mirror of
https://github.com/OPM/opm-simulators.git
synced 2025-02-25 18:55:30 -06:00
Added some whitespace around brackets in accordance with OPM suggested coding standard
This commit is contained in:
T.D. (Tongdong) Qiu
parent
950d1c92c1
commit
a491e64883
@ -251,13 +251,13 @@ protected:
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const double tolerance = EWOMS_GET_PARAM(TypeTag, double, LinearSolverReduction);
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const bool matrix_add_well_contributions = EWOMS_GET_PARAM(TypeTag, bool, MatrixAddWellContributions);
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const int linear_solver_verbosity = parameters_.linear_solver_verbosity_;
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if(use_gpu && !matrix_add_well_contributions){
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if (use_gpu && !matrix_add_well_contributions) {
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OPM_THROW(std::logic_error,"Error cannot use GPU solver if command line parameter --matrix-add-well-contributions is false, because the GPU solver performs a standard bicgstab");
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}
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bdaBridge.reset(new BdaBridge(use_gpu, linear_solver_verbosity, maxit, tolerance));
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#else
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const bool use_gpu = EWOMS_GET_PARAM(TypeTag, bool, UseGpu);
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if(use_gpu){
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if (use_gpu) {
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OPM_THROW(std::logic_error,"Error cannot use GPU solver since CUDA was not found during compilation");
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}
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#endif
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@ -462,13 +462,13 @@ protected:
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#if HAVE_CUDA
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bdaBridge->solve_system(matrix_.get(), istlb, result);
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if(result.converged){
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if (result.converged) {
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// get result vector x from non-Dune backend, iff solve was successful
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bdaBridge->get_result(x);
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}else{
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} else {
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// CPU fallback, or default case for Dune
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const bool use_gpu = EWOMS_GET_PARAM(TypeTag, bool, UseGpu);
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if(use_gpu){
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if (use_gpu) {
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OpmLog::warning("cusparseSolver did not converge, now trying Dune to solve current linear system...");
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}
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auto precond = constructPrecond(linearOperator, parallelInformation_arg);
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@ -37,7 +37,7 @@ namespace Opm
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BdaBridge::BdaBridge(bool use_gpu_, int linear_solver_verbosity OPM_UNUSED, int maxit OPM_UNUSED, double tolerance OPM_UNUSED) : use_gpu(use_gpu_) {
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#if HAVE_CUDA
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if(use_gpu){
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if (use_gpu) {
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backend.reset(new cusparseSolverBackend(linear_solver_verbosity, maxit, tolerance));
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}
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#endif
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@ -52,16 +52,16 @@ int checkZeroDiagonal(BridgeMatrix& mat) {
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int numZeros = 0;
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const int dim = 3;
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const double zero_replace = 1e-15;
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if(diag_indices.size() == 0){
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if (diag_indices.size() == 0) {
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int N = mat.N();
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diag_indices.reserve(N);
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int roff = 0;
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for(typename BridgeMatrix::iterator r = mat.begin(); r != mat.end(); ++r){
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for (typename BridgeMatrix::iterator r = mat.begin(); r != mat.end(); ++r) {
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auto diag = r->find(r.index()); // diag is an iterator
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assert(diag.index() == r.index());
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for(int rr = 0; rr < dim; ++rr){
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for (int rr = 0; rr < dim; ++rr) {
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auto& val = (*diag)[rr][rr]; // reference to easily change the value
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if (val == 0.0){ // could be replaced by '< 1e-30' or similar
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if (val == 0.0) { // could be replaced by '< 1e-30' or similar
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val = zero_replace;
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++numZeros;
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}
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@ -69,12 +69,12 @@ int checkZeroDiagonal(BridgeMatrix& mat) {
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diag_indices.emplace_back(diag.offset());
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}
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}else{
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for(typename BridgeMatrix::iterator r = mat.begin(); r != mat.end(); ++r){
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for (typename BridgeMatrix::iterator r = mat.begin(); r != mat.end(); ++r) {
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typename BridgeMatrix::size_type offset = diag_indices[r.index()];
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auto& diag_block = r->getptr()[offset]; // diag_block is a reference to MatrixBlock, located on column r of row r
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for(int rr = 0; rr < dim; ++rr){
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for (int rr = 0; rr < dim; ++rr) {
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auto& val = diag_block[rr][rr];
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if(val == 0.0){ // could be replaced by '< 1e-30' or similar
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if (val == 0.0) { // could be replaced by '< 1e-30' or similar
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val = zero_replace;
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++numZeros;
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}
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@ -94,18 +94,18 @@ void getSparsityPattern(BridgeMatrix& mat, std::vector<int> &h_rows, std::vector
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int sum_nnzs = 0;
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// convert colIndices and rowPointers
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if(h_rows.size() == 0){
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if (h_rows.size() == 0) {
