// MODIFIED FOR TEST CODE
double MonteCarlo_integrate(int Num_samples, int print)
{
Random R = new_Random_seed(SEED);
int under_curve = 0;
int count;
for (count=0; count<Num_samples; count++)
{
double x= Random_nextDouble(R);
double y= Random_nextDouble(R);
if ( x*x + y*y <= 1.0)
under_curve ++;
}
Random_delete(R);
// ADDED FOR TEST CODE print results
if( print )
{
printf("\n%d\n", under_curve);
}
return ((double) under_curve / Num_samples) * 4.0;
}
/*
* This is a straightforward copy/adaptation of scimark2.c:main.
*
* @note: default to the (small) cache-contained version.
*
* @param s_large [logical(1)]
* Perform SciMark LARGE, i.e., memory-focused version of the benchmark?
* Default is FALSE.
* @param s_min_time [numeric(1)]
* Minimum time to run each of the benchmarks, in seconds.
* @return [numeric(6)]
*/
SEXP c_rscimark(SEXP s_large, SEXP s_min_time) {
int large = asLogical(s_large);
double min_time = asReal(s_min_time);
int FFT_size = FFT_SIZE;
int SOR_size = SOR_SIZE;
int Sparse_size_M = SPARSE_SIZE_M;
int Sparse_size_nz = SPARSE_SIZE_nz;
int LU_size = LU_SIZE;
Random R = new_Random_seed(RANDOM_SEED);
if (large) {
FFT_size = LG_FFT_SIZE;
SOR_size = LG_SOR_SIZE;
Sparse_size_M = LG_SPARSE_SIZE_M;
Sparse_size_nz = LG_SPARSE_SIZE_nz;
LU_size = LG_LU_SIZE;
}
/* allocate memory for return value */
SEXP s_res = NEW_NUMERIC(6);
double* res = REAL(s_res);
/* do the benchmark stuff */
res[1] = kernel_measureFFT(FFT_size, min_time, R);
res[2] = kernel_measureSOR(SOR_size, min_time, R);
res[3] = kernel_measureMonteCarlo(min_time, R);
res[4] = kernel_measureSparseMatMult(
Sparse_size_M, Sparse_size_nz, min_time, R);
res[5] = kernel_measureLU(LU_size, min_time, R);
/* composite value */
res[0] = (res[1] + res[2] + res[3] + res[4] + res[5]) / 5;
Random_delete(R);
return s_res;
}
int main(int argc, char *argv[])
{
/* default to the (small) cache-contained version */
double min_time = RESOLUTION_DEFAULT;
int FFT_size = FFT_SIZE;
int SOR_size = SOR_SIZE;
int Sparse_size_M = SPARSE_SIZE_M;
int Sparse_size_nz = SPARSE_SIZE_nz;
int LU_size = LU_SIZE;
/* run the benchmark */
double res[6] = {0.0};
Random R = new_Random_seed(RANDOM_SEED);
if (argc > 1)
{
int current_arg = 1;
if (strcmp(argv[1], "-help")==0 ||
strcmp(argv[1], "-h") == 0)
{
fprintf(stderr, "Usage: [-large] [minimum_time]\n");
exit(0);
}
if (strcmp(argv[1], "-large")==0)
{
FFT_size = LG_FFT_SIZE;
SOR_size = LG_SOR_SIZE;
Sparse_size_M = LG_SPARSE_SIZE_M;
Sparse_size_nz = LG_SPARSE_SIZE_nz;
LU_size = LG_LU_SIZE;
current_arg++;
}
if (current_arg < argc)
{
min_time = atof(argv[current_arg]);
}
}
print_banner();
printf("Using %10.2f seconds min time per kenel.\n", min_time);
res[1] = kernel_measureFFT( FFT_size, min_time, R);
res[2] = kernel_measureSOR( SOR_size, min_time, R);
res[3] = kernel_measureMonteCarlo(min_time, R);
res[4] = kernel_measureSparseMatMult( Sparse_size_M,
Sparse_size_nz, min_time, R);
res[5] = kernel_measureLU( LU_size, min_time, R);
res[0] = (res[1] + res[2] + res[3] + res[4] + res[5]) / 5;
/* print out results */
printf("Composite Score: %8.2f\n" ,res[0]);
printf("FFT Mflops: %8.2f (N=%d)\n", res[1], FFT_size);
printf("SOR Mflops: %8.2f (%d x %d)\n",
res[2], SOR_size, SOR_size);
printf("MonteCarlo: Mflops: %8.2f\n", res[3]);
printf("Sparse matmult Mflops: %8.2f (N=%d, nz=%d)\n", res[4],
Sparse_size_M, Sparse_size_nz);
printf("LU Mflops: %8.2f (M=%d, N=%d)\n", res[5],
LU_size, LU_size);
Random_delete(R);
return 0;
}