mirror of
https://github.com/NVIDIA/cuda-samples.git
synced 2024-12-01 10:59:16 +08:00
401 lines
13 KiB
C++
401 lines
13 KiB
C++
/*
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* Copyright 2015 NVIDIA Corporation. All rights reserved.
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*
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* Please refer to the NVIDIA end user license agreement (EULA) associated
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* with this source code for terms and conditions that govern your use of
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* this software. Any use, reproduction, disclosure, or distribution of
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* this software and related documentation outside the terms of the EULA
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* is strictly prohibited.
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*
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*/
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#include <stdio.h>
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#include <stdlib.h>
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#include <string.h>
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#include <ctype.h>
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#include <assert.h>
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#include "cusolverSp.h"
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#include "cusolverSp_LOWLEVEL_PREVIEW.h"
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#include <cuda_runtime.h>
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#include "helper_cuda.h"
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#include "helper_cusolver.h"
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template <typename T_ELEM>
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int loadMMSparseMatrix(
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char *filename,
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char elem_type,
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bool csrFormat,
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int *m,
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int *n,
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int *nnz,
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T_ELEM **aVal,
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int **aRowInd,
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int **aColInd,
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int extendSymMatrix);
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void UsageSP(void)
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{
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printf( "<options>\n");
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printf( "-h : display this help\n");
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printf( "-file=<filename> : filename containing a matrix in MM format\n");
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printf( "-device=<device_id> : <device_id> if want to run on specific GPU\n");
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exit( 0 );
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}
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void parseCommandLineArguments(int argc, char *argv[], struct testOpts &opts)
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{
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memset(&opts, 0, sizeof(opts));
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if (checkCmdLineFlag(argc, (const char **)argv, "-h"))
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{
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UsageSP();
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}
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if (checkCmdLineFlag(argc, (const char **)argv, "file"))
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{
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char *fileName = 0;
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getCmdLineArgumentString(argc, (const char **)argv, "file", &fileName);
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if (fileName)
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{
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opts.sparse_mat_filename = fileName;
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}
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else
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{
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printf("\nIncorrect filename passed to -file \n ");
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UsageSP();
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}
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}
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}
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int main (int argc, char *argv[])
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{
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struct testOpts opts;
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cusolverSpHandle_t cusolverSpH = NULL; // reordering, permutation and 1st LU factorization
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cusparseHandle_t cusparseH = NULL; // residual evaluation
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cudaStream_t stream = NULL;
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cusparseMatDescr_t descrA = NULL; // A is a base-0 general matrix
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csrcholInfoHost_t h_info = NULL; // opaque info structure for LU with parital pivoting
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csrcholInfo_t d_info = NULL; // opaque info structure for LU with parital pivoting
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int rowsA = 0; // number of rows of A
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int colsA = 0; // number of columns of A
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int nnzA = 0; // number of nonzeros of A
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int baseA = 0; // base index in CSR format
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// CSR(A) from I/O
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int *h_csrRowPtrA = NULL; // <int> n+1
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int *h_csrColIndA = NULL; // <int> nnzA
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double *h_csrValA = NULL; // <double> nnzA
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double *h_x = NULL; // <double> n, x = A \ b
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double *h_b = NULL; // <double> n, b = ones(m,1)
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double *h_r = NULL; // <double> n, r = b - A*x
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size_t size_internal = 0;
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size_t size_chol = 0; // size of working space for csrlu
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void *buffer_cpu = NULL; // working space for Cholesky
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void *buffer_gpu = NULL; // working space for Cholesky