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h_rows.emplace_back(0);
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for(typename BridgeMatrix::const_iterator r = mat.begin(); r != mat.end(); ++r){
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for (typename BridgeMatrix::const_iterator r = mat.begin(); r != mat.end(); ++r) {
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int size_row = 0;
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for(auto c = r->begin(); c != r->end(); ++c){
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for (auto c = r->begin(); c != r->end(); ++c) {
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h_cols.emplace_back(c.index());
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size_row++;
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}
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sum_nnzs += size_row;
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h_rows.emplace_back(sum_nnzs);
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}
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if(h_rows[mat.N()] != mat.nonzeroes()){
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if (h_rows[mat.N()] != mat.nonzeroes()) {
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OPM_THROW(std::logic_error, "Error size of rows do not sum to number of nonzeroes in BdaBridge::getSparsityPattern()");
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}
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}
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@ -118,7 +118,7 @@ void BdaBridge::solve_system(BridgeMatrix *mat OPM_UNUSED, BridgeVector &b OPM_U
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{
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#if HAVE_CUDA
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if(use_gpu){
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if (use_gpu) {
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BdaResult result;
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result.converged = false;
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static std::vector<int> h_rows;
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@ -127,12 +127,12 @@ void BdaBridge::solve_system(BridgeMatrix *mat OPM_UNUSED, BridgeVector &b OPM_U
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int N = mat->N()*dim;
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int nnz = (h_rows.empty()) ? mat->nonzeroes()*dim*dim : h_rows.back()*dim*dim;
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if(dim != 3){
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if (dim != 3) {
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OpmLog::warning("cusparseSolver only accepts blocksize = 3 at this time, will use Dune for the remainder of the program");
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use_gpu = false;
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}
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if(h_rows.capacity() == 0){
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if (h_rows.capacity() == 0) {
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h_rows.reserve(N+1);
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h_cols.reserve(nnz);
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#if PRINT_TIMERS_BRIDGE
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@ -163,7 +163,7 @@ void BdaBridge::solve_system(BridgeMatrix *mat OPM_UNUSED, BridgeVector &b OPM_U
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typedef cusparseSolverBackend::cusparseSolverStatus cusparseSolverStatus;
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// assume that underlying data (nonzeroes) from mat (Dune::BCRSMatrix) are contiguous, if this is not the case, cusparseSolver is expected to perform undefined behaviour
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cusparseSolverStatus status = backend->solve_system(N, nnz, dim, static_cast<double*>(&(((*mat)[0][0][0][0]))), h_rows.data(), h_cols.data(), static_cast<double*>(&(b[0][0])), result);
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switch(status){
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switch(status) {
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case cusparseSolverStatus::CUSPARSE_SOLVER_SUCCESS:
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//OpmLog::info("cusparseSolver converged");
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break;
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@ -190,9 +190,9 @@ void BdaBridge::solve_system(BridgeMatrix *mat OPM_UNUSED, BridgeVector &b OPM_U
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template <class BridgeVector>
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void BdaBridge::get_result(BridgeVector &x OPM_UNUSED){
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void BdaBridge::get_result(BridgeVector &x OPM_UNUSED) {
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#if HAVE_CUDA
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if(use_gpu){
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if (use_gpu) {
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backend->post_process(static_cast<double*>(&(x[0][0])));
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}
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#endif
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@ -45,20 +45,20 @@ namespace Opm
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const cusparseOperation_t operation = CUSPARSE_OPERATION_NON_TRANSPOSE;
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const cusparseDirection_t order = CUSPARSE_DIRECTION_ROW;
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double second(void){
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double second(void) {
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struct timeval tv;
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gettimeofday(&tv, nullptr);
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return (double)tv.tv_sec + (double)tv.tv_usec / 1000000.0;
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}
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cusparseSolverBackend::cusparseSolverBackend(int verbosity_, int maxit_, double tolerance_) : verbosity(verbosity_), maxit(maxit_), tolerance(tolerance_), minit(0){
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cusparseSolverBackend::cusparseSolverBackend(int verbosity_, int maxit_, double tolerance_) : verbosity(verbosity_), maxit(maxit_), tolerance(tolerance_), minit(0) {
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}
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cusparseSolverBackend::~cusparseSolverBackend(){
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cusparseSolverBackend::~cusparseSolverBackend() {
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finalize();
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}
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void cusparseSolverBackend::gpu_pbicgstab(BdaResult& res){
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void cusparseSolverBackend::gpu_pbicgstab(BdaResult& res) {
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double t_total1, t_total2;
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int n = N;
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double rho = 1.0, rhop;
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@ -80,17 +80,17 @@ namespace Opm
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cublasDcopy(cublasHandle, n, d_r, 1, d_p, 1);
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cublasDnrm2(cublasHandle, n, d_r, 1, &norm_0);
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if(verbosity > 1){
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if (verbosity > 1) {
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std::ostringstream out;
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out << std::scientific << "cusparseSolver initial norm: " << norm_0;
