ITER: 0 | i: 8 | j: 4 Result(i: 2.828427 | j: 2.000000 | ij:
5.656854): 258.276300
ITER: 0 | i: 8 | j: 5 Result(i: 2.828427 | j: 2.236068 | ij:
6.324555): 264.600855
ITER: 0 | i: 8 | j: 6 Result(i: 2.828427 | j: 2.449490 | ij:
6.928203): 271.529058
ITER: 0 | i: 8 | j: 7 Result(i: 2.828427 | j: 2.645751 | ij:
7.483315): 279.012373
ITER: 0 | i: 8 | j: 8 Result(i: 2.828427 | j: 2.828427 | ij:
8.000000): 287.012373
ITER: 0 | i: 8 | j: 9 Result(i: 2.828427 | j: 3.000000 | ij:
8.485281): 295.497654
ITER: 0 | i: 9 | j: 0 Result(i: 3.000000 | j: 0.000000 | ij:
0.000000): 295.497654
ITER: 0 | i: 9 | j: 1 Result(i: 3.000000 | j: 1.000000 | ij:
3.000000): 298.497654
ITER: 0 | i: 9 | j: 2 Result(i: 3.000000 | j: 1.414214 | ij:
4.242641): 302.740295
ITER: 0 | i: 9 | j: 3 Result(i: 3.000000 | j: 1.732051 | ij:
5.196152): 307.936447
ITER: 0 | i: 9 | j: 4 Result(i: 3.000000 | j: 2.000000 | ij:
6.000000): 313.936447
ITER: 0 | i: 9 | j: 5 Result(i: 3.000000 | j: 2.236068 | ij:
6.708204): 320.644651
ITER: 0 | i: 9 | j: 6 Result(i: 3.000000 | j: 2.449490 | ij:
7.348469): 327.993120
ITER: 0 | i: 9 | j: 7 Result(i: 3.000000 | j: 2.645751 | ij:
7.937254): 335.930374
ITER: 0 | i: 9 | j: 8 Result(i: 3.000000 | j: 2.828427 | ij:
8.485281): 344.415656
ITER: 0 | i: 9 | j: 9 Result(i: 3.000000 | j: 3.000000 | ij:
9.000000): 353.415656
Final Result: 353.415656
ITER: 1 | i: 0 | j: 0 Result(i: 0.000000 | j: 0.000000 | ij:
0.000000): 0.000000
ITER: 1 | i: 0 | j: 1 Result(i: 0.000000 | j: 1.000000 | ij:
0.000000): 0.000000
ITER: 1 | i: 0 | j: 2 Result(i: 0.000000 | j: 1.414214 | ij:
0.000000): 0.000000
ITER: 1 | i: 0 | j: 3 Result(i: 0.000000 | j: 1.732051 | ij:
0.000000): 0.000000
ITER: 1 | i: 0 | j: 4 Result(i: 0.000000 | j: 2.000000 | ij:
0.000000): 0.000000
ITER: 1 | i: 0 | j: 5 Result(i: 0.000000 | j: 2.236068 | ij:
0.000000): 0.000000
ITER: 1 | i: 0 | j: 6 Result(i: 0.000000 | j: 2.449490 | ij:
0.000000): 0.000000
ITER: 1 | i: 0 | j: 7 Result(i: 0.000000 | j: 2.645751 | ij:
0.000000): 0.000000
ITER: 1 | i: 0 | j: 8 Result(i: 0.000000 | j: 2.828427 | ij:
0.000000): 0.000000
ITER: 1 | i: 0 | j: 9 Result(i: 0.000000 | j: 3.000000 | ij:
0.000000): 0.000000
ITER: 1 | i: 1 | j: 0 Result(i: 1.000000 | j: 0.000000 | ij:
0.000000): 0.000000
ITER: 1 | i: 1 | j: 1 Result(i: 1.000000 | j: 1.000000 | ij:
1.000000): 1.000000
ITER: 1 | i: 1 | j: 2 Result(i: 1.000000 | j: 1.414214 | ij:
1.414214): 2.414214
ITER: 1 | i: 1 | j: 3 Result(i: 1.000000 | j: 1.732051 | ij:
1.732051): 4.146264
ITER: 1 | i: 1 | j: 4 Result(i: 1.000000 | j: 2.000000 | ij:
2.000000): 6.146264
ITER: 1 | i: 1 | j: 5 Result(i: 1.000000 | j: 2.236068 | ij:
2.236068): 8.382332
ITER: 1 | i: 1 | j: 6 Result(i: 1.000000 | j: 2.449490 | ij:
2.449490): 10.831822
ITER: 1 | i: 1 | j: 7 Result(i: 1.000000 | j: 2.645751 | ij:
2.645751): 13.477573
ITER: 1 | i: 1 | j: 8 Result(i: 1.000000 | j: 2.828427 | ij:
2.828427): 16.306001
ITER: 1 | i: 1 | j: 9 Result(i: 1.000000 | j: 3.000000 | ij:
3.000000): 19.306001
ITER: 1 | i: 2 | j: 0 Result(i: 1.414214 | j: 0.000000 | ij:
0.000000): 19.306001
ITER: 1 | i: 2 | j: 1 Result(i: 1.414214 | j: 1.000000 | ij:
1.414214): 20.720214
ITER: 1 | i: 2 | j: 2 Result(i: 1.414214 | j: 1.414214 | ij:
2.000000): 22.720214
ITER: 1 | i: 2 | j: 3 Result(i: 1.414214 | j: 1.732051 | ij:
2.449490): 25.169704
ITER: 1 | i: 2 | j: 4 Result(i: 1.414214 | j: 2.000000 | ij:
2.828427): 27.998131
ITER: 1 | i: 2 | j: 5 Result(i: 1.414214 | j: 2.236068 | ij:
3.162278): 31.160409
ITER: 1 | i: 2 | j: 6 Result(i: 1.414214 | j: 2.449490 | ij:
3.464102): 34.624510
ITER: 1 | i: 2 | j: 7 Result(i: 1.414214 | j: 2.645751 | ij:
3.741657): 38.366168
ITER: 1 | i: 2 | j: 8 Result(i: 1.414214 | j: 2.828427 | ij:
4.000000): 42.366168
ITER: 1 | i: 2 | j: 9 Result(i: 1.414214 | j: 3.000000 | ij:
4.242641): 46.608808
ITER: 1 | i: 3 | j: 0 Result(i: 1.732051 | j: 0.000000 | ij:
0.000000): 46.608808
ITER: 1 | i: 3 | j: 1 Result(i: 1.732051 | j: 1.000000 | ij:
1.732051): 48.340859
ITER: 1 | i: 3 | j: 2 Result(i: 1.732051 | j: 1.414214 | ij:
2.449490): 50.790349
ITER: 1 | i: 3 | j: 3 Result(i: 1.732051 | j: 1.732051 | ij:
3.000000): 53.790349
ITER: 1 | i: 3 | j: 4 Result(i: 1.732051 | j: 2.000000 | ij:
3.464102): 57.254450
ITER: 1 | i: 3 | j: 5 Result(i: 1.732051 | j: 2.236068 | ij:
3.872983): 61.127434
ITER: 1 | i: 3 | j: 6 Result(i: 1.732051 | j: 2.449490 | ij:
4.242641): 65.370075
ITER: 1 | i: 3 | j: 7 Result(i: 1.732051 | j: 2.645751 | ij:
4.582576): 69.952650
ITER: 1 | i: 3 | j: 8 Result(i: 1.732051 | j: 2.828427 | ij:
4.898979): 74.851630
ITER: 1 | i: 3 | j: 9 Result(i: 1.732051 | j: 3.000000 | ij:
5.196152): 80.047782
ITER: 1 | i: 4 | j: 0 Result(i: 2.000000 | j: 0.000000 | ij:
0.000000): 80.047782
ITER: 1 | i: 4 | j: 1 Result(i: 2.000000 | j: 1.000000 | ij:
2.000000): 82.047782
ITER: 1 | i: 4 | j: 2 Result(i: 2.000000 | j: 1.414214 | ij:
2.828427): 84.876209
ITER: 1 | i: 4 | j: 3 Result(i: 2.000000 | j: 1.732051 | ij:
3.464102): 88.340311
ITER: 1 | i: 4 | j: 4 Result(i: 2.000000 | j: 2.000000 | ij:
4.000000): 92.340311
ITER: 1 | i: 4 | j: 5 Result(i: 2.000000 | j: 2.236068 | ij:
4.472136): 96.812447
ITER: 1 | i: 4 | j: 6 Result(i: 2.000000 | j: 2.449490 | ij:
4.898979): 101.711426
ITER: 1 | i: 4 | j: 7 Result(i: 2.000000 | j: 2.645751 | ij:
5.291503): 107.002929
ITER: 1 | i: 4 | j: 8 Result(i: 2.000000 | j: 2.828427 | ij:
5.656854): 112.659783
ITER: 1 | i: 4 | j: 9 Result(i: 2.000000 | j: 3.000000 | ij:
6.000000): 118.659783
ITER: 1 | i: 5 | j: 0 Result(i: 2.236068 | j: 0.000000 | ij:
0.000000): 118.659783
ITER: 1 | i: 5 | j: 1 Result(i: 2.236068 | j: 1.000000 | ij:
2.236068): 120.895851
ITER: 1 | i: 5 | j: 2 Result(i: 2.236068 | j: 1.414214 | ij:
3.162278): 124.058129
ITER: 1 | i: 5 | j: 3 Result(i: 2.236068 | j: 1.732051 | ij:
3.872983): 127.931112
ITER: 1 | i: 5 | j: 4 Result(i: 2.236068 | j: 2.000000 | ij:
4.472136): 132.403248
ITER: 1 | i: 5 | j: 5 Result(i: 2.236068 | j: 2.236068 | ij:
5.000000): 137.403248
ITER: 1 | i: 5 | j: 6 Result(i: 2.236068 | j: 2.449490 | ij:
5.477226): 142.880474
ITER: 1 | i: 5 | j: 7 Result(i: 2.236068 | j: 2.645751 | ij:
5.916080): 148.796553
ITER: 1 | i: 5 | j: 8 Result(i: 2.236068 | j: 2.828427 | ij:
6.324555): 155.121109
ITER: 1 | i: 5 | j: 9 Result(i: 2.236068 | j: 3.000000 | ij:
6.708204): 161.829313
ITER: 1 | i: 6 | j: 0 Result(i: 2.449490 | j: 0.000000 | ij:
0.000000): 161.829313
ITER: 1 | i: 6 | j: 1 Result(i: 2.449490 | j: 1.000000 | ij:
2.449490): 164.278802
ITER: 1 | i: 6 | j: 2 Result(i: 2.449490 | j: 1.414214 | ij:
3.464102): 167.742904
ITER: 1 | i: 6 | j: 3 Result(i: 2.449490 | j: 1.732051 | ij:
4.242641): 171.985545
ITER: 1 | i: 6 | j: 4 Result(i: 2.449490 | j: 2.000000 | ij:
4.898979): 176.884524
ITER: 1 | i: 6 | j: 5 Result(i: 2.449490 | j: 2.236068 | ij:
5.477226): 182.361750
ITER: 1 | i: 6 | j: 6 Result(i: 2.449490 | j: 2.449490 | ij:
6.000000): 188.361750
ITER: 1 | i: 6 | j: 7 Result(i: 2.449490 | j: 2.645751 | ij:
6.480741): 194.842491
ITER: 1 | i: 6 | j: 8 Result(i: 2.449490 | j: 2.828427 | ij:
6.928203): 201.770694
ITER: 1 | i: 6 | j: 9 Result(i: 2.449490 | j: 3.000000 | ij:
7.348469): 209.119163
ITER: 1 | i: 7 | j: 0 Result(i: 2.645751 | j: 0.000000 | ij:
0.000000): 209.119163
ITER: 1 | i: 7 | j: 1 Result(i: 2.645751 | j: 1.000000 | ij:
2.645751): 211.764914
ITER: 1 | i: 7 | j: 2 Result(i: 2.645751 | j: 1.414214 | ij:
3.741657): 215.506572
ITER: 1 | i: 7 | j: 3 Result(i: 2.645751 | j: 1.732051 | ij:
4.582576): 220.089147
ITER: 1 | i: 7 | j: 4 Result(i: 2.645751 | j: 2.000000 | ij:
5.291503): 225.380650
ITER: 1 | i: 7 | j: 5 Result(i: 2.645751 | j: 2.236068 | ij:
5.916080): 231.296730
ITER: 1 | i: 7 | j: 6 Result(i: 2.645751 | j: 2.449490 | ij:
6.480741): 237.777470
ITER: 1 | i: 7 | j: 7 Result(i: 2.645751 | j: 2.645751 | ij:
7.000000): 244.777470
ITER: 1 | i: 7 | j: 8 Result(i: 2.645751 | j: 2.828427 | ij:
7.483315): 252.260785
ITER: 1 | i: 7 | j: 9 Result(i: 2.645751 | j: 3.000000 | ij:
7.937254): 260.198039
ITER: 1 | i: 8 | j: 0 Result(i: 2.828427 | j: 0.000000 | ij:
0.000000): 260.198039
ITER: 1 | i: 8 | j: 1 Result(i: 2.828427 | j: 1.000000 | ij:
2.828427): 263.026466
ITER: 1 | i: 8 | j: 2 Result(i: 2.828427 | j: 1.414214 | ij:
4.000000): 267.026466
ITER: 1 | i: 8 | j: 3 Result(i: 2.828427 | j: 1.732051 | ij:
4.898979): 271.925446
ITER: 1 | i: 8 | j: 4 Result(i: 2.828427 | j: 2.000000 | ij:
5.656854): 277.582300
ITER: 1 | i: 8 | j: 5 Result(i: 2.828427 | j: 2.236068 | ij:
6.324555): 283.906855
ITER: 1 | i: 8 | j: 6 Result(i: 2.828427 | j: 2.449490 | ij:
6.928203): 290.835059
ITER: 1 | i: 8 | j: 7 Result(i: 2.828427 | j: 2.645751 | ij:
7.483315): 298.318373
ITER: 1 | i: 8 | j: 8 Result(i: 2.828427 | j: 2.828427 | ij:
8.000000): 306.318373
ITER: 1 | i: 8 | j: 9 Result(i: 2.828427 | j: 3.000000 | ij:
8.485281): 314.803655
ITER: 1 | i: 9 | j: 0 Result(i: 3.000000 | j: 0.000000 | ij:
0.000000): 314.803655
ITER: 1 | i: 9 | j: 1 Result(i: 3.000000 | j: 1.000000 | ij:
3.000000): 317.803655
ITER: 1 | i: 9 | j: 2 Result(i: 3.000000 | j: 1.414214 | ij:
4.242641): 322.046295
ITER: 1 | i: 9 | j: 3 Result(i: 3.000000 | j: 1.732051 | ij:
5.196152): 327.242448
ITER: 1 | i: 9 | j: 4 Result(i: 3.000000 | j: 2.000000 | ij:
6.000000): 333.242448
ITER: 1 | i: 9 | j: 5 Result(i: 3.000000 | j: 2.236068 | ij:
6.708204): 339.950652
ITER: 1 | i: 9 | j: 6 Result(i: 3.000000 | j: 2.449490 | ij:
7.348469): 347.299121
ITER: 1 | i: 9 | j: 7 Result(i: 3.000000 | j: 2.645751 | ij:
7.937254): 355.236375
ITER: 1 | i: 9 | j: 8 Result(i: 3.000000 | j: 2.828427 | ij:
8.485281): 363.721656
ITER: 1 | i: 9 | j: 9 Result(i: 3.000000 | j: 3.000000 | ij:
9.000000): 372.721656
Final Result: 372.721656
As we can see in the following iterations the sqrt(1) as well as
result is set to zero for some reason.
ITER: 0 | i: 1 | j: 4 Result(i: 0.000000 | j: 2.000000 | ij:
0.000000): 0.000000
ITER: 0 | i: 1 | j: 5 Result(i: 0.000000 | j: 2.236068 | ij:
0.000000): 0.000000
ITER: 0 | i: 1 | j: 6 Result(i: 0.000000 | j: 2.449490 | ij:
0.000000): 0.000000
ITER: 0 | i: 1 | j: 7 Result(i: 0.000000 | j: 2.645751 | ij:
0.000000): 0.000000
ITER: 0 | i: 1 | j: 8 Result(i: 0.000000 | j: 2.828427 | ij:
0.000000): 0.000000
ITER: 0 | i: 1 | j: 9 Result(i: 0.000000 | j: 3.000000 | ij:
0.000000): 0.000000
Please help me to resolve the accuracy issue! I think that it will
be very useful for gem5 community.
To be noticed, I find the correct simulated tick in which the
application started in FS (using m5 dumpstats), and I start the
--debug-start, but the trace file which is generated is 10x larger
than SE mode for the same application. How can I compare them?
Thank you in advance!
Best regards,
Nikos
Quoting Νικόλαος Ταμπουρατζής ntampouratzis@ece.auth.gr:
Dear Jason,
I am trying to use --debug-start but in FS mode it is very
difficult to find the tick on which the application is started!
However, I am writing the following very simple c++ program:
#include <cmath>
#include <stdio.h>
int main(){
int dim = 4096;
double result;
for (int iter = 0; iter < 2; iter++){
result = 0;
for (int i = 0; i < dim; i++){
for (int j = 0; j < dim; j++){
result += sqrt(i) * sqrt(j);
}
}
printf("Result: %lf\n", result); //Result:
}
}
I cross-compile it using: riscv64-linux-gnu-g++ -static -O3 -o
test_riscv test_riscv.cpp
While in X86 (without cross-compilation of course), QEMU-RISCV,
GEM5-SE the result is the same (30530733453.127449), in GEM5-FS
result is different! In addition, the result is also different
between the 2 iterations.
Please reproduce the error if you want in order to verify my
Ηow can the issue be resolved?
Thank you in advance!
Best regards,
Nikos
Quoting Jason Lowe-Power jason@lowepower.com:
Hi Nikos,
You can use --debug-start to start the debugging after some
ticks. Also, I would expect that the difference should come up
no need to run the program to the end.
For the FS mode one, you will want to just start the trace as
application starts. This could be a bit of a pain.
I'm not really sure what fundamentally could be different. FS
use the exact same code for executing instructions, so I don't
the problem. Have you tried running for smaller inputs or just
Dear Bobby,
Iam trying to add --debug-flags=Exec (building the gem5 for
not for gem5.fast which I had) but the debug traces exceed the
(and it is not finished yet) for less than 1 simulated second.
I reduce the size of the debug-flags (or set something more
In contrast I build the HPCG benchmark with DHPCG_DEBUG flag.
want, you can compare these two output files
(hpcg20010909T014640_SE_Mode & HPCG-Benchmark_3.1_FS_Mode). As
see, something goes wrong with the accuracy of calculations in
(benchmark uses double precission). You can find the files
That's quite odd that it works in SE mode but not FS mode!
I would suggest running with --debug-flags=Exec for both and
diff to see how they differ.
Cheers,
Jason
On Tue, Sep 20, 2022 at 2:45 PM Νικόλαος Ταμπουρατζής <
ntampouratzis@ece.auth.gr> wrote:
Dear Bobby,
In QEMU I get the same (correct) results that I get in SE
simulation. I get invalid results in FS simulation (in both
riscv-fs.py and riscv-ubuntu-run.py). I cannot access real
hardware at this moment, however, if you want you may
Please let me know if you have any updates!
Best regards,
Nikos
Quoting Jason Lowe-Power jason@lowepower.com:
Hi Nikos,
I notice you said the following in your original email:
In addition, I used the RISCV Ubuntu image
I installed the gcc compiler, compile it (through qemu)
Is this saying you get the wrong results is QEMU? If so,
or the HPCG workload, not in gem5. If not, I would test in
sure the binary works there. Another way you could test to
problem is your binary or gem5 would be to run it on real
access to some RISC-V hardware here at UC Davis, if you
Dear Bobby,
- I use the original riscv-fs.py which is provided in the
release.
I run the gem5 once (./build/RISCV/gem5.fast -d
./configs/example/gem5_library/riscv-fs.py) in order to
riscv-bootloader-vmlinux-5.10 and riscv-disk-img.
After this I mount the riscv-disk-img (sudo mount -o loop
riscv-disk-img /mnt), put the xhpcg executable and I do the
changes in riscv-fs.py to boot the riscv-disk-img with
image = CustomDiskImageResource(
local_path =
"/home/cossim/.cache/gem5/riscv-disk-img",
)
Set the Full System workload.
board.set_kernel_disk_workload(
kernel=Resource("riscv-bootloader-vmlinux-5.10"),
disk_image=image,
)
Finally, in the
gem5/src/python/gem5/components/boards/riscv_board.py
I change the last line to "return ["console=ttyS0",
"root={root_value}", "rw"]" in order to allow the write
the image.
- The HPCG benchmark after some iterations calculates if
are valid or not valid. In the case of FS it gives invalid
I see from the results, one (at least) problem is that
different results in each HPCG execution (with the same
I'm going to need a bit more information to help:
- In what way have you modified
./configs/example/gem5_library/riscv-fs.py? Can you
- What error are you getting or in what way are the
Dear gem5 community,
I have successfully cross-compile the HPCG benchmark for
version, without MPI and OpenMP). While it working
mode (./build/RISCV/gem5.fast -d ./HPCG_SE_results
./configs/example/se.py -c xhpcg --options '--nx=16
--npx=1 --npy=1 --npz=1 --rt=0.1'), I get invalid
simulation using "./build/RISCV/gem5.fast -d
./configs/example/gem5_library/riscv-fs.py" (I mount the
and put it).
Can you help me please?
In addition, I used the RISCV Ubuntu image
(
I installed the gcc compiler, compile it (through qemu)
wrong results too.
Here is the Makefile which I use, the hpcg executable
Dear Bobby, Jason, all,
Is there any update about the accuracy of RISC-V FS?
Best regards,
Nikos
Quoting Bobby Bruce <bbruce@ucdavis.edu>:
> Jason and I had a theory that this may be due to the "Rounding Mode" for
> floating pointing being set incorrectly in FS mode. That's set via a macro
> here:
> https://gem5.googlesource.com/public/gem5/+/refs/tags/v22.0.0.2/src/arch/riscv/fp_inst.hh#36
>
> I manually expanded the macro here:
> https://gem5.googlesource.com/public/gem5/+/refs/tags/v22.0.0.2/src/arch/riscv/isa/decoder.isa#1495,
> inside the "fsqrt_d" definition then compiled "build/ALL/gem5.debug". Then
> used gdb to add a breakpoint in the "Fsqrt_d::execute" function (in the
> generated "build/ALL/arch/riscv/generated/exec-ns.cc.inc" file).
>
> ```
> gdb build/ALL/gem5.opt
> break Fsqrt_d::execute
> run bug-recreation/se-mode-run.py # or `run bug-recreation/fs-mode-run.py`
> ```
>
> Stepping through with gdb I the rounding mode is `0` for SE mode and `0`
> for FS mode as well. So, no luck with that theory.
>
> My new theory is that this bug has something to do with thread context
> switching being implemented incorrectly in RISC-V somehow. I find it
> strange that the sqrt(1) works fine for a while (i.e. returns `1`) then
> suddenly starts returning zero after a certain point in the execution. In
> addition, it's odd that the loop is not returning the same value each time
> despite executing the same code. It'd make sense to me that the thread is
> being stored and then resumed with some corruption of the floating point
> data. This would also explain why this bug only occurs in FS mode.
