我有矩阵的CSR坐标。
/* alloc space for COO matrix */
int *coo_rows = (int*) malloc(K.n_rows * sizeof(int));
int *coo_cols = (int*) malloc(K.n_rows * sizeof(int));
float *coo_vals = (float*) malloc(K.n_rows * sizeof(float));
/*Load coo values*/
int *rowptrs = (int*) malloc((N_unique+1)*sizeof(int));
int *colinds = (int*) malloc(K.n_rows *sizeof(int));
double *vals = (double*) malloc(K.n_rows *sizeof(double));
/* take csr values */
int job[] = {
2, // job(1)=2 (coo->csr with sorting)
0, // job(2)=1 (one-based indexing for csr matrix)
0, // job(3)=1 (one-based indexing for coo matrix)
0, // empty
n1, // job(5)=nnz (sets nnz for csr matrix)
0 // job(6)=0 (all output arrays filled)
};
int info;
mkl_scsrcoo(job, &n, vals, colinds, rowptrs, &n1, coo_vals, coo_rows, coo_cols, &info);
assert(info == 0 && "Converted COO->CSR");
现在，我想将mkl_dcsrmm函数应用于C := alpha*A*B + beta*C来计算beta = 0;
/* function declaration */
void mkl_dcsrmm (char *transa, MKL_INT *m, MKL_INT *n, MKL_INT *k, double *alpha, char *matdescra, double *val, MKL_INT *indx, MKL_INT *pntrb, MKL_INT *pntre, double *b, MKL_INT *ldb, double *beta, double *c, MKL_INT *ldc);
从现在开始。
int A_rows = ..., A_cols = ..., C_cols = ...
double alpha = 1.0;
mkl_dcsrmm ((char*)"N", &A_rows, &C_cols, &A_cols, &alpha, char *matdescra, vals, coo_cols, rowptrs, colinds , double *b, MKL_INT *ldb, double *beta, double *c, MKL_INT *ldc);
我发现填写输入有些困难。你能帮我填写剩下的输入吗？
我想要了解更详细的具体输入是matdescra。我从cspblas_ccsr示例
char matdescra[6];
matdescra[0] = 'g';
matdescra[1] = 'l';
matdescra[2] = 'n';
matdescra[3] = 'c';
但我对此有一些疑问。我工作的矩阵A不是三角形的，这个char数组的初始化会让你做出这样的声明，我应该如何配置matdescra数组的参数？
答案 0 :(得分：3)
以下是我使用的内容，以及适用于我的内容。
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.
*/
这是一个完整的例子。我首先通过填充具有指定密度的非零元素的密集矩阵来创建随机矩阵。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），我使用密集矩阵的cblas_dgemm函数进行相同的乘法。
#include "mkl.h"
#include "mkl_spblas.h"
#include <stddef.h> // For NULL
#include <stdlib.h> // for rand()
#include <assert.h>
#include <stdio.h>
#include <limits.h>
// 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;
}