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OpmLog::info(out.str());
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}
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for(it = 0.5; it < maxit; it+=0.5){
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for (it = 0.5; it < maxit; it+=0.5) {
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rhop = rho;
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cublasDdot(cublasHandle, n, d_rw, 1, d_r, 1, &rho);
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if(it > 1){
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if (it > 1) {
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beta = (rho/rhop) * (alpha/omega);
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nomega = -omega;
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cublasDaxpy(cublasHandle, n, &nomega, d_v, 1, d_p, 1);
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@ -118,7 +118,7 @@ namespace Opm
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cublasDaxpy(cublasHandle, n, &alpha, d_pw, 1, d_x, 1);
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cublasDnrm2(cublasHandle, n, d_r, 1, &norm);
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if(norm < tolerance * norm_0 && it > minit){
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if (norm < tolerance * norm_0 && it > minit) {
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break;
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}
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@ -147,11 +147,11 @@ namespace Opm
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cublasDnrm2(cublasHandle, n, d_r, 1, &norm);
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if(norm < tolerance * norm_0 && it > minit){
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if (norm < tolerance * norm_0 && it > minit) {
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break;
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}
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if(verbosity > 1){
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if (verbosity > 1) {
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std::ostringstream out;
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out << "it: " << it << std::scientific << ", norm: " << norm;
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OpmLog::info(out.str());
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@ -166,7 +166,7 @@ namespace Opm
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res.elapsed = t_total2 - t_total1;
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res.converged = (it != (maxit + 0.5));
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if(verbosity > 0){
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if (verbosity > 0) {
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std::ostringstream out;
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out << "=== converged: " << res.converged << ", conv_rate: " << res.conv_rate << ", time: " << res.elapsed << \
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", time per iteration: " << res.elapsed/it << ", iterations: " << it;
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@ -175,7 +175,7 @@ namespace Opm
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}
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void cusparseSolverBackend::initialize(int N, int nnz, int dim){
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void cusparseSolverBackend::initialize(int N, int nnz, int dim) {
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this->N = N;
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this->nnz = nnz;
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this->BLOCK_SIZE = dim;
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@ -235,7 +235,7 @@ namespace Opm
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initialized = true;
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} // end initialize()
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void cusparseSolverBackend::finalize(){
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void cusparseSolverBackend::finalize() {
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cudaFree(d_x);
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cudaFree(d_b);
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cudaFree(d_r);
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@ -267,10 +267,10 @@ namespace Opm
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} // end finalize()
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void cusparseSolverBackend::copy_system_to_gpu(double *vals, int *rows, int *cols, double *b){
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void cusparseSolverBackend::copy_system_to_gpu(double *vals, int *rows, int *cols, double *b) {
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double t1, t2;
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if(verbosity > 2){
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if (verbosity > 2) {
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t1 = second();
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}
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@ -290,7 +290,7 @@ namespace Opm
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this->cols = cols;
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this->rows = rows;
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if(verbosity > 2){
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if (verbosity > 2) {
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cudaStreamSynchronize(stream);
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t2 = second();
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std::ostringstream out;
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@ -301,10 +301,10 @@ namespace Opm
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// don't copy rowpointers and colindices, they stay the same
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void cusparseSolverBackend::update_system_on_gpu(double *vals, double *b){
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void cusparseSolverBackend::update_system_on_gpu(double *vals, double *b) {
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double t1, t2;
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if(verbosity > 2){
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if (verbosity > 2) {
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t1 = second();
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}
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@ -312,7 +312,7 @@ namespace Opm
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cudaMemcpyAsync(d_b, b, N * sizeof(double), cudaMemcpyHostToDevice, stream);
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cudaMemsetAsync(d_x, 0, sizeof(double) * N, stream);
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if(verbosity > 2){
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if (verbosity > 2) {
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cudaStreamSynchronize(stream);
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t2 = second();
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std::ostringstream out;
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@ -322,17 +322,17 @@ namespace Opm
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} // end update_system_on_gpu()
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void cusparseSolverBackend::reset_prec_on_gpu(){
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void cusparseSolverBackend::reset_prec_on_gpu() {
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cudaMemcpyAsync(d_mVals, d_bVals, nnz * sizeof(double), cudaMemcpyDeviceToDevice, stream);
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}
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bool cusparseSolverBackend::analyse_matrix(){
