本文整理汇总了C++中cusparseSetMatType函数的典型用法代码示例。如果您正苦于以下问题:C++ cusparseSetMatType函数的具体用法?C++ cusparseSetMatType怎么用?C++ cusparseSetMatType使用的例子?那么恭喜您, 这里精选的函数代码示例或许可以为您提供帮助。
在下文中一共展示了cusparseSetMatType函数的20个代码示例,这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞,您的评价将有助于我们的系统推荐出更棒的C++代码示例。
示例1: xDense2Csr
xDense2Csr(StatisticalTimer& timer) : cusparseFunc(timer)
{
cusparseStatus_t err = cusparseCreateMatDescr(&descrA);
CUDA_V_THROW(err, "cusparseCreateMatDescr failed");
err = cusparseSetMatType(descrA, CUSPARSE_MATRIX_TYPE_GENERAL);
CUDA_V_THROW(err, "cusparseSetMatType failed");
err = cusparseSetMatIndexBase(descrA, CUSPARSE_INDEX_BASE_ZERO);
CUDA_V_THROW(err, "cusparseSetMatIndexBase failed");
n_rows = 0;
n_cols = 0;
n_vals = 0;
device_col_indices = nullptr;
device_row_offsets = nullptr;
device_values = nullptr;
device_A = nullptr;
nnzPerRow = nullptr;
devRowOffsets = nullptr;
devColIndices = nullptr;
devValues = nullptr;
}// end
开发者ID:nagyist,项目名称:clSPARSE,代码行数:26,代码来源:cufunc_xDense2Csr.hpp
示例2: fprintf
// initialize CUDA
ssp_cuda *ssp_init_cuda() {
ssp_cuda *cudaHandle = (ssp_cuda*)malloc(sizeof(ssp_cuda));
if (!cudaHandle) {
fprintf(stderr,"ssp_init_cuda: cudaHandle memory allocation failed.\n");
return NULL;
}
cudaHandle->cusparse_handle = 0;
cudaHandle->cusparse_matDescr = 0;
cusparseStatus_t status = cusparseCreate(&cudaHandle->cusparse_handle);
if (status != CUSPARSE_STATUS_SUCCESS) {
ssp_finalize_cuda(cudaHandle);
fprintf(stderr,"ssp_init_cuda: cusparse initialization failed.\n");
return NULL;
}
status = cusparseCreateMatDescr(&cudaHandle->cusparse_matDescr);
if (status != CUSPARSE_STATUS_SUCCESS) {
ssp_finalize_cuda(cudaHandle);
fprintf(stderr,"ssp_init_cuda: cusparse matrix setup failed.\n");
return NULL;
}
cusparseSetMatType(cudaHandle->cusparse_matDescr,CUSPARSE_MATRIX_TYPE_GENERAL);
cusparseSetMatIndexBase(cudaHandle->cusparse_matDescr,CUSPARSE_INDEX_BASE_ZERO);
return cudaHandle;
}
开发者ID:nefan,项目名称:ssparse,代码行数:32,代码来源:ssp_cuda.cpp
示例3: cublas_handle_
Caffe::Caffe()
: cublas_handle_(NULL),cusparse_handle_(NULL),cusparse_descr_(NULL),curand_generator_(NULL),random_generator_(),mode_(Caffe::CPU), solver_count_(1), root_solver_(true){
// Try to create a cublas handler, and report an error if failed (but we will
// keep the program running as one might just want to run CPU code).
LOG(INFO)<<"caffe init.";
if (cublasCreate(&cublas_handle_) != CUBLAS_STATUS_SUCCESS) {
LOG(ERROR) << "Cannot create Cublas handle. Cublas won't be available.";
}
//add cusparse handler
if (cusparseCreate(&cusparse_handle_)!=CUSPARSE_STATUS_SUCCESS){
LOG(ERROR) << "cannot create Cusparse handle,Cusparse won't be available.";
}
if(cusparseCreateMatDescr(&cusparse_descr_)!=CUSPARSE_STATUS_SUCCESS){
LOG(ERROR) << "cannot create Cusparse descr,descr won't be available.";
}else{
cusparseSetMatType(cusparse_descr_,CUSPARSE_MATRIX_TYPE_GENERAL);
cusparseSetMatIndexBase(cusparse_descr_,CUSPARSE_INDEX_BASE_ZERO);
LOG(INFO)<<"init descr";
}
// Try to create a curand handler.
