MKL源码安装，支持多核环境配置

MKL源码安装，支持多核环境配置

1. 安装包下载并解压

链接: Intel® Math Kernel Library (Intel® MKL).

新的改变

2. 安装

1）. 解压至任意目录
2）. 运行安装脚本, 默认安装至 /opt/, 可配置安装路径（建议默认安装）

$ ./install.sh

3）. 在 /etc/ld.so.conf.d 下创建名为 intel-mkl.conf 的文件，内容为

/opt/intel/mkl/lib/intel64
/opt/intel/lib/intel64

然后执行

$  ldconfig -v

4). 执行

/opt/intel/mkl/bin/mklvars.sh intel64 mod

3. 环境变量设置

1） 进入startup文件编辑(bash shell)

$ vi ~/.bashrc

2）添加环境变量

export PATH=/opt/intel/bin:$PATH
export LD_LIBRARY_PATH=/opt/intel/lib/intel64:/opt/intel/mkl/lib/intel64:$LD_LIBRARY_P
source /opt/intel/compilers_and_libraries/linux/mkl/bin/mklvars.sh intel64

第三条语句用于 -lmkl_intel_thread库的调用实现多核并行计算
3）生效环境变量

$ source ~/.bashrc

4. 官方样例

dgemm_example.c

编译命令

gcc -I/opt/intel/mkl/include dgemm_example.c -lmkl_rt -L/opt/intel/mkl/lib/intel64 -L/opt/intel/lib/intel64

#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, p, 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, p = 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, p, p, 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*p*sizeof( double ), 64 );
    B = (double *)mkl_malloc( p*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*p); i++) {
        A[i] = (double)(i+1);
    }
 
    for (i = 0; i < (p*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, p, alpha, A, p, 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(p,6); j++) {
            printf ("%12.0f", A[j+i*p]);
        }
        printf ("\n");
    }
 
    printf ("\n Top left corner of matrix B: \n");
    for (i=0; i<min(p,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;
}