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https://github.com/OPM/opm-simulators.git
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Allow to choose amgcl backend at runtime, added default amgcl params
This commit is contained in:
Tong Dong Qiu
parent
282f611f92
commit
e556124405
@ -65,19 +65,37 @@ void amgclSolverBackend<block_size>::initialize(int N_, int nnz_, int dim, doubl
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rhs.resize(N);
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x.resize(N);
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#if AMGCL_CUDA
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cudaDeviceProp prop;
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cudaGetDeviceProperties(&prop, deviceID);
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std::cout << prop.name << std::endl;
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cusparseCreate(&bprm.cusparse_handle);
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#endif
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// try to read amgcl parameters via json file
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std::string filename = "amgcl_options.json";
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std::ifstream file(filename);
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// read amgcl parameters via json file
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std::ifstream file("amgcl_options.json");
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boost::property_tree::read_json(file, prm);
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if (file.is_open()) { // if file exists, read parameters from file
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boost::property_tree::read_json(file, prm);
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// print json parameters
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boost::property_tree::write_json(std::cout, prm);
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backend_type_cuda = prm.get("backend_type_cuda", false); // defaults to false if not specified
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out << "Using parameters from " << filename << ":\n";
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} else { // otherwise use default parameters, same as Dune
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prm.put("backend_type_cuda", false);
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prm.put("precond.class", "relaxation");
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prm.put("precond.type", "ilu0");
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prm.put("precond.damping", 0.9);
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prm.put("solver.type", "bicgstab");
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prm.put("solver.tol", tolerance);
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prm.put("solver.maxiter", maxit);
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prm.put("solver.verbose", verbosity >= 2);
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out << "Using default amgcl parameters:\n";
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}
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boost::property_tree::write_json(out, prm); // print amgcl parameters
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prm.erase("backend_type_cuda"); // delete custom parameter, otherwise amgcl prints a warning
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if (backend_type_cuda) {
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cudaDeviceProp prop;
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cudaGetDeviceProperties(&prop, deviceID);
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out << prop.name << std::endl;
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cusparseCreate(&CUDA_bprm.cusparse_handle);
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}
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OpmLog::info(out.str());
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initialized = true;
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} // end initialize()
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@ -146,46 +164,56 @@ void amgclSolverBackend<block_size>::solve_system(double *b, WellContributions &
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double error = 0.0;
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try {
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// create matrix object
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#if AMGCL_CUDA
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auto A = std::tie(N, A_rows, A_cols, A_vals);
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#else
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auto Atmp = std::tie(N, A_rows, A_cols, A_vals);
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auto A = amgcl::adapter::block_matrix<dmat_type>(Atmp);
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#endif
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if (backend_type_cuda) { // use CUDA
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// create matrix object
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auto A = std::tie(N, A_rows, A_cols, A_vals);
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// create solver and construct preconditioner
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// don't reuse this unless the preconditioner can be reused
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Solver solve(A, prm, bprm);
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// create solver and construct preconditioner
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// don't reuse this unless the preconditioner can be reused
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CUDA_Solver solve(A, prm, CUDA_bprm);
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// print info (once)
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std::call_once(print_info, [&](){
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// print solver structure
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std::cout << solve << std::endl;
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});
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// print solver structure (once)
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std::call_once(print_info, [&](){
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std::ostringstream out;
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out << solve << std::endl;
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OpmLog::info(out.str());
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});
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#if AMGCL_CUDA
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thrust::device_vector<double> B(b, b + N);
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thrust::device_vector<double> X(N, 0.0);
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thrust::device_vector<double> B(b, b + N);
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thrust::device_vector<double> X(N, 0.0);
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// actually solve
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std::tie(iters, error) = solve(B, X);
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// actually solve