if (curandCreateGenerator(&curand_generator_, CURAND_RNG_PSEUDO_DEFAULT)
!= CURAND_STATUS_SUCCESS ||
curandSetPseudoRandomGeneratorSeed(curand_generator_, cluster_seedgen())
!= CURAND_STATUS_SUCCESS) {
LOG(ERROR) << "Cannot create Curand generator. Curand won't be available.";
}
LOG(INFO)<<"caffe finish";
}
开发者ID:ZhouYuSong,项目名称:caffe-pruned,代码行数:28,代码来源:common.cpp
示例4: CudaSparseSingleton
CudaSparseSingleton()
{
cusparseCreate( & handle );
cusparseCreateMatDescr( & descra );
cusparseSetMatType( descra , CUSPARSE_MATRIX_TYPE_GENERAL );
cusparseSetMatIndexBase( descra , CUSPARSE_INDEX_BASE_ZERO );
}
开发者ID:ProgramFan,项目名称:kokkos,代码行数:7,代码来源:SparseLinearSystem.hpp
示例5: cuSparseHandleType
cuSparseHandleType(bool transposeA, bool transposeB){
cusparseStatus_t status;
status= cusparseCreate(&handle);
if (status != CUSPARSE_STATUS_SUCCESS) {
std::cerr << ("cusparseCreate ERROR") << std::endl;
return;
}
cusparseSetPointerMode(handle, CUSPARSE_POINTER_MODE_HOST);
if (transposeA){
transA = CUSPARSE_OPERATION_TRANSPOSE;
}
else {
transA = CUSPARSE_OPERATION_NON_TRANSPOSE;
}
if (transposeB){
transB = CUSPARSE_OPERATION_TRANSPOSE;
}
else {
transB = CUSPARSE_OPERATION_NON_TRANSPOSE;
}
status = cusparseCreateMatDescr(&a_descr);
if (status != CUSPARSE_STATUS_SUCCESS) {
std::cerr << "cusparseCreateMatDescr a_descr ERROR" << std::endl;
return;
}
cusparseSetMatType(a_descr,CUSPARSE_MATRIX_TYPE_GENERAL);
cusparseSetMatIndexBase(a_descr,CUSPARSE_INDEX_BASE_ZERO);
status = cusparseCreateMatDescr(&b_descr);
if (status != CUSPARSE_STATUS_SUCCESS) {
std::cerr << ("cusparseCreateMatDescr b_descr ERROR") << std::endl;
return;
}
cusparseSetMatType(b_descr,CUSPARSE_MATRIX_TYPE_GENERAL);
cusparseSetMatIndexBase(b_descr,CUSPARSE_INDEX_BASE_ZERO);
status = cusparseCreateMatDescr(&c_descr);
if (status != CUSPARSE_STATUS_SUCCESS) {
std::cerr << ("cusparseCreateMatDescr c_descr ERROR") << std::endl;
return;
}
cusparseSetMatType(c_descr,CUSPARSE_MATRIX_TYPE_GENERAL);
cusparseSetMatIndexBase(c_descr,CUSPARSE_INDEX_BASE_ZERO);
}
开发者ID:crtrott,项目名称:Trilinos,代码行数:47,代码来源:KokkosKernels_SPGEMMHandle.hpp
示例6: magma_dapplycuicc_l
magma_int_t
magma_dapplycuicc_l( magma_d_vector b, magma_d_vector *x,
magma_d_preconditioner *precond ){
double one = MAGMA_D_MAKE( 1.0, 0.0);
// CUSPARSE context //
cusparseHandle_t cusparseHandle;
cusparseStatus_t cusparseStatus;
cusparseStatus = cusparseCreate(&cusparseHandle);
if(cusparseStatus != 0) printf("error in Handle.\n");
cusparseMatDescr_t descrL;
cusparseStatus = cusparseCreateMatDescr(&descrL);
if(cusparseStatus != 0) printf("error in MatrDescr.\n");
cusparseStatus =
cusparseSetMatType(descrL,CUSPARSE_MATRIX_TYPE_TRIANGULAR);
if(cusparseStatus != 0) printf("error in MatrType.\n");
cusparseStatus =
cusparseSetMatDiagType (descrL, CUSPARSE_DIAG_TYPE_NON_UNIT);
if(cusparseStatus != 0) printf("error in DiagType.\n");
cusparseStatus =
cusparseSetMatFillMode(descrL,CUSPARSE_FILL_MODE_LOWER);
if(cusparseStatus != 0) printf("error in fillmode.\n");
cusparseStatus =
cusparseSetMatIndexBase(descrL,CUSPARSE_INDEX_BASE_ZERO);
if(cusparseStatus != 0) printf("error in IndexBase.\n");
// end CUSPARSE context //
cusparseStatus =
cusparseDcsrsv_solve( cusparseHandle,
CUSPARSE_OPERATION_NON_TRANSPOSE,
precond->M.num_rows, &one,
descrL,
precond->M.val,
precond->M.row,
precond->M.col,
precond->cuinfoL,
b.val,
x->val );
if(cusparseStatus != 0) printf("error in L triangular solve:%p.\n", precond->cuinfoL );
cusparseDestroyMatDescr( descrL );
cusparseDestroy( cusparseHandle );
magma_device_sync();
return MAGMA_SUCCESS;
}
开发者ID:XapaJIaMnu,项目名称:magma,代码行数:58,代码来源:dcuilu.cpp
示例7: descrA
sparse_matrix::sparse_matrix(sparse_matrix::descriptor_t descriptor,
int rows, int cols, int nonzeros,
const double* values, const int* col_ptr, const int* row_ind)
: descrA(), m(), n(), nnz(), csrValA(), csrRowPtrA(), csrColIndA()
{
// Create descriptor
assert(cusparseCreateMatDescr(&descrA) == cudaSuccess);
assert(cusparseSetMatIndexBase(descrA, CUSPARSE_INDEX_BASE_ZERO) == cudaSuccess);
// Set descriptor fields
switch (descriptor) {
case non_symmetric:
assert(cusparseSetMatDiagType(descrA, CUSPARSE_DIAG_TYPE_NON_UNIT) == cudaSuccess);
assert(cusparseSetMatType (descrA, CUSPARSE_MATRIX_TYPE_GENERAL) == cudaSuccess);
assert(cusparseSetMatFillMode(descrA, CUSPARSE_FILL_MODE_LOWER) == cudaSuccess); // doesn't matter which, presumably