>
> I'll try to find time to figure out a good test for this. If anyone has any
> other theories or ideas then let me know.
>
> --
> Dr. Bobby R. Bruce
> Room 3050,
> Kemper Hall, UC Davis
> Davis,
> CA, 95616
>
> web: https://www.bobbybruce.net
>
>
> On Fri, Oct 7, 2022 at 12:50 PM Νικόλαος Ταμπουρατζής <
> ntampouratzis@ece.auth.gr> wrote:
>>
>> Dear Jason & Boddy,
>>
>> Unfortunately, I have tried my simple example without the sqrt
>> function and the problem remains. Specifically, I have the following
>> simple code:
>>
>>
>> #include <cmath>
>> #include <stdio.h>
>>
>> int main(){
>>
>> int dim = 1024;
>>
>> double result;
>>
>> for (int iter = 0; iter < 2; iter++){
>> result = 0;
>> for (int i = 0; i < dim; i++){
>> for (int j = 0; j < dim; j++){
>> result += i * j;
>> }
>> }
>> printf("Final Result: %lf\n", result);
>> }
>> }
>>
>>
>> In the above code, the correct result is 274341298176.000000 (from
>> RISCV-SE mode and x86), while in FS mode I get sometimes the correct
>> result and other times a different number.
>>
>> Best regards,
>> Nikos
>>
>>
>> Quoting Jason Lowe-Power <jason@lowepower.com>:
>>
>> > I have an idea...
>> >
>> > Have you put a breakpoint in the implementation of the fsqrt_d
> function? I
>> > would like to know if when running in SE mode and running in FS mode we
> are
>> > using the same rounding mode. My hypothesis is that in FS mode the
> rounding
>> > mode is set differently.
>> >
>> > Cheers,
>> > Jason
>> >
>> > On Fri, Oct 7, 2022 at 12:15 AM Νικόλαος Ταμπουρατζής <
>> > ntampouratzis@ece.auth.gr> wrote:
>> >
>> >> Dear Boddy,
>> >>
>> >> Thanks a lot for the effort! I looked in detail and I observe that the
>> >> problem is created only using float and double variables (in the case
>> >> of int it is working properly in FS mode). Specifically, in the case
>> >> of float the variables are set to "nan", while in the case of double
>> >> the variables are set to 0.000000 (in random time - probably from some
>> >> instruction of simulated OS?). You may use a simple c/c++ example in
>> >> order to get some traces before going to HPCG...
>> >>
>> >> Thank you in advance!!
>> >> Best regards,
>> >> Nikos
>> >>
>> >>
>> >> Quoting Bobby Bruce <bbruce@ucdavis.edu>:
>> >>
>> >> > Hey Niko,
>> >> >
>> >> > Thanks for this analysis. I jumped a little into this today but
> didn't
>> >> get
>> >> > as far as you did. I wanted to find a quick way to recreate the
>> >> following:
>> >> > https://gem5-review.googlesource.com/c/public/gem5/+/64211. Please
> feel
>> >> > free to use this, if it helps any.
>> >> >
>> >> > It's very strange to me that this bug hasn't manifested itself
> before but
>> >> > it's undeniably there. I'll try to spend more time looking at this
>> >> tomorrow
>> >> > with some traces and debug flags and see if I can narrow down the
>> >> problem.
>> >> >
>> >> > --
>> >> > Dr. Bobby R. Bruce
>> >> > Room 3050,
>> >> > Kemper Hall, UC Davis
>> >> > Davis,
>> >> > CA, 95616
>> >> >
>> >> > web: https://www.bobbybruce.net
>> >> >
>> >> >
>> >> > On Wed, Oct 5, 2022 at 2:26 PM Νικόλαος Ταμπουρατζής <
>> >> > ntampouratzis@ece.auth.gr> wrote:
>> >> >
>> >> >> In my previous results, I had used double (not float) for the
>> >> >> following variables: result, sq_i and sq_j. In the case of float
>> >> >> instead of double I get "nan" and not 0.000000.
>> >> >>
>> >> >> Quoting Νικόλαος Ταμπουρατζής <ntampouratzis@ece.auth.gr>:
>> >> >>
>> >> >> > Dear Jason, all,
>> >> >> >
>> >> >> > I am trying to find the accuracy problem with RISCV-FS and I
> observe
>> >> >> > that the problem is created (at least in my dummy example) because
>> >> >> > the variables (double) are set to zero in random simulated time
> (for
>> >> >> > this reason I get different results among executions of the same
>> >> >> > code). Specifically for the following dummy code:
>> >> >> >
>> >> >> >
>> >> >> > #include <cmath>
>> >> >> > #include <stdio.h>
>> >> >> >
>> >> >> > int main(){
>> >> >> >
>> >> >> > int dim = 10;
>> >> >> >
>> >> >> > float result;
>> >> >> >
>> >> >> > for (int iter = 0; iter < 2; iter++){
>> >> >> > result = 0;
>> >> >> > for (int i = 0; i < dim; i++){
>> >> >> > for (int j = 0; j < dim; j++){
>> >> >> > float sq_i = sqrt(i);
>> >> >> > float sq_j = sqrt(j);
>> >> >> > result += sq_i * sq_j;
>> >> >> > printf("ITER: %d | i: %d | j: %d Result(i: %f | j:
>> >> >> > %f | i*j: %f): %f\n", iter, i , j, sq_i, sq_j, sq_i * sq_j,
> result);
>> >> >> > }
>> >> >> > }
>> >> >> > printf("Final Result: %lf\n", result);
>> >> >> > }
>> >> >> > }
>> >> >> >
>> >> >> >
>> >> >> > The correct Final Result in both iterations is 372.721656.
> However,
>> >> >> > I get the following results in FS:
>> >> >> >
>> >> >> > ITER: 0 | i: 0 | j: 0 Result(i: 0.000000 | j: 0.000000 | i*j:
>> >> >> > 0.000000): 0.000000
>> >> >> > ITER: 0 | i: 0 | j: 1 Result(i: 0.000000 | j: 1.000000 | i*j:
>> >> >> > 0.000000): 0.000000
>> >> >> > ITER: 0 | i: 0 | j: 2 Result(i: 0.000000 | j: 1.414214 | i*j:
>> >> >> > 0.000000): 0.000000
>> >> >> > ITER: 0 | i: 0 | j: 3 Result(i: 0.000000 | j: 1.732051 | i*j:
>> >> >> > 0.000000): 0.000000
>> >> >> > ITER: 0 | i: 0 | j: 4 Result(i: 0.000000 | j: 2.000000 | i*j:
>> >> >> > 0.000000): 0.000000
>> >> >> > ITER: 0 | i: 0 | j: 5 Result(i: 0.000000 | j: 2.236068 | i*j:
>> >> >> > 0.000000): 0.000000
>> >> >> > ITER: 0 | i: 0 | j: 6 Result(i: 0.000000 | j: 2.449490 | i*j:
>> >> >> > 0.000000): 0.000000
>> >> >> > ITER: 0 | i: 0 | j: 7 Result(i: 0.000000 | j: 2.645751 | i*j:
>> >> >> > 0.000000): 0.000000
>> >> >> > ITER: 0 | i: 0 | j: 8 Result(i: 0.000000 | j: 2.828427 | i*j:
>> >> >> > 0.000000): 0.000000
>> >> >> > ITER: 0 | i: 0 | j: 9 Result(i: 0.000000 | j: 3.000000 | i*j:
>> >> >> > 0.000000): 0.000000
>> >> >> > ITER: 0 | i: 1 | j: 0 Result(i: 1.000000 | j: 0.000000 | i*j:
>> >> >> > 0.000000): 0.000000
>> >> >> > ITER: 0 | i: 1 | j: 1 Result(i: 1.000000 | j: 1.000000 | i*j:
>> >> >> > 1.000000): 1.000000
>> >> >> > ITER: 0 | i: 1 | j: 2 Result(i: 1.000000 | j: 1.414214 | i*j:
>> >> >> > 1.414214): 2.414214
>> >> >> > ITER: 0 | i: 1 | j: 3 Result(i: 1.000000 | j: 1.732051 | i*j:
>> >> >> > 1.732051): 4.146264
>> >> >> > ITER: 0 | i: 1 | j: 4 Result(i: 0.000000 | j: 2.000000 | i*j:
>> >> >> > 0.000000): 0.000000
>> >> >> > ITER: 0 | i: 1 | j: 5 Result(i: 0.000000 | j: 2.236068 | i*j:
>> >> >> > 0.000000): 0.000000
>> >> >> > ITER: 0 | i: 1 | j: 6 Result(i: 0.000000 | j: 2.449490 | i*j:
>> >> >> > 0.000000): 0.000000
>> >> >> > ITER: 0 | i: 1 | j: 7 Result(i: 0.000000 | j: 2.645751 | i*j:
>> >> >> > 0.000000): 0.000000
>> >> >> > ITER: 0 | i: 1 | j: 8 Result(i: 0.000000 | j: 2.828427 | i*j:
>> >> >> > 0.000000): 0.000000
>> >> >> > ITER: 0 | i: 1 | j: 9 Result(i: 0.000000 | j: 3.000000 | i*j:
>> >> >> > 0.000000): 0.000000
>> >> >> > ITER: 0 | i: 2 | j: 0 Result(i: 1.414214 | j: 0.000000 | i*j:
>> >> >> > 0.000000): 0.000000
>> >> >> > ITER: 0 | i: 2 | j: 1 Result(i: 1.414214 | j: 1.000000 | i*j:
>> >> >> > 1.414214): 1.414214
>> >> >> > ITER: 0 | i: 2 | j: 2 Result(i: 1.414214 | j: 1.414214 | i*j:
>> >> >> > 2.000000): 3.414214
>> >> >> > ITER: 0 | i: 2 | j: 3 Result(i: 1.414214 | j: 1.732051 | i*j:
>> >> >> > 2.449490): 5.863703
>> >> >> > ITER: 0 | i: 2 | j: 4 Result(i: 1.414214 | j: 2.000000 | i*j:
>> >> >> > 2.828427): 8.692130
>> >> >> > ITER: 0 | i: 2 | j: 5 Result(i: 1.414214 | j: 2.236068 | i*j:
>> >> >> > 3.162278): 11.854408
>> >> >> > ITER: 0 | i: 2 | j: 6 Result(i: 1.414214 | j: 2.449490 | i*j:
>> >> >> > 3.464102): 15.318510
>> >> >> > ITER: 0 | i: 2 | j: 7 Result(i: 1.414214 | j: 2.645751 | i*j:
>> >> >> > 3.741657): 19.060167
>> >> >> > ITER: 0 | i: 2 | j: 8 Result(i: 1.414214 | j: 2.828427 | i*j:
>> >> >> > 4.000000): 23.060167
>> >> >> > ITER: 0 | i: 2 | j: 9 Result(i: 1.414214 | j: 3.000000 | i*j:
>> >> >> > 4.242641): 27.302808
>> >> >> > ITER: 0 | i: 3 | j: 0 Result(i: 1.732051 | j: 0.000000 | i*j:
>> >> >> > 0.000000): 27.302808
>> >> >> > ITER: 0 | i: 3 | j: 1 Result(i: 1.732051 | j: 1.000000 | i*j:
>> >> >> > 1.732051): 29.034859
>> >> >> > ITER: 0 | i: 3 | j: 2 Result(i: 1.732051 | j: 1.414214 | i*j:
>> >> >> > 2.449490): 31.484348
>> >> >> > ITER: 0 | i: 3 | j: 3 Result(i: 1.732051 | j: 1.732051 | i*j:
>> >> >> > 3.000000): 34.484348
>> >> >> > ITER: 0 | i: 3 | j: 4 Result(i: 1.732051 | j: 2.000000 | i*j:
>> >> >> > 3.464102): 37.948450
>> >> >> > ITER: 0 | i: 3 | j: 5 Result(i: 1.732051 | j: 2.236068 | i*j:
>> >> >> > 3.872983): 41.821433
>> >> >> > ITER: 0 | i: 3 | j: 6 Result(i: 1.732051 | j: 2.449490 | i*j:
>> >> >> > 4.242641): 46.064074
>> >> >> > ITER: 0 | i: 3 | j: 7 Result(i: 1.732051 | j: 2.645751 | i*j:
>> >> >> > 4.582576): 50.646650
>> >> >> > ITER: 0 | i: 3 | j: 8 Result(i: 1.732051 | j: 2.828427 | i*j:
>> >> >> > 4.898979): 55.545629
>> >> >> > ITER: 0 | i: 3 | j: 9 Result(i: 1.732051 | j: 3.000000 | i*j:
>> >> >> > 5.196152): 60.741782
>> >> >> > ITER: 0 | i: 4 | j: 0 Result(i: 2.000000 | j: 0.000000 | i*j:
>> >> >> > 0.000000): 60.741782
>> >> >> > ITER: 0 | i: 4 | j: 1 Result(i: 2.000000 | j: 1.000000 | i*j:
>> >> >> > 2.000000): 62.741782
>> >> >> > ITER: 0 | i: 4 | j: 2 Result(i: 2.000000 | j: 1.414214 | i*j:
>> >> >> > 2.828427): 65.570209
>> >> >> > ITER: 0 | i: 4 | j: 3 Result(i: 2.000000 | j: 1.732051 | i*j:
>> >> >> > 3.464102): 69.034310
>> >> >> > ITER: 0 | i: 4 | j: 4 Result(i: 2.000000 | j: 2.000000 | i*j:
>> >> >> > 4.000000): 73.034310
>> >> >> > ITER: 0 | i: 4 | j: 5 Result(i: 2.000000 | j: 2.236068 | i*j:
>> >> >> > 4.472136): 77.506446
>> >> >> > ITER: 0 | i: 4 | j: 6 Result(i: 2.000000 | j: 2.449490 | i*j:
>> >> >> > 4.898979): 82.405426
>> >> >> > ITER: 0 | i: 4 | j: 7 Result(i: 2.000000 | j: 2.645751 | i*j:
>> >> >> > 5.291503): 87.696928
>> >> >> > ITER: 0 | i: 4 | j: 8 Result(i: 2.000000 | j: 2.828427 | i*j:
>> >> >> > 5.656854): 93.353783
>> >> >> > ITER: 0 | i: 4 | j: 9 Result(i: 2.000000 | j: 3.000000 | i*j:
>> >> >> > 6.000000): 99.353783
>> >> >> > ITER: 0 | i: 5 | j: 0 Result(i: 2.236068 | j: 0.000000 | i*j:
>> >> >> > 0.000000): 99.353783
>> >> >> > ITER: 0 | i: 5 | j: 1 Result(i: 2.236068 | j: 1.000000 | i*j:
>> >> >> > 2.236068): 101.589851
>> >> >> > ITER: 0 | i: 5 | j: 2 Result(i: 2.236068 | j: 1.414214 | i*j:
>> >> >> > 3.162278): 104.752128
>> >> >> > ITER: 0 | i: 5 | j: 3 Result(i: 2.236068 | j: 1.732051 | i*j:
>> >> >> > 3.872983): 108.625112
>> >> >> > ITER: 0 | i: 5 | j: 4 Result(i: 2.236068 | j: 2.000000 | i*j:
>> >> >> > 4.472136): 113.097248
>> >> >> > ITER: 0 | i: 5 | j: 5 Result(i: 2.236068 | j: 2.236068 | i*j:
>> >> >> > 5.000000): 118.097248
>> >> >> > ITER: 0 | i: 5 | j: 6 Result(i: 2.236068 | j: 2.449490 | i*j:
>> >> >> > 5.477226): 123.574473
>> >> >> > ITER: 0 | i: 5 | j: 7 Result(i: 2.236068 | j: 2.645751 | i*j:
>> >> >> > 5.916080): 129.490553
>> >> >> > ITER: 0 | i: 5 | j: 8 Result(i: 2.236068 | j: 2.828427 | i*j:
>> >> >> > 6.324555): 135.815108
>> >> >> > ITER: 0 | i: 5 | j: 9 Result(i: 2.236068 | j: 3.000000 | i*j:
>> >> >> > 6.708204): 142.523312
>> >> >> > ITER: 0 | i: 6 | j: 0 Result(i: 2.449490 | j: 0.000000 | i*j:
>> >> >> > 0.000000): 142.523312
>> >> >> > ITER: 0 | i: 6 | j: 1 Result(i: 2.449490 | j: 1.000000 | i*j:
>> >> >> > 2.449490): 144.972802
>> >> >> > ITER: 0 | i: 6 | j: 2 Result(i: 2.449490 | j: 1.414214 | i*j:
>> >> >> > 3.464102): 148.436904
>> >> >> > ITER: 0 | i: 6 | j: 3 Result(i: 2.449490 | j: 1.732051 | i*j:
>> >> >> > 4.242641): 152.679544
>> >> >> > ITER: 0 | i: 6 | j: 4 Result(i: 2.449490 | j: 2.000000 | i*j:
>> >> >> > 4.898979): 157.578524
>> >> >> > ITER: 0 | i: 6 | j: 5 Result(i: 2.449490 | j: 2.236068 | i*j:
>> >> >> > 5.477226): 163.055749
>> >> >> > ITER: 0 | i: 6 | j: 6 Result(i: 2.449490 | j: 2.449490 | i*j:
>> >> >> > 6.000000): 169.055749
>> >> >> > ITER: 0 | i: 6 | j: 7 Result(i: 2.449490 | j: 2.645751 | i*j:
>> >> >> > 6.480741): 175.536490
>> >> >> > ITER: 0 | i: 6 | j: 8 Result(i: 2.449490 | j: 2.828427 | i*j:
>> >> >> > 6.928203): 182.464693
>> >> >> > ITER: 0 | i: 6 | j: 9 Result(i: 2.449490 | j: 3.000000 | i*j:
>> >> >> > 7.348469): 189.813162
>> >> >> > ITER: 0 | i: 7 | j: 0 Result(i: 2.645751 | j: 0.000000 | i*j:
>> >> >> > 0.000000): 189.813162
>> >> >> > ITER: 0 | i: 7 | j: 1 Result(i: 2.645751 | j: 1.000000 | i*j:
>> >> >> > 2.645751): 192.458914
>> >> >> > ITER: 0 | i: 7 | j: 2 Result(i: 2.645751 | j: 1.414214 | i*j:
>> >> >> > 3.741657): 196.200571
>> >> >> > ITER: 0 | i: 7 | j: 3 Result(i: 2.645751 | j: 1.732051 | i*j:
>> >> >> > 4.582576): 200.783147
>> >> >> > ITER: 0 | i: 7 | j: 4 Result(i: 2.645751 | j: 2.000000 | i*j:
>> >> >> > 5.291503): 206.074649
>> >> >> > ITER: 0 | i: 7 | j: 5 Result(i: 2.645751 | j: 2.236068 | i*j:
>> >> >> > 5.916080): 211.990729
>> >> >> > ITER: 0 | i: 7 | j: 6 Result(i: 2.645751 | j: 2.449490 | i*j:
>> >> >> > 6.480741): 218.471470
>> >> >> > ITER: 0 | i: 7 | j: 7 Result(i: 2.645751 | j: 2.645751 | i*j:
>> >> >> > 7.000000): 225.471470
>> >> >> > ITER: 0 | i: 7 | j: 8 Result(i: 2.645751 | j: 2.828427 | i*j:
>> >> >> > 7.483315): 232.954785
>> >> >> > ITER: 0 | i: 7 | j: 9 Result(i: 2.645751 | j: 3.000000 | i*j:
>> >> >> > 7.937254): 240.892039
>> >> >> > ITER: 0 | i: 8 | j: 0 Result(i: 2.828427 | j: 0.000000 | i*j:
>> >> >> > 0.000000): 240.892039
>> >> >> > ITER: 0 | i: 8 | j: 1 Result(i: 2.828427 | j: 1.000000 | i*j:
>> >> >> > 2.828427): 243.720466
>> >> >> > ITER: 0 | i: 8 | j: 2 Result(i: 2.828427 | j: 1.414214 | i*j:
>> >> >> > 4.000000): 247.720466
>> >> >> > ITER: 0 | i: 8 | j: 3 Result(i: 2.828427 | j: 1.732051 | i*j:
>> >> >> > 4.898979): 252.619445
>> >> >> > ITER: 0 | i: 8 | j: 4 Result(i: 2.828427 | j: 2.000000 | i*j:
>> >> >> > 5.656854): 258.276300
>> >> >> > ITER: 0 | i: 8 | j: 5 Result(i: 2.828427 | j: 2.236068 | i*j:
>> >> >> > 6.324555): 264.600855
>> >> >> > ITER: 0 | i: 8 | j: 6 Result(i: 2.828427 | j: 2.449490 | i*j:
>> >> >> > 6.928203): 271.529058
>> >> >> > ITER: 0 | i: 8 | j: 7 Result(i: 2.828427 | j: 2.645751 | i*j:
>> >> >> > 7.483315): 279.012373
>> >> >> > ITER: 0 | i: 8 | j: 8 Result(i: 2.828427 | j: 2.828427 | i*j:
>> >> >> > 8.000000): 287.012373
>> >> >> > ITER: 0 | i: 8 | j: 9 Result(i: 2.828427 | j: 3.000000 | i*j:
>> >> >> > 8.485281): 295.497654
>> >> >> > ITER: 0 | i: 9 | j: 0 Result(i: 3.000000 | j: 0.000000 | i*j:
>> >> >> > 0.000000): 295.497654
>> >> >> > ITER: 0 | i: 9 | j: 1 Result(i: 3.000000 | j: 1.000000 | i*j:
>> >> >> > 3.000000): 298.497654
>> >> >> > ITER: 0 | i: 9 | j: 2 Result(i: 3.000000 | j: 1.414214 | i*j:
>> >> >> > 4.242641): 302.740295
>> >> >> > ITER: 0 | i: 9 | j: 3 Result(i: 3.000000 | j: 1.732051 | i*j:
>> >> >> > 5.196152): 307.936447
>> >> >> > ITER: 0 | i: 9 | j: 4 Result(i: 3.000000 | j: 2.000000 | i*j:
>> >> >> > 6.000000): 313.936447
>> >> >> > ITER: 0 | i: 9 | j: 5 Result(i: 3.000000 | j: 2.236068 | i*j:
>> >> >> > 6.708204): 320.644651
>> >> >> > ITER: 0 | i: 9 | j: 6 Result(i: 3.000000 | j: 2.449490 | i*j:
>> >> >> > 7.348469): 327.993120
>> >> >> > ITER: 0 | i: 9 | j: 7 Result(i: 3.000000 | j: 2.645751 | i*j:
>> >> >> > 7.937254): 335.930374
>> >> >> > ITER: 0 | i: 9 | j: 8 Result(i: 3.000000 | j: 2.828427 | i*j:
>> >> >> > 8.485281): 344.415656
>> >> >> > ITER: 0 | i: 9 | j: 9 Result(i: 3.000000 | j: 3.000000 | i*j:
>> >> >> > 9.000000): 353.415656
>> >> >> > Final Result: 353.415656
>> >> >> > ITER: 1 | i: 0 | j: 0 Result(i: 0.000000 | j: 0.000000 | i*j:
>> >> >> > 0.000000): 0.000000
>> >> >> > ITER: 1 | i: 0 | j: 1 Result(i: 0.000000 | j: 1.000000 | i*j:
