这是我使用的，以及对我有用的东西。

char matdescra[6] = {'g', 'l', 'n', 'c', 'x', 'x'};

/* https://software.intel.com/sites/products/documentation/hpc/mkl/mklman/GUID-34C8DB79-0139-46E0-8B53-99F3BEE7B2D4.htm#TBL2-6

G: General. D: Diagonal

L/U Lower/Upper triangular (ignored with G)

N: non-unit diagonal (ignored with G)

C: zero-based indexing.

*/

完整的例子

下面是一个完整的例子。我首先通过用指定密度的非零元素填充密集矩阵来创建一个随机矩阵。然后我将它转换为CSR格式的稀疏矩阵。最后，我使用mkl_dcsrmm来进行乘法运算。作为可能的检查(检查未完成)，我使用密集矩阵使用cblas_dgemm函数进行相同的乘法运算。

#include "mkl.h"

#include "mkl_spblas.h"

#include // For NULL

#include // for rand()

#include

#include

#include

// Compute C = A * B; where A is sparse and B is dense.

int main() {

MKL_INT m=10, n=5, k=11;

const double sparsity = 0.9; ///< @param sparsity Values below which are set to zero (sampled from uniform(0,1)-distribution).

double *A_dense;

double *B;

double *C;

double alpha = 1.0;

double beta = 0.0;

const int allignment = 64;

// Seed the RNG to always be the same

srand(42);

// Allocate memory to matrices

A_dense = (double *)mkl_malloc(m*k*sizeof(double), allignment);

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

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

if (A_dense == NULL || B == NULL || C == NULL) {

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

mkl_free(A_dense);

mkl_free(B);

mkl_free(C);

return 1;

}

// Initializing matrix data

int i;

int nzmax = 0;

for (i = 0; i < (m*k); i++) {

double val = rand()/(double)RAND_MAX;

if (val < sparsity) {

A_dense[i] = 0.0;

} else {

A_dense[i] = val;

nzmax++;

}

}

for (i = 0; i < (k*n); i++) {

B[i] = rand();

}

for (i = 0; i < (m*n); i++) {

C[i] = 0.0;

}

// Convert A to a sparse matrix in CSR format.

// INFO: https://software.intel.com/sites/products/documentation/hpc/mkl/mklman/GUID-AD67DD8D-4C22-4232-8D3F-AF97DC2ABBC8.htm#GUID-AD67DD8D-4C22-4232-8D3F-AF97DC2ABBC8

MKL_INT job[6];

job[0] = 0; // convert TO CSR.

job[1] = 0; // Zero-based indexing for input.

job[2] = 0; // Zero-based indexing for output.

job[3] = 2; // adns is a whole matrix A.

job[4] = nzmax; // Maximum number of non-zero elements allowed.

job[5] = 3; // all 3 arays are generated for output.

/* JOB: conversion parameters

* m: number of rows of A.

* k: number of columns of A.

* adns: (input/output). Array containing non-zero elements of the matrix A.

* lda: specifies the leading dimension of adns. must be at least max(1, m).

* acsr: (input/output) array containing non-zero elements of the matrix A.

* ja: array containing the column indices.

* ia length m+1, rowIndex.

* OUTPUT:

* info: 0 if successful. i if interrupted at i-th row because of lack of space.

*/

int info = -1;

printf("nzmax:\t %d\n", nzmax);

double *A_sparse = mkl_malloc(nzmax * sizeof(double), allignment);

if (A_sparse == NULL) {

printf("ERROR: Could not allocate enough space to A_sparse.\n");

return 1;

}

MKL_INT *A_sparse_cols = mkl_malloc(nzmax * sizeof(MKL_INT), allignment);

if (A_sparse_cols == NULL) {

printf("ERROR: Could not allocate enough space to A_sparse_cols.\n");

return 1;

}

MKL_INT *A_sparse_rowInd = mkl_malloc((m+1) * sizeof(MKL_INT), allignment);

if (A_sparse_rowInd == NULL) {

printf("ERROR: Could not allocate enough space to A_sparse_rowInd.\n");

return 1;

}

mkl_ddnscsr(job, &m, &k, A_dense, &k, A_sparse, A_sparse_cols, A_sparse_rowInd, &info);

if(info != 0) {

printf("WARNING: info=%d, expected 0.\n", info);

}

assert(info == 0);

char transa = 'n';

MKL_INT ldb = n, ldc=n;

char matdescra[6] = {'g', 'l', 'n', 'c', 'x', 'x'};

/* https://software.intel.com/sites/products/documentation/hpc/mkl/mklman/GUID-34C8DB79-0139-46E0-8B53-99F3BEE7B2D4.htm#TBL2-6

G: General. D: Diagonal

L/U Lower/Upper triangular (ignored with G)

N: non-unit diagonal (ignored with G)

C: zero-based indexing.

*/

mkl_dcsrmm(&transa, &m, &n, &m, &alpha, matdescra, A_sparse, A_sparse_cols,

A_sparse_rowInd, &(A_sparse_rowInd[1]), B, &ldb, &beta, C, &ldc);

// The same computation in dense format

cblas_dgemm(CblasRowMajor, CblasNoTrans, CblasNoTrans,

m, n, k, alpha, A_dense, k, B, n, beta, C, n);

mkl_free(A_dense);

mkl_free(A_sparse);

mkl_free(A_sparse_cols);

mkl_free(A_sparse_rowInd);

mkl_free(B);

mkl_free(C);

return 0;

}