Measuring Performance with Intel® MKL Support Functions

Intel MKL provides functions to measure performance. This provides a way of quantifying the performance improvement resulting from using Intel MKL routines in this tutorial.

Measure Performance of dgemm

Use the dsecnd routine to return the elapsed CPU time in seconds.

Note

The quick execution of the dgemm routine makes it difficult to measure its speed, even for an operation on a large matrix. For this reason, the exercises perform the multiplication multiple times. You should set the value of the LOOP_COUNT constant so that the total execution time is about one second.

/* C source code is found in dgemm_with_timing.c */

    printf (" Making the first run of matrix product using Intel(R) MKL dgemm function \n"
            " via CBLAS interface to get stable run time measurements \n\n");
    cblas_dgemm(CblasRowMajor, CblasNoTrans, CblasNoTrans, 
                m, n, k, alpha, A, k, B, n, beta, C, n);

    printf (" Measuring performance of matrix product using Intel(R) MKL dgemm function \n"
            " via CBLAS interface \n\n");
    s_initial = dsecnd();
    for (r = 0; r < LOOP_COUNT; r++) {
        cblas_dgemm(CblasRowMajor, CblasNoTrans, CblasNoTrans, 
                    m, n, k, alpha, A, k, 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 == \n\n", (s_elapsed * 1000));

Measure Performance Without Using dgemm

In order to show the improvement resulting from using dgemm, perform the same measurement, but use a triply-nested loop to multiply the matrices.

/* C source code is found in matrix_multiplication.c */

    printf (" Making the first run of matrix product using triple nested loop\n"
            " to get stable run time measurements \n\n");
    for (i = 0; i < m; i++) {
        for (j = 0; j < n; j++) {
            sum = 0.0;
            for (l = 0; l < k; l++)
                sum += A[k*i+l] * B[n*l+j];
            C[n*i+j] = sum;
        }
    }

    printf (" Measuring performance of matrix product using triple nested loop \n\n");
    s_initial = dsecnd();
    for (r = 0; r < LOOP_COUNT; r++) {
        for (i = 0; i < m; i++) {
            for (j = 0; j < n; j++) {
                sum = 0.0;
                for (l = 0; l < k; l++)
                    sum += A[k*i+l] * B[n*l+j];
                C[n*i+j] = sum;
            }
        }
    }
    s_elapsed = (dsecnd() - s_initial) / LOOP_COUNT;
    
    printf (" == Matrix multiplication using triple nested loop completed == \n"
            " == at %.5f milliseconds == \n\n", (s_elapsed * 1000));

Compare the results in the first exercise using dgemm to the results of the second exercise without using dgemm.

You can find more information about measuring Intel MKL performance from the article "A simple example to measure the performance of an Intel MKL function" in the Intel Math Kernel Library Knowledge Base.

Previous Next

See Also


For more complete information about compiler optimizations, see our Optimization Notice.