VS2019 MKL configuration tutorial (Windows)

Download Link: https://software.intel.com/en-us/mkl

 

1. Download the file

After the official website registration, select MKL download, install to the specified directory on the line, not to say.

 

2. Profiles

First, create a Windows desktop items, add a CPP source file.

 

Open the project properties page - configuration properties, will be more of Intel Performance ... this one, see the figure configuration

 

In the open VC ++ directory, configuration. I installed MKL place in the D: \ IntelSWTools

Open the D: \ IntelSWTools \ compilers_and_libraries_2019.5.281 \ windows, due to the different versions, the latter may update the version date may be different. Add the following depending on your situation.

Executables directory: D: \ IntelSWTools \ compilers_and_libraries_2019.5.281 \ Windows \ MKL \ bin

包含目录：D:\IntelSWTools\compilers_and_libraries_2019.5.281\windows\mkl\include

Library catalog:

D:\IntelSWTools\compilers_and_libraries_2019.5.281\windows\compiler\lib\ia32_win

D:\IntelSWTools\compilers_and_libraries_2019.5.281\windows\mkl\lib\ia32_win

 

Open link, add in the Additional Dependencies

mkl_intel_c.lib;mkl_intel_thread.lib;mkl_core.lib;libiomp5md.lib;

 

3. Configuration Test

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

#include "mkl.h"

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

    system("PAUSE");
    return 0;
}