>> >> >> > 0.000000): 0.000000
>> >> >> > ITER: 1 | i: 0 | j: 2 Result(i: 0.000000 | j: 1.414214 | i*j:
>> >> >> > 0.000000): 0.000000
>> >> >> > ITER: 1 | i: 0 | j: 3 Result(i: 0.000000 | j: 1.732051 | i*j:
>> >> >> > 0.000000): 0.000000
>> >> >> > ITER: 1 | i: 0 | j: 4 Result(i: 0.000000 | j: 2.000000 | i*j:
>> >> >> > 0.000000): 0.000000
>> >> >> > ITER: 1 | i: 0 | j: 5 Result(i: 0.000000 | j: 2.236068 | i*j:
>> >> >> > 0.000000): 0.000000
>> >> >> > ITER: 1 | i: 0 | j: 6 Result(i: 0.000000 | j: 2.449490 | i*j:
>> >> >> > 0.000000): 0.000000
>> >> >> > ITER: 1 | i: 0 | j: 7 Result(i: 0.000000 | j: 2.645751 | i*j:
>> >> >> > 0.000000): 0.000000
>> >> >> > ITER: 1 | i: 0 | j: 8 Result(i: 0.000000 | j: 2.828427 | i*j:
>> >> >> > 0.000000): 0.000000
>> >> >> > ITER: 1 | i: 0 | j: 9 Result(i: 0.000000 | j: 3.000000 | i*j:
>> >> >> > 0.000000): 0.000000
>> >> >> > ITER: 1 | i: 1 | j: 0 Result(i: 1.000000 | j: 0.000000 | i*j:
>> >> >> > 0.000000): 0.000000
>> >> >> > ITER: 1 | i: 1 | j: 1 Result(i: 1.000000 | j: 1.000000 | i*j:
>> >> >> > 1.000000): 1.000000
>> >> >> > ITER: 1 | i: 1 | j: 2 Result(i: 1.000000 | j: 1.414214 | i*j:
>> >> >> > 1.414214): 2.414214
>> >> >> > ITER: 1 | i: 1 | j: 3 Result(i: 1.000000 | j: 1.732051 | i*j:
>> >> >> > 1.732051): 4.146264
>> >> >> > ITER: 1 | i: 1 | j: 4 Result(i: 1.000000 | j: 2.000000 | i*j:
>> >> >> > 2.000000): 6.146264
>> >> >> > ITER: 1 | i: 1 | j: 5 Result(i: 1.000000 | j: 2.236068 | i*j:
>> >> >> > 2.236068): 8.382332
>> >> >> > ITER: 1 | i: 1 | j: 6 Result(i: 1.000000 | j: 2.449490 | i*j:
>> >> >> > 2.449490): 10.831822
>> >> >> > ITER: 1 | i: 1 | j: 7 Result(i: 1.000000 | j: 2.645751 | i*j:
>> >> >> > 2.645751): 13.477573
>> >> >> > ITER: 1 | i: 1 | j: 8 Result(i: 1.000000 | j: 2.828427 | i*j:
>> >> >> > 2.828427): 16.306001
>> >> >> > ITER: 1 | i: 1 | j: 9 Result(i: 1.000000 | j: 3.000000 | i*j:
>> >> >> > 3.000000): 19.306001
>> >> >> > ITER: 1 | i: 2 | j: 0 Result(i: 1.414214 | j: 0.000000 | i*j:
>> >> >> > 0.000000): 19.306001
>> >> >> > ITER: 1 | i: 2 | j: 1 Result(i: 1.414214 | j: 1.000000 | i*j:
>> >> >> > 1.414214): 20.720214
>> >> >> > ITER: 1 | i: 2 | j: 2 Result(i: 1.414214 | j: 1.414214 | i*j:
>> >> >> > 2.000000): 22.720214
>> >> >> > ITER: 1 | i: 2 | j: 3 Result(i: 1.414214 | j: 1.732051 | i*j:
>> >> >> > 2.449490): 25.169704
>> >> >> > ITER: 1 | i: 2 | j: 4 Result(i: 1.414214 | j: 2.000000 | i*j:
>> >> >> > 2.828427): 27.998131
>> >> >> > ITER: 1 | i: 2 | j: 5 Result(i: 1.414214 | j: 2.236068 | i*j:
>> >> >> > 3.162278): 31.160409
>> >> >> > ITER: 1 | i: 2 | j: 6 Result(i: 1.414214 | j: 2.449490 | i*j:
>> >> >> > 3.464102): 34.624510
>> >> >> > ITER: 1 | i: 2 | j: 7 Result(i: 1.414214 | j: 2.645751 | i*j:
>> >> >> > 3.741657): 38.366168
>> >> >> > ITER: 1 | i: 2 | j: 8 Result(i: 1.414214 | j: 2.828427 | i*j:
>> >> >> > 4.000000): 42.366168
>> >> >> > ITER: 1 | i: 2 | j: 9 Result(i: 1.414214 | j: 3.000000 | i*j:
>> >> >> > 4.242641): 46.608808
>> >> >> > ITER: 1 | i: 3 | j: 0 Result(i: 1.732051 | j: 0.000000 | i*j:
>> >> >> > 0.000000): 46.608808
>> >> >> > ITER: 1 | i: 3 | j: 1 Result(i: 1.732051 | j: 1.000000 | i*j:
>> >> >> > 1.732051): 48.340859
>> >> >> > ITER: 1 | i: 3 | j: 2 Result(i: 1.732051 | j: 1.414214 | i*j:
>> >> >> > 2.449490): 50.790349
>> >> >> > ITER: 1 | i: 3 | j: 3 Result(i: 1.732051 | j: 1.732051 | i*j:
>> >> >> > 3.000000): 53.790349
>> >> >> > ITER: 1 | i: 3 | j: 4 Result(i: 1.732051 | j: 2.000000 | i*j:
>> >> >> > 3.464102): 57.254450
>> >> >> > ITER: 1 | i: 3 | j: 5 Result(i: 1.732051 | j: 2.236068 | i*j:
>> >> >> > 3.872983): 61.127434
>> >> >> > ITER: 1 | i: 3 | j: 6 Result(i: 1.732051 | j: 2.449490 | i*j:
>> >> >> > 4.242641): 65.370075
>> >> >> > ITER: 1 | i: 3 | j: 7 Result(i: 1.732051 | j: 2.645751 | i*j:
>> >> >> > 4.582576): 69.952650
>> >> >> > ITER: 1 | i: 3 | j: 8 Result(i: 1.732051 | j: 2.828427 | i*j:
>> >> >> > 4.898979): 74.851630
>> >> >> > ITER: 1 | i: 3 | j: 9 Result(i: 1.732051 | j: 3.000000 | i*j:
>> >> >> > 5.196152): 80.047782
>> >> >> > ITER: 1 | i: 4 | j: 0 Result(i: 2.000000 | j: 0.000000 | i*j:
>> >> >> > 0.000000): 80.047782
>> >> >> > ITER: 1 | i: 4 | j: 1 Result(i: 2.000000 | j: 1.000000 | i*j:
>> >> >> > 2.000000): 82.047782
>> >> >> > ITER: 1 | i: 4 | j: 2 Result(i: 2.000000 | j: 1.414214 | i*j:
>> >> >> > 2.828427): 84.876209
>> >> >> > ITER: 1 | i: 4 | j: 3 Result(i: 2.000000 | j: 1.732051 | i*j:
>> >> >> > 3.464102): 88.340311
>> >> >> > ITER: 1 | i: 4 | j: 4 Result(i: 2.000000 | j: 2.000000 | i*j:
>> >> >> > 4.000000): 92.340311
>> >> >> > ITER: 1 | i: 4 | j: 5 Result(i: 2.000000 | j: 2.236068 | i*j:
>> >> >> > 4.472136): 96.812447
>> >> >> > ITER: 1 | i: 4 | j: 6 Result(i: 2.000000 | j: 2.449490 | i*j:
>> >> >> > 4.898979): 101.711426
>> >> >> > ITER: 1 | i: 4 | j: 7 Result(i: 2.000000 | j: 2.645751 | i*j:
>> >> >> > 5.291503): 107.002929
>> >> >> > ITER: 1 | i: 4 | j: 8 Result(i: 2.000000 | j: 2.828427 | i*j:
>> >> >> > 5.656854): 112.659783
>> >> >> > ITER: 1 | i: 4 | j: 9 Result(i: 2.000000 | j: 3.000000 | i*j:
>> >> >> > 6.000000): 118.659783
>> >> >> > ITER: 1 | i: 5 | j: 0 Result(i: 2.236068 | j: 0.000000 | i*j:
>> >> >> > 0.000000): 118.659783
>> >> >> > ITER: 1 | i: 5 | j: 1 Result(i: 2.236068 | j: 1.000000 | i*j:
>> >> >> > 2.236068): 120.895851
>> >> >> > ITER: 1 | i: 5 | j: 2 Result(i: 2.236068 | j: 1.414214 | i*j:
>> >> >> > 3.162278): 124.058129
>> >> >> > ITER: 1 | i: 5 | j: 3 Result(i: 2.236068 | j: 1.732051 | i*j:
>> >> >> > 3.872983): 127.931112
>> >> >> > ITER: 1 | i: 5 | j: 4 Result(i: 2.236068 | j: 2.000000 | i*j:
>> >> >> > 4.472136): 132.403248
>> >> >> > ITER: 1 | i: 5 | j: 5 Result(i: 2.236068 | j: 2.236068 | i*j:
>> >> >> > 5.000000): 137.403248
>> >> >> > ITER: 1 | i: 5 | j: 6 Result(i: 2.236068 | j: 2.449490 | i*j:
>> >> >> > 5.477226): 142.880474
>> >> >> > ITER: 1 | i: 5 | j: 7 Result(i: 2.236068 | j: 2.645751 | i*j:
>> >> >> > 5.916080): 148.796553
>> >> >> > ITER: 1 | i: 5 | j: 8 Result(i: 2.236068 | j: 2.828427 | i*j:
>> >> >> > 6.324555): 155.121109
>> >> >> > ITER: 1 | i: 5 | j: 9 Result(i: 2.236068 | j: 3.000000 | i*j:
>> >> >> > 6.708204): 161.829313
>> >> >> > ITER: 1 | i: 6 | j: 0 Result(i: 2.449490 | j: 0.000000 | i*j:
>> >> >> > 0.000000): 161.829313
>> >> >> > ITER: 1 | i: 6 | j: 1 Result(i: 2.449490 | j: 1.000000 | i*j:
>> >> >> > 2.449490): 164.278802
>> >> >> > ITER: 1 | i: 6 | j: 2 Result(i: 2.449490 | j: 1.414214 | i*j:
>> >> >> > 3.464102): 167.742904
>> >> >> > ITER: 1 | i: 6 | j: 3 Result(i: 2.449490 | j: 1.732051 | i*j:
>> >> >> > 4.242641): 171.985545
>> >> >> > ITER: 1 | i: 6 | j: 4 Result(i: 2.449490 | j: 2.000000 | i*j:
>> >> >> > 4.898979): 176.884524
>> >> >> > ITER: 1 | i: 6 | j: 5 Result(i: 2.449490 | j: 2.236068 | i*j:
>> >> >> > 5.477226): 182.361750
>> >> >> > ITER: 1 | i: 6 | j: 6 Result(i: 2.449490 | j: 2.449490 | i*j:
>> >> >> > 6.000000): 188.361750
>> >> >> > ITER: 1 | i: 6 | j: 7 Result(i: 2.449490 | j: 2.645751 | i*j:
>> >> >> > 6.480741): 194.842491
>> >> >> > ITER: 1 | i: 6 | j: 8 Result(i: 2.449490 | j: 2.828427 | i*j:
>> >> >> > 6.928203): 201.770694
>> >> >> > ITER: 1 | i: 6 | j: 9 Result(i: 2.449490 | j: 3.000000 | i*j:
>> >> >> > 7.348469): 209.119163
>> >> >> > ITER: 1 | i: 7 | j: 0 Result(i: 2.645751 | j: 0.000000 | i*j:
>> >> >> > 0.000000): 209.119163
>> >> >> > ITER: 1 | i: 7 | j: 1 Result(i: 2.645751 | j: 1.000000 | i*j:
>> >> >> > 2.645751): 211.764914
>> >> >> > ITER: 1 | i: 7 | j: 2 Result(i: 2.645751 | j: 1.414214 | i*j:
>> >> >> > 3.741657): 215.506572
>> >> >> > ITER: 1 | i: 7 | j: 3 Result(i: 2.645751 | j: 1.732051 | i*j:
>> >> >> > 4.582576): 220.089147
>> >> >> > ITER: 1 | i: 7 | j: 4 Result(i: 2.645751 | j: 2.000000 | i*j:
>> >> >> > 5.291503): 225.380650
>> >> >> > ITER: 1 | i: 7 | j: 5 Result(i: 2.645751 | j: 2.236068 | i*j:
>> >> >> > 5.916080): 231.296730
>> >> >> > ITER: 1 | i: 7 | j: 6 Result(i: 2.645751 | j: 2.449490 | i*j:
>> >> >> > 6.480741): 237.777470
>> >> >> > ITER: 1 | i: 7 | j: 7 Result(i: 2.645751 | j: 2.645751 | i*j:
>> >> >> > 7.000000): 244.777470
>> >> >> > ITER: 1 | i: 7 | j: 8 Result(i: 2.645751 | j: 2.828427 | i*j:
>> >> >> > 7.483315): 252.260785
>> >> >> > ITER: 1 | i: 7 | j: 9 Result(i: 2.645751 | j: 3.000000 | i*j:
>> >> >> > 7.937254): 260.198039
>> >> >> > ITER: 1 | i: 8 | j: 0 Result(i: 2.828427 | j: 0.000000 | i*j:
>> >> >> > 0.000000): 260.198039
>> >> >> > ITER: 1 | i: 8 | j: 1 Result(i: 2.828427 | j: 1.000000 | i*j:
>> >> >> > 2.828427): 263.026466
>> >> >> > ITER: 1 | i: 8 | j: 2 Result(i: 2.828427 | j: 1.414214 | i*j:
>> >> >> > 4.000000): 267.026466
>> >> >> > ITER: 1 | i: 8 | j: 3 Result(i: 2.828427 | j: 1.732051 | i*j:
>> >> >> > 4.898979): 271.925446
>> >> >> > ITER: 1 | i: 8 | j: 4 Result(i: 2.828427 | j: 2.000000 | i*j:
>> >> >> > 5.656854): 277.582300
>> >> >> > ITER: 1 | i: 8 | j: 5 Result(i: 2.828427 | j: 2.236068 | i*j:
>> >> >> > 6.324555): 283.906855
>> >> >> > ITER: 1 | i: 8 | j: 6 Result(i: 2.828427 | j: 2.449490 | i*j:
>> >> >> > 6.928203): 290.835059
>> >> >> > ITER: 1 | i: 8 | j: 7 Result(i: 2.828427 | j: 2.645751 | i*j:
>> >> >> > 7.483315): 298.318373
>> >> >> > ITER: 1 | i: 8 | j: 8 Result(i: 2.828427 | j: 2.828427 | i*j:
>> >> >> > 8.000000): 306.318373
>> >> >> > ITER: 1 | i: 8 | j: 9 Result(i: 2.828427 | j: 3.000000 | i*j:
>> >> >> > 8.485281): 314.803655
>> >> >> > ITER: 1 | i: 9 | j: 0 Result(i: 3.000000 | j: 0.000000 | i*j:
>> >> >> > 0.000000): 314.803655
>> >> >> > ITER: 1 | i: 9 | j: 1 Result(i: 3.000000 | j: 1.000000 | i*j:
>> >> >> > 3.000000): 317.803655
>> >> >> > ITER: 1 | i: 9 | j: 2 Result(i: 3.000000 | j: 1.414214 | i*j:
>> >> >> > 4.242641): 322.046295
>> >> >> > ITER: 1 | i: 9 | j: 3 Result(i: 3.000000 | j: 1.732051 | i*j:
>> >> >> > 5.196152): 327.242448
>> >> >> > ITER: 1 | i: 9 | j: 4 Result(i: 3.000000 | j: 2.000000 | i*j:
>> >> >> > 6.000000): 333.242448
>> >> >> > ITER: 1 | i: 9 | j: 5 Result(i: 3.000000 | j: 2.236068 | i*j:
>> >> >> > 6.708204): 339.950652
>> >> >> > ITER: 1 | i: 9 | j: 6 Result(i: 3.000000 | j: 2.449490 | i*j:
>> >> >> > 7.348469): 347.299121
>> >> >> > ITER: 1 | i: 9 | j: 7 Result(i: 3.000000 | j: 2.645751 | i*j:
>> >> >> > 7.937254): 355.236375
>> >> >> > ITER: 1 | i: 9 | j: 8 Result(i: 3.000000 | j: 2.828427 | i*j:
>> >> >> > 8.485281): 363.721656
>> >> >> > ITER: 1 | i: 9 | j: 9 Result(i: 3.000000 | j: 3.000000 | i*j:
>> >> >> > 9.000000): 372.721656
>> >> >> > Final Result: 372.721656
>> >> >> >
>> >> >> >
>> >> >> >
>> >> >> > As we can see in the following iterations the sqrt(1) as well as
> the
>> >> >> > result is set to zero for some reason.
>> >> >> >
>> >> >> > ITER: 0 | i: 1 | j: 4 Result(i: 0.000000 | j: 2.000000 | i*j:
>> >> >> > 0.000000): 0.000000
>> >> >> > ITER: 0 | i: 1 | j: 5 Result(i: 0.000000 | j: 2.236068 | i*j:
>> >> >> > 0.000000): 0.000000
>> >> >> > ITER: 0 | i: 1 | j: 6 Result(i: 0.000000 | j: 2.449490 | i*j:
>> >> >> > 0.000000): 0.000000
>> >> >> > ITER: 0 | i: 1 | j: 7 Result(i: 0.000000 | j: 2.645751 | i*j:
>> >> >> > 0.000000): 0.000000
>> >> >> > ITER: 0 | i: 1 | j: 8 Result(i: 0.000000 | j: 2.828427 | i*j:
>> >> >> > 0.000000): 0.000000
>> >> >> > ITER: 0 | i: 1 | j: 9 Result(i: 0.000000 | j: 3.000000 | i*j:
>> >> >> > 0.000000): 0.000000
>> >> >> >
>> >> >> > Please help me to resolve the accuracy issue! I think that it will
>> >> >> > be very useful for gem5 community.
>> >> >> >
>> >> >> > To be noticed, I find the correct simulated tick in which the
>> >> >> > application started in FS (using m5 dumpstats), and I start the
>> >> >> > --debug-start, but the trace file which is generated is 10x larger
>> >> >> > than SE mode for the same application. How can I compare them?
>> >> >> >
>> >> >> > Thank you in advance!
>> >> >> > Best regards,
>> >> >> > Nikos
>> >> >> >
>> >> >> > Quoting Νικόλαος Ταμπουρατζής <ntampouratzis@ece.auth.gr>:
>> >> >> >
>> >> >> >> Dear Jason,
>> >> >> >>
>> >> >> >> I am trying to use --debug-start but in FS mode it is very
>> >> >> >> difficult to find the tick on which the application is started!
>> >> >> >>
>> >> >> >> However, I am writing the following very simple c++ program:
>> >> >> >>
>> >> >> >> #include <cmath>
>> >> >> >> #include <stdio.h>
>> >> >> >>
>> >> >> >> int main(){
>> >> >> >>
>> >> >> >> int dim = 4096;
>> >> >> >>
>> >> >> >> double result;
>> >> >> >>
>> >> >> >> for (int iter = 0; iter < 2; iter++){
>> >> >> >> result = 0;
>> >> >> >> for (int i = 0; i < dim; i++){
>> >> >> >> for (int j = 0; j < dim; j++){
>> >> >> >> result += sqrt(i) * sqrt(j);
>> >> >> >> }
>> >> >> >> }
>> >> >> >> printf("Result: %lf\n", result); //Result:
> 30530733453.127449
>> >> >> >> }
>> >> >> >> }
>> >> >> >>
>> >> >> >> I cross-compile it using: riscv64-linux-gnu-g++ -static -O3 -o
>> >> >> >> test_riscv test_riscv.cpp
>> >> >> >>
>> >> >> >>
>> >> >> >> While in X86 (without cross-compilation of course), QEMU-RISCV,
>> >> >> >> GEM5-SE the result is the same (30530733453.127449), in GEM5-FS
> the
>> >> >> >> result is different! In addition, the result is also different
>> >> >> >> between the 2 iterations.
>> >> >> >>
>> >> >> >> Please reproduce the error if you want in order to verify my
> result.
>> >> >> >> Ηow can the issue be resolved?
>> >> >> >>
>> >> >> >> Thank you in advance!
>> >> >> >>
>> >> >> >> Best regards,
>> >> >> >> Nikos
>> >> >> >>
>> >> >> >>
>> >> >> >> Quoting Jason Lowe-Power <jason@lowepower.com>:
>> >> >> >>
>> >> >> >>> Hi Nikos,
>> >> >> >>>
>> >> >> >>> You can use --debug-start to start the debugging after some
> number
>> >> of
>> >> >> >>> ticks. Also, I would expect that the difference should come up
>> >> >> quickly, so
>> >> >> >>> no need to run the program to the end.
>> >> >> >>>
>> >> >> >>> For the FS mode one, you will want to just start the trace as
> the
>> >> >> >>> application starts. This could be a bit of a pain.
>> >> >> >>>
>> >> >> >>> I'm not really sure what fundamentally could be different. FS
> and SE
>> >> >> mode
>> >> >> >>> use the exact same code for executing instructions, so I don't
> think
>> >> >> that's
>> >> >> >>> the problem. Have you tried running for smaller inputs or just
> one
>> >> >> >>> iteration?
>> >> >> >>>
>> >> >> >>> Jason
>> >> >> >>>
>> >> >> >>>
>> >> >> >>>
>> >> >> >>> On Wed, Sep 21, 2022 at 9:04 AM Νικόλαος Ταμπουρατζής <
>> >> >> >>> ntampouratzis@ece.auth.gr> wrote:
>> >> >> >>>
>> >> >> >>>> Dear Bobby,
>> >> >> >>>>
>> >> >> >>>> Iam trying to add --debug-flags=Exec (building the gem5 for
>> >> gem5.opt
>> >> >> >>>> not for gem5.fast which I had) but the debug traces exceed the
> 20GB
>> >> >> >>>> (and it is not finished yet) for less than 1 simulated second.
> How
>> >> can
>> >> >> >>>> I reduce the size of the debug-flags (or set something more
>> >> specific)?
>> >> >> >>>>
>> >> >> >>>> In contrast I build the HPCG benchmark with DHPCG_DEBUG flag.
> If
>> >> you
>> >> >> >>>> want, you can compare these two output files
>> >> >> >>>> (hpcg20010909T014640_SE_Mode & HPCG-Benchmark_3.1_FS_Mode). As
> you
>> >> can
>> >> >> >>>> see, something goes wrong with the accuracy of calculations in
> FS
>> >> mode
>> >> >> >>>> (benchmark uses double precission). You can find the files
> here:
>> >> >> >>>> http://kition.mhl.tuc.gr:8000/d/68d82f3533/
>> >> >> >>>>
>> >> >> >>>> Best regards,
>> >> >> >>>> Nikos
>> >> >> >>>>
>> >> >> >>>> Quoting Jason Lowe-Power <jason@lowepower.com>:
>> >> >> >>>>
>> >> >> >>>>> That's quite odd that it works in SE mode but not FS mode!