break;
case symmetric_lower:
assert(cusparseSetMatDiagType(descrA, CUSPARSE_DIAG_TYPE_NON_UNIT) == cudaSuccess);
assert(cusparseSetMatType (descrA, CUSPARSE_MATRIX_TYPE_SYMMETRIC) == cudaSuccess);
assert(cusparseSetMatFillMode(descrA, CUSPARSE_FILL_MODE_UPPER) == cudaSuccess); // upper since we're coming with CSC and storing as CSR
break;
case symmetric_upper:
assert(cusparseSetMatDiagType(descrA, CUSPARSE_DIAG_TYPE_NON_UNIT) == cudaSuccess);
assert(cusparseSetMatType (descrA, CUSPARSE_MATRIX_TYPE_SYMMETRIC) == cudaSuccess);
assert(cusparseSetMatFillMode(descrA, CUSPARSE_FILL_MODE_LOWER) == cudaSuccess); // lower since we're coming with CSC and storing as CSR
break;
}
// Switch rows and cols becuase we're coming with CSC and storing as CSR
n = rows;
m = cols;
nnz = nonzeros;
// Allocate memory
assert(cudaMalloc(reinterpret_cast<void**>(&csrValA), nnz * sizeof(double)) == cudaSuccess);
assert(cudaMalloc(reinterpret_cast<void**>(&csrRowPtrA), (m+1) * sizeof(int)) == cudaSuccess);
assert(cudaMalloc(reinterpret_cast<void**>(&csrColIndA), nnz * sizeof(int)) == cudaSuccess);
// Copy values
assert(cudaMemcpy(csrValA, values, nnz * sizeof(double), cudaMemcpyHostToDevice) == cudaSuccess);
assert(cudaMemcpy(csrRowPtrA, col_ptr, (m+1) * sizeof(int), cudaMemcpyHostToDevice) == cudaSuccess);
assert(cudaMemcpy(csrColIndA, row_ind, nnz * sizeof(int), cudaMemcpyHostToDevice) == cudaSuccess);
}
开发者ID:m-farquhar,项目名称:GPUMatfun,代码行数:43,代码来源:cusparse_wrapper.cpp
示例8: spmat_hyb
spmat_hyb(
const command_queue &queue,
int n, int m,
const row_t *row_begin,
const col_t *col_begin,
const val_t *val_begin
)
: handle( cusparse_handle(queue) ),
desc ( create_description(), detail::deleter() ),
mat ( create_matrix(), detail::deleter() )
{
cuda_check( cusparseSetMatType(desc.get(), CUSPARSE_MATRIX_TYPE_GENERAL) );
cuda_check( cusparseSetMatIndexBase(desc.get(), CUSPARSE_INDEX_BASE_ZERO) );
fill_matrix(queue, n, m, row_begin, col_begin, val_begin);
}
开发者ID:mariomulansky,项目名称:vexcl,代码行数:16,代码来源:cusparse.hpp
示例9: xCsr2Dense
xCsr2Dense( StatisticalTimer& timer, bool read_explicit_zeroes = true ): cusparseFunc( timer )
{
cusparseStatus_t err = cusparseCreateMatDescr( &descrA );
CUDA_V_THROW( err, "cusparseCreateMatDescr failed" );
err = cusparseSetMatType( descrA, CUSPARSE_MATRIX_TYPE_GENERAL );
CUDA_V_THROW( err, "cusparseSetMatType failed" );
err = cusparseSetMatIndexBase( descrA, CUSPARSE_INDEX_BASE_ZERO );
CUDA_V_THROW( err, "cusparseSetMatIndexBase failed" );
n_rows = 0;
n_cols = 0;
n_vals = 0;
explicit_zeroes = read_explicit_zeroes;
}
开发者ID:10imaging,项目名称:clSPARSE,代码行数:16,代码来源:cufunc_xCsr2dense.hpp
示例10: CudaSparseSingleton
CudaSparseSingleton()
{
status = cusparseCreate(&handle);
if(status != CUSPARSE_STATUS_SUCCESS)
{
throw std::runtime_error( std::string("ERROR - CUSPARSE Library Initialization failed" ) );
}
status = cusparseCreateMatDescr(&descra);
if(status != CUSPARSE_STATUS_SUCCESS)
{
throw std::runtime_error( std::string("ERROR - CUSPARSE Library Matrix descriptor failed" ) );
}
cusparseSetMatType(descra , CUSPARSE_MATRIX_TYPE_GENERAL);
cusparseSetMatIndexBase(descra , CUSPARSE_INDEX_BASE_ZERO);
}
开发者ID:gitter-badger,项目名称:quinoa,代码行数:17,代码来源:Stokhos_Cuda_CrsMatrix.hpp
示例11: magma_capplycumicc_l
extern "C" magma_int_t
magma_capplycumicc_l(
magma_c_matrix b,
magma_c_matrix *x,
magma_c_preconditioner *precond,
magma_queue_t queue )
{
magma_int_t info = 0;
cusparseHandle_t cusparseHandle=NULL;
cusparseMatDescr_t descrL=NULL;
magmaFloatComplex one = MAGMA_C_MAKE( 1.0, 0.0);
// CUSPARSE context //
CHECK_CUSPARSE( cusparseCreate( &cusparseHandle ));
CHECK_CUSPARSE( cusparseSetStream( cusparseHandle, queue ));
CHECK_CUSPARSE( cusparseCreateMatDescr( &descrL ));
CHECK_CUSPARSE( cusparseSetMatType( descrL, CUSPARSE_MATRIX_TYPE_TRIANGULAR ));
CHECK_CUSPARSE( cusparseSetMatDiagType( descrL, CUSPARSE_DIAG_TYPE_NON_UNIT ));
CHECK_CUSPARSE( cusparseSetMatFillMode( descrL, CUSPARSE_FILL_MODE_LOWER ));
CHECK_CUSPARSE( cusparseSetMatIndexBase( descrL, CUSPARSE_INDEX_BASE_ZERO ));
CHECK_CUSPARSE( cusparseCcsrsm_solve( cusparseHandle,
CUSPARSE_OPERATION_NON_TRANSPOSE,
precond->M.num_rows,
b.num_rows*b.num_cols/precond->M.num_rows,
&one,
descrL,
precond->M.dval,
precond->M.drow,
precond->M.dcol,
precond->cuinfoL,
b.dval,
