Use dgemm to Multiply Matrices

This exercise demonstrates declaring variables, storing matrix values in the arrays, and calling dgemm to compute the product of the matrices. The arrays are used to store these matrices:

The one-dimensional arrays in the exercises store the matrices by placing the elements of each column in successive cells of the arrays.

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

#define min(x,y) (((x) < (y)) ? (x) : (y))

#include <stdio.h>
#include <stdlib.h>
#include "mkl.h"

int main()
{
    double *A, *B, *C;
    int m, n, k, i, j;
    double alpha, beta;

    printf ("\n This example computes real matrix C=alpha*A*B+beta*C using \n"
            " Intel(R) MKL function dgemm, where A, B, and  C are matrices and \n"
            " alpha and beta are double precision scalars\n\n");

    m = 2000, k = 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, k, k, 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*k*sizeof( double ), 64 );
    B = (double *)mkl_malloc( k*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*k); i++) {
        A[i] = (double)(i+1);
    }

    for (i = 0; i < (k*n); i++) {
        B[i] = (double)(-i-1);
    }

    for (i = 0; i < (m*n); i++) {
        C[i] = 0.0;
    }

    printf (" Computing matrix product using Intel(R) MKL dgemm function via CBLAS interface \n\n");
    cblas_dgemm(CblasRowMajor, CblasNoTrans, CblasNoTrans, 
                m, n, k, alpha, A, k, B, n, beta, C, n);
    printf ("\n Computations completed.\n\n");

    printf (" Top left corner of matrix A: \n");
    for (i=0; i<min(m,6); i++) {
      for (j=0; j<min(k,6); j++) {
        printf ("%12.0f", A[j+i*k]);
      }
      printf ("\n");
    }

    printf ("\n Top left corner of matrix B: \n");
    for (i=0; i<min(k,6); i++) {
      for (j=0; j<min(n,6); j++) {
        printf ("%12.0f", B[j+i*n]);
      }
      printf ("\n");
    }
    
    printf ("\n Top left corner of matrix C: \n");
    for (i=0; i<min(m,6); i++) {
      for (j=0; j<min(n,6); j++) {
        printf ("%12.5G", C[j+i*n]);
      }
      printf ("\n");
    }

    printf ("\n Deallocating memory \n\n");
    mkl_free(A);
    mkl_free(B);
    mkl_free(C);

    printf (" Example completed. \n\n");
    return 0;
}

This call to the dgemm routine multiplies the matrices:

cblas_dgemm(CblasRowMajor, CblasNoTrans, CblasNoTrans,
           m, n, k, alpha, A, k, B, n, beta, C, n);

The arguments provide options for how oneMKL performs the operation. In this case:

CblasRowMajor

Indicates that the matrices are stored in row major order, with the elements of each row of the matrix stored contiguously as shown in the figure above.

CblasNoTrans

Enumeration type indicating that the matrices A and B should not be transposed or conjugate transposed before multiplication.

m, n, k

Integers indicating the size of the matrices:

• A: m rows by k columns

• B: k rows by n columns

• C: m rows by n columns

alpha

Real value used to scale the product of matrices A and B.

A

Array used to store matrix A.

k

Leading dimension of array A, or the number of elements between successive rows (for row major storage) in memory. In the case of this exercise the leading dimension is the same as the number of columns.

B

Array used to store matrix B.

n

Leading dimension of array B, or the number of elements between successive rows (for row major storage) in memory. In the case of this exercise the leading dimension is the same as the number of columns.

beta

Real value used to scale matrix C.

C

Array used to store matrix C.

n

Leading dimension of array C, or the number of elements between successive rows (for row major storage) in memory. In the case of this exercise the leading dimension is the same as the number of columns.

Compile and Link Your Code

oneMKL provides many options for creating code for multiple processors and operating systems, compatible with different compilers and third-party libraries, and with different interfaces. To compile and link the exercises in this tutorial with Intel® Parallel Studio XE Composer Edition, type

• Windows* OS: icx /Qmkl src\dgemm_example.c
• Linux* OS: icpx -qmkl src/dgemm_example.c

Alternatively, you can use the supplied build scripts to build and run the executables.

• Windows* OS:

   build
  build run_dgemm_example

• Linux* OS, macOS*:

  make
  make run_dgemm_example

For the executables in this tutorial, the build scripts are named:

For other compilers, use the oneMKL Link Line Advisor to generate a command line to compile and link the exercises in this tutorial: https://www.intel.com/content/www/us/en/developer/tools/oneapi/onemkl-link-line-advisor.html.

After compiling and linking, execute the resulting executable file, named dgemm_example.exe on Windows* OS or a.out on Linux* OS and macOS*.