>> >> >> >>>>>
>> >> >> >>>>> I would suggest running with --debug-flags=Exec for both and
> then
>> >> >> >>>> perform a
>> >> >> >>>>> diff to see how they differ.
>> >> >> >>>>>
>> >> >> >>>>> Cheers,
>> >> >> >>>>> Jason
>> >> >> >>>>>
>> >> >> >>>>> On Tue, Sep 20, 2022 at 2:45 PM Νικόλαος Ταμπουρατζής <
>> >> >> >>>>> ntampouratzis@ece.auth.gr> wrote:
>> >> >> >>>>>
>> >> >> >>>>>> Dear Bobby,
>> >> >> >>>>>>
>> >> >> >>>>>> In QEMU I get the same (correct) results that I get in SE
> mode
>> >> >> >>>>>> simulation. I get invalid results in FS simulation (in both
>> >> >> >>>>>> riscv-fs.py and riscv-ubuntu-run.py). I cannot access real
> RISCV
>> >> >> >>>>>> hardware at this moment, however, if you want you may
> execute my
>> >> >> xhpcg
>> >> >> >>>>>> binary (http://kition.mhl.tuc.gr:8000/f/4ca25fdd3c/) with the
>> >> >> >>>>>> following configuration:
>> >> >> >>>>>>
>> >> >> >>>>>> ./xhpcg --nx=16 --ny=16 --nz=16 --npx=1 --npy=1 --npz=1
> --rt=0.1
>> >> >> >>>>>>
>> >> >> >>>>>> Please let me know if you have any updates!
>> >> >> >>>>>>
>> >> >> >>>>>> Best regards,
>> >> >> >>>>>> Nikos
>> >> >> >>>>>>
>> >> >> >>>>>>
>> >> >> >>>>>> Quoting Jason Lowe-Power <jason@lowepower.com>:
>> >> >> >>>>>>
>> >> >> >>>>>>> Hi Nikos,
>> >> >> >>>>>>>
>> >> >> >>>>>>> I notice you said the following in your original email:
>> >> >> >>>>>>>
>> >> >> >>>>>>> In addition, I used the RISCV Ubuntu image
>> >> >> >>>>>>>> (
>> >> >> https://github.com/gem5/gem5-resources/tree/stable/src/riscv-ubuntu
>> >> >> >>>> ),
>> >> >> >>>>>>>> I installed the gcc compiler, compile it (through qemu)
> and I
>> >> get
>> >> >> >>>>>>>> wrong results too.
>> >> >> >>>>>>>
>> >> >> >>>>>>>
>> >> >> >>>>>>> Is this saying you get the wrong results is QEMU? If so,
> the bug
>> >> >> is in
>> >> >> >>>>>> GCC
>> >> >> >>>>>>> or the HPCG workload, not in gem5. If not, I would test in
> QEMU
>> >> to
>> >> >> >>>> make
>> >> >> >>>>>>> sure the binary works there. Another way you could test to
> see
>> >> if
>> >> >> the
>> >> >> >>>>>>> problem is your binary or gem5 would be to run it on real
>> >> >> hardware. We
>> >> >> >>>>>> have
>> >> >> >>>>>>> access to some RISC-V hardware here at UC Davis, if you
> don't
>> >> have
>> >> >> >>>> access
>> >> >> >>>>>>> to it.
>> >> >> >>>>>>>
>> >> >> >>>>>>> Cheers,
>> >> >> >>>>>>> Jason
>> >> >> >>>>>>>
>> >> >> >>>>>>> On Tue, Sep 20, 2022 at 12:58 AM Νικόλαος Ταμπουρατζής <
>> >> >> >>>>>>> ntampouratzis@ece.auth.gr> wrote:
>> >> >> >>>>>>>
>> >> >> >>>>>>>> Dear Bobby,
>> >> >> >>>>>>>>
>> >> >> >>>>>>>> 1) I use the original riscv-fs.py which is provided in the
>> >> latest
>> >> >> >>>> gem5
>> >> >> >>>>>>>> release.
>> >> >> >>>>>>>> I run the gem5 once (./build/RISCV/gem5.fast -d
>> >> ./HPCG_FS_results
>> >> >> >>>>>>>> ./configs/example/gem5_library/riscv-fs.py) in order to
>> >> download
>> >> >> the
>> >> >> >>>>>>>> riscv-bootloader-vmlinux-5.10 and riscv-disk-img.
>> >> >> >>>>>>>> After this I mount the riscv-disk-img (sudo mount -o loop
>> >> >> >>>>>>>> riscv-disk-img /mnt), put the xhpcg executable and I do the
>> >> >> following
>> >> >> >>>>>>>> changes in riscv-fs.py to boot the riscv-disk-img with
>> >> executable:
>> >> >> >>>>>>>>
>> >> >> >>>>>>>> image = CustomDiskImageResource(
>> >> >> >>>>>>>> local_path =
> "/home/cossim/.cache/gem5/riscv-disk-img",
>> >> >> >>>>>>>> )
>> >> >> >>>>>>>>
>> >> >> >>>>>>>> # Set the Full System workload.
>> >> >> >>>>>>>> board.set_kernel_disk_workload(
>> >> >> >>>>>>>>
>> >> >> kernel=Resource("riscv-bootloader-vmlinux-5.10"),
>> >> >> >>>>>>>> disk_image=image,
>> >> >> >>>>>>>> )
>> >> >> >>>>>>>>
>> >> >> >>>>>>>> Finally, in the
>> >> >> gem5/src/python/gem5/components/boards/riscv_board.py
>> >> >> >>>>>>>> I change the last line to "return ["console=ttyS0",
>> >> >> >>>>>>>> "root={root_value}", "rw"]" in order to allow the write
>> >> >> permissions
>> >> >> >>>> in
>> >> >> >>>>>>>> the image.
>> >> >> >>>>>>>>
>> >> >> >>>>>>>>
>> >> >> >>>>>>>> 2) The HPCG benchmark after some iterations calculates if
> the
>> >> >> results
>> >> >> >>>>>>>> are valid or not valid. In the case of FS it gives invalid
>> >> >> results.
>> >> >> >>>> As
>> >> >> >>>>>>>> I see from the results, one (at least) problem is that
> produces
>> >> >> >>>>>>>> different results in each HPCG execution (with the same
>> >> >> >>>> configuration).
>> >> >> >>>>>>>>
>> >> >> >>>>>>>> Here is the HPCG output and riscv-fs.py
>> >> >> >>>>>>>> (http://kition.mhl.tuc.gr:8000/d/68d82f3533/). You may
>> >> reproduce
>> >> >> the
>> >> >> >>>>>>>> results in the video if you use the xhpcg executable
>> >> >> >>>>>>>> (http://kition.mhl.tuc.gr:8000/f/4ca25fdd3c/)
>> >> >> >>>>>>>>
>> >> >> >>>>>>>> Please help me in order to solve it!
>> >> >> >>>>>>>>
>> >> >> >>>>>>>> Finally, I get invalid results in the HPL benchmark in FS
> mode
>> >> >> too.
>> >> >> >>>>>>>>
>> >> >> >>>>>>>> Best regards,
>> >> >> >>>>>>>> Nikos
>> >> >> >>>>>>>>
>> >> >> >>>>>>>>
>> >> >> >>>>>>>> Quoting Bobby Bruce <bbruce@ucdavis.edu>:
>> >> >> >>>>>>>>
>> >> >> >>>>>>>> > I'm going to need a bit more information to help:
>> >> >> >>>>>>>> >
>> >> >> >>>>>>>> > 1. In what way have you modified
>> >> >> >>>>>>>> > ./configs/example/gem5_library/riscv-fs.py? Can you
> attach
>> >> the
>> >> >> >>>> script
>> >> >> >>>>>>>> here?
>> >> >> >>>>>>>> > 2. What error are you getting or in what way are the
> results
>> >> >> >>>> invalid?
>> >> >> >>>>>>>> >
>> >> >> >>>>>>>> > -
>> >> >> >>>>>>>> > Dr. Bobby R. Bruce
>> >> >> >>>>>>>> > Room 3050,
>> >> >> >>>>>>>> > Kemper Hall, UC Davis
>> >> >> >>>>>>>> > Davis,
>> >> >> >>>>>>>> > CA, 95616
>> >> >> >>>>>>>> >
>> >> >> >>>>>>>> > web: https://www.bobbybruce.net
>> >> >> >>>>>>>> >
>> >> >> >>>>>>>> >
>> >> >> >>>>>>>> > On Mon, Sep 19, 2022 at 1:43 PM Νικόλαος Ταμπουρατζής <
>> >> >> >>>>>>>> > ntampouratzis@ece.auth.gr> wrote:
>> >> >> >>>>>>>> >
>> >> >> >>>>>>>> >>
>> >> >> >>>>>>>> >> Dear gem5 community,
>> >> >> >>>>>>>> >>
>> >> >> >>>>>>>> >> I have successfully cross-compile the HPCG benchmark for
>> >> RISCV
>> >> >> >>>>>> (Serial
>> >> >> >>>>>>>> >> version, without MPI and OpenMP). While it working
> properly
>> >> in
>> >> >> >>>> gem5
>> >> >> >>>>>> SE
>> >> >> >>>>>>>> >> mode (./build/RISCV/gem5.fast -d ./HPCG_SE_results
>> >> >> >>>>>>>> >> ./configs/example/se.py -c xhpcg --options '--nx=16
> --ny=16
>> >> >> >>>> --nz=16
>> >> >> >>>>>>>> >> --npx=1 --npy=1 --npz=1 --rt=0.1'), I get invalid
> results
>> >> in FS
>> >> >> >>>>>>>> >> simulation using "./build/RISCV/gem5.fast -d
>> >> ./HPCG_FS_results
>> >> >> >>>>>>>> >> ./configs/example/gem5_library/riscv-fs.py" (I mount the
>> >> riscv
>> >> >> >>>> image
>> >> >> >>>>>>>> >> and put it).
>> >> >> >>>>>>>> >>
>> >> >> >>>>>>>> >> Can you help me please?
>> >> >> >>>>>>>> >>
>> >> >> >>>>>>>> >> In addition, I used the RISCV Ubuntu image
>> >> >> >>>>>>>> >> (
>> >> >> >>>>
>> >> https://github.com/gem5/gem5-resources/tree/stable/src/riscv-ubuntu
>> >> >> >>>>>> ),
>> >> >> >>>>>>>> >> I installed the gcc compiler, compile it (through qemu)
> and
>> >> I
>> >> >> get
>> >> >> >>>>>>>> >> wrong results too.
>> >> >> >>>>>>>> >>
>> >> >> >>>>>>>> >> Here is the Makefile which I use, the hpcg executable
> for
>> >> RISCV
>> >> >> >>>>>>>> >> (xhpcg), and a video that shows the results
>> >> >> >>>>>>>> >> (http://kition.mhl.tuc.gr:8000/f/4ca25fdd3c/).
>> >> >> >>>>>>>> >>
>> >> >> >>>>>>>> >> P.S. I use the latest gem5 version.
>> >> >> >>>>>>>> >>
>> >> >> >>>>>>>> >> Thank you in advance! :)
>> >> >> >>>>>>>> >>
>> >> >> >>>>>>>> >> Best regards,
>> >> >> >>>>>>>> >> Nikos
>> >> >> >>>>>>>> >> _______________________________________________
>> >> >> >>>>>>>> >> gem5-users mailing list -- gem5-users@gem5.org
>> >> >> >>>>>>>> >> To unsubscribe send an email to
> gem5-users-leave@gem5.org
>> >> >> >>>>>>>> >>
>> >> >> >>>>>>>>
>> >> >> >>>>>>>>
>> >> >> >>>>>>>> _______________________________________________
>> >> >> >>>>>>>> gem5-users mailing list -- gem5-users@gem5.org
>> >> >> >>>>>>>> To unsubscribe send an email to gem5-users-leave@gem5.org
>> >> >> >>>>>>>>
>> >> >> >>>>>>
>> >> >> >>>>>>
>> >> >> >>>>>> _______________________________________________
>> >> >> >>>>>> gem5-users mailing list -- gem5-users@gem5.org
>> >> >> >>>>>> To unsubscribe send an email to gem5-users-leave@gem5.org
>> >> >> >>>>>>
>> >> >> >>>>
>> >> >> >>>>
>> >> >> >>>> _______________________________________________
>> >> >> >>>> gem5-users mailing list -- gem5-users@gem5.org
>> >> >> >>>> To unsubscribe send an email to gem5-users-leave@gem5.org
>> >> >> >>>>
>> >> >> >>
>> >> >> >>
>> >> >> >> _______________________________________________
>> >> >> >> gem5-users mailing list -- gem5-users@gem5.org
>> >> >> >> To unsubscribe send an email to gem5-users-leave@gem5.org
>> >> >> >
>> >> >> >
>> >> >> > _______________________________________________
>> >> >> > gem5-users mailing list -- gem5-users@gem5.org
>> >> >> > To unsubscribe send an email to gem5-users-leave@gem5.org
>> >> >>
>> >> >>
>> >> >> _______________________________________________
>> >> >> gem5-users mailing list -- gem5-users@gem5.org
>> >> >> To unsubscribe send an email to gem5-users-leave@gem5.org
>> >> >>
>> >>
>> >>
>> >> _______________________________________________
>> >> gem5-users mailing list -- gem5-users@gem5.org
>> >> To unsubscribe send an email to gem5-users-leave@gem5.org
>> >>
>>
>>
>> _______________________________________________
>> gem5-users mailing list -- gem5-users@gem5.org
>> To unsubscribe send an email to gem5-users-leave@gem5.org
BB
Bobby Bruce
Mon, Oct 31, 2022 11:37 PM
You mean this bug? Unfortunately not, I've been very busy with the upcoming
gem5 release and haven't had time to investigate this further.
--
Dr. Bobby R. Bruce
Room 3050,
Kemper Hall, UC Davis
Davis,
CA, 95616
web: https://www.bobbybruce.net
On Mon, Oct 31, 2022 at 1:45 AM Νικόλαος Ταμπουρατζής via gem5-users <
gem5-users@gem5.org> wrote:
Dear Bobby, Jason, all,
Is there any update about the accuracy of RISC-V FS?
Best regards,
Nikos
Quoting Bobby Bruce bbruce@ucdavis.edu:
Jason and I had a theory that this may be due to the "Rounding Mode" for
floating pointing being set incorrectly in FS mode. That's set via a
I manually expanded the macro here:
inside the "fsqrt_d" definition then compiled "build/ALL/gem5.debug".
used gdb to add a breakpoint in the "Fsqrt_d::execute" function (in the
generated "build/ALL/arch/riscv/generated/exec-ns.cc.inc" file).
gdb build/ALL/gem5.opt
break Fsqrt_d::execute
run bug-recreation/se-mode-run.py # or `run
bug-recreation/fs-mode-run.py`
Stepping through with gdb I the rounding mode is `0` for SE mode and `0`
for FS mode as well. So, no luck with that theory.
My new theory is that this bug has something to do with thread context
switching being implemented incorrectly in RISC-V somehow. I find it
strange that the sqrt(1) works fine for a while (i.e. returns `1`) then
suddenly starts returning zero after a certain point in the execution. In
addition, it's odd that the loop is not returning the same value each
despite executing the same code. It'd make sense to me that the thread is
being stored and then resumed with some corruption of the floating point
data. This would also explain why this bug only occurs in FS mode.
I'll try to find time to figure out a good test for this. If anyone has
Dear Jason & Boddy,
Unfortunately, I have tried my simple example without the sqrt
function and the problem remains. Specifically, I have the following
simple code:
#include <cmath>
#include <stdio.h>
int main(){
int dim = 1024;
double result;
for (int iter = 0; iter < 2; iter++){
result = 0;
for (int i = 0; i < dim; i++){
for (int j = 0; j < dim; j++){
result += i * j;
}
}
printf("Final Result: %lf\n", result);
}
}
In the above code, the correct result is 274341298176.000000 (from
RISCV-SE mode and x86), while in FS mode I get sometimes the correct
result and other times a different number.
Best regards,
Nikos
Quoting Jason Lowe-Power jason@lowepower.com:
I have an idea...
Have you put a breakpoint in the implementation of the fsqrt_d
would like to know if when running in SE mode and running in FS mode
using the same rounding mode. My hypothesis is that in FS mode the
mode is set differently.
Cheers,
Jason
On Fri, Oct 7, 2022 at 12:15 AM Νικόλαος Ταμπουρατζής <
ntampouratzis@ece.auth.gr> wrote:
Dear Boddy,
Thanks a lot for the effort! I looked in detail and I observe that
problem is created only using float and double variables (in the case
of int it is working properly in FS mode). Specifically, in the case
of float the variables are set to "nan", while in the case of double
the variables are set to 0.000000 (in random time - probably from
instruction of simulated OS?). You may use a simple c/c++ example in
order to get some traces before going to HPCG...
Thank you in advance!!
Best regards,
Nikos
Quoting Bobby Bruce bbruce@ucdavis.edu:
Hey Niko,
Thanks for this analysis. I jumped a little into this today but
as far as you did. I wanted to find a quick way to recreate the
free to use this, if it helps any.
It's very strange to me that this bug hasn't manifested itself
it's undeniably there. I'll try to spend more time looking at this
with some traces and debug flags and see if I can narrow down the
In my previous results, I had used double (not float) for the
following variables: result, sq_i and sq_j. In the case of float
instead of double I get "nan" and not 0.000000.
Quoting Νικόλαος Ταμπουρατζής ntampouratzis@ece.auth.gr:
Dear Jason, all,
I am trying to find the accuracy problem with RISCV-FS and I
that the problem is created (at least in my dummy example)
the variables (double) are set to zero in random simulated time
this reason I get different results among executions of the same
code). Specifically for the following dummy code:
#include <cmath>
#include <stdio.h>
int main(){
int dim = 10;
float result;
for (int iter = 0; iter < 2; iter++){
result = 0;
for (int i = 0; i < dim; i++){
for (int j = 0; j < dim; j++){
float sq_i = sqrt(i);
float sq_j = sqrt(j);
result += sq_i * sq_j;
printf("ITER: %d | i: %d | j: %d Result(i: %f |
%f | i*j: %f): %f\n", iter, i , j, sq_i, sq_j, sq_i * sq_j,
}
}
printf("Final Result: %lf\n", result);
}
}
The correct Final Result in both iterations is 372.721656.