precond->M.num_rows,
x->dval,
precond->M.num_rows ));
magma_device_sync();
cleanup:
cusparseDestroyMatDescr( descrL );
cusparseDestroy( cusparseHandle );
return info;
}
开发者ID:cjy7117,项目名称:FT-MAGMA,代码行数:44,代码来源:ccumilu.cpp
示例12: spmat_crs
spmat_crs(
const command_queue &queue,
int n, int m,
const row_t *row_begin,
const col_t *col_begin,
const val_t *val_begin
)
: n(n), m(m), nnz(static_cast<unsigned>(row_begin[n] - row_begin[0])),
handle( cusparse_handle(queue) ),
desc ( create_description(), detail::deleter() ),
row(queue, n+1, row_begin),
col(queue, nnz, col_begin + row_begin[0]),
val(queue, nnz, val_begin + row_begin[0])
{
if (row_begin[0] != 0)
vector<int>(queue, row) -= row_begin[0];
cuda_check( cusparseSetMatType(desc.get(), CUSPARSE_MATRIX_TYPE_GENERAL) );
cuda_check( cusparseSetMatIndexBase(desc.get(), CUSPARSE_INDEX_BASE_ZERO) );
}
开发者ID:mariomulansky,项目名称:vexcl,代码行数:20,代码来源:cusparse.hpp
示例13: magma_ccustomicsetup
magma_int_t
magma_ccustomicsetup(
magma_c_matrix A,
magma_c_matrix b,
magma_c_preconditioner *precond,
magma_queue_t queue )
{
magma_int_t info = 0;
cusparseHandle_t cusparseHandle=NULL;
cusparseMatDescr_t descrL=NULL;
cusparseMatDescr_t descrU=NULL;
magma_c_matrix hA={Magma_CSR};
char preconditionermatrix[255];
snprintf( preconditionermatrix, sizeof(preconditionermatrix),
"/Users/hanzt0114cl306/work/matrices/ani/ani7_crop_ichol.mtx" );
CHECK( magma_c_csr_mtx( &hA, preconditionermatrix , queue) );
// for CUSPARSE
CHECK( magma_cmtransfer( hA, &precond->M, Magma_CPU, Magma_DEV , queue ));
// copy the matrix to precond->L and (transposed) to precond->U
CHECK( magma_cmtransfer(precond->M, &(precond->L), Magma_DEV, Magma_DEV, queue ));
CHECK( magma_cmtranspose( precond->L, &(precond->U), queue ));
// extract the diagonal of L into precond->d
CHECK( magma_cjacobisetup_diagscal( precond->L, &precond->d, queue ));
CHECK( magma_cvinit( &precond->work1, Magma_DEV, hA.num_rows, 1, MAGMA_C_ZERO, queue ));
// extract the diagonal of U into precond->d2
CHECK( magma_cjacobisetup_diagscal( precond->U, &precond->d2, queue ));
CHECK( magma_cvinit( &precond->work2, Magma_DEV, hA.num_rows, 1, MAGMA_C_ZERO, queue ));
// CUSPARSE context //
CHECK_CUSPARSE( cusparseCreate( &cusparseHandle ));
CHECK_CUSPARSE( cusparseCreateMatDescr( &descrL ));
CHECK_CUSPARSE( cusparseSetMatType( descrL, CUSPARSE_MATRIX_TYPE_TRIANGULAR ));
CHECK_CUSPARSE( cusparseSetMatDiagType( descrL, CUSPARSE_DIAG_TYPE_NON_UNIT ));
CHECK_CUSPARSE( cusparseSetMatIndexBase( descrL, CUSPARSE_INDEX_BASE_ZERO ));
CHECK_CUSPARSE( cusparseSetMatFillMode( descrL, CUSPARSE_FILL_MODE_LOWER ));
CHECK_CUSPARSE( cusparseCreateSolveAnalysisInfo( &precond->cuinfoL ));
CHECK_CUSPARSE( cusparseCcsrsv_analysis( cusparseHandle,
CUSPARSE_OPERATION_NON_TRANSPOSE, precond->M.num_rows,
precond->M.nnz, descrL,
precond->M.val, precond->M.row, precond->M.col, precond->cuinfoL ));
CHECK_CUSPARSE( cusparseCreateMatDescr( &descrU ));
CHECK_CUSPARSE( cusparseSetMatType( descrU, CUSPARSE_MATRIX_TYPE_TRIANGULAR ));
CHECK_CUSPARSE( cusparseSetMatDiagType( descrU, CUSPARSE_DIAG_TYPE_NON_UNIT ));
CHECK_CUSPARSE( cusparseSetMatIndexBase( descrU, CUSPARSE_INDEX_BASE_ZERO ));
CHECK_CUSPARSE( cusparseSetMatFillMode( descrU, CUSPARSE_FILL_MODE_LOWER ));
CHECK_CUSPARSE( cusparseCreateSolveAnalysisInfo( &precond->cuinfoU ));
CHECK_CUSPARSE( cusparseCcsrsv_analysis( cusparseHandle,
CUSPARSE_OPERATION_TRANSPOSE, precond->M.num_rows,
precond->M.nnz, descrU,
precond->M.val, precond->M.row, precond->M.col, precond->cuinfoU ));
cleanup:
cusparseDestroy( cusparseHandle );
cusparseDestroyMatDescr( descrL );
cusparseDestroyMatDescr( descrU );
cusparseHandle=NULL;
descrL=NULL;
descrU=NULL;
magma_cmfree( &hA, queue );
return info;
}
开发者ID:maxhutch,项目名称:magma,代码行数:74,代码来源:ccustomic.cpp
示例14: i
int TxMatrixOptimizationDataCU::ingestLocalMatrix(SparseMatrix& A) {
std::vector<local_int_t> i(A.localNumberOfRows + 1, 0);
// Slight overallocation for these arrays
std::vector<local_int_t> j;
j.reserve(A.localNumberOfNonzeros);
std::vector<double> a;
a.reserve(A.localNumberOfNonzeros);
scatterFromHalo.setNumRows(A.localNumberOfRows);
scatterFromHalo.setNumCols(A.localNumberOfColumns);
scatterFromHalo.clear();
// We're splitting the matrix into diagonal and off-diagonal block to
// enable overlapping of computation and communication.