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int *d_csrRowPtrA = NULL; // <int> n+1
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int *d_csrColIndA = NULL; // <int> nnzA
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double *d_csrValA = NULL; // <double> nnzA
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double *d_x = NULL; // <double> n, x = A \ b
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double *d_b = NULL; // <double> n, a copy of h_b
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double *d_r = NULL; // <double> n, r = b - A*x
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// the constants used in residual evaluation, r = b - A*x
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const double minus_one = -1.0;
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const double one = 1.0;
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// the constant used in cusolverSp
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// singularity is -1 if A is invertible under tol
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// tol determines the condition of singularity
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int singularity = 0;
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const double tol = 1.e-14;
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double x_inf = 0.0; // |x|
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double r_inf = 0.0; // |r|
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double A_inf = 0.0; // |A|
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int errors = 0;
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parseCommandLineArguments(argc, argv, opts);
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findCudaDevice(argc, (const char **)argv);
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if (opts.sparse_mat_filename == NULL)
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{
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opts.sparse_mat_filename = sdkFindFilePath("lap2D_5pt_n100.mtx", argv[0]);
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if (opts.sparse_mat_filename != NULL)
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printf("Using default input file [%s]\n", opts.sparse_mat_filename);
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else
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printf("Could not find lap2D_5pt_n100.mtx\n");
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}
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else
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{
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printf("Using input file [%s]\n", opts.sparse_mat_filename);
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}
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printf("step 1: read matrix market format\n");
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if (opts.sparse_mat_filename)
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{
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if (loadMMSparseMatrix<double>(opts.sparse_mat_filename, 'd', true , &rowsA, &colsA,
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&nnzA, &h_csrValA, &h_csrRowPtrA, &h_csrColIndA, true))
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{
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return 1;
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}
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baseA = h_csrRowPtrA[0]; // baseA = {0,1}
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}
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else
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{
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fprintf(stderr, "Error: input matrix is not provided\n");
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return 1;
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}
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if ( rowsA != colsA )
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{
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fprintf(stderr, "Error: only support square matrix\n");
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return 1;
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}
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printf("sparse matrix A is %d x %d with %d nonzeros, base=%d\n", rowsA, colsA, nnzA, baseA);
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checkCudaErrors(cusolverSpCreate(&cusolverSpH));
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checkCudaErrors(cusparseCreate(&cusparseH));
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checkCudaErrors(cudaStreamCreate(&stream));
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checkCudaErrors(cusolverSpSetStream(cusolverSpH, stream));
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checkCudaErrors(cusparseSetStream(cusparseH, stream));
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checkCudaErrors(cusparseCreateMatDescr(&descrA));
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checkCudaErrors(cusparseSetMatType(descrA, CUSPARSE_MATRIX_TYPE_GENERAL));
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if (baseA)
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{
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checkCudaErrors(cusparseSetMatIndexBase(descrA, CUSPARSE_INDEX_BASE_ONE));
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}
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else
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{
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checkCudaErrors(cusparseSetMatIndexBase(descrA, CUSPARSE_INDEX_BASE_ZERO));
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}
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h_x = (double*)malloc(sizeof(double)*colsA);
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h_b = (double*)malloc(sizeof(double)*rowsA);
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h_r = (double*)malloc(sizeof(double)*rowsA);
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assert(NULL != h_x);
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assert(NULL != h_b);
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assert(NULL != h_r);
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checkCudaErrors(cudaMalloc((void **)&d_csrRowPtrA, sizeof(int)*(rowsA+1)));
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checkCudaErrors(cudaMalloc((void **)&d_csrColIndA, sizeof(int)*nnzA));
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checkCudaErrors(cudaMalloc((void **)&d_csrValA , sizeof(double)*nnzA));
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checkCudaErrors(cudaMalloc((void **)&d_x, sizeof(double)*colsA));
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checkCudaErrors(cudaMalloc((void **)&d_b, sizeof(double)*rowsA));
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checkCudaErrors(cudaMalloc((void **)&d_r, sizeof(double)*rowsA));
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for(int row = 0 ; row < rowsA ; row++)
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{
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h_b[row] = 1.0;
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}