I get the following results in FS:
ITER: 0 | i: 0 | j: 0 Result(i: 0.000000 | j: 0.000000 | ij:
0.000000): 0.000000
ITER: 0 | i: 0 | j: 1 Result(i: 0.000000 | j: 1.000000 | ij:
0.000000): 0.000000
ITER: 0 | i: 0 | j: 2 Result(i: 0.000000 | j: 1.414214 | ij:
0.000000): 0.000000
ITER: 0 | i: 0 | j: 3 Result(i: 0.000000 | j: 1.732051 | ij:
0.000000): 0.000000
ITER: 0 | i: 0 | j: 4 Result(i: 0.000000 | j: 2.000000 | ij:
0.000000): 0.000000
ITER: 0 | i: 0 | j: 5 Result(i: 0.000000 | j: 2.236068 | ij:
0.000000): 0.000000
ITER: 0 | i: 0 | j: 6 Result(i: 0.000000 | j: 2.449490 | ij:
0.000000): 0.000000
ITER: 0 | i: 0 | j: 7 Result(i: 0.000000 | j: 2.645751 | ij:
0.000000): 0.000000
ITER: 0 | i: 0 | j: 8 Result(i: 0.000000 | j: 2.828427 | ij:
0.000000): 0.000000
ITER: 0 | i: 0 | j: 9 Result(i: 0.000000 | j: 3.000000 | ij:
0.000000): 0.000000
ITER: 0 | i: 1 | j: 0 Result(i: 1.000000 | j: 0.000000 | ij:
0.000000): 0.000000
ITER: 0 | i: 1 | j: 1 Result(i: 1.000000 | j: 1.000000 | ij:
1.000000): 1.000000
ITER: 0 | i: 1 | j: 2 Result(i: 1.000000 | j: 1.414214 | ij:
1.414214): 2.414214
ITER: 0 | i: 1 | j: 3 Result(i: 1.000000 | j: 1.732051 | ij:
1.732051): 4.146264
ITER: 0 | i: 1 | j: 4 Result(i: 0.000000 | j: 2.000000 | ij:
0.000000): 0.000000
ITER: 0 | i: 1 | j: 5 Result(i: 0.000000 | j: 2.236068 | ij:
0.000000): 0.000000
ITER: 0 | i: 1 | j: 6 Result(i: 0.000000 | j: 2.449490 | ij:
0.000000): 0.000000
ITER: 0 | i: 1 | j: 7 Result(i: 0.000000 | j: 2.645751 | ij:
0.000000): 0.000000
ITER: 0 | i: 1 | j: 8 Result(i: 0.000000 | j: 2.828427 | ij:
0.000000): 0.000000
ITER: 0 | i: 1 | j: 9 Result(i: 0.000000 | j: 3.000000 | ij:
0.000000): 0.000000
ITER: 0 | i: 2 | j: 0 Result(i: 1.414214 | j: 0.000000 | ij:
0.000000): 0.000000
ITER: 0 | i: 2 | j: 1 Result(i: 1.414214 | j: 1.000000 | ij:
1.414214): 1.414214
ITER: 0 | i: 2 | j: 2 Result(i: 1.414214 | j: 1.414214 | ij:
2.000000): 3.414214
ITER: 0 | i: 2 | j: 3 Result(i: 1.414214 | j: 1.732051 | ij:
2.449490): 5.863703
ITER: 0 | i: 2 | j: 4 Result(i: 1.414214 | j: 2.000000 | ij:
2.828427): 8.692130
ITER: 0 | i: 2 | j: 5 Result(i: 1.414214 | j: 2.236068 | ij:
3.162278): 11.854408
ITER: 0 | i: 2 | j: 6 Result(i: 1.414214 | j: 2.449490 | ij:
3.464102): 15.318510
ITER: 0 | i: 2 | j: 7 Result(i: 1.414214 | j: 2.645751 | ij:
3.741657): 19.060167
ITER: 0 | i: 2 | j: 8 Result(i: 1.414214 | j: 2.828427 | ij:
4.000000): 23.060167
ITER: 0 | i: 2 | j: 9 Result(i: 1.414214 | j: 3.000000 | ij:
4.242641): 27.302808
ITER: 0 | i: 3 | j: 0 Result(i: 1.732051 | j: 0.000000 | ij:
0.000000): 27.302808
ITER: 0 | i: 3 | j: 1 Result(i: 1.732051 | j: 1.000000 | ij:
1.732051): 29.034859
ITER: 0 | i: 3 | j: 2 Result(i: 1.732051 | j: 1.414214 | ij:
2.449490): 31.484348
ITER: 0 | i: 3 | j: 3 Result(i: 1.732051 | j: 1.732051 | ij:
3.000000): 34.484348
ITER: 0 | i: 3 | j: 4 Result(i: 1.732051 | j: 2.000000 | ij:
3.464102): 37.948450
ITER: 0 | i: 3 | j: 5 Result(i: 1.732051 | j: 2.236068 | ij:
3.872983): 41.821433
ITER: 0 | i: 3 | j: 6 Result(i: 1.732051 | j: 2.449490 | ij:
4.242641): 46.064074
ITER: 0 | i: 3 | j: 7 Result(i: 1.732051 | j: 2.645751 | ij:
4.582576): 50.646650
ITER: 0 | i: 3 | j: 8 Result(i: 1.732051 | j: 2.828427 | ij:
4.898979): 55.545629
ITER: 0 | i: 3 | j: 9 Result(i: 1.732051 | j: 3.000000 | ij:
5.196152): 60.741782
ITER: 0 | i: 4 | j: 0 Result(i: 2.000000 | j: 0.000000 | ij:
0.000000): 60.741782
ITER: 0 | i: 4 | j: 1 Result(i: 2.000000 | j: 1.000000 | ij:
2.000000): 62.741782
ITER: 0 | i: 4 | j: 2 Result(i: 2.000000 | j: 1.414214 | ij:
2.828427): 65.570209
ITER: 0 | i: 4 | j: 3 Result(i: 2.000000 | j: 1.732051 | ij:
3.464102): 69.034310
ITER: 0 | i: 4 | j: 4 Result(i: 2.000000 | j: 2.000000 | ij:
4.000000): 73.034310
ITER: 0 | i: 4 | j: 5 Result(i: 2.000000 | j: 2.236068 | ij:
4.472136): 77.506446
ITER: 0 | i: 4 | j: 6 Result(i: 2.000000 | j: 2.449490 | ij:
4.898979): 82.405426
ITER: 0 | i: 4 | j: 7 Result(i: 2.000000 | j: 2.645751 | ij:
5.291503): 87.696928
ITER: 0 | i: 4 | j: 8 Result(i: 2.000000 | j: 2.828427 | ij:
5.656854): 93.353783
ITER: 0 | i: 4 | j: 9 Result(i: 2.000000 | j: 3.000000 | ij:
6.000000): 99.353783
ITER: 0 | i: 5 | j: 0 Result(i: 2.236068 | j: 0.000000 | ij:
0.000000): 99.353783
ITER: 0 | i: 5 | j: 1 Result(i: 2.236068 | j: 1.000000 | ij:
2.236068): 101.589851
ITER: 0 | i: 5 | j: 2 Result(i: 2.236068 | j: 1.414214 | ij:
3.162278): 104.752128
ITER: 0 | i: 5 | j: 3 Result(i: 2.236068 | j: 1.732051 | ij:
3.872983): 108.625112
ITER: 0 | i: 5 | j: 4 Result(i: 2.236068 | j: 2.000000 | ij:
4.472136): 113.097248
ITER: 0 | i: 5 | j: 5 Result(i: 2.236068 | j: 2.236068 | ij:
5.000000): 118.097248
ITER: 0 | i: 5 | j: 6 Result(i: 2.236068 | j: 2.449490 | ij:
5.477226): 123.574473
ITER: 0 | i: 5 | j: 7 Result(i: 2.236068 | j: 2.645751 | ij:
5.916080): 129.490553
ITER: 0 | i: 5 | j: 8 Result(i: 2.236068 | j: 2.828427 | ij:
6.324555): 135.815108
ITER: 0 | i: 5 | j: 9 Result(i: 2.236068 | j: 3.000000 | ij:
6.708204): 142.523312
ITER: 0 | i: 6 | j: 0 Result(i: 2.449490 | j: 0.000000 | ij:
0.000000): 142.523312
ITER: 0 | i: 6 | j: 1 Result(i: 2.449490 | j: 1.000000 | ij:
2.449490): 144.972802
ITER: 0 | i: 6 | j: 2 Result(i: 2.449490 | j: 1.414214 | ij:
3.464102): 148.436904
ITER: 0 | i: 6 | j: 3 Result(i: 2.449490 | j: 1.732051 | ij:
4.242641): 152.679544
ITER: 0 | i: 6 | j: 4 Result(i: 2.449490 | j: 2.000000 | ij:
4.898979): 157.578524
ITER: 0 | i: 6 | j: 5 Result(i: 2.449490 | j: 2.236068 | ij:
5.477226): 163.055749
ITER: 0 | i: 6 | j: 6 Result(i: 2.449490 | j: 2.449490 | ij:
6.000000): 169.055749
ITER: 0 | i: 6 | j: 7 Result(i: 2.449490 | j: 2.645751 | ij:
6.480741): 175.536490
ITER: 0 | i: 6 | j: 8 Result(i: 2.449490 | j: 2.828427 | ij:
6.928203): 182.464693
ITER: 0 | i: 6 | j: 9 Result(i: 2.449490 | j: 3.000000 | ij:
7.348469): 189.813162
ITER: 0 | i: 7 | j: 0 Result(i: 2.645751 | j: 0.000000 | ij:
0.000000): 189.813162
ITER: 0 | i: 7 | j: 1 Result(i: 2.645751 | j: 1.000000 | ij:
2.645751): 192.458914
ITER: 0 | i: 7 | j: 2 Result(i: 2.645751 | j: 1.414214 | ij:
3.741657): 196.200571
ITER: 0 | i: 7 | j: 3 Result(i: 2.645751 | j: 1.732051 | ij:
4.582576): 200.783147
ITER: 0 | i: 7 | j: 4 Result(i: 2.645751 | j: 2.000000 | ij:
5.291503): 206.074649
ITER: 0 | i: 7 | j: 5 Result(i: 2.645751 | j: 2.236068 | ij:
5.916080): 211.990729
ITER: 0 | i: 7 | j: 6 Result(i: 2.645751 | j: 2.449490 | ij:
6.480741): 218.471470
ITER: 0 | i: 7 | j: 7 Result(i: 2.645751 | j: 2.645751 | ij:
7.000000): 225.471470
ITER: 0 | i: 7 | j: 8 Result(i: 2.645751 | j: 2.828427 | ij:
7.483315): 232.954785
ITER: 0 | i: 7 | j: 9 Result(i: 2.645751 | j: 3.000000 | ij:
7.937254): 240.892039
ITER: 0 | i: 8 | j: 0 Result(i: 2.828427 | j: 0.000000 | ij:
0.000000): 240.892039
ITER: 0 | i: 8 | j: 1 Result(i: 2.828427 | j: 1.000000 | ij:
2.828427): 243.720466
ITER: 0 | i: 8 | j: 2 Result(i: 2.828427 | j: 1.414214 | ij:
4.000000): 247.720466
ITER: 0 | i: 8 | j: 3 Result(i: 2.828427 | j: 1.732051 | ij:
4.898979): 252.619445
ITER: 0 | i: 8 | j: 4 Result(i: 2.828427 | j: 2.000000 | ij:
5.656854): 258.276300
ITER: 0 | i: 8 | j: 5 Result(i: 2.828427 | j: 2.236068 | ij:
6.324555): 264.600855
ITER: 0 | i: 8 | j: 6 Result(i: 2.828427 | j: 2.449490 | ij:
6.928203): 271.529058
ITER: 0 | i: 8 | j: 7 Result(i: 2.828427 | j: 2.645751 | ij:
7.483315): 279.012373
ITER: 0 | i: 8 | j: 8 Result(i: 2.828427 | j: 2.828427 | ij:
8.000000): 287.012373
ITER: 0 | i: 8 | j: 9 Result(i: 2.828427 | j: 3.000000 | ij:
8.485281): 295.497654
ITER: 0 | i: 9 | j: 0 Result(i: 3.000000 | j: 0.000000 | ij:
0.000000): 295.497654
ITER: 0 | i: 9 | j: 1 Result(i: 3.000000 | j: 1.000000 | ij:
3.000000): 298.497654
ITER: 0 | i: 9 | j: 2 Result(i: 3.000000 | j: 1.414214 | ij:
4.242641): 302.740295
ITER: 0 | i: 9 | j: 3 Result(i: 3.000000 | j: 1.732051 | ij:
5.196152): 307.936447
ITER: 0 | i: 9 | j: 4 Result(i: 3.000000 | j: 2.000000 | ij:
6.000000): 313.936447
ITER: 0 | i: 9 | j: 5 Result(i: 3.000000 | j: 2.236068 | ij:
6.708204): 320.644651
ITER: 0 | i: 9 | j: 6 Result(i: 3.000000 | j: 2.449490 | ij:
7.348469): 327.993120
ITER: 0 | i: 9 | j: 7 Result(i: 3.000000 | j: 2.645751 | ij:
7.937254): 335.930374
ITER: 0 | i: 9 | j: 8 Result(i: 3.000000 | j: 2.828427 | ij:
8.485281): 344.415656
ITER: 0 | i: 9 | j: 9 Result(i: 3.000000 | j: 3.000000 | ij:
9.000000): 353.415656
Final Result: 353.415656
ITER: 1 | i: 0 | j: 0 Result(i: 0.000000 | j: 0.000000 | ij:
0.000000): 0.000000
ITER: 1 | i: 0 | j: 1 Result(i: 0.000000 | j: 1.000000 | ij:
0.000000): 0.000000
ITER: 1 | i: 0 | j: 2 Result(i: 0.000000 | j: 1.414214 | ij:
0.000000): 0.000000
ITER: 1 | i: 0 | j: 3 Result(i: 0.000000 | j: 1.732051 | ij:
0.000000): 0.000000
ITER: 1 | i: 0 | j: 4 Result(i: 0.000000 | j: 2.000000 | ij:
0.000000): 0.000000
ITER: 1 | i: 0 | j: 5 Result(i: 0.000000 | j: 2.236068 | ij:
0.000000): 0.000000
ITER: 1 | i: 0 | j: 6 Result(i: 0.000000 | j: 2.449490 | ij:
0.000000): 0.000000
ITER: 1 | i: 0 | j: 7 Result(i: 0.000000 | j: 2.645751 | ij:
0.000000): 0.000000
ITER: 1 | i: 0 | j: 8 Result(i: 0.000000 | j: 2.828427 | ij:
0.000000): 0.000000
ITER: 1 | i: 0 | j: 9 Result(i: 0.000000 | j: 3.000000 | ij:
0.000000): 0.000000
ITER: 1 | i: 1 | j: 0 Result(i: 1.000000 | j: 0.000000 | ij:
0.000000): 0.000000
ITER: 1 | i: 1 | j: 1 Result(i: 1.000000 | j: 1.000000 | ij:
1.000000): 1.000000
ITER: 1 | i: 1 | j: 2 Result(i: 1.000000 | j: 1.414214 | ij:
1.414214): 2.414214
ITER: 1 | i: 1 | j: 3 Result(i: 1.000000 | j: 1.732051 | ij:
1.732051): 4.146264
ITER: 1 | i: 1 | j: 4 Result(i: 1.000000 | j: 2.000000 | ij:
2.000000): 6.146264
ITER: 1 | i: 1 | j: 5 Result(i: 1.000000 | j: 2.236068 | ij:
2.236068): 8.382332
ITER: 1 | i: 1 | j: 6 Result(i: 1.000000 | j: 2.449490 | ij:
2.449490): 10.831822
ITER: 1 | i: 1 | j: 7 Result(i: 1.000000 | j: 2.645751 | ij:
2.645751): 13.477573
ITER: 1 | i: 1 | j: 8 Result(i: 1.000000 | j: 2.828427 | ij:
2.828427): 16.306001
ITER: 1 | i: 1 | j: 9 Result(i: 1.000000 | j: 3.000000 | ij:
3.000000): 19.306001
ITER: 1 | i: 2 | j: 0 Result(i: 1.414214 | j: 0.000000 | ij:
0.000000): 19.306001
ITER: 1 | i: 2 | j: 1 Result(i: 1.414214 | j: 1.000000 | ij:
1.414214): 20.720214
ITER: 1 | i: 2 | j: 2 Result(i: 1.414214 | j: 1.414214 | ij:
2.000000): 22.720214
ITER: 1 | i: 2 | j: 3 Result(i: 1.414214 | j: 1.732051 | ij:
2.449490): 25.169704
ITER: 1 | i: 2 | j: 4 Result(i: 1.414214 | j: 2.000000 | ij:
2.828427): 27.998131
ITER: 1 | i: 2 | j: 5 Result(i: 1.414214 | j: 2.236068 | ij:
3.162278): 31.160409
ITER: 1 | i: 2 | j: 6 Result(i: 1.414214 | j: 2.449490 | ij:
3.464102): 34.624510
ITER: 1 | i: 2 | j: 7 Result(i: 1.414214 | j: 2.645751 | ij:
3.741657): 38.366168
ITER: 1 | i: 2 | j: 8 Result(i: 1.414214 | j: 2.828427 | ij:
4.000000): 42.366168
ITER: 1 | i: 2 | j: 9 Result(i: 1.414214 | j: 3.000000 | ij:
4.242641): 46.608808
ITER: 1 | i: 3 | j: 0 Result(i: 1.732051 | j: 0.000000 | ij:
0.000000): 46.608808
ITER: 1 | i: 3 | j: 1 Result(i: 1.732051 | j: 1.000000 | ij:
1.732051): 48.340859
ITER: 1 | i: 3 | j: 2 Result(i: 1.732051 | j: 1.414214 | ij:
2.449490): 50.790349
ITER: 1 | i: 3 | j: 3 Result(i: 1.732051 | j: 1.732051 | ij:
3.000000): 53.790349
ITER: 1 | i: 3 | j: 4 Result(i: 1.732051 | j: 2.000000 | ij:
3.464102): 57.254450
ITER: 1 | i: 3 | j: 5 Result(i: 1.732051 | j: 2.236068 | ij:
3.872983): 61.127434
ITER: 1 | i: 3 | j: 6 Result(i: 1.732051 | j: 2.449490 | ij:
4.242641): 65.370075
ITER: 1 | i: 3 | j: 7 Result(i: 1.732051 | j: 2.645751 | ij:
4.582576): 69.952650
ITER: 1 | i: 3 | j: 8 Result(i: 1.732051 | j: 2.828427 | ij:
4.898979): 74.851630
ITER: 1 | i: 3 | j: 9 Result(i: 1.732051 | j: 3.000000 | ij:
5.196152): 80.047782
ITER: 1 | i: 4 | j: 0 Result(i: 2.000000 | j: 0.000000 | ij:
0.000000): 80.047782
ITER: 1 | i: 4 | j: 1 Result(i: 2.000000 | j: 1.000000 | ij:
2.000000): 82.047782
ITER: 1 | i: 4 | j: 2 Result(i: 2.000000 | j: 1.414214 | ij:
2.828427): 84.876209
ITER: 1 | i: 4 | j: 3 Result(i: 2.000000 | j: 1.732051 | ij:
3.464102): 88.340311
ITER: 1 | i: 4 | j: 4 Result(i: 2.000000 | j: 2.000000 | ij:
4.000000): 92.340311
ITER: 1 | i: 4 | j: 5 Result(i: 2.000000 | j: 2.236068 | ij:
4.472136): 96.812447
ITER: 1 | i: 4 | j: 6 Result(i: 2.000000 | j: 2.449490 | ij:
4.898979): 101.711426
ITER: 1 | i: 4 | j: 7 Result(i: 2.000000 | j: 2.645751 | ij:
5.291503): 107.002929
ITER: 1 | i: 4 | j: 8 Result(i: 2.000000 | j: 2.828427 | ij:
5.656854): 112.659783
ITER: 1 | i: 4 | j: 9 Result(i: 2.000000 | j: 3.000000 | ij:
6.000000): 118.659783
ITER: 1 | i: 5 | j: 0 Result(i: 2.236068 | j: 0.000000 | ij:
0.000000): 118.659783
ITER: 1 | i: 5 | j: 1 Result(i: 2.236068 | j: 1.000000 | ij:
2.236068): 120.895851
ITER: 1 | i: 5 | j: 2 Result(i: 2.236068 | j: 1.414214 | ij:
3.162278): 124.058129
ITER: 1 | i: 5 | j: 3 Result(i: 2.236068 | j: 1.732051 | ij:
3.872983): 127.931112
ITER: 1 | i: 5 | j: 4 Result(i: 2.236068 | j: 2.000000 | ij:
4.472136): 132.403248
ITER: 1 | i: 5 | j: 5 Result(i: 2.236068 | j: 2.236068 | ij:
5.000000): 137.403248
ITER: 1 | i: 5 | j: 6 Result(i: 2.236068 | j: 2.449490 | ij:
5.477226): 142.880474
ITER: 1 | i: 5 | j: 7 Result(i: 2.236068 | j: 2.645751 | ij:
5.916080): 148.796553
ITER: 1 | i: 5 | j: 8 Result(i: 2.236068 | j: 2.828427 | ij:
6.324555): 155.121109
ITER: 1 | i: 5 | j: 9 Result(i: 2.236068 | j: 3.000000 | ij:
6.708204): 161.829313
ITER: 1 | i: 6 | j: 0 Result(i: 2.449490 | j: 0.000000 | ij:
0.000000): 161.829313
ITER: 1 | i: 6 | j: 1 Result(i: 2.449490 | j: 1.000000 | ij:
2.449490): 164.278802
ITER: 1 | i: 6 | j: 2 Result(i: 2.449490 | j: 1.414214 | ij:
3.464102): 167.742904
ITER: 1 | i: 6 | j: 3 Result(i: 2.449490 | j: 1.732051 | ij:
4.242641): 171.985545
ITER: 1 | i: 6 | j: 4 Result(i: 2.449490 | j: 2.000000 | ij:
4.898979): 176.884524
ITER: 1 | i: 6 | j: 5 Result(i: 2.449490 | j: 2.236068 | ij:
5.477226): 182.361750
ITER: 1 | i: 6 | j: 6 Result(i: 2.449490 | j: 2.449490 | ij:
6.000000): 188.361750
ITER: 1 | i: 6 | j: 7 Result(i: 2.449490 | j: 2.645751 | ij:
6.480741): 194.842491
ITER: 1 | i: 6 | j: 8 Result(i: 2.449490 | j: 2.828427 | ij:
6.928203): 201.770694
ITER: 1 | i: 6 | j: 9 Result(i: 2.449490 | j: 3.000000 | ij:
7.348469): 209.119163
ITER: 1 | i: 7 | j: 0 Result(i: 2.645751 | j: 0.000000 | ij:
0.000000): 209.119163
ITER: 1 | i: 7 | j: 1 Result(i: 2.645751 | j: 1.000000 | ij:
2.645751): 211.764914
ITER: 1 | i: 7 | j: 2 Result(i: 2.645751 | j: 1.414214 | ij:
3.741657): 215.506572
ITER: 1 | i: 7 | j: 3 Result(i: 2.645751 | j: 1.732051 | ij:
4.582576): 220.089147
ITER: 1 | i: 7 | j: 4 Result(i: 2.645751 | j: 2.000000 | ij:
5.291503): 225.380650
ITER: 1 | i: 7 | j: 5 Result(i: 2.645751 | j: 2.236068 | ij:
5.916080): 231.296730
ITER: 1 | i: 7 | j: 6 Result(i: 2.645751 | j: 2.449490 | ij:
6.480741): 237.777470
ITER: 1 | i: 7 | j: 7 Result(i: 2.645751 | j: 2.645751 | ij:
7.000000): 244.777470
ITER: 1 | i: 7 | j: 8 Result(i: 2.645751 | j: 2.828427 | ij:
7.483315): 252.260785
ITER: 1 | i: 7 | j: 9 Result(i: 2.645751 | j: 3.000000 | ij:
7.937254): 260.198039
ITER: 1 | i: 8 | j: 0 Result(i: 2.828427 | j: 0.000000 | ij:
0.000000): 260.198039
ITER: 1 | i: 8 | j: 1 Result(i: 2.828427 | j: 1.000000 | ij:
2.828427): 263.026466
ITER: 1 | i: 8 | j: 2 Result(i: 2.828427 | j: 1.414214 | ij:
4.000000): 267.026466
ITER: 1 | i: 8 | j: 3 Result(i: 2.828427 | j: 1.732051 | ij:
4.898979): 271.925446
ITER: 1 | i: 8 | j: 4 Result(i: 2.828427 | j: 2.000000 | ij:
5.656854): 277.582300
ITER: 1 | i: 8 | j: 5 Result(i: 2.828427 | j: 2.236068 | ij:
6.324555): 283.906855
ITER: 1 | i: 8 | j: 6 Result(i: 2.828427 | j: 2.449490 | ij:
6.928203): 290.835059
ITER: 1 | i: 8 | j: 7 Result(i: 2.828427 | j: 2.645751 | ij:
7.483315): 298.318373
ITER: 1 | i: 8 | j: 8 Result(i: 2.828427 | j: 2.828427 | ij:
8.000000): 306.318373
ITER: 1 | i: 8 | j: 9 Result(i: 2.828427 | j: 3.000000 | ij:
8.485281): 314.803655
ITER: 1 | i: 9 | j: 0 Result(i: 3.000000 | j: 0.000000 | ij:
0.000000): 314.803655
ITER: 1 | i: 9 | j: 1 Result(i: 3.000000 | j: 1.000000 | ij:
3.000000): 317.803655
ITER: 1 | i: 9 | j: 2 Result(i: 3.000000 | j: 1.414214 | ij:
4.242641): 322.046295
ITER: 1 | i: 9 | j: 3 Result(i: 3.000000 | j: 1.732051 | ij:
5.196152): 327.242448
ITER: 1 | i: 9 | j: 4 Result(i: 3.000000 | j: 2.000000 | ij:
6.000000): 333.242448
ITER: 1 | i: 9 | j: 5 Result(i: 3.000000 | j: 2.236068 | ij:
6.708204): 339.950652
ITER: 1 | i: 9 | j: 6 Result(i: 3.000000 | j: 2.449490 | ij:
7.348469): 347.299121
ITER: 1 | i: 9 | j: 7 Result(i: 3.000000 | j: 2.645751 | ij:
7.937254): 355.236375
ITER: 1 | i: 9 | j: 8 Result(i: 3.000000 | j: 2.828427 | ij:
8.485281): 363.721656
ITER: 1 | i: 9 | j: 9 Result(i: 3.000000 | j: 3.000000 | ij:
9.000000): 372.721656
Final Result: 372.721656
As we can see in the following iterations the sqrt(1) as well as
result is set to zero for some reason.
ITER: 0 | i: 1 | j: 4 Result(i: 0.000000 | j: 2.000000 | ij:
0.000000): 0.000000
ITER: 0 | i: 1 | j: 5 Result(i: 0.000000 | j: 2.236068 | ij:
0.000000): 0.000000
ITER: 0 | i: 1 | j: 6 Result(i: 0.000000 | j: 2.449490 | ij:
0.000000): 0.000000
ITER: 0 | i: 1 | j: 7 Result(i: 0.000000 | j: 2.645751 | ij:
0.000000): 0.000000
ITER: 0 | i: 1 | j: 8 Result(i: 0.000000 | j: 2.828427 | ij:
0.000000): 0.000000
ITER: 0 | i: 1 | j: 9 Result(i: 0.000000 | j: 3.000000 | ij:
0.000000): 0.000000
Please help me to resolve the accuracy issue! I think that it
be very useful for gem5 community.
To be noticed, I find the correct simulated tick in which the
application started in FS (using m5 dumpstats), and I start the
--debug-start, but the trace file which is generated is 10x
than SE mode for the same application. How can I compare them?
Thank you in advance!