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bool cusparseSolverBackend::analyse_matrix() {
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int d_bufferSize_M, d_bufferSize_L, d_bufferSize_U, d_bufferSize;
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double t1, t2;
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if(verbosity > 2){
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if (verbosity > 2) {
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t1 = second();
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}
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@ -381,7 +381,7 @@ namespace Opm
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int structural_zero;
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cusparseStatus_t status = cusparseXbsrilu02_zeroPivot(cusparseHandle, info_M, &structural_zero);
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if(CUSPARSE_STATUS_ZERO_PIVOT == status){
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if (CUSPARSE_STATUS_ZERO_PIVOT == status) {
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return false;
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}
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@ -395,7 +395,7 @@ namespace Opm
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BLOCK_SIZE, info_U, policy, d_buffer);
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cudaCheckLastError("Could not analyse level information");
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if(verbosity > 2){
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if (verbosity > 2) {
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cudaStreamSynchronize(stream);
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t2 = second();
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std::ostringstream out;
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@ -406,10 +406,10 @@ namespace Opm
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return true;
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} // end analyse_matrix()
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bool cusparseSolverBackend::create_preconditioner(){
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bool cusparseSolverBackend::create_preconditioner() {
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double t1, t2;
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if(verbosity > 2){
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if (verbosity > 2) {
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t1 = second();
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}
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@ -422,11 +422,11 @@ namespace Opm
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int structural_zero;
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// cusparseXbsrilu02_zeroPivot() calls cudaDeviceSynchronize()
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cusparseStatus_t status = cusparseXbsrilu02_zeroPivot(cusparseHandle, info_M, &structural_zero);
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if(CUSPARSE_STATUS_ZERO_PIVOT == status){
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if (CUSPARSE_STATUS_ZERO_PIVOT == status) {
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return false;
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}
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if(verbosity > 2){
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if (verbosity > 2) {
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cudaStreamSynchronize(stream);
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t2 = second();
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std::ostringstream out;
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@ -437,7 +437,7 @@ namespace Opm
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} // end create_preconditioner()
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void cusparseSolverBackend::solve_system(BdaResult &res){
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void cusparseSolverBackend::solve_system(BdaResult &res) {
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// actually solve
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gpu_pbicgstab(res);
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cudaStreamSynchronize(stream);
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@ -447,21 +447,21 @@ namespace Opm
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// copy result to host memory
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// caller must be sure that x is a valid array
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void cusparseSolverBackend::post_process(double *x){
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void cusparseSolverBackend::post_process(double *x) {
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if(!initialized){
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if (!initialized) {
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cudaHostRegister(x, N * sizeof(double), cudaHostRegisterDefault);
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}
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double t1, t2;
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if(verbosity > 2){
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if (verbosity > 2) {
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t1 = second();
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}
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cudaMemcpyAsync(x, d_x, N * sizeof(double), cudaMemcpyDeviceToHost, stream);
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cudaStreamSynchronize(stream);
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if(verbosity > 2){
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if (verbosity > 2) {
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t2 = second();
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std::ostringstream out;
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out << "cusparseSolver::post_process(): " << t2-t1 << " s";
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@ -472,20 +472,20 @@ namespace Opm
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typedef cusparseSolverBackend::cusparseSolverStatus cusparseSolverStatus;
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cusparseSolverStatus cusparseSolverBackend::solve_system(int N, int nnz, int dim, double *vals, int *rows, int *cols, double *b, BdaResult &res){
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if(initialized == false){
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cusparseSolverStatus cusparseSolverBackend::solve_system(int N, int nnz, int dim, double *vals, int *rows, int *cols, double *b, BdaResult &res) {
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if (initialized == false) {
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initialize(N, nnz, dim);
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copy_system_to_gpu(vals, rows, cols, b);
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}else{
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update_system_on_gpu(vals, b);
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}
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if(analysis_done == false){
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if(!analyse_matrix()){
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if (analysis_done == false) {
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if (!analyse_matrix()) {
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return cusparseSolverStatus::CUSPARSE_SOLVER_ANALYSIS_FAILED;
|
||||
}
|
||||
}
|
||||
reset_prec_on_gpu();
|
||||
if(create_preconditioner()){
|
||||
if (create_preconditioner()) {
|
||||
solve_system(res);
|
||||
}else{
|
||||
return cusparseSolverStatus::CUSPARSE_SOLVER_CREATE_PRECONDITIONER_FAILED;
|
||||
|
||||
Loading…
Reference in New Issue
Block a user