VS2019配置MKL教程(Windows)

下载链接：https://software.intel.com/en-us/mkl

文件下载

官网注册后，选择MKL下载下来，安装到指定目录就行，不在多说。

配置文件

首先创建一个Windows桌面项目，再添加一个CPP源文件。

打开项目属性页--配置属性，会多出Intel Performance...这一项，看下图配置

在打开VC++目录，进行配置。我安装MKL的地方在D:\IntelSWTools

打开D:\IntelSWTools\compilers_and_libraries_2019.5.281\windows，由于版本不同，可能后面的版本更新日期可能不同。按照下面根据你的情况添加。

可执行文件目录：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

库目录：

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

注意：在选择生成程序时，选择生成x86程序。如果要生成x64程序，那么库文件那里选择intel64_win。

打开链接器，在附加依赖项添加（如果配置64位程序，需要将mkl_intel_c.lib改成mkl_intel_lp64.lib）

配置测试

#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 = , k = , n = ;

    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), );

    B = (double*)mkl_malloc(k * n * sizeof(double), );

    C = (double*)mkl_malloc(m * n * sizeof(double), );

    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 ;

    }

    printf(" Intializing matrix data \n\n");

    for (i = ; i < (m * k); i++) {

        A[i] = (double)(i + );

    }

    for (i = ; i < (k * n); i++) {

        B[i] = (double)(-i - );

    }

    for (i = ; 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 = ; i < min(m, ); i++) {

        for (j = ; j < min(k, ); j++) {

            printf("%12.0f", A[j + i * k]);

        }

        printf("\n");

    }

    printf("\n Top left corner of matrix B: \n");

    for (i = ; i < min(k, ); i++) {

        for (j = ; j < min(n, ); j++) {

            printf("%12.0f", B[j + i * n]);

        }

        printf("\n");

    }

    printf("\n Top left corner of matrix C: \n");

    for (i = ; i < min(m, ); i++) {

        for (j = ; j < min(n, ); 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 ;

}