Best regards,
Nikos
Quoting Νικόλαος Ταμπουρατζής ntampouratzis@ece.auth.gr:
Dear Jason,
I am trying to use --debug-start but in FS mode it is very
difficult to find the tick on which the application is started!
However, I am writing the following very simple c++ program:
#include <cmath>
#include <stdio.h>
int main(){
int dim = 4096;
double result;
for (int iter = 0; iter < 2; iter++){
result = 0;
for (int i = 0; i < dim; i++){
for (int j = 0; j < dim; j++){
result += sqrt(i) * sqrt(j);
}
}
printf("Result: %lf\n", result); //Result:
}
}
I cross-compile it using: riscv64-linux-gnu-g++ -static -O3 -o
test_riscv test_riscv.cpp
While in X86 (without cross-compilation of course), QEMU-RISCV,
GEM5-SE the result is the same (30530733453.127449), in GEM5-FS
result is different! In addition, the result is also different
between the 2 iterations.
Please reproduce the error if you want in order to verify my
Ηow can the issue be resolved?
Thank you in advance!
Best regards,
Nikos
Quoting Jason Lowe-Power jason@lowepower.com:
Hi Nikos,
You can use --debug-start to start the debugging after some
ticks. Also, I would expect that the difference should come up
no need to run the program to the end.
For the FS mode one, you will want to just start the trace as
application starts. This could be a bit of a pain.
I'm not really sure what fundamentally could be different. FS
use the exact same code for executing instructions, so I don't
the problem. Have you tried running for smaller inputs or just
Dear Bobby,
Iam trying to add --debug-flags=Exec (building the gem5 for
not for gem5.fast which I had) but the debug traces exceed
(and it is not finished yet) for less than 1 simulated
I reduce the size of the debug-flags (or set something more
In contrast I build the HPCG benchmark with DHPCG_DEBUG flag.
want, you can compare these two output files
(hpcg20010909T014640_SE_Mode & HPCG-Benchmark_3.1_FS_Mode).
see, something goes wrong with the accuracy of calculations
(benchmark uses double precission). You can find the files
That's quite odd that it works in SE mode but not FS mode!
I would suggest running with --debug-flags=Exec for both and
diff to see how they differ.
Cheers,
Jason
On Tue, Sep 20, 2022 at 2:45 PM Νικόλαος Ταμπουρατζής <
ntampouratzis@ece.auth.gr> wrote:
Dear Bobby,
In QEMU I get the same (correct) results that I get in SE
simulation. I get invalid results in FS simulation (in both
riscv-fs.py and riscv-ubuntu-run.py). I cannot access real
hardware at this moment, however, if you want you may
following configuration:
./xhpcg --nx=16 --ny=16 --nz=16 --npx=1 --npy=1 --npz=1
Please let me know if you have any updates!
Best regards,
Nikos
Quoting Jason Lowe-Power jason@lowepower.com:
Hi Nikos,
I notice you said the following in your original email:
In addition, I used the RISCV Ubuntu image
I installed the gcc compiler, compile it (through qemu)
Is this saying you get the wrong results is QEMU? If so,
or the HPCG workload, not in gem5. If not, I would test in
sure the binary works there. Another way you could test to
problem is your binary or gem5 would be to run it on real
access to some RISC-V hardware here at UC Davis, if you
Dear Bobby,
- I use the original riscv-fs.py which is provided in
release.
I run the gem5 once (./build/RISCV/gem5.fast -d
./configs/example/gem5_library/riscv-fs.py) in order to
riscv-bootloader-vmlinux-5.10 and riscv-disk-img.
After this I mount the riscv-disk-img (sudo mount -o loop
riscv-disk-img /mnt), put the xhpcg executable and I do
changes in riscv-fs.py to boot the riscv-disk-img with
image = CustomDiskImageResource(
local_path =
"/home/cossim/.cache/gem5/riscv-disk-img",
)
Set the Full System workload.
board.set_kernel_disk_workload(
kernel=Resource("riscv-bootloader-vmlinux-5.10"),
disk_image=image,
)
Finally, in the
gem5/src/python/gem5/components/boards/riscv_board.py
I change the last line to "return ["console=ttyS0",
"root={root_value}", "rw"]" in order to allow the write
the image.
- The HPCG benchmark after some iterations calculates if
are valid or not valid. In the case of FS it gives
I see from the results, one (at least) problem is that
different results in each HPCG execution (with the same
I'm going to need a bit more information to help:
- In what way have you modified
./configs/example/gem5_library/riscv-fs.py? Can you
- What error are you getting or in what way are the
Dear gem5 community,
I have successfully cross-compile the HPCG benchmark
version, without MPI and OpenMP). While it working
mode (./build/RISCV/gem5.fast -d ./HPCG_SE_results
./configs/example/se.py -c xhpcg --options '--nx=16
--npx=1 --npy=1 --npz=1 --rt=0.1'), I get invalid
simulation using "./build/RISCV/gem5.fast -d
./configs/example/gem5_library/riscv-fs.py" (I mount
and put it).
Can you help me please?
In addition, I used the RISCV Ubuntu image
(
I installed the gcc compiler, compile it (through
wrong results too.
Here is the Makefile which I use, the hpcg executable
You mean this bug? Unfortunately not, I've been very busy with the upcoming
gem5 release and haven't had time to investigate this further.
--
Dr. Bobby R. Bruce
Room 3050,
Kemper Hall, UC Davis
Davis,
CA, 95616
web: https://www.bobbybruce.net
On Mon, Oct 31, 2022 at 1:45 AM Νικόλαος Ταμπουρατζής via gem5-users <
gem5-users@gem5.org> wrote:
> Dear Bobby, Jason, all,
>
> Is there any update about the accuracy of RISC-V FS?
>
> Best regards,
> Nikos
>
>
> Quoting Bobby Bruce <bbruce@ucdavis.edu>:
>
> > Jason and I had a theory that this may be due to the "Rounding Mode" for
> > floating pointing being set incorrectly in FS mode. That's set via a
> macro
> > here:
> >
> https://gem5.googlesource.com/public/gem5/+/refs/tags/v22.0.0.2/src/arch/riscv/fp_inst.hh#36
> >
> > I manually expanded the macro here:
> >
> https://gem5.googlesource.com/public/gem5/+/refs/tags/v22.0.0.2/src/arch/riscv/isa/decoder.isa#1495
> ,
> > inside the "fsqrt_d" definition then compiled "build/ALL/gem5.debug".
> Then
> > used gdb to add a breakpoint in the "Fsqrt_d::execute" function (in the
> > generated "build/ALL/arch/riscv/generated/exec-ns.cc.inc" file).
> >
> > ```
> > gdb build/ALL/gem5.opt
> > break Fsqrt_d::execute
> > run bug-recreation/se-mode-run.py # or `run
> bug-recreation/fs-mode-run.py`
> > ```
> >
> > Stepping through with gdb I the rounding mode is `0` for SE mode and `0`
> > for FS mode as well. So, no luck with that theory.
> >
> > My new theory is that this bug has something to do with thread context
> > switching being implemented incorrectly in RISC-V somehow. I find it
> > strange that the sqrt(1) works fine for a while (i.e. returns `1`) then
> > suddenly starts returning zero after a certain point in the execution. In
> > addition, it's odd that the loop is not returning the same value each
> time
> > despite executing the same code. It'd make sense to me that the thread is
> > being stored and then resumed with some corruption of the floating point
> > data. This would also explain why this bug only occurs in FS mode.
> >
> > I'll try to find time to figure out a good test for this. If anyone has
> any
> > other theories or ideas then let me know.
> >
> > --
> > Dr. Bobby R. Bruce
> > Room 3050,
> > Kemper Hall, UC Davis
> > Davis,
> > CA, 95616
> >
> > web: https://www.bobbybruce.net
> >
> >
> > On Fri, Oct 7, 2022 at 12:50 PM Νικόλαος Ταμπουρατζής <
> > ntampouratzis@ece.auth.gr> wrote:
> >>
> >> Dear Jason & Boddy,
> >>
> >> Unfortunately, I have tried my simple example without the sqrt
> >> function and the problem remains. Specifically, I have the following
> >> simple code:
> >>
> >>
> >> #include <cmath>
> >> #include <stdio.h>
> >>
> >> int main(){
> >>
> >> int dim = 1024;
> >>
> >> double result;
> >>
> >> for (int iter = 0; iter < 2; iter++){
> >> result = 0;
> >> for (int i = 0; i < dim; i++){
> >> for (int j = 0; j < dim; j++){
> >> result += i * j;
> >> }
> >> }
> >> printf("Final Result: %lf\n", result);
> >> }
> >> }
> >>
> >>
> >> In the above code, the correct result is 274341298176.000000 (from
> >> RISCV-SE mode and x86), while in FS mode I get sometimes the correct
> >> result and other times a different number.
> >>
> >> Best regards,
> >> Nikos
> >>
> >>
> >> Quoting Jason Lowe-Power <jason@lowepower.com>:
> >>
> >> > I have an idea...
> >> >
> >> > Have you put a breakpoint in the implementation of the fsqrt_d
> > function? I
> >> > would like to know if when running in SE mode and running in FS mode
> we
> > are
> >> > using the same rounding mode. My hypothesis is that in FS mode the
> > rounding
> >> > mode is set differently.
> >> >
> >> > Cheers,
> >> > Jason
> >> >
> >> > On Fri, Oct 7, 2022 at 12:15 AM Νικόλαος Ταμπουρατζής <
> >> > ntampouratzis@ece.auth.gr> wrote:
> >> >
> >> >> Dear Boddy,
> >> >>
> >> >> Thanks a lot for the effort! I looked in detail and I observe that
> the
> >> >> problem is created only using float and double variables (in the case
> >> >> of int it is working properly in FS mode). Specifically, in the case
> >> >> of float the variables are set to "nan", while in the case of double
> >> >> the variables are set to 0.000000 (in random time - probably from
> some
> >> >> instruction of simulated OS?). You may use a simple c/c++ example in
> >> >> order to get some traces before going to HPCG...
> >> >>
> >> >> Thank you in advance!!
> >> >> Best regards,
> >> >> Nikos
> >> >>
> >> >>
> >> >> Quoting Bobby Bruce <bbruce@ucdavis.edu>:
> >> >>
> >> >> > Hey Niko,
> >> >> >
> >> >> > Thanks for this analysis. I jumped a little into this today but
> > didn't
> >> >> get
> >> >> > as far as you did. I wanted to find a quick way to recreate the
> >> >> following:
> >> >> > https://gem5-review.googlesource.com/c/public/gem5/+/64211.
> Please
> > feel
> >> >> > free to use this, if it helps any.
> >> >> >
> >> >> > It's very strange to me that this bug hasn't manifested itself
> > before but
> >> >> > it's undeniably there. I'll try to spend more time looking at this
> >> >> tomorrow
> >> >> > with some traces and debug flags and see if I can narrow down the
> >> >> problem.
> >> >> >
> >> >> > --
> >> >> > Dr. Bobby R. Bruce
> >> >> > Room 3050,
> >> >> > Kemper Hall, UC Davis
> >> >> > Davis,
> >> >> > CA, 95616
> >> >> >
> >> >> > web: https://www.bobbybruce.net
> >> >> >
> >> >> >
> >> >> > On Wed, Oct 5, 2022 at 2:26 PM Νικόλαος Ταμπουρατζής <
> >> >> > ntampouratzis@ece.auth.gr> wrote:
> >> >> >
> >> >> >> In my previous results, I had used double (not float) for the
> >> >> >> following variables: result, sq_i and sq_j. In the case of float
> >> >> >> instead of double I get "nan" and not 0.000000.
> >> >> >>
> >> >> >> Quoting Νικόλαος Ταμπουρατζής <ntampouratzis@ece.auth.gr>:
> >> >> >>
> >> >> >> > Dear Jason, all,
> >> >> >> >
> >> >> >> > I am trying to find the accuracy problem with RISCV-FS and I
> > observe
> >> >> >> > that the problem is created (at least in my dummy example)
> because
> >> >> >> > the variables (double) are set to zero in random simulated time
> > (for
> >> >> >> > this reason I get different results among executions of the same
> >> >> >> > code). Specifically for the following dummy code:
> >> >> >> >
> >> >> >> >
> >> >> >> > #include <cmath>
> >> >> >> > #include <stdio.h>
> >> >> >> >
> >> >> >> > int main(){
> >> >> >> >
> >> >> >> > int dim = 10;
> >> >> >> >
> >> >> >> > float result;
> >> >> >> >
> >> >> >> > for (int iter = 0; iter < 2; iter++){
> >> >> >> > result = 0;
> >> >> >> > for (int i = 0; i < dim; i++){
> >> >> >> > for (int j = 0; j < dim; j++){
> >> >> >> > float sq_i = sqrt(i);
> >> >> >> > float sq_j = sqrt(j);
> >> >> >> > result += sq_i * sq_j;
> >> >> >> > printf("ITER: %d | i: %d | j: %d Result(i: %f |
> j:
> >> >> >> > %f | i*j: %f): %f\n", iter, i , j, sq_i, sq_j, sq_i * sq_j,
> > result);
> >> >> >> > }
> >> >> >> > }
> >> >> >> > printf("Final Result: %lf\n", result);
> >> >> >> > }
> >> >> >> > }
> >> >> >> >
> >> >> >> >
> >> >> >> > The correct Final Result in both iterations is 372.721656.
> > However,
> >> >> >> > I get the following results in FS:
> >> >> >> >
> >> >> >> > ITER: 0 | i: 0 | j: 0 Result(i: 0.000000 | j: 0.000000 | i*j:
> >> >> >> > 0.000000): 0.000000
> >> >> >> > ITER: 0 | i: 0 | j: 1 Result(i: 0.000000 | j: 1.000000 | i*j:
> >> >> >> > 0.000000): 0.000000
> >> >> >> > ITER: 0 | i: 0 | j: 2 Result(i: 0.000000 | j: 1.414214 | i*j:
> >> >> >> > 0.000000): 0.000000
> >> >> >> > ITER: 0 | i: 0 | j: 3 Result(i: 0.000000 | j: 1.732051 | i*j:
> >> >> >> > 0.000000): 0.000000
> >> >> >> > ITER: 0 | i: 0 | j: 4 Result(i: 0.000000 | j: 2.000000 | i*j:
> >> >> >> > 0.000000): 0.000000
> >> >> >> > ITER: 0 | i: 0 | j: 5 Result(i: 0.000000 | j: 2.236068 | i*j:
> >> >> >> > 0.000000): 0.000000
> >> >> >> > ITER: 0 | i: 0 | j: 6 Result(i: 0.000000 | j: 2.449490 | i*j:
> >> >> >> > 0.000000): 0.000000
> >> >> >> > ITER: 0 | i: 0 | j: 7 Result(i: 0.000000 | j: 2.645751 | i*j:
> >> >> >> > 0.000000): 0.000000
> >> >> >> > ITER: 0 | i: 0 | j: 8 Result(i: 0.000000 | j: 2.828427 | i*j:
> >> >> >> > 0.000000): 0.000000
> >> >> >> > ITER: 0 | i: 0 | j: 9 Result(i: 0.000000 | j: 3.000000 | i*j:
> >> >> >> > 0.000000): 0.000000
> >> >> >> > ITER: 0 | i: 1 | j: 0 Result(i: 1.000000 | j: 0.000000 | i*j:
> >> >> >> > 0.000000): 0.000000
> >> >> >> > ITER: 0 | i: 1 | j: 1 Result(i: 1.000000 | j: 1.000000 | i*j:
> >> >> >> > 1.000000): 1.000000
> >> >> >> > ITER: 0 | i: 1 | j: 2 Result(i: 1.000000 | j: 1.414214 | i*j:
> >> >> >> > 1.414214): 2.414214
> >> >> >> > ITER: 0 | i: 1 | j: 3 Result(i: 1.000000 | j: 1.732051 | i*j:
> >> >> >> > 1.732051): 4.146264
> >> >> >> > ITER: 0 | i: 1 | j: 4 Result(i: 0.000000 | j: 2.000000 | i*j:
> >> >> >> > 0.000000): 0.000000
> >> >> >> > ITER: 0 | i: 1 | j: 5 Result(i: 0.000000 | j: 2.236068 | i*j:
> >> >> >> > 0.000000): 0.000000
> >> >> >> > ITER: 0 | i: 1 | j: 6 Result(i: 0.000000 | j: 2.449490 | i*j:
> >> >> >> > 0.000000): 0.000000
> >> >> >> > ITER: 0 | i: 1 | j: 7 Result(i: 0.000000 | j: 2.645751 | i*j:
> >> >> >> > 0.000000): 0.000000
> >> >> >> > ITER: 0 | i: 1 | j: 8 Result(i: 0.000000 | j: 2.828427 | i*j:
> >> >> >> > 0.000000): 0.000000
> >> >> >> > ITER: 0 | i: 1 | j: 9 Result(i: 0.000000 | j: 3.000000 | i*j:
> >> >> >> > 0.000000): 0.000000
> >> >> >> > ITER: 0 | i: 2 | j: 0 Result(i: 1.414214 | j: 0.000000 | i*j:
> >> >> >> > 0.000000): 0.000000
> >> >> >> > ITER: 0 | i: 2 | j: 1 Result(i: 1.414214 | j: 1.000000 | i*j:
> >> >> >> > 1.414214): 1.414214
> >> >> >> > ITER: 0 | i: 2 | j: 2 Result(i: 1.414214 | j: 1.414214 | i*j:
> >> >> >> > 2.000000): 3.414214
> >> >> >> > ITER: 0 | i: 2 | j: 3 Result(i: 1.414214 | j: 1.732051 | i*j:
> >> >> >> > 2.449490): 5.863703
> >> >> >> > ITER: 0 | i: 2 | j: 4 Result(i: 1.414214 | j: 2.000000 | i*j:
> >> >> >> > 2.828427): 8.692130
> >> >> >> > ITER: 0 | i: 2 | j: 5 Result(i: 1.414214 | j: 2.236068 | i*j:
> >> >> >> > 3.162278): 11.854408
> >> >> >> > ITER: 0 | i: 2 | j: 6 Result(i: 1.414214 | j: 2.449490 | i*j:
> >> >> >> > 3.464102): 15.318510
> >> >> >> > ITER: 0 | i: 2 | j: 7 Result(i: 1.414214 | j: 2.645751 | i*j:
> >> >> >> > 3.741657): 19.060167
> >> >> >> > ITER: 0 | i: 2 | j: 8 Result(i: 1.414214 | j: 2.828427 | i*j:
> >> >> >> > 4.000000): 23.060167
> >> >> >> > ITER: 0 | i: 2 | j: 9 Result(i: 1.414214 | j: 3.000000 | i*j:
> >> >> >> > 4.242641): 27.302808
> >> >> >> > ITER: 0 | i: 3 | j: 0 Result(i: 1.732051 | j: 0.000000 | i*j:
> >> >> >> > 0.000000): 27.302808
> >> >> >> > ITER: 0 | i: 3 | j: 1 Result(i: 1.732051 | j: 1.000000 | i*j:
> >> >> >> > 1.732051): 29.034859
> >> >> >> > ITER: 0 | i: 3 | j: 2 Result(i: 1.732051 | j: 1.414214 | i*j:
> >> >> >> > 2.449490): 31.484348
> >> >> >> > ITER: 0 | i: 3 | j: 3 Result(i: 1.732051 | j: 1.732051 | i*j:
> >> >> >> > 3.000000): 34.484348
> >> >> >> > ITER: 0 | i: 3 | j: 4 Result(i: 1.732051 | j: 2.000000 | i*j:
> >> >> >> > 3.464102): 37.948450
> >> >> >> > ITER: 0 | i: 3 | j: 5 Result(i: 1.732051 | j: 2.236068 | i*j:
> >> >> >> > 3.872983): 41.821433
> >> >> >> > ITER: 0 | i: 3 | j: 6 Result(i: 1.732051 | j: 2.449490 | i*j:
> >> >> >> > 4.242641): 46.064074
> >> >> >> > ITER: 0 | i: 3 | j: 7 Result(i: 1.732051 | j: 2.645751 | i*j:
> >> >> >> > 4.582576): 50.646650
> >> >> >> > ITER: 0 | i: 3 | j: 8 Result(i: 1.732051 | j: 2.828427 | i*j:
> >> >> >> > 4.898979): 55.545629
> >> >> >> > ITER: 0 | i: 3 | j: 9 Result(i: 1.732051 | j: 3.000000 | i*j:
> >> >> >> > 5.196152): 60.741782
> >> >> >> > ITER: 0 | i: 4 | j: 0 Result(i: 2.000000 | j: 0.000000 | i*j:
> >> >> >> > 0.000000): 60.741782
> >> >> >> > ITER: 0 | i: 4 | j: 1 Result(i: 2.000000 | j: 1.000000 | i*j:
> >> >> >> > 2.000000): 62.741782
> >> >> >> > ITER: 0 | i: 4 | j: 2 Result(i: 2.000000 | j: 1.414214 | i*j:
> >> >> >> > 2.828427): 65.570209
> >> >> >> > ITER: 0 | i: 4 | j: 3 Result(i: 2.000000 | j: 1.732051 | i*j:
> >> >> >> > 3.464102): 69.034310
> >> >> >> > ITER: 0 | i: 4 | j: 4 Result(i: 2.000000 | j: 2.000000 | i*j:
> >> >> >> > 4.000000): 73.034310
> >> >> >> > ITER: 0 | i: 4 | j: 5 Result(i: 2.000000 | j: 2.236068 | i*j:
> >> >> >> > 4.472136): 77.506446