i[0] = 0;
for (local_int_t m = 0; m < A.localNumberOfRows; ++m) {
local_int_t nonzerosInRow = 0;
for (local_int_t n = 0; n < A.nonzerosInRow[m]; ++n) {
local_int_t col = A.mtxIndL[m][n];
if (col < A.localNumberOfRows) {
j.push_back(col);
a.push_back(A.matrixValues[m][n]);
++nonzerosInRow;
} else {
scatterFromHalo.addEntry(m, col, A.matrixValues[m][n]);
}
}
i[m + 1] = i[m] + nonzerosInRow;
}
// Setup SpMV data on Device
cudaError_t err = cudaSuccess;
int* i_d;
err = cudaMalloc((void**)&i_d, i.size() * sizeof(i[0]));
CHKCUDAERR(err);
err = cudaMemcpy(i_d, &i[0], i.size() * sizeof(i[0]), cudaMemcpyHostToDevice);
CHKCUDAERR(err);
int* j_d;
err = cudaMalloc((void**)&j_d, j.size() * sizeof(j[0]));
CHKCUDAERR(err);
err = cudaMemcpy(j_d, &j[0], j.size() * sizeof(j[0]), cudaMemcpyHostToDevice);
CHKCUDAERR(err);
double* a_d;
err = cudaMalloc((void**)&a_d, a.size() * sizeof(a[0]));
CHKCUDAERR(err);
err = cudaMemcpy(a_d, &a[0], a.size() * sizeof(a[0]), cudaMemcpyHostToDevice);
CHKCUDAERR(err);
cusparseStatus_t cerr = CUSPARSE_STATUS_SUCCESS;
cerr = cusparseCreateMatDescr(&matDescr);
CHKCUSPARSEERR(cerr);
cerr = cusparseSetMatIndexBase(matDescr, CUSPARSE_INDEX_BASE_ZERO);
CHKCUSPARSEERR(cerr);
cerr = cusparseSetMatType(matDescr, CUSPARSE_MATRIX_TYPE_GENERAL);
CHKCUSPARSEERR(cerr);
cerr = cusparseCreateHybMat(&localMatrix);
CHKCUSPARSEERR(cerr);
cerr = cusparseDcsr2hyb(handle, A.localNumberOfRows, A.localNumberOfColumns,
matDescr, a_d, i_d, j_d, localMatrix, 27,
CUSPARSE_HYB_PARTITION_USER);
CHKCUSPARSEERR(cerr);
#ifndef HPCG_NOMPI
err = cudaMalloc((void**)&elementsToSend,
A.totalToBeSent * sizeof(*elementsToSend));
CHKCUDAERR(err);
err = cudaMemcpy(elementsToSend, A.elementsToSend,
A.totalToBeSent * sizeof(*elementsToSend),
cudaMemcpyHostToDevice);
CHKCUDAERR(err);
err = cudaMalloc((void**)&sendBuffer_d, A.totalToBeSent * sizeof(double));
CHKCUDAERR(err);
#endif
// Set up the GS data.
gelusStatus_t gerr = GELUS_STATUS_SUCCESS;
gelusSolveDescription_t solveDescr;
gerr = gelusCreateSolveDescr(&solveDescr);
CHKGELUSERR(gerr);
gerr = gelusSetSolveOperation(solveDescr, GELUS_OPERATION_NON_TRANSPOSE);
CHKGELUSERR(gerr);
gerr = gelusSetSolveFillMode(solveDescr, GELUS_FILL_MODE_FULL);
CHKGELUSERR(gerr);
gerr = gelusSetSolveStorageFormat(solveDescr, GELUS_STORAGE_FORMAT_HYB);
CHKGELUSERR(gerr);
gerr = gelusSetOptimizationLevel(solveDescr, GELUS_OPTIMIZATION_LEVEL_THREE);
CHKGELUSERR(gerr);
gerr = cugelusCreateSorIterationData(&gsContext);
CHKGELUSERR(gerr);
#ifdef HPCG_DEBUG
std::cout << A.localNumberOfRows << std::endl;
std::cout << A.localNumberOfColumns << std::endl;
std::cout << A.localNumberOfNonzeros << std::endl;
int myrank;
MPI_Comm_rank(MPI_COMM_WORLD, &myrank);
if (myrank == 0) {
dumpMatrix(std::cout, i, j, a);
}
#endif
gerr = cugelusDcsrsor_iteration_analysis(
//.........这里部分代码省略.........
