I have the CSR coordinates of a matrix.
/* 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");
Now I want to apply the mkl_dcsrmm function to compute C := alpha*A*B + beta*C with 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);
Since now I have.
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);
I found some difficulties on filling the inputs. Could you please help me to fill the rest of the inputs?
A specific input for which I want to go in more details is the matdescra. I borrowed the following code from cspblas_ccsr example
char matdescra[6]; matdescra[0] = 'g'; matdescra[1] = 'l'; matdescra[2] = 'n'; matdescra[3] = 'c';
but I have some questions about that. The matrix A I am working is not triangular and the initialization of this char array engage you to make such a declaration, how should I configure the parameters of the matdescra array?
Here is what I use, and what works for me.
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. */
Complete Example
Here is a complete example. I first create a random matrix by filling a dense matrix with a specified density of Non-Zero elements. Then I convert it to a sparse matrix in CSR-format. Finally, I do the multiplication using mkl_dcsrmm. As a possible check (check not done), I do the same multiplication using the cblas_dgemm function with the dense matrix.
#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; }