> >> >> >> > ITER: 0 | i: 4 | j: 6 Result(i: 2.000000 | j: 2.449490 | i*j:
> >> >> >> > 4.898979): 82.405426
> >> >> >> > ITER: 0 | i: 4 | j: 7 Result(i: 2.000000 | j: 2.645751 | i*j:
> >> >> >> > 5.291503): 87.696928
> >> >> >> > ITER: 0 | i: 4 | j: 8 Result(i: 2.000000 | j: 2.828427 | i*j:
> >> >> >> > 5.656854): 93.353783
> >> >> >> > ITER: 0 | i: 4 | j: 9 Result(i: 2.000000 | j: 3.000000 | i*j:
> >> >> >> > 6.000000): 99.353783
> >> >> >> > ITER: 0 | i: 5 | j: 0 Result(i: 2.236068 | j: 0.000000 | i*j:
> >> >> >> > 0.000000): 99.353783
> >> >> >> > ITER: 0 | i: 5 | j: 1 Result(i: 2.236068 | j: 1.000000 | i*j:
> >> >> >> > 2.236068): 101.589851
> >> >> >> > ITER: 0 | i: 5 | j: 2 Result(i: 2.236068 | j: 1.414214 | i*j:
> >> >> >> > 3.162278): 104.752128
> >> >> >> > ITER: 0 | i: 5 | j: 3 Result(i: 2.236068 | j: 1.732051 | i*j:
> >> >> >> > 3.872983): 108.625112
> >> >> >> > ITER: 0 | i: 5 | j: 4 Result(i: 2.236068 | j: 2.000000 | i*j:
> >> >> >> > 4.472136): 113.097248
> >> >> >> > ITER: 0 | i: 5 | j: 5 Result(i: 2.236068 | j: 2.236068 | i*j:
> >> >> >> > 5.000000): 118.097248
> >> >> >> > ITER: 0 | i: 5 | j: 6 Result(i: 2.236068 | j: 2.449490 | i*j:
> >> >> >> > 5.477226): 123.574473
> >> >> >> > ITER: 0 | i: 5 | j: 7 Result(i: 2.236068 | j: 2.645751 | i*j:
> >> >> >> > 5.916080): 129.490553
> >> >> >> > ITER: 0 | i: 5 | j: 8 Result(i: 2.236068 | j: 2.828427 | i*j:
> >> >> >> > 6.324555): 135.815108
> >> >> >> > ITER: 0 | i: 5 | j: 9 Result(i: 2.236068 | j: 3.000000 | i*j:
> >> >> >> > 6.708204): 142.523312
> >> >> >> > ITER: 0 | i: 6 | j: 0 Result(i: 2.449490 | j: 0.000000 | i*j:
> >> >> >> > 0.000000): 142.523312
> >> >> >> > ITER: 0 | i: 6 | j: 1 Result(i: 2.449490 | j: 1.000000 | i*j:
> >> >> >> > 2.449490): 144.972802
> >> >> >> > ITER: 0 | i: 6 | j: 2 Result(i: 2.449490 | j: 1.414214 | i*j:
> >> >> >> > 3.464102): 148.436904
> >> >> >> > ITER: 0 | i: 6 | j: 3 Result(i: 2.449490 | j: 1.732051 | i*j:
> >> >> >> > 4.242641): 152.679544
> >> >> >> > ITER: 0 | i: 6 | j: 4 Result(i: 2.449490 | j: 2.000000 | i*j:
> >> >> >> > 4.898979): 157.578524
> >> >> >> > ITER: 0 | i: 6 | j: 5 Result(i: 2.449490 | j: 2.236068 | i*j:
> >> >> >> > 5.477226): 163.055749
> >> >> >> > ITER: 0 | i: 6 | j: 6 Result(i: 2.449490 | j: 2.449490 | i*j:
> >> >> >> > 6.000000): 169.055749
> >> >> >> > ITER: 0 | i: 6 | j: 7 Result(i: 2.449490 | j: 2.645751 | i*j:
> >> >> >> > 6.480741): 175.536490
> >> >> >> > ITER: 0 | i: 6 | j: 8 Result(i: 2.449490 | j: 2.828427 | i*j:
> >> >> >> > 6.928203): 182.464693
> >> >> >> > ITER: 0 | i: 6 | j: 9 Result(i: 2.449490 | j: 3.000000 | i*j:
> >> >> >> > 7.348469): 189.813162
> >> >> >> > ITER: 0 | i: 7 | j: 0 Result(i: 2.645751 | j: 0.000000 | i*j:
> >> >> >> > 0.000000): 189.813162
> >> >> >> > ITER: 0 | i: 7 | j: 1 Result(i: 2.645751 | j: 1.000000 | i*j:
> >> >> >> > 2.645751): 192.458914
> >> >> >> > ITER: 0 | i: 7 | j: 2 Result(i: 2.645751 | j: 1.414214 | i*j:
> >> >> >> > 3.741657): 196.200571
> >> >> >> > ITER: 0 | i: 7 | j: 3 Result(i: 2.645751 | j: 1.732051 | i*j:
> >> >> >> > 4.582576): 200.783147
> >> >> >> > ITER: 0 | i: 7 | j: 4 Result(i: 2.645751 | j: 2.000000 | i*j:
> >> >> >> > 5.291503): 206.074649
> >> >> >> > ITER: 0 | i: 7 | j: 5 Result(i: 2.645751 | j: 2.236068 | i*j:
> >> >> >> > 5.916080): 211.990729
> >> >> >> > ITER: 0 | i: 7 | j: 6 Result(i: 2.645751 | j: 2.449490 | i*j:
> >> >> >> > 6.480741): 218.471470
> >> >> >> > ITER: 0 | i: 7 | j: 7 Result(i: 2.645751 | j: 2.645751 | i*j:
> >> >> >> > 7.000000): 225.471470
> >> >> >> > ITER: 0 | i: 7 | j: 8 Result(i: 2.645751 | j: 2.828427 | i*j:
> >> >> >> > 7.483315): 232.954785
> >> >> >> > ITER: 0 | i: 7 | j: 9 Result(i: 2.645751 | j: 3.000000 | i*j:
> >> >> >> > 7.937254): 240.892039
> >> >> >> > ITER: 0 | i: 8 | j: 0 Result(i: 2.828427 | j: 0.000000 | i*j:
> >> >> >> > 0.000000): 240.892039
> >> >> >> > ITER: 0 | i: 8 | j: 1 Result(i: 2.828427 | j: 1.000000 | i*j:
> >> >> >> > 2.828427): 243.720466
> >> >> >> > ITER: 0 | i: 8 | j: 2 Result(i: 2.828427 | j: 1.414214 | i*j:
> >> >> >> > 4.000000): 247.720466
> >> >> >> > ITER: 0 | i: 8 | j: 3 Result(i: 2.828427 | j: 1.732051 | i*j:
> >> >> >> > 4.898979): 252.619445
> >> >> >> > ITER: 0 | i: 8 | j: 4 Result(i: 2.828427 | j: 2.000000 | i*j:
> >> >> >> > 5.656854): 258.276300
> >> >> >> > ITER: 0 | i: 8 | j: 5 Result(i: 2.828427 | j: 2.236068 | i*j:
> >> >> >> > 6.324555): 264.600855
> >> >> >> > ITER: 0 | i: 8 | j: 6 Result(i: 2.828427 | j: 2.449490 | i*j:
> >> >> >> > 6.928203): 271.529058
> >> >> >> > ITER: 0 | i: 8 | j: 7 Result(i: 2.828427 | j: 2.645751 | i*j:
> >> >> >> > 7.483315): 279.012373
> >> >> >> > ITER: 0 | i: 8 | j: 8 Result(i: 2.828427 | j: 2.828427 | i*j:
> >> >> >> > 8.000000): 287.012373
> >> >> >> > ITER: 0 | i: 8 | j: 9 Result(i: 2.828427 | j: 3.000000 | i*j:
> >> >> >> > 8.485281): 295.497654
> >> >> >> > ITER: 0 | i: 9 | j: 0 Result(i: 3.000000 | j: 0.000000 | i*j:
> >> >> >> > 0.000000): 295.497654
> >> >> >> > ITER: 0 | i: 9 | j: 1 Result(i: 3.000000 | j: 1.000000 | i*j:
> >> >> >> > 3.000000): 298.497654
> >> >> >> > ITER: 0 | i: 9 | j: 2 Result(i: 3.000000 | j: 1.414214 | i*j:
> >> >> >> > 4.242641): 302.740295
> >> >> >> > ITER: 0 | i: 9 | j: 3 Result(i: 3.000000 | j: 1.732051 | i*j:
> >> >> >> > 5.196152): 307.936447
> >> >> >> > ITER: 0 | i: 9 | j: 4 Result(i: 3.000000 | j: 2.000000 | i*j:
> >> >> >> > 6.000000): 313.936447
> >> >> >> > ITER: 0 | i: 9 | j: 5 Result(i: 3.000000 | j: 2.236068 | i*j:
> >> >> >> > 6.708204): 320.644651
> >> >> >> > ITER: 0 | i: 9 | j: 6 Result(i: 3.000000 | j: 2.449490 | i*j:
> >> >> >> > 7.348469): 327.993120
> >> >> >> > ITER: 0 | i: 9 | j: 7 Result(i: 3.000000 | j: 2.645751 | i*j:
> >> >> >> > 7.937254): 335.930374
> >> >> >> > ITER: 0 | i: 9 | j: 8 Result(i: 3.000000 | j: 2.828427 | i*j:
> >> >> >> > 8.485281): 344.415656
> >> >> >> > ITER: 0 | i: 9 | j: 9 Result(i: 3.000000 | j: 3.000000 | i*j:
> >> >> >> > 9.000000): 353.415656
> >> >> >> > Final Result: 353.415656
> >> >> >> > ITER: 1 | i: 0 | j: 0 Result(i: 0.000000 | j: 0.000000 | i*j:
> >> >> >> > 0.000000): 0.000000
> >> >> >> > ITER: 1 | i: 0 | j: 1 Result(i: 0.000000 | j: 1.000000 | i*j:
> >> >> >> > 0.000000): 0.000000
> >> >> >> > ITER: 1 | i: 0 | j: 2 Result(i: 0.000000 | j: 1.414214 | i*j:
> >> >> >> > 0.000000): 0.000000
> >> >> >> > ITER: 1 | i: 0 | j: 3 Result(i: 0.000000 | j: 1.732051 | i*j:
> >> >> >> > 0.000000): 0.000000
> >> >> >> > ITER: 1 | i: 0 | j: 4 Result(i: 0.000000 | j: 2.000000 | i*j:
> >> >> >> > 0.000000): 0.000000
> >> >> >> > ITER: 1 | i: 0 | j: 5 Result(i: 0.000000 | j: 2.236068 | i*j:
> >> >> >> > 0.000000): 0.000000
> >> >> >> > ITER: 1 | i: 0 | j: 6 Result(i: 0.000000 | j: 2.449490 | i*j:
> >> >> >> > 0.000000): 0.000000
> >> >> >> > ITER: 1 | i: 0 | j: 7 Result(i: 0.000000 | j: 2.645751 | i*j:
> >> >> >> > 0.000000): 0.000000
> >> >> >> > ITER: 1 | i: 0 | j: 8 Result(i: 0.000000 | j: 2.828427 | i*j:
> >> >> >> > 0.000000): 0.000000
> >> >> >> > ITER: 1 | i: 0 | j: 9 Result(i: 0.000000 | j: 3.000000 | i*j:
> >> >> >> > 0.000000): 0.000000
> >> >> >> > ITER: 1 | i: 1 | j: 0 Result(i: 1.000000 | j: 0.000000 | i*j:
> >> >> >> > 0.000000): 0.000000
> >> >> >> > ITER: 1 | i: 1 | j: 1 Result(i: 1.000000 | j: 1.000000 | i*j:
> >> >> >> > 1.000000): 1.000000
> >> >> >> > ITER: 1 | i: 1 | j: 2 Result(i: 1.000000 | j: 1.414214 | i*j:
> >> >> >> > 1.414214): 2.414214
> >> >> >> > ITER: 1 | i: 1 | j: 3 Result(i: 1.000000 | j: 1.732051 | i*j:
> >> >> >> > 1.732051): 4.146264
> >> >> >> > ITER: 1 | i: 1 | j: 4 Result(i: 1.000000 | j: 2.000000 | i*j:
> >> >> >> > 2.000000): 6.146264
> >> >> >> > ITER: 1 | i: 1 | j: 5 Result(i: 1.000000 | j: 2.236068 | i*j:
> >> >> >> > 2.236068): 8.382332
> >> >> >> > ITER: 1 | i: 1 | j: 6 Result(i: 1.000000 | j: 2.449490 | i*j:
> >> >> >> > 2.449490): 10.831822
> >> >> >> > ITER: 1 | i: 1 | j: 7 Result(i: 1.000000 | j: 2.645751 | i*j:
> >> >> >> > 2.645751): 13.477573
> >> >> >> > ITER: 1 | i: 1 | j: 8 Result(i: 1.000000 | j: 2.828427 | i*j:
> >> >> >> > 2.828427): 16.306001
> >> >> >> > ITER: 1 | i: 1 | j: 9 Result(i: 1.000000 | j: 3.000000 | i*j:
> >> >> >> > 3.000000): 19.306001
> >> >> >> > ITER: 1 | i: 2 | j: 0 Result(i: 1.414214 | j: 0.000000 | i*j:
> >> >> >> > 0.000000): 19.306001
> >> >> >> > ITER: 1 | i: 2 | j: 1 Result(i: 1.414214 | j: 1.000000 | i*j:
> >> >> >> > 1.414214): 20.720214
> >> >> >> > ITER: 1 | i: 2 | j: 2 Result(i: 1.414214 | j: 1.414214 | i*j:
> >> >> >> > 2.000000): 22.720214
> >> >> >> > ITER: 1 | i: 2 | j: 3 Result(i: 1.414214 | j: 1.732051 | i*j:
> >> >> >> > 2.449490): 25.169704
> >> >> >> > ITER: 1 | i: 2 | j: 4 Result(i: 1.414214 | j: 2.000000 | i*j:
> >> >> >> > 2.828427): 27.998131
> >> >> >> > ITER: 1 | i: 2 | j: 5 Result(i: 1.414214 | j: 2.236068 | i*j:
> >> >> >> > 3.162278): 31.160409
> >> >> >> > ITER: 1 | i: 2 | j: 6 Result(i: 1.414214 | j: 2.449490 | i*j:
> >> >> >> > 3.464102): 34.624510
> >> >> >> > ITER: 1 | i: 2 | j: 7 Result(i: 1.414214 | j: 2.645751 | i*j:
> >> >> >> > 3.741657): 38.366168
> >> >> >> > ITER: 1 | i: 2 | j: 8 Result(i: 1.414214 | j: 2.828427 | i*j:
> >> >> >> > 4.000000): 42.366168
> >> >> >> > ITER: 1 | i: 2 | j: 9 Result(i: 1.414214 | j: 3.000000 | i*j:
> >> >> >> > 4.242641): 46.608808
> >> >> >> > ITER: 1 | i: 3 | j: 0 Result(i: 1.732051 | j: 0.000000 | i*j:
> >> >> >> > 0.000000): 46.608808
> >> >> >> > ITER: 1 | i: 3 | j: 1 Result(i: 1.732051 | j: 1.000000 | i*j:
> >> >> >> > 1.732051): 48.340859
> >> >> >> > ITER: 1 | i: 3 | j: 2 Result(i: 1.732051 | j: 1.414214 | i*j:
> >> >> >> > 2.449490): 50.790349
> >> >> >> > ITER: 1 | i: 3 | j: 3 Result(i: 1.732051 | j: 1.732051 | i*j:
> >> >> >> > 3.000000): 53.790349
> >> >> >> > ITER: 1 | i: 3 | j: 4 Result(i: 1.732051 | j: 2.000000 | i*j:
> >> >> >> > 3.464102): 57.254450
> >> >> >> > ITER: 1 | i: 3 | j: 5 Result(i: 1.732051 | j: 2.236068 | i*j:
> >> >> >> > 3.872983): 61.127434
> >> >> >> > ITER: 1 | i: 3 | j: 6 Result(i: 1.732051 | j: 2.449490 | i*j:
> >> >> >> > 4.242641): 65.370075
> >> >> >> > ITER: 1 | i: 3 | j: 7 Result(i: 1.732051 | j: 2.645751 | i*j:
> >> >> >> > 4.582576): 69.952650
> >> >> >> > ITER: 1 | i: 3 | j: 8 Result(i: 1.732051 | j: 2.828427 | i*j:
> >> >> >> > 4.898979): 74.851630
> >> >> >> > ITER: 1 | i: 3 | j: 9 Result(i: 1.732051 | j: 3.000000 | i*j:
> >> >> >> > 5.196152): 80.047782
> >> >> >> > ITER: 1 | i: 4 | j: 0 Result(i: 2.000000 | j: 0.000000 | i*j:
> >> >> >> > 0.000000): 80.047782
> >> >> >> > ITER: 1 | i: 4 | j: 1 Result(i: 2.000000 | j: 1.000000 | i*j:
> >> >> >> > 2.000000): 82.047782
> >> >> >> > ITER: 1 | i: 4 | j: 2 Result(i: 2.000000 | j: 1.414214 | i*j:
> >> >> >> > 2.828427): 84.876209
> >> >> >> > ITER: 1 | i: 4 | j: 3 Result(i: 2.000000 | j: 1.732051 | i*j:
> >> >> >> > 3.464102): 88.340311
> >> >> >> > ITER: 1 | i: 4 | j: 4 Result(i: 2.000000 | j: 2.000000 | i*j:
> >> >> >> > 4.000000): 92.340311
> >> >> >> > ITER: 1 | i: 4 | j: 5 Result(i: 2.000000 | j: 2.236068 | i*j:
> >> >> >> > 4.472136): 96.812447
> >> >> >> > ITER: 1 | i: 4 | j: 6 Result(i: 2.000000 | j: 2.449490 | i*j:
> >> >> >> > 4.898979): 101.711426
> >> >> >> > ITER: 1 | i: 4 | j: 7 Result(i: 2.000000 | j: 2.645751 | i*j:
> >> >> >> > 5.291503): 107.002929
> >> >> >> > ITER: 1 | i: 4 | j: 8 Result(i: 2.000000 | j: 2.828427 | i*j:
> >> >> >> > 5.656854): 112.659783
> >> >> >> > ITER: 1 | i: 4 | j: 9 Result(i: 2.000000 | j: 3.000000 | i*j:
> >> >> >> > 6.000000): 118.659783
> >> >> >> > ITER: 1 | i: 5 | j: 0 Result(i: 2.236068 | j: 0.000000 | i*j:
> >> >> >> > 0.000000): 118.659783
> >> >> >> > ITER: 1 | i: 5 | j: 1 Result(i: 2.236068 | j: 1.000000 | i*j:
> >> >> >> > 2.236068): 120.895851
> >> >> >> > ITER: 1 | i: 5 | j: 2 Result(i: 2.236068 | j: 1.414214 | i*j:
> >> >> >> > 3.162278): 124.058129
> >> >> >> > ITER: 1 | i: 5 | j: 3 Result(i: 2.236068 | j: 1.732051 | i*j:
> >> >> >> > 3.872983): 127.931112
> >> >> >> > ITER: 1 | i: 5 | j: 4 Result(i: 2.236068 | j: 2.000000 | i*j:
> >> >> >> > 4.472136): 132.403248
> >> >> >> > ITER: 1 | i: 5 | j: 5 Result(i: 2.236068 | j: 2.236068 | i*j:
> >> >> >> > 5.000000): 137.403248
> >> >> >> > ITER: 1 | i: 5 | j: 6 Result(i: 2.236068 | j: 2.449490 | i*j:
> >> >> >> > 5.477226): 142.880474
> >> >> >> > ITER: 1 | i: 5 | j: 7 Result(i: 2.236068 | j: 2.645751 | i*j:
> >> >> >> > 5.916080): 148.796553
> >> >> >> > ITER: 1 | i: 5 | j: 8 Result(i: 2.236068 | j: 2.828427 | i*j:
> >> >> >> > 6.324555): 155.121109
> >> >> >> > ITER: 1 | i: 5 | j: 9 Result(i: 2.236068 | j: 3.000000 | i*j:
> >> >> >> > 6.708204): 161.829313
> >> >> >> > ITER: 1 | i: 6 | j: 0 Result(i: 2.449490 | j: 0.000000 | i*j:
> >> >> >> > 0.000000): 161.829313
> >> >> >> > ITER: 1 | i: 6 | j: 1 Result(i: 2.449490 | j: 1.000000 | i*j:
> >> >> >> > 2.449490): 164.278802
> >> >> >> > ITER: 1 | i: 6 | j: 2 Result(i: 2.449490 | j: 1.414214 | i*j:
> >> >> >> > 3.464102): 167.742904
> >> >> >> > ITER: 1 | i: 6 | j: 3 Result(i: 2.449490 | j: 1.732051 | i*j:
> >> >> >> > 4.242641): 171.985545
> >> >> >> > ITER: 1 | i: 6 | j: 4 Result(i: 2.449490 | j: 2.000000 | i*j:
> >> >> >> > 4.898979): 176.884524
> >> >> >> > ITER: 1 | i: 6 | j: 5 Result(i: 2.449490 | j: 2.236068 | i*j:
> >> >> >> > 5.477226): 182.361750
> >> >> >> > ITER: 1 | i: 6 | j: 6 Result(i: 2.449490 | j: 2.449490 | i*j:
> >> >> >> > 6.000000): 188.361750
> >> >> >> > ITER: 1 | i: 6 | j: 7 Result(i: 2.449490 | j: 2.645751 | i*j:
> >> >> >> > 6.480741): 194.842491
> >> >> >> > ITER: 1 | i: 6 | j: 8 Result(i: 2.449490 | j: 2.828427 | i*j:
> >> >> >> > 6.928203): 201.770694
> >> >> >> > ITER: 1 | i: 6 | j: 9 Result(i: 2.449490 | j: 3.000000 | i*j:
> >> >> >> > 7.348469): 209.119163
> >> >> >> > ITER: 1 | i: 7 | j: 0 Result(i: 2.645751 | j: 0.000000 | i*j:
> >> >> >> > 0.000000): 209.119163
> >> >> >> > ITER: 1 | i: 7 | j: 1 Result(i: 2.645751 | j: 1.000000 | i*j:
> >> >> >> > 2.645751): 211.764914
> >> >> >> > ITER: 1 | i: 7 | j: 2 Result(i: 2.645751 | j: 1.414214 | i*j:
> >> >> >> > 3.741657): 215.506572
> >> >> >> > ITER: 1 | i: 7 | j: 3 Result(i: 2.645751 | j: 1.732051 | i*j:
> >> >> >> > 4.582576): 220.089147
> >> >> >> > ITER: 1 | i: 7 | j: 4 Result(i: 2.645751 | j: 2.000000 | i*j:
> >> >> >> > 5.291503): 225.380650
> >> >> >> > ITER: 1 | i: 7 | j: 5 Result(i: 2.645751 | j: 2.236068 | i*j:
> >> >> >> > 5.916080): 231.296730
> >> >> >> > ITER: 1 | i: 7 | j: 6 Result(i: 2.645751 | j: 2.449490 | i*j:
> >> >> >> > 6.480741): 237.777470
> >> >> >> > ITER: 1 | i: 7 | j: 7 Result(i: 2.645751 | j: 2.645751 | i*j:
> >> >> >> > 7.000000): 244.777470
> >> >> >> > ITER: 1 | i: 7 | j: 8 Result(i: 2.645751 | j: 2.828427 | i*j:
> >> >> >> > 7.483315): 252.260785
> >> >> >> > ITER: 1 | i: 7 | j: 9 Result(i: 2.645751 | j: 3.000000 | i*j:
> >> >> >> > 7.937254): 260.198039
> >> >> >> > ITER: 1 | i: 8 | j: 0 Result(i: 2.828427 | j: 0.000000 | i*j:
> >> >> >> > 0.000000): 260.198039
> >> >> >> > ITER: 1 | i: 8 | j: 1 Result(i: 2.828427 | j: 1.000000 | i*j:
> >> >> >> > 2.828427): 263.026466
> >> >> >> > ITER: 1 | i: 8 | j: 2 Result(i: 2.828427 | j: 1.414214 | i*j:
> >> >> >> > 4.000000): 267.026466
> >> >> >> > ITER: 1 | i: 8 | j: 3 Result(i: 2.828427 | j: 1.732051 | i*j:
> >> >> >> > 4.898979): 271.925446
> >> >> >> > ITER: 1 | i: 8 | j: 4 Result(i: 2.828427 | j: 2.000000 | i*j:
> >> >> >> > 5.656854): 277.582300
> >> >> >> > ITER: 1 | i: 8 | j: 5 Result(i: 2.828427 | j: 2.236068 | i*j:
> >> >> >> > 6.324555): 283.906855
> >> >> >> > ITER: 1 | i: 8 | j: 6 Result(i: 2.828427 | j: 2.449490 | i*j:
> >> >> >> > 6.928203): 290.835059
> >> >> >> > ITER: 1 | i: 8 | j: 7 Result(i: 2.828427 | j: 2.645751 | i*j:
> >> >> >> > 7.483315): 298.318373
> >> >> >> > ITER: 1 | i: 8 | j: 8 Result(i: 2.828427 | j: 2.828427 | i*j:
> >> >> >> > 8.000000): 306.318373
> >> >> >> > ITER: 1 | i: 8 | j: 9 Result(i: 2.828427 | j: 3.000000 | i*j:
> >> >> >> > 8.485281): 314.803655
> >> >> >> > ITER: 1 | i: 9 | j: 0 Result(i: 3.000000 | j: 0.000000 | i*j:
> >> >> >> > 0.000000): 314.803655
> >> >> >> > ITER: 1 | i: 9 | j: 1 Result(i: 3.000000 | j: 1.000000 | i*j:
> >> >> >> > 3.000000): 317.803655
> >> >> >> > ITER: 1 | i: 9 | j: 2 Result(i: 3.000000 | j: 1.414214 | i*j:
> >> >> >> > 4.242641): 322.046295
> >> >> >> > ITER: 1 | i: 9 | j: 3 Result(i: 3.000000 | j: 1.732051 | i*j:
> >> >> >> > 5.196152): 327.242448
> >> >> >> > ITER: 1 | i: 9 | j: 4 Result(i: 3.000000 | j: 2.000000 | i*j:
> >> >> >> > 6.000000): 333.242448
> >> >> >> > ITER: 1 | i: 9 | j: 5 Result(i: 3.000000 | j: 2.236068 | i*j:
> >> >> >> > 6.708204): 339.950652
> >> >> >> > ITER: 1 | i: 9 | j: 6 Result(i: 3.000000 | j: 2.449490 | i*j:
> >> >> >> > 7.348469): 347.299121
> >> >> >> > ITER: 1 | i: 9 | j: 7 Result(i: 3.000000 | j: 2.645751 | i*j:
> >> >> >> > 7.937254): 355.236375
> >> >> >> > ITER: 1 | i: 9 | j: 8 Result(i: 3.000000 | j: 2.828427 | i*j:
> >> >> >> > 8.485281): 363.721656
> >> >> >> > ITER: 1 | i: 9 | j: 9 Result(i: 3.000000 | j: 3.000000 | i*j:
> >> >> >> > 9.000000): 372.721656
> >> >> >> > Final Result: 372.721656
> >> >> >> >
> >> >> >> >
> >> >> >> >
> >> >> >> > As we can see in the following iterations the sqrt(1) as well as
> > the
> >> >> >> > result is set to zero for some reason.
