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:IntelSWToolscompilers_and_libraries_2019.5.281windows，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:IntelSWToolscompilers_and_libraries_2019.5.281windowsmklin

包含目录：D:IntelSWToolscompilers_and_libraries_2019.5.281windowsmklinclude

库目录：

D:IntelSWToolscompilers_and_libraries_2019.5.281windowscompilerlibia32_win

D:IntelSWToolscompilers_and_libraries_2019.5.281windowsmkllibia32_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）

mkl_intel_c.lib;mkl_intel_thread.lib;mkl_core.lib;libiomp5md.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("
 This example computes real matrix C=alpha*A*B+beta*C using 
"
        " Intel(R) MKL function dgemm, where A, B, and  C are matrices and 
"
        " alpha and beta are double precision scalars

");

    m = 2000, k = 200, n = 1000;
    printf(" Initializing data for matrix multiplication C=A*B for matrix 
"
        " A(%ix%i) and matrix B(%ix%i)

", m, k, k, n);
    alpha = 1.0; beta = 0.0;

    printf(" Allocating memory for matrices aligned on 64-byte boundary for better 
"
        " performance 

");
    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("
 ERROR: Can't allocate memory for matrices. Aborting... 

");
        mkl_free(A);
        mkl_free(B);
        mkl_free(C);
        return 1;
    }

    printf(" Intializing matrix data 

");
    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 

");
    cblas_dgemm(CblasRowMajor, CblasNoTrans, CblasNoTrans,
        m, n, k, alpha, A, k, B, n, beta, C, n);
    printf("
 Computations completed.

");

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

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

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

    printf("
 Deallocating memory 

");
    mkl_free(A);
    mkl_free(B);
    mkl_free(C);

    printf(" Example completed. 

");

    system("PAUSE");
    return 0;
}