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std::tie(iters, error) = solve(B, X);
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thrust::copy(X.begin(), X.end(), x.begin());
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#else
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// reset x vector
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std::fill(x.begin(), x.end(), 0.0);
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thrust::copy(X.begin(), X.end(), x.begin());
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} else { // use builtin backend (CPU)
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// create matrix object
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auto Atmp = std::tie(N, A_rows, A_cols, A_vals);
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auto A = amgcl::adapter::block_matrix<dmat_type>(Atmp);
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// create blocked vectors
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auto b_ptr = reinterpret_cast<dvec_type*>(b);
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auto x_ptr = reinterpret_cast<dvec_type*>(x.data());
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auto B = amgcl::make_iterator_range(b_ptr, b_ptr + N / block_size);
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auto X = amgcl::make_iterator_range(x_ptr, x_ptr + N / block_size);
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// create solver and construct preconditioner
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// don't reuse this unless the preconditioner can be reused
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CPU_Solver solve(A, prm);
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// actually solve
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std::tie(iters, error) = solve(B, X);
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#endif
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// print solver structure (once)
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std::call_once(print_info, [&](){
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std::ostringstream out;
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out << solve << std::endl;
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OpmLog::info(out.str());
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});
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// reset x vector
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std::fill(x.begin(), x.end(), 0.0);
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// create blocked vectors
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auto b_ptr = reinterpret_cast<dvec_type*>(b);
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auto x_ptr = reinterpret_cast<dvec_type*>(x.data());
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auto B = amgcl::make_iterator_range(b_ptr, b_ptr + N / block_size);
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auto X = amgcl::make_iterator_range(x_ptr, x_ptr + N / block_size);
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// actually solve
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std::tie(iters, error) = solve(B, X);
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}
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} catch (const std::exception& ex) {
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std::cerr << "Caught exception: " << ex.what() << std::endl;
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throw ex;
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@ -43,8 +43,6 @@
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#include <amgcl/relaxation/cusparse_ilu0.hpp>
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#endif
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#define AMGCL_CUDA 0
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namespace bda
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{
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@ -66,18 +64,13 @@ class amgclSolverBackend : public BdaSolver<block_size>
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using Base::tolerance;
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using Base::initialized;
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#if AMGCL_CUDA
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#if !HAVE_CUDA
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#error "Error amgcl is trying to use CUDA, but CUDA was not found by CMake"
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#endif
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typedef amgcl::backend::cuda<double> Backend;
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#else
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typedef amgcl::backend::cuda<double> CUDA_Backend;
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typedef amgcl::static_matrix<double, block_size, block_size> dmat_type; // matrix value type in double precision
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typedef amgcl::static_matrix<double, block_size, 1> dvec_type; // the corresponding vector value type
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typedef amgcl::backend::builtin<dmat_type> Backend;
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#endif
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typedef amgcl::backend::builtin<dmat_type> CPU_Backend;
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typedef amgcl::make_solver<amgcl::runtime::preconditioner<Backend>, amgcl::runtime::solver::wrapper<Backend> > Solver;
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typedef amgcl::make_solver<amgcl::runtime::preconditioner<CUDA_Backend>, amgcl::runtime::solver::wrapper<CUDA_Backend> > CUDA_Solver;
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typedef amgcl::make_solver<amgcl::runtime::preconditioner<CPU_Backend>, amgcl::runtime::solver::wrapper<CPU_Backend> > CPU_Solver;
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private:
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// store matrix in CSR format
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@ -85,9 +78,11 @@ private:
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std::vector<double> A_vals, rhs;
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std::vector<double> x;
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std::once_flag print_info;
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bool backend_type_cuda = false; // true if amgcl uses cuda, otherwise use cpu backend
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// if more backend are supported (vexcl), turn into enum
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boost::property_tree::ptree prm;
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typename Backend::params bprm; // amgcl backend parameters, only used for cusparseHandle
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boost::property_tree::ptree prm; // amgcl parameters
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typename CUDA_Backend::params CUDA_bprm; // amgcl backend parameters, only used for cusparseHandle
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/// Initialize GPU and allocate memory
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/// \param[in] N number of nonzeroes, divide by dim*dim to get number of blocks
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