本文整理汇总了C++中cusparseSetMatIndexBase函数的典型用法代码示例。如果您正苦于以下问题:C++ cusparseSetMatIndexBase函数的具体用法?C++ cusparseSetMatIndexBase怎么用?C++ cusparseSetMatIndexBase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cusparseSetMatIndexBase函数的15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,您的评价将有助于系统推荐出更棒的C++代码示例。
示例1: 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;
}
示例2: 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
示例3: CudaSparseSingleton
CudaSparseSingleton()
{
cusparseCreate( & handle );
cusparseCreateMatDescr( & descra );
cusparseSetMatType( descra , CUSPARSE_MATRIX_TYPE_GENERAL );
cusparseSetMatIndexBase( descra , CUSPARSE_INDEX_BASE_ZERO );
}
示例4: 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";
}
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);
}
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;
}
示例7: 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);
}
示例8: 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;
}
示例9: 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);
}
示例10: 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;
}
示例11: 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);
}
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) );
}
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;
}
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.........
示例15: 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.........