开发者ID:NobodyInAmerica,项目名称:libTxHPCG,代码行数:101,代码来源:TxMatrixOptimizationDataCU.cpp
示例15: _tmain
int _tmain(int argc, _TCHAR* argv[])
{
int M = 0, N = 0, nz = 0, *I = NULL, *J = NULL;
cuDoubleComplex *val = NULL;
cuDoubleComplex *x, *y;
cuDoubleComplex *d_x, *d_y;
double duration, duration_setup;
std::clock_t setup_clock;
setup_clock = std::clock();
// This will pick the best possible CUDA capable device
cudaDeviceProp deviceProp;
int devID = findCudaDevice(argc, (const char **)argv);
if (devID < 0)
{
printf("no devices found...\n");
exit(EXIT_SUCCESS);
}
checkCudaErrors(cudaGetDeviceProperties(&deviceProp, devID));
// Statistics about the GPU device
printf("> GPU device has %d Multi-Processors, SM %d.%d compute capabilities\n\n",
deviceProp.multiProcessorCount, deviceProp.major, deviceProp.minor);
int version = (deviceProp.major * 0x10 + deviceProp.minor);
if (version < 0x11)
{
printf("Requires a minimum CUDA compute 1.1 capability\n");
cudaDeviceReset();
exit(EXIT_SUCCESS);
}
M = N = 8388608; //2 ^ 23
//M = N = 4194304; //2 ^ 22
//M = N = 2097152; //2 ^ 21
//M = N = 1048576; //2 ^ 20
//M = N = 524288; //2 ^ 19
nz = N * 8;
I = (int *)malloc(sizeof(int)*(N + 1));
J = (int *)malloc(sizeof(int)*nz);
val = (cuDoubleComplex *)malloc(sizeof(cuDoubleComplex)*nz);
genTridiag(I, J, val, N, nz);
x = (cuDoubleComplex*)malloc(sizeof(cuDoubleComplex)* N);
y = (cuDoubleComplex*)malloc(sizeof(cuDoubleComplex)* N);
//create an array for the answer array (Y) and set all of the answers to 0 for the test (could do random)
for (int i = 0; i < N; i++)
{
y[i] = make_cuDoubleComplex(0.0, 0.0);
}
//Get handle to the CUBLAS context
cublasHandle_t cublasHandle = 0;
cublasStatus_t cublasStatus;
cublasStatus = cublasCreate(&cublasHandle);
checkCudaErrors(cublasStatus);
//Get handle to the CUSPARSE context
cusparseHandle_t cusparseHandle = 0;
cusparseStatus_t cusparseStatus;
cusparseStatus = cusparseCreate(&cusparseHandle);
checkCudaErrors(cusparseStatus);
//Get handle to a CUSPARSE matrix descriptor
cusparseMatDescr_t descr = 0;
cusparseStatus = cusparseCreateMatDescr(&descr);
checkCudaErrors(cusparseStatus);
//Get handle to a matrix_solve_info object
cusparseSolveAnalysisInfo_t info = 0;
cusparseStatus = cusparseCreateSolveAnalysisInfo(&info);
checkCudaErrors(cusparseStatus);
cusparseSetMatType(descr, CUSPARSE_MATRIX_TYPE_GENERAL);
cusparseSetMatIndexBase(descr, CUSPARSE_INDEX_BASE_ZERO);
duration_setup = (std::clock() - setup_clock) / (double)CLOCKS_PER_SEC;
printf("setup_time: %f\r\n", duration_setup);
std::clock_t start;
start = std::clock();
checkCudaErrors(cudaMalloc((void **)&d_x, N*sizeof(float)));
checkCudaErrors(cudaMalloc((void **)&d_y, N*sizeof(float)));
cudaMemcpy(d_x, x, N*sizeof(float), cudaMemcpyHostToDevice);
cudaMemcpy(d_y, y, N*sizeof(float), cudaMemcpyHostToDevice);
//Analyze the matrix. The info variable is needed to perform additional operations on the matrix
cusparseStatus = cusparseZcsrsv_analysis(cusparseHandle, CUSPARSE_OPERATION_NON_TRANSPOSE, N, nz, descr, val, J, I, info);
//Uses infor gathered from the matrix to solve the matrix.
//.........这里部分代码省略.........
开发者ID:davidhauck,项目名称:MatrixSolver,代码行数:101,代码来源:CudaTest.cpp
示例16: main
int main(int argc, char **argv)
{
int N = 0, nz = 0, *I = NULL, *J = NULL;
float *val = NULL;
const float tol = 1e-5f;
const int max_iter = 10000;
float *x;
float *rhs;
float a, b, na, r0, r1;
float dot;
float *r, *p, *Ax;
int k;
float alpha, beta, alpham1;
printf("Starting [%s]...\n", sSDKname);
// This will pick the best possible CUDA capable device
cudaDeviceProp deviceProp;
int devID = findCudaDevice(argc, (const char **)argv);
checkCudaErrors(cudaGetDeviceProperties(&deviceProp, devID));
#if defined(__APPLE__) || defined(MACOSX)
fprintf(stderr, "Unified Memory not currently supported on OS X\n");
cudaDeviceReset();
exit(EXIT_WAIVED);
#endif
if (sizeof(void *) != 8)
{
fprintf(stderr, "Unified Memory requires compiling for a 64-bit system.\n");
cudaDeviceReset();
exit(EXIT_WAIVED);
}
if (((deviceProp.major << 4) + deviceProp.minor) < 0x30)
{
fprintf(stderr, "%s requires Compute Capability of SM 3.0 or higher to run.\nexiting...\n", argv[0]);
cudaDeviceReset();
exit(EXIT_WAIVED);
}
// Statistics about the GPU device
printf("> GPU device has %d Multi-Processors, SM %d.%d compute capabilities\n\n",
deviceProp.multiProcessorCount, deviceProp.major, deviceProp.minor);
/* Generate a random tridiagonal symmetric matrix in CSR format */
N = 1048576;
nz = (N-2)*3 + 4;
cudaMallocManaged((void **)&I, sizeof(int)*(N+1));
cudaMallocManaged((void **)&J, sizeof(int)*nz);
cudaMallocManaged((void **)&val, sizeof(float)*nz);
genTridiag(I, J, val, N, nz);
cudaMallocManaged((void **)&x, sizeof(float)*N);
cudaMallocManaged((void **)&rhs, sizeof(float)*N);
for (int i = 0; i < N; i++)
{
rhs[i] = 1.0;
x[i] = 0.0;
}
/* Get handle to the CUBLAS context */
cublasHandle_t cublasHandle = 0;
cublasStatus_t cublasStatus;
cublasStatus = cublasCreate(&cublasHandle);
checkCudaErrors(cublasStatus);
/* Get handle to the CUSPARSE context */
cusparseHandle_t cusparseHandle = 0;
cusparseStatus_t cusparseStatus;
cusparseStatus = cusparseCreate(&cusparseHandle);
checkCudaErrors(cusparseStatus);
cusparseMatDescr_t descr = 0;
cusparseStatus = cusparseCreateMatDescr(&descr);
checkCudaErrors(cusparseStatus);
cusparseSetMatType(descr,CUSPARSE_MATRIX_TYPE_GENERAL);
cusparseSetMatIndexBase(descr,CUSPARSE_INDEX_BASE_ZERO);
// temp memory for CG
checkCudaErrors(cudaMallocManaged((void **)&r, N*sizeof(float)));
checkCudaErrors(cudaMallocManaged((void **)&p, N*sizeof(float)));
checkCudaErrors(cudaMallocManaged((void **)&Ax, N*sizeof(float)));
cudaDeviceSynchronize();
for (int i=0; i < N; i++)
{
r[i] = rhs[i];
}
alpha = 1.0;
//.........这里部分代码省略.........
