Using Intel® Math Kernel Library for Matrix Multiplication - C

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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.
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.
Optimization Notice
Intel's compilers may or may not optimize to the same degree for non-Intel microprocessors for optimizations that are not unique to Intel microprocessors. These optimizations include SSE2, SSE3, and SSSE3 instruction sets and other optimizations. Intel does not guarantee the availability, functionality, or effectiveness of any optimization on microprocessors not manufactured by Intel. Microprocessor-dependent optimizations in this product are intended for use with Intel microprocessors. Certain optimizations not specific to Intel microarchitecture are reserved for Intel microprocessors. Please refer to the applicable product User and Reference Guides for more information regarding the specific instruction sets covered by this notice.
Notice revision #20110804
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Product and Performance Information

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Performance varies by use, configuration and other factors. Learn more at www.Intel.com/PerformanceIndex.