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printf("step 2: create opaque info structure\n");
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checkCudaErrors(cusolverSpCreateCsrcholInfoHost(&h_info));
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printf("step 3: analyze chol(A) to know structure of L\n");
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checkCudaErrors(cusolverSpXcsrcholAnalysisHost(
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cusolverSpH, rowsA, nnzA,
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descrA, h_csrRowPtrA, h_csrColIndA,
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h_info));
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printf("step 4: workspace for chol(A)\n");
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checkCudaErrors(cusolverSpDcsrcholBufferInfoHost(
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cusolverSpH, rowsA, nnzA,
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descrA, h_csrValA, h_csrRowPtrA, h_csrColIndA,
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h_info,
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&size_internal,
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&size_chol));
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if (buffer_cpu)
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{
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free(buffer_cpu);
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}
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buffer_cpu = (void*)malloc(sizeof(char)*size_chol);
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assert(NULL != buffer_cpu);
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printf("step 5: compute A = L*L^T \n");
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checkCudaErrors(cusolverSpDcsrcholFactorHost(
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cusolverSpH, rowsA, nnzA,
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descrA, h_csrValA, h_csrRowPtrA, h_csrColIndA,
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h_info,
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buffer_cpu));
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printf("step 6: check if the matrix is singular \n");
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checkCudaErrors(cusolverSpDcsrcholZeroPivotHost(
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cusolverSpH, h_info, tol, &singularity));
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if ( 0 <= singularity)
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{
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fprintf(stderr, "Error: A is not invertible, singularity=%d\n", singularity);
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return 1;
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}
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printf("step 7: solve A*x = b \n");
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checkCudaErrors(cusolverSpDcsrcholSolveHost(
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cusolverSpH, rowsA, h_b, h_x, h_info, buffer_cpu));
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printf("step 8: evaluate residual r = b - A*x (result on CPU)\n");
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// use GPU gemv to compute r = b - A*x
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checkCudaErrors(cudaMemcpy(d_csrRowPtrA, h_csrRowPtrA, sizeof(int)*(rowsA+1), cudaMemcpyHostToDevice));
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checkCudaErrors(cudaMemcpy(d_csrColIndA, h_csrColIndA, sizeof(int)*nnzA , cudaMemcpyHostToDevice));
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checkCudaErrors(cudaMemcpy(d_csrValA , h_csrValA , sizeof(double)*nnzA , cudaMemcpyHostToDevice));
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checkCudaErrors(cudaMemcpy(d_r, h_b, sizeof(double)*rowsA, cudaMemcpyHostToDevice));
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checkCudaErrors(cudaMemcpy(d_x, h_x, sizeof(double)*colsA, cudaMemcpyHostToDevice));
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/* Wrap raw data into cuSPARSE generic API objects */
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cusparseSpMatDescr_t matA = NULL;
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if (baseA)
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{
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checkCudaErrors(cusparseCreateCsr(
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&matA, rowsA, colsA, nnzA, d_csrRowPtrA, d_csrColIndA, d_csrValA, CUSPARSE_INDEX_32I,
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CUSPARSE_INDEX_32I, CUSPARSE_INDEX_BASE_ONE, CUDA_R_64F));
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}
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else
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{
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checkCudaErrors(cusparseCreateCsr(
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&matA, rowsA, colsA, nnzA, d_csrRowPtrA, d_csrColIndA, d_csrValA, CUSPARSE_INDEX_32I,
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CUSPARSE_INDEX_32I, CUSPARSE_INDEX_BASE_ZERO, CUDA_R_64F));
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}
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cusparseDnVecDescr_t vecx = NULL;
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checkCudaErrors(cusparseCreateDnVec(&vecx, colsA, d_x, CUDA_R_64F));
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cusparseDnVecDescr_t vecAx = NULL;
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checkCudaErrors(cusparseCreateDnVec(&vecAx, rowsA, d_r, CUDA_R_64F));
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/* Allocate workspace for cuSPARSE */
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size_t bufferSize = 0;
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checkCudaErrors(cusparseSpMV_bufferSize(
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cusparseH, CUSPARSE_OPERATION_NON_TRANSPOSE, &minus_one, matA, vecx,
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&one, vecAx, CUDA_R_64F, CUSPARSE_SPMV_ALG_DEFAULT, &bufferSize));
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void *buffer = NULL;
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checkCudaErrors(cudaMalloc(&buffer, bufferSize));
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checkCudaErrors(cusparseSpMV(
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cusparseH, CUSPARSE_OPERATION_NON_TRANSPOSE, &minus_one, matA, vecx,
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&one, vecAx, CUDA_R_64F, CUSPARSE_SPMV_ALG_DEFAULT, buffer));
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checkCudaErrors(cudaMemcpy(h_r, d_r, sizeof(double)*rowsA, cudaMemcpyDeviceToHost));
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x_inf = vec_norminf(colsA, h_x);
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r_inf = vec_norminf(rowsA, h_r);
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A_inf = csr_mat_norminf(rowsA, colsA, nnzA, descrA, h_csrValA, h_csrRowPtrA, h_csrColIndA);
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printf("(CPU) |b - A*x| = %E \n", r_inf);
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printf("(CPU) |A| = %E \n", A_inf);