开发者ID:ziyuhe,项目名称:cuda_project,代码行数:101,代码来源:main.cpp
示例17: main
//.........这里部分代码省略.........
}
*/
cerr<<"Solving Equations "<<endl;
double r1, b, alpha, alpham1, beta, r0, a, na;
const double tol = 0.1;
const int max_iter = 1000000;
int *d_col, *d_row;
double *d_val, *d_x, dot;
double *d_r, *d_p, *d_Ax;
int k;
cublasHandle_t cublasHandle = 0;
cublasStatus_t cublasStatus;
cublasStatus = cublasCreate(&cublasHandle);
checkCudaErrors(cublasStatus);
/* Get handle to the CUSPARSE context */
cusparseHandle_t cusparseHandle = 0;
cusparseStatus_t cusparseStatus;
cusparseStatus = cusparseCreate(&cusparseHandle);
checkCudaErrors(cusparseStatus);
cusparseMatDescr_t descr = 0;
cusparseStatus = cusparseCreateMatDescr(&descr);
checkCudaErrors(cusparseStatus);
cusparseSetMatType(descr,CUSPARSE_MATRIX_TYPE_GENERAL);
cusparseSetMatIndexBase(descr,CUSPARSE_INDEX_BASE_ZERO);
checkCudaErrors(cudaMalloc((void **)&d_col, G.nonzero*sizeof(int)));
checkCudaErrors(cudaMalloc((void **)&d_row, (m+n+1)*sizeof(int)));
checkCudaErrors(cudaMalloc((void **)&d_val, G.nonzero*sizeof(double)));
checkCudaErrors(cudaMalloc((void **)&d_x, (m+n)*sizeof(double)));
checkCudaErrors(cudaMalloc((void **)&d_r, (m+n)*sizeof(double)));
checkCudaErrors(cudaMalloc((void **)&d_p, (m+n)*sizeof(double)));
checkCudaErrors(cudaMalloc((void **)&d_Ax, (m+n)*sizeof(double)));
cudaMemcpy(d_col, G.columns, G.nonzero*sizeof(int), cudaMemcpyHostToDevice);
cudaMemcpy(d_row, G.rowIndex, (m+n+1)*sizeof(int), cudaMemcpyHostToDevice);
cudaMemcpy(d_val, G.Mat_val, G.nonzero*sizeof(double), cudaMemcpyHostToDevice);
cudaMemcpy(d_x, G.x, (m+n)*sizeof(double), cudaMemcpyHostToDevice);
cudaMemcpy(d_r, G.b, (m+n)*sizeof(double), cudaMemcpyHostToDevice);
alpha = 1.0;
alpham1 = -1.0;
beta = 0.0;
r0 = 0.;
printf("\n Data transferred\n");
cudaEvent_t start,stop;
cudaEventCreate(&start);
cudaEventCreate(&stop);
cudaEventRecord(start, 0);
cusparseDcsrmv(cusparseHandle,CUSPARSE_OPERATION_NON_TRANSPOSE, (m+n), (m+n), G.nonzero, &alpha, descr, d_val, d_row, d_col, d_x, &beta, d_Ax);
cublasDaxpy(cublasHandle, (m+n), &alpham1, d_Ax, 1, d_r, 1);
开发者ID:gcoe-iitb,项目名称:MNA-based-power-grid-solver,代码行数:67,代码来源:main.cpp
示例18: magma_d_spmv
extern "C" magma_int_t
magma_d_spmv(
double alpha,
magma_d_matrix A,
magma_d_matrix x,
double beta,
magma_d_matrix y,
magma_queue_t queue )
{
magma_int_t info = 0;
magma_d_matrix x2={Magma_CSR};
cusparseHandle_t cusparseHandle = 0;
cusparseMatDescr_t descr = 0;
// make sure RHS is a dense matrix
if ( x.storage_type != Magma_DENSE ) {
printf("error: only dense vectors are supported for SpMV.\n");
info = MAGMA_ERR_NOT_SUPPORTED;
goto cleanup;
}
if ( A.memory_location != x.memory_location ||
x.memory_location != y.memory_location ) {
printf("error: linear algebra objects are not located in same memory!\n");
printf("memory locations are: %d %d %d\n",
A.memory_location, x.memory_location, y.memory_location );
info = MAGMA_ERR_INVALID_PTR;
goto cleanup;
}
// DEV case
if ( A.memory_location == Magma_DEV ) {
if ( A.num_cols == x.num_rows && x.num_cols == 1 ) {
if ( A.storage_type == Magma_CSR || A.storage_type == Magma_CUCSR
|| A.storage_type == Magma_CSRL
|| A.storage_type == Magma_CSRU ) {
CHECK_CUSPARSE( cusparseCreate( &cusparseHandle ));
CHECK_CUSPARSE( cusparseSetStream( cusparseHandle, queue->cuda_stream() ));
CHECK_CUSPARSE( cusparseCreateMatDescr( &descr ));
CHECK_CUSPARSE( cusparseSetMatType( descr, CUSPARSE_MATRIX_TYPE_GENERAL ));
CHECK_CUSPARSE( cusparseSetMatIndexBase( descr, CUSPARSE_INDEX_BASE_ZERO ));
cusparseDcsrmv( cusparseHandle,CUSPARSE_OPERATION_NON_TRANSPOSE,
A.num_rows, A.num_cols, A.nnz, &alpha, descr,
A.dval, A.drow, A.dcol, x.dval, &beta, y.dval );
}
else if ( A.storage_type == Magma_ELL ) {
//printf("using ELLPACKT kernel for SpMV: ");
CHECK( magma_dgeelltmv( MagmaNoTrans, A.num_rows, A.num_cols,
A.max_nnz_row, alpha, A.dval, A.dcol, x.dval, beta,
y.dval, queue ));
//printf("done.\n");
}
else if ( A.storage_type == Magma_ELLPACKT ) {
//printf("using ELL kernel for SpMV: ");
