Intel MKL 在VS中的配置与安装笔记

本文主要是介绍Intel MKL 在VS中的配置与安装笔记，希望对大家解决编程问题提供一定的参考价值，需要的开发者们随着小编来一起学习吧！

转自：https://blog.csdn.net/caoenze/article/details/46699327

mkl 使用手册下载：http://download.csdn.net/detail/caoenze/8855821

从intel官网下载c_studio_xe_2013_sp1_update3_setup.exe文件（完全离线安装包）
双击.exe文件，自动提取文件并进入安装引导
安装完成后，配置VS2010（前提是本机已正确安装过VS2010）
新建一C++项目，比如win32控制台项目：MKL_TEST
点击菜单栏项目——》MKL_TEST属性——》配置属性——》VC++目录：
可执行文件目录添加：C:\Program Files (x86)\Intel\Composer XE\mkl\bin\ia32
包含目录添加：C:\Program Files (x86)\Intel\Composer XE\mkl\include
库目录添加：C:\Program Files (x86)\Intel\Composer XE\mkl\lib\ia32
注意：包含目录不区分ia32和intel64
Bin和lib目录区分ia32和intel64根据自己的CPU架构选择。
IA32可以认为是X86或者X86-32
Intel64:intel与HP联合开发的64-bits全新架构，与X86不兼容，没有太大市场。
6 、连接器——》输入
附加依赖项：添加
mkl_intel_c.lib
mkl_intel_thread.lib
mkl_core.lib
libiomp5mt.lib//我只添加了前三个，添加第4个，编译时提示找不到此库
7、配置属性——Intel Performance Library
右侧Use Intel MKL :
选择Parallel
其它两项可以选择性配置，不配置也可以。
8、至此，VS2010调用MKL已配置完毕，可在MKL_TEST项目里添加源文件main.c 测试代码如下：

#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® 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® 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");}getchar();printf ("\n Deallocating memory \n\n");mkl_free(A);mkl_free(B);mkl_free(C);printf (" Example completed. \n\n");return 0;
}

结果展示