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clock_t startTime = clock();
}
}
clock_t endTime = clock();
<br />
! 6
|-
| 15009000
| 15.7
| 7.7
When mkl_get_max_threads is equal to the number of physical cores, the performance is the best, not the number of threads, which is the following 3 instead of 6. <br />
==Source Code==
=Serial=
#include <stdio.h>
#include <stdlib.h>
#include <time.h>
/* Consider adjusting LOOP_COUNT based on the performance of your computer */
/* to make sure that total run time is at least 1 second */
#define LOOP_COUNT 220 //220 for more accurate statistics
int main()
{
double* A, * B, * C;
int m, n, p, i, j, k, r;
double alpha, beta;
double sum;
double s_initial, s_elapsed;
printf("\n This example demonstrates threading impact on computing real matrix product \n"
" C=alpha*A*B+beta*C using Intel(R) MKL function dgemm, where A, B, and C are \n"
" matrices and alpha and beta are double precision scalars \n\n");
m = 2000, p = 200, n = 1000;
printf(" Initializing data for matrix multiplication C=A*B for matrix \n"
" A(%ix%i) and matrix B(%ix%i)\n\n", m, p, p, n);
alpha = 1.0; beta = 0.0;
printf(" Allocating memory for matrices aligned on 64-byte boundary for better \n"
" performance \n\n");
A = (double*)malloc(m * p * sizeof(double), 64);
B = (double*)malloc(p * n * sizeof(double), 64);
C = (double*)malloc(m * n * sizeof(double), 64);
if (A == NULL || B == NULL || C == NULL) {
printf("\n ERROR: Can't allocate memory for matrices. Aborting... \n\n");
free(A);
free(B);
free(C);
return 1;
}
printf(" Intializing matrix data \n\n");
for (i = 0; i < (m * p); i++) {
A[i] = (double)(i + 1);
}
for (i = 0; i < (p * n); i++) {
B[i] = (double)(-i - 1);
}
for (i = 0; i < (m * n); i++) {
C[i] = 0.0;
}
clock_t startTime = clock();
for (i = 0; i < m; i++) {
for (j = 0; j < n; j++) {
sum = 0.0;
for (k = 0; k < p; k++)
sum += A[p * i + k] * B[n * k + j];
C[n * i + j] = sum;
}
}
clock_t endTime = clock();
s_elapsed = (endTime - startTime) / LOOP_COUNT;
printf(" == Matrix multiplication using triple nested loop completed == \n"
" == at %.5f milliseconds == \n\n", (s_elapsed * 1000));
printf(" Deallocating memory \n\n");
free(A);
free(B);
free(C);
if (s_elapsed < 0.9 / LOOP_COUNT) {
s_elapsed = 1.0 / LOOP_COUNT / s_elapsed;
i = (int)(s_elapsed * LOOP_COUNT) + 1;
printf(" It is highly recommended to define LOOP_COUNT for this example on your \n"
" computer as %i to have total execution time about 1 second for reliability \n"
" of measurements\n\n", i);
}
printf(" Example completed. \n\n");
return 0;
}
=MKL version=
#include <stdio.h>
#include <stdlib.h>
#include "mkl.h"
/* Consider adjusting LOOP_COUNT based on the performance of your computer */
/* to make sure that total run time is at least 1 second */
#define LOOP_COUNT 220 // 220 用于更精确的统计
int main()
{
double* A, * B, * C;
int m, n, p, i, j, r, max_threads;
double alpha, beta;
double s_initial, s_elapsed;
printf("\n This example demonstrates threading impact on computing real matrix product \n"
" C=alpha*A*B+beta*C using Intel(R) MKL function dgemm, where A, B, and C are \n"
" matrices and alpha and beta are double precision scalars \n\n");
m = 2000, p = 200, n = 1000;
printf(" Initializing data for matrix multiplication C=A*B for matrix \n"
" A(%ix%i) and matrix B(%ix%i)\n\n", m, p, p, n);
alpha = 1.0; beta = 0.0;
printf(" Allocating memory for matrices aligned on 64-byte boundary for better \n"
" performance \n\n");
A = (double*)mkl_malloc(m * p * sizeof(double), 64);
B = (double*)mkl_malloc(p * n * sizeof(double), 64);
C = (double*)mkl_malloc(m * n * sizeof(double), 64);
if (A == NULL || B == NULL || C == NULL) {
printf("\n ERROR: Can't allocate memory for matrices. Aborting... \n\n");
mkl_free(A);
mkl_free(B);
mkl_free(C);
return 1;
}
printf(" Intializing matrix data \n\n");
for (i = 0; i < (m * p); i++) {
A[i] = (double)(i + 1);
}
for (i = 0; i < (p * n); i++) {
B[i] = (double)(-i - 1);
}
for (i = 0; i < (m * n); i++) {
C[i] = 0.0;
}
max_threads = mkl_get_max_threads();
printf(" Finding max number %d of threads Intel(R) MKL can use for parallel runs \n\n", max_threads);
printf(" Running Intel(R) MKL from 1 to %i threads \n\n", max_threads * 2);
for (i = 1; i <= max_threads * 2; i++) {
for (j = 0; j < (m * n); j++)
C[j] = 0.0;
mkl_set_num_threads(i);
cblas_dgemm(CblasRowMajor, CblasNoTrans, CblasNoTrans,
m, n, p, alpha, A, p, B, n, beta, C, n);
s_initial = dsecnd();
for (r = 0; r < LOOP_COUNT; r++) {
cblas_dgemm(CblasRowMajor, CblasNoTrans, CblasNoTrans,
m, n, p, alpha, A, p, B, n, beta, C, n);
}
s_elapsed = (dsecnd() - s_initial) / LOOP_COUNT;
printf(" == Matrix multiplication using Intel(R) MKL dgemm completed ==\n"
" == at %.5f milliseconds using %d thread(s) ==\n\n", (s_elapsed * 1000), i);
}
printf(" Deallocating memory \n\n");
mkl_free(A);
mkl_free(B);
mkl_free(C);
if (s_elapsed < 0.9 / LOOP_COUNT) {
s_elapsed = 1.0 / LOOP_COUNT / s_elapsed;
i = (int)(s_elapsed * LOOP_COUNT) + 1;
printf(" It is highly recommended to define LOOP_COUNT for this example on your \n"
" computer as %i to have total execution time about 1 second for reliability \n"
" of measurements\n\n", i);
}
printf(" Example completed. \n\n");
return 0;
}
==References==