CHECK( magma_dgeellmv( MagmaNoTrans, A.num_rows, A.num_cols,
A.max_nnz_row, alpha, A.dval, A.dcol, x.dval, beta,
y.dval, queue ));
//printf("done.\n");
}
else if ( A.storage_type == Magma_ELLRT ) {
//printf("using ELLRT kernel for SpMV: ");
CHECK( magma_dgeellrtmv( MagmaNoTrans, A.num_rows, A.num_cols,
A.max_nnz_row, alpha, A.dval, A.dcol, A.drow, x.dval,
beta, y.dval, A.alignment, A.blocksize, queue ));
//printf("done.\n");
}
else if ( A.storage_type == Magma_SELLP ) {
//printf("using SELLP kernel for SpMV: ");
CHECK( magma_dgesellpmv( MagmaNoTrans, A.num_rows, A.num_cols,
A.blocksize, A.numblocks, A.alignment,
alpha, A.dval, A.dcol, A.drow, x.dval, beta, y.dval, queue ));
//printf("done.\n");
}
else if ( A.storage_type == Magma_DENSE ) {
//printf("using DENSE kernel for SpMV: ");
magmablas_dgemv( MagmaNoTrans, A.num_rows, A.num_cols, alpha,
A.dval, A.num_rows, x.dval, 1, beta, y.dval,
1, queue );
//printf("done.\n");
}
else if ( A.storage_type == Magma_SPMVFUNCTION ) {
//printf("using DENSE kernel for SpMV: ");
CHECK( magma_dcustomspmv( alpha, x, beta, y, queue ));
//printf("done.\n");
}
else if ( A.storage_type == Magma_BCSR ) {
//printf("using CUSPARSE BCSR kernel for SpMV: ");
// CUSPARSE context //
cusparseDirection_t dirA = CUSPARSE_DIRECTION_ROW;
int mb = magma_ceildiv( A.num_rows, A.blocksize );
int nb = magma_ceildiv( A.num_cols, A.blocksize );
CHECK_CUSPARSE( cusparseCreate( &cusparseHandle ));
CHECK_CUSPARSE( cusparseSetStream( cusparseHandle, queue->cuda_stream() ));
CHECK_CUSPARSE( cusparseCreateMatDescr( &descr ));
cusparseDbsrmv( cusparseHandle, dirA,
CUSPARSE_OPERATION_NON_TRANSPOSE, mb, nb, A.numblocks,
//.........这里部分代码省略.........
开发者ID:xulunfan,项目名称:magma,代码行数:101,代码来源:magma_d_blaswrapper.cpp
示例19: main
//.........这里部分代码省略.........
val = (float *)malloc(sizeof(float)*nz); // csr values for matrix A
x = (float *)malloc(sizeof(float)*N);
rhs = (float *)malloc(sizeof(float)*N);
for (int i = 0; i < N; i++)
{
rhs[i] = 0.0; // Initialize RHS
x[i] = 0.0; // Initial approximation of solution
}
genLaplace(I, J, val, M, N, nz, rhs);
/* Create CUBLAS context */
cublasHandle_t cublasHandle = 0;
cublasStatus_t cublasStatus;
cublasStatus = cublasCreate(&cublasHandle);
checkCudaErrors(cublasStatus);
/* Create CUSPARSE context */
cusparseHandle_t cusparseHandle = 0;
cusparseStatus_t cusparseStatus;
cusparseStatus = cusparseCreate(&cusparseHandle);
checkCudaErrors(cusparseStatus);
/* Description of the A matrix*/
cusparseMatDescr_t descr = 0;
cusparseStatus = cusparseCreateMatDescr(&descr);
checkCudaErrors(cusparseStatus);
/* Define the properties of the matrix */
cusparseSetMatType(descr,CUSPARSE_MATRIX_TYPE_GENERAL);
cusparseSetMatIndexBase(descr,CUSPARSE_INDEX_BASE_ZERO);
/* Allocate required memory */
checkCudaErrors(cudaMalloc((void **)&d_col, nz*sizeof(int)));
checkCudaErrors(cudaMalloc((void **)&d_row, (N+1)*sizeof(int)));
checkCudaErrors(cudaMalloc((void **)&d_val, nz*sizeof(float)));
checkCudaErrors(cudaMalloc((void **)&d_x, N*sizeof(float)));
checkCudaErrors(cudaMalloc((void **)&d_y, N*sizeof(float)));
checkCudaErrors(cudaMalloc((void **)&d_r, N*sizeof(float)));
checkCudaErrors(cudaMalloc((void **)&d_p, N*sizeof(float)));
checkCudaErrors(cudaMalloc((void **)&d_omega, N*sizeof(float)));
cudaMemcpy(d_col, J, nz*sizeof(int), cudaMemcpyHostToDevice);
cudaMemcpy(d_row, I, (N+1)*sizeof(int), cudaMemcpyHostToDevice);
cudaMemcpy(d_val, val, nz*sizeof(float), cudaMemcpyHostToDevice);
cudaMemcpy(d_x, x, N*sizeof(float), cudaMemcpyHostToDevice);
cudaMemcpy(d_r, rhs, N*sizeof(float), cudaMemcpyHostToDevice);
/* Conjugate gradient without preconditioning.
------------------------------------------
Follows the description by Golub & Van Loan, "Matrix Computations 3rd ed.", Section 10.2.6 */
printf("Convergence of conjugate gradient without preconditioning: \n");
k = 0;
r0 = 0;
cublasSdot(cublasHandle, N, d_r, 1, d_r, 1, &r1);
while (r1 > tol*tol && k <= max_iter)
{
k++;
if (k == 1)
开发者ID:drolfe00,项目名称:CUDAVerificationkernels,代码行数:67, |
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