Multiplying Matrices Using dgemm
Intel MKL provides several routines for multiplying matrices. The most widely used is the dgemm
routine, which calculates the product of double precision matrices:
The dgemm
routine can perform several calculations. For example, you can perform this operation with the transpose or conjugate transpose of A and B. The complete details of capabilities of the dgemm
routine and all of its arguments can be found in the ?gemm
topic in the Intel Math Kernel Library Reference Manual.
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 onedimensional arrays in the exercises store the matrices by placing the elements of each column in successive cells of the arrays.
Note
The C source code for the exercises in this tutorial is found in <installdir>\Samples\enUS\mkl\tutorials.zip (Windows* OS), or <installdir>/Samples/enUS/mkl/tutorials.zip (Linux* OS/OS X*).
After you unzip the tutorials.zip file, the Fortran source code can be found in the mkl_mmx_f directory, and the C source code can be found in the mkl_mmx_c directory.
/* 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 64byte 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)(i1); } 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; }
Note
This exercise illustrates how to call the dgemm
routine. An actual application would make use of the result of the matrix multiplication.
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 Intel MKL 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:
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
Intel MKL provides many options for creating code for multiple processors and operating systems, compatible with different compilers and thirdparty libraries, and with different interfaces. To compile and link the exercises in this tutorial with Intel® Composer XE, type
Note
This assumes that you have installed Intel MKL and set environment variables as described in http://software.intel.com/enus/articles/intelmkl111gettingstarted/.
For other compilers, use the Intel MKL Link Line Advisor to generate a command line to compile and link the exercises in this tutorial: http://software.intel.com/enus/articles/intelmkllinklineadvisor/.
After compiling and linking, execute the resulting executable file, named dgemm_example.exe on Windows* OS or a.out on Linux* OS and OS X*.
Intel's compilers may or may not optimize to the same degree for nonIntel 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. Microprocessordependent 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 