> >> >> >> >
> >> >> >> > ITER: 0 | i: 1 | j: 4 Result(i: 0.000000 | j: 2.000000 | i*j:
> >> >> >> > 0.000000): 0.000000
> >> >> >> > ITER: 0 | i: 1 | j: 5 Result(i: 0.000000 | j: 2.236068 | i*j:
> >> >> >> > 0.000000): 0.000000
> >> >> >> > ITER: 0 | i: 1 | j: 6 Result(i: 0.000000 | j: 2.449490 | i*j:
> >> >> >> > 0.000000): 0.000000
> >> >> >> > ITER: 0 | i: 1 | j: 7 Result(i: 0.000000 | j: 2.645751 | i*j:
> >> >> >> > 0.000000): 0.000000
> >> >> >> > ITER: 0 | i: 1 | j: 8 Result(i: 0.000000 | j: 2.828427 | i*j:
> >> >> >> > 0.000000): 0.000000
> >> >> >> > ITER: 0 | i: 1 | j: 9 Result(i: 0.000000 | j: 3.000000 | i*j:
> >> >> >> > 0.000000): 0.000000
> >> >> >> >
> >> >> >> > Please help me to resolve the accuracy issue! I think that it
> will
> >> >> >> > be very useful for gem5 community.
> >> >> >> >
> >> >> >> > To be noticed, I find the correct simulated tick in which the
> >> >> >> > application started in FS (using m5 dumpstats), and I start the
> >> >> >> > --debug-start, but the trace file which is generated is 10x
> larger
> >> >> >> > than SE mode for the same application. How can I compare them?
> >> >> >> >
> >> >> >> > Thank you in advance!
> >> >> >> > Best regards,
> >> >> >> > Nikos
> >> >> >> >
> >> >> >> > Quoting Νικόλαος Ταμπουρατζής <ntampouratzis@ece.auth.gr>:
> >> >> >> >
> >> >> >> >> Dear Jason,
> >> >> >> >>
> >> >> >> >> I am trying to use --debug-start but in FS mode it is very
> >> >> >> >> difficult to find the tick on which the application is started!
> >> >> >> >>
> >> >> >> >> However, I am writing the following very simple c++ program:
> >> >> >> >>
> >> >> >> >> #include <cmath>
> >> >> >> >> #include <stdio.h>
> >> >> >> >>
> >> >> >> >> int main(){
> >> >> >> >>
> >> >> >> >> int dim = 4096;
> >> >> >> >>
> >> >> >> >> double result;
> >> >> >> >>
> >> >> >> >> for (int iter = 0; iter < 2; iter++){
> >> >> >> >> result = 0;
> >> >> >> >> for (int i = 0; i < dim; i++){
> >> >> >> >> for (int j = 0; j < dim; j++){
> >> >> >> >> result += sqrt(i) * sqrt(j);
> >> >> >> >> }
> >> >> >> >> }
> >> >> >> >> printf("Result: %lf\n", result); //Result:
> > 30530733453.127449
> >> >> >> >> }
> >> >> >> >> }
> >> >> >> >>
> >> >> >> >> I cross-compile it using: riscv64-linux-gnu-g++ -static -O3 -o
> >> >> >> >> test_riscv test_riscv.cpp
> >> >> >> >>
> >> >> >> >>
> >> >> >> >> While in X86 (without cross-compilation of course), QEMU-RISCV,
> >> >> >> >> GEM5-SE the result is the same (30530733453.127449), in GEM5-FS
> > the
> >> >> >> >> result is different! In addition, the result is also different
> >> >> >> >> between the 2 iterations.
> >> >> >> >>
> >> >> >> >> Please reproduce the error if you want in order to verify my
> > result.
> >> >> >> >> Ηow can the issue be resolved?
> >> >> >> >>
> >> >> >> >> Thank you in advance!
> >> >> >> >>
> >> >> >> >> Best regards,
> >> >> >> >> Nikos
> >> >> >> >>
> >> >> >> >>
> >> >> >> >> Quoting Jason Lowe-Power <jason@lowepower.com>:
> >> >> >> >>
> >> >> >> >>> Hi Nikos,
> >> >> >> >>>
> >> >> >> >>> You can use --debug-start to start the debugging after some
> > number
> >> >> of
> >> >> >> >>> ticks. Also, I would expect that the difference should come up
> >> >> >> quickly, so
> >> >> >> >>> no need to run the program to the end.
> >> >> >> >>>
> >> >> >> >>> For the FS mode one, you will want to just start the trace as
> > the
> >> >> >> >>> application starts. This could be a bit of a pain.
> >> >> >> >>>
> >> >> >> >>> I'm not really sure what fundamentally could be different. FS
> > and SE
> >> >> >> mode
> >> >> >> >>> use the exact same code for executing instructions, so I don't
> > think
> >> >> >> that's
> >> >> >> >>> the problem. Have you tried running for smaller inputs or just
> > one
> >> >> >> >>> iteration?
> >> >> >> >>>
> >> >> >> >>> Jason
> >> >> >> >>>
> >> >> >> >>>
> >> >> >> >>>
> >> >> >> >>> On Wed, Sep 21, 2022 at 9:04 AM Νικόλαος Ταμπουρατζής <
> >> >> >> >>> ntampouratzis@ece.auth.gr> wrote:
> >> >> >> >>>
> >> >> >> >>>> Dear Bobby,
> >> >> >> >>>>
> >> >> >> >>>> Iam trying to add --debug-flags=Exec (building the gem5 for
> >> >> gem5.opt
> >> >> >> >>>> not for gem5.fast which I had) but the debug traces exceed
> the
> > 20GB
> >> >> >> >>>> (and it is not finished yet) for less than 1 simulated
> second.
> > How
> >> >> can
> >> >> >> >>>> I reduce the size of the debug-flags (or set something more
> >> >> specific)?
> >> >> >> >>>>
> >> >> >> >>>> In contrast I build the HPCG benchmark with DHPCG_DEBUG flag.
> > If
> >> >> you
> >> >> >> >>>> want, you can compare these two output files
> >> >> >> >>>> (hpcg20010909T014640_SE_Mode & HPCG-Benchmark_3.1_FS_Mode).
> As
> > you
> >> >> can
> >> >> >> >>>> see, something goes wrong with the accuracy of calculations
> in
> > FS
> >> >> mode
> >> >> >> >>>> (benchmark uses double precission). You can find the files
> > here:
> >> >> >> >>>> http://kition.mhl.tuc.gr:8000/d/68d82f3533/
> >> >> >> >>>>
> >> >> >> >>>> Best regards,
> >> >> >> >>>> Nikos
> >> >> >> >>>>
> >> >> >> >>>> Quoting Jason Lowe-Power <jason@lowepower.com>:
> >> >> >> >>>>
> >> >> >> >>>>> That's quite odd that it works in SE mode but not FS mode!
> >> >> >> >>>>>
> >> >> >> >>>>> I would suggest running with --debug-flags=Exec for both and
> > then
> >> >> >> >>>> perform a
> >> >> >> >>>>> diff to see how they differ.
> >> >> >> >>>>>
> >> >> >> >>>>> Cheers,
> >> >> >> >>>>> Jason
> >> >> >> >>>>>
> >> >> >> >>>>> On Tue, Sep 20, 2022 at 2:45 PM Νικόλαος Ταμπουρατζής <
> >> >> >> >>>>> ntampouratzis@ece.auth.gr> wrote:
> >> >> >> >>>>>
> >> >> >> >>>>>> Dear Bobby,
> >> >> >> >>>>>>
> >> >> >> >>>>>> In QEMU I get the same (correct) results that I get in SE
> > mode
> >> >> >> >>>>>> simulation. I get invalid results in FS simulation (in both
> >> >> >> >>>>>> riscv-fs.py and riscv-ubuntu-run.py). I cannot access real
> > RISCV
> >> >> >> >>>>>> hardware at this moment, however, if you want you may
> > execute my
> >> >> >> xhpcg
> >> >> >> >>>>>> binary (http://kition.mhl.tuc.gr:8000/f/4ca25fdd3c/) with
> the
> >> >> >> >>>>>> following configuration:
> >> >> >> >>>>>>
> >> >> >> >>>>>> ./xhpcg --nx=16 --ny=16 --nz=16 --npx=1 --npy=1 --npz=1
> > --rt=0.1
> >> >> >> >>>>>>
> >> >> >> >>>>>> Please let me know if you have any updates!
> >> >> >> >>>>>>
> >> >> >> >>>>>> Best regards,
> >> >> >> >>>>>> Nikos
> >> >> >> >>>>>>
> >> >> >> >>>>>>
> >> >> >> >>>>>> Quoting Jason Lowe-Power <jason@lowepower.com>:
> >> >> >> >>>>>>
> >> >> >> >>>>>>> Hi Nikos,
> >> >> >> >>>>>>>
> >> >> >> >>>>>>> I notice you said the following in your original email:
> >> >> >> >>>>>>>
> >> >> >> >>>>>>> In addition, I used the RISCV Ubuntu image
> >> >> >> >>>>>>>> (
> >> >> >>
> https://github.com/gem5/gem5-resources/tree/stable/src/riscv-ubuntu
> >> >> >> >>>> ),
> >> >> >> >>>>>>>> I installed the gcc compiler, compile it (through qemu)
> > and I
> >> >> get
> >> >> >> >>>>>>>> wrong results too.
> >> >> >> >>>>>>>
> >> >> >> >>>>>>>
> >> >> >> >>>>>>> Is this saying you get the wrong results is QEMU? If so,
> > the bug
> >> >> >> is in
> >> >> >> >>>>>> GCC
> >> >> >> >>>>>>> or the HPCG workload, not in gem5. If not, I would test in
> > QEMU
> >> >> to
> >> >> >> >>>> make
> >> >> >> >>>>>>> sure the binary works there. Another way you could test to
> > see
> >> >> if
> >> >> >> the
> >> >> >> >>>>>>> problem is your binary or gem5 would be to run it on real
> >> >> >> hardware. We
> >> >> >> >>>>>> have
> >> >> >> >>>>>>> access to some RISC-V hardware here at UC Davis, if you
> > don't
> >> >> have
> >> >> >> >>>> access
> >> >> >> >>>>>>> to it.
> >> >> >> >>>>>>>
> >> >> >> >>>>>>> Cheers,
> >> >> >> >>>>>>> Jason
> >> >> >> >>>>>>>
> >> >> >> >>>>>>> On Tue, Sep 20, 2022 at 12:58 AM Νικόλαος Ταμπουρατζής <
> >> >> >> >>>>>>> ntampouratzis@ece.auth.gr> wrote:
> >> >> >> >>>>>>>
> >> >> >> >>>>>>>> Dear Bobby,
> >> >> >> >>>>>>>>
> >> >> >> >>>>>>>> 1) I use the original riscv-fs.py which is provided in
> the
> >> >> latest
> >> >> >> >>>> gem5
> >> >> >> >>>>>>>> release.
> >> >> >> >>>>>>>> I run the gem5 once (./build/RISCV/gem5.fast -d
> >> >> ./HPCG_FS_results
> >> >> >> >>>>>>>> ./configs/example/gem5_library/riscv-fs.py) in order to
> >> >> download
> >> >> >> the
> >> >> >> >>>>>>>> riscv-bootloader-vmlinux-5.10 and riscv-disk-img.
> >> >> >> >>>>>>>> After this I mount the riscv-disk-img (sudo mount -o loop
> >> >> >> >>>>>>>> riscv-disk-img /mnt), put the xhpcg executable and I do
> the
> >> >> >> following
> >> >> >> >>>>>>>> changes in riscv-fs.py to boot the riscv-disk-img with
> >> >> executable:
> >> >> >> >>>>>>>>
> >> >> >> >>>>>>>> image = CustomDiskImageResource(
> >> >> >> >>>>>>>> local_path =
> > "/home/cossim/.cache/gem5/riscv-disk-img",
> >> >> >> >>>>>>>> )
> >> >> >> >>>>>>>>
> >> >> >> >>>>>>>> # Set the Full System workload.
> >> >> >> >>>>>>>> board.set_kernel_disk_workload(
> >> >> >> >>>>>>>>
> >> >> >> kernel=Resource("riscv-bootloader-vmlinux-5.10"),
> >> >> >> >>>>>>>> disk_image=image,
> >> >> >> >>>>>>>> )
> >> >> >> >>>>>>>>
> >> >> >> >>>>>>>> Finally, in the
> >> >> >> gem5/src/python/gem5/components/boards/riscv_board.py
> >> >> >> >>>>>>>> I change the last line to "return ["console=ttyS0",
> >> >> >> >>>>>>>> "root={root_value}", "rw"]" in order to allow the write
> >> >> >> permissions
> >> >> >> >>>> in
> >> >> >> >>>>>>>> the image.
> >> >> >> >>>>>>>>
> >> >> >> >>>>>>>>
> >> >> >> >>>>>>>> 2) The HPCG benchmark after some iterations calculates if
> > the
> >> >> >> results
> >> >> >> >>>>>>>> are valid or not valid. In the case of FS it gives
> invalid
> >> >> >> results.
> >> >> >> >>>> As
> >> >> >> >>>>>>>> I see from the results, one (at least) problem is that
> > produces
> >> >> >> >>>>>>>> different results in each HPCG execution (with the same
> >> >> >> >>>> configuration).
> >> >> >> >>>>>>>>
> >> >> >> >>>>>>>> Here is the HPCG output and riscv-fs.py
> >> >> >> >>>>>>>> (http://kition.mhl.tuc.gr:8000/d/68d82f3533/). You may
> >> >> reproduce
> >> >> >> the
> >> >> >> >>>>>>>> results in the video if you use the xhpcg executable
> >> >> >> >>>>>>>> (http://kition.mhl.tuc.gr:8000/f/4ca25fdd3c/)
> >> >> >> >>>>>>>>
> >> >> >> >>>>>>>> Please help me in order to solve it!
> >> >> >> >>>>>>>>
> >> >> >> >>>>>>>> Finally, I get invalid results in the HPL benchmark in FS
> > mode
> >> >> >> too.
> >> >> >> >>>>>>>>
> >> >> >> >>>>>>>> Best regards,
> >> >> >> >>>>>>>> Nikos
> >> >> >> >>>>>>>>
> >> >> >> >>>>>>>>
> >> >> >> >>>>>>>> Quoting Bobby Bruce <bbruce@ucdavis.edu>:
> >> >> >> >>>>>>>>
> >> >> >> >>>>>>>> > I'm going to need a bit more information to help:
> >> >> >> >>>>>>>> >
> >> >> >> >>>>>>>> > 1. In what way have you modified
> >> >> >> >>>>>>>> > ./configs/example/gem5_library/riscv-fs.py? Can you
> > attach
> >> >> the
> >> >> >> >>>> script
> >> >> >> >>>>>>>> here?
> >> >> >> >>>>>>>> > 2. What error are you getting or in what way are the
> > results
> >> >> >> >>>> invalid?
> >> >> >> >>>>>>>> >
> >> >> >> >>>>>>>> > -
> >> >> >> >>>>>>>> > Dr. Bobby R. Bruce
> >> >> >> >>>>>>>> > Room 3050,
> >> >> >> >>>>>>>> > Kemper Hall, UC Davis
> >> >> >> >>>>>>>> > Davis,
> >> >> >> >>>>>>>> > CA, 95616
> >> >> >> >>>>>>>> >
> >> >> >> >>>>>>>> > web: https://www.bobbybruce.net
> >> >> >> >>>>>>>> >
> >> >> >> >>>>>>>> >
> >> >> >> >>>>>>>> > On Mon, Sep 19, 2022 at 1:43 PM Νικόλαος Ταμπουρατζής <
> >> >> >> >>>>>>>> > ntampouratzis@ece.auth.gr> wrote:
> >> >> >> >>>>>>>> >
> >> >> >> >>>>>>>> >>
> >> >> >> >>>>>>>> >> Dear gem5 community,
> >> >> >> >>>>>>>> >>
> >> >> >> >>>>>>>> >> I have successfully cross-compile the HPCG benchmark
> for
> >> >> RISCV
> >> >> >> >>>>>> (Serial
> >> >> >> >>>>>>>> >> version, without MPI and OpenMP). While it working
> > properly
> >> >> in
> >> >> >> >>>> gem5
> >> >> >> >>>>>> SE
> >> >> >> >>>>>>>> >> mode (./build/RISCV/gem5.fast -d ./HPCG_SE_results
> >> >> >> >>>>>>>> >> ./configs/example/se.py -c xhpcg --options '--nx=16
> > --ny=16
> >> >> >> >>>> --nz=16
> >> >> >> >>>>>>>> >> --npx=1 --npy=1 --npz=1 --rt=0.1'), I get invalid
> > results
> >> >> in FS
> >> >> >> >>>>>>>> >> simulation using "./build/RISCV/gem5.fast -d
> >> >> ./HPCG_FS_results
> >> >> >> >>>>>>>> >> ./configs/example/gem5_library/riscv-fs.py" (I mount
> the
> >> >> riscv
> >> >> >> >>>> image
> >> >> >> >>>>>>>> >> and put it).
> >> >> >> >>>>>>>> >>
> >> >> >> >>>>>>>> >> Can you help me please?
> >> >> >> >>>>>>>> >>
> >> >> >> >>>>>>>> >> In addition, I used the RISCV Ubuntu image
> >> >> >> >>>>>>>> >> (
> >> >> >> >>>>
> >> >> https://github.com/gem5/gem5-resources/tree/stable/src/riscv-ubuntu
> >> >> >> >>>>>> ),
> >> >> >> >>>>>>>> >> I installed the gcc compiler, compile it (through
> qemu)
> > and
> >> >> I
> >> >> >> get
> >> >> >> >>>>>>>> >> wrong results too.
> >> >> >> >>>>>>>> >>
> >> >> >> >>>>>>>> >> Here is the Makefile which I use, the hpcg executable
> > for
> >> >> RISCV
> >> >> >> >>>>>>>> >> (xhpcg), and a video that shows the results
> >> >> >> >>>>>>>> >> (http://kition.mhl.tuc.gr:8000/f/4ca25fdd3c/).
> >> >> >> >>>>>>>> >>
> >> >> >> >>>>>>>> >> P.S. I use the latest gem5 version.
> >> >> >> >>>>>>>> >>
> >> >> >> >>>>>>>> >> Thank you in advance! :)
> >> >> >> >>>>>>>> >>
> >> >> >> >>>>>>>> >> Best regards,
> >> >> >> >>>>>>>> >> Nikos
> >> >> >> >>>>>>>> >> _______________________________________________
> >> >> >> >>>>>>>> >> gem5-users mailing list -- gem5-users@gem5.org
> >> >> >> >>>>>>>> >> To unsubscribe send an email to
> > gem5-users-leave@gem5.org
> >> >> >> >>>>>>>> >>
> >> >> >> >>>>>>>>
> >> >> >> >>>>>>>>
> >> >> >> >>>>>>>> _______________________________________________
> >> >> >> >>>>>>>> gem5-users mailing list -- gem5-users@gem5.org
> >> >> >> >>>>>>>> To unsubscribe send an email to
> gem5-users-leave@gem5.org
> >> >> >> >>>>>>>>
> >> >> >> >>>>>>
> >> >> >> >>>>>>
> >> >> >> >>>>>> _______________________________________________
> >> >> >> >>>>>> gem5-users mailing list -- gem5-users@gem5.org
> >> >> >> >>>>>> To unsubscribe send an email to gem5-users-leave@gem5.org
> >> >> >> >>>>>>
> >> >> >> >>>>
> >> >> >> >>>>
> >> >> >> >>>> _______________________________________________
> >> >> >> >>>> gem5-users mailing list -- gem5-users@gem5.org
> >> >> >> >>>> To unsubscribe send an email to gem5-users-leave@gem5.org
> >> >> >> >>>>
> >> >> >> >>
> >> >> >> >>
> >> >> >> >> _______________________________________________
> >> >> >> >> gem5-users mailing list -- gem5-users@gem5.org
> >> >> >> >> To unsubscribe send an email to gem5-users-leave@gem5.org
> >> >> >> >
> >> >> >> >
> >> >> >> > _______________________________________________
> >> >> >> > gem5-users mailing list -- gem5-users@gem5.org
> >> >> >> > To unsubscribe send an email to gem5-users-leave@gem5.org
> >> >> >>
> >> >> >>
> >> >> >> _______________________________________________
> >> >> >> gem5-users mailing list -- gem5-users@gem5.org
> >> >> >> To unsubscribe send an email to gem5-users-leave@gem5.org
> >> >> >>
> >> >>
> >> >>
> >> >> _______________________________________________
> >> >> gem5-users mailing list -- gem5-users@gem5.org
> >> >> To unsubscribe send an email to gem5-users-leave@gem5.org
> >> >>
> >>
> >>
> >> _______________________________________________
> >> gem5-users mailing list -- gem5-users@gem5.org
> >> To unsubscribe send an email to gem5-users-leave@gem5.org
>
>
> _______________________________________________
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