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printf("(CPU) |x| = %E \n", x_inf);
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printf("(CPU) |b - A*x|/(|A|*|x|) = %E \n", r_inf/(A_inf * x_inf));
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printf("step 9: create opaque info structure\n");
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checkCudaErrors(cusolverSpCreateCsrcholInfo(&d_info));
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checkCudaErrors(cudaMemcpy(d_csrRowPtrA, h_csrRowPtrA, sizeof(int)*(rowsA+1), cudaMemcpyHostToDevice));
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checkCudaErrors(cudaMemcpy(d_csrColIndA, h_csrColIndA, sizeof(int)*nnzA , cudaMemcpyHostToDevice));
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checkCudaErrors(cudaMemcpy(d_csrValA , h_csrValA , sizeof(double)*nnzA , cudaMemcpyHostToDevice));
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checkCudaErrors(cudaMemcpy(d_b, h_b, sizeof(double)*rowsA, cudaMemcpyHostToDevice));
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printf("step 10: analyze chol(A) to know structure of L\n");
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checkCudaErrors(cusolverSpXcsrcholAnalysis(
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cusolverSpH, rowsA, nnzA,
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descrA, d_csrRowPtrA, d_csrColIndA,
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d_info));
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printf("step 11: workspace for chol(A)\n");
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checkCudaErrors(cusolverSpDcsrcholBufferInfo(
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cusolverSpH, rowsA, nnzA,
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descrA, d_csrValA, d_csrRowPtrA, d_csrColIndA,
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d_info,
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&size_internal,
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&size_chol));
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if (buffer_gpu) {
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checkCudaErrors(cudaFree(buffer_gpu));
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}
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checkCudaErrors(cudaMalloc(&buffer_gpu, sizeof(char)*size_chol));
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printf("step 12: compute A = L*L^T \n");
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checkCudaErrors(cusolverSpDcsrcholFactor(
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cusolverSpH, rowsA, nnzA,
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descrA, d_csrValA, d_csrRowPtrA, d_csrColIndA,
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d_info,
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buffer_gpu));
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printf("step 13: check if the matrix is singular \n");
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checkCudaErrors(cusolverSpDcsrcholZeroPivot(
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cusolverSpH, d_info, tol, &singularity));
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if ( 0 <= singularity){
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fprintf(stderr, "Error: A is not invertible, singularity=%d\n", singularity);
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return 1;
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}
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printf("step 14: solve A*x = b \n");
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checkCudaErrors(cusolverSpDcsrcholSolve(
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cusolverSpH, rowsA, d_b, d_x, d_info, buffer_gpu));
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checkCudaErrors(cudaMemcpy(d_r, h_b, sizeof(double)*rowsA, cudaMemcpyHostToDevice));
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checkCudaErrors(cusparseSpMV(
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cusparseH, CUSPARSE_OPERATION_NON_TRANSPOSE, &minus_one, matA, vecx,
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&one, vecAx, CUDA_R_64F, CUSPARSE_SPMV_ALG_DEFAULT, buffer));
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checkCudaErrors(cudaMemcpy(h_r, d_r, sizeof(double)*rowsA, cudaMemcpyDeviceToHost));
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r_inf = vec_norminf(rowsA, h_r);
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printf("(GPU) |b - A*x| = %E \n", r_inf);
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printf("(GPU) |b - A*x|/(|A|*|x|) = %E \n", r_inf/(A_inf * x_inf));
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if (cusolverSpH) { checkCudaErrors(cusolverSpDestroy(cusolverSpH)); }
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if (cusparseH ) { checkCudaErrors(cusparseDestroy(cusparseH)); }
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if (stream ) { checkCudaErrors(cudaStreamDestroy(stream)); }
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if (descrA ) { checkCudaErrors(cusparseDestroyMatDescr(descrA)); }
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if (h_info ) { checkCudaErrors(cusolverSpDestroyCsrcholInfoHost(h_info)); }
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if (d_info ) { checkCudaErrors(cusolverSpDestroyCsrcholInfo(d_info)); }
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if (matA ) { checkCudaErrors(cusparseDestroySpMat(matA)); }
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if (vecx ) { checkCudaErrors(cusparseDestroyDnVec(vecx)); }
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if (vecAx ) { checkCudaErrors(cusparseDestroyDnVec(vecAx)); }
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if (h_csrValA ) { free(h_csrValA); }
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if (h_csrRowPtrA) { free(h_csrRowPtrA); }
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if (h_csrColIndA) { free(h_csrColIndA); }
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if (h_x ) { free(h_x); }
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if (h_b ) { free(h_b); }
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if (h_r ) { free(h_r); }
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if (buffer_cpu) { free(buffer_cpu); }
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if (buffer_gpu) { checkCudaErrors(cudaFree(buffer_gpu)); }
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if (d_csrValA ) { checkCudaErrors(cudaFree(d_csrValA)); }
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if (d_csrRowPtrA) { checkCudaErrors(cudaFree(d_csrRowPtrA)); }
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if (d_csrColIndA) { checkCudaErrors(cudaFree(d_csrColIndA)); }
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if (d_x) { checkCudaErrors(cudaFree(d_x)); }
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if (d_b) { checkCudaErrors(cudaFree(d_b)); }
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if (d_r) { checkCudaErrors(cudaFree(d_r)); }
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return 0;
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}
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