/* Copyright 2019 Equinor ASA This file is part of the Open Porous Media project (OPM). OPM is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. OPM is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with OPM. If not, see . */ #ifndef OPM_CUSPARSESOLVER_BACKEND_HEADER_INCLUDED #define OPM_CUSPARSESOLVER_BACKEND_HEADER_INCLUDED #include "cublas_v2.h" #include "cusparse_v2.h" #include #include #include namespace bda { /// This class implements a cusparse-based ilu0-bicgstab solver on GPU template class cusparseSolverBackend : public BdaSolver { typedef BdaSolver Base; using Base::N; using Base::Nb; using Base::nnz; using Base::nnzb; using Base::verbosity; using Base::deviceID; using Base::maxit; using Base::tolerance; using Base::initialized; private: cublasHandle_t cublasHandle; cusparseHandle_t cusparseHandle; cudaStream_t stream; cusparseMatDescr_t descr_B, descr_M, descr_L, descr_U; bsrilu02Info_t info_M; bsrsv2Info_t info_L, info_U; // b: bsr matrix, m: preconditioner double *d_bVals, *d_mVals; int *d_bCols, *d_mCols; int *d_bRows, *d_mRows; double *d_x, *d_b, *d_r, *d_rw, *d_p; // vectors, used during linear solve double *d_pw, *d_s, *d_t, *d_v; void *d_buffer; double *vals_contiguous; // only used if COPY_ROW_BY_ROW is true in cusparseSolverBackend.cpp bool analysis_done = false; /// Solve linear system using ilu0-bicgstab /// \param[in] wellContribs contains all WellContributions, to apply them separately, instead of adding them to matrix A /// \param[inout] res summary of solver result void gpu_pbicgstab(WellContributions& wellContribs, BdaResult& res); /// Initialize GPU and allocate memory /// \param[in] N number of nonzeroes, divide by dim*dim to get number of blocks /// \param[in] nnz number of nonzeroes, divide by dim*dim to get number of blocks /// \param[in] dim size of block void initialize(int N, int nnz, int dim); /// Clean memory void finalize(); /// Copy linear system to GPU /// \param[in] vals array of nonzeroes, each block is stored row-wise, contains nnz values /// \param[in] rows array of rowPointers, contains N/dim+1 values /// \param[in] cols array of columnIndices, contains nnz values /// \param[in] b input vector, contains N values void copy_system_to_gpu(double *vals, int *rows, int *cols, double *b); // Update linear system on GPU, don't copy rowpointers and colindices, they stay the same /// \param[in] vals array of nonzeroes, each block is stored row-wise, contains nnz values /// \param[in] rows array of rowPointers, contains N/dim+1 values, only used if COPY_ROW_BY_ROW is true /// \param[in] b input vector, contains N values void update_system_on_gpu(double *vals, int *rows, double *b); /// Reset preconditioner on GPU, ilu0-decomposition is done inplace by cusparse void reset_prec_on_gpu(); /// Analyse sparsity pattern to extract parallelism /// \return true iff analysis was successful bool analyse_matrix(); /// Perform ilu0-decomposition /// \return true iff decomposition was successful bool create_preconditioner(); /// Solve linear system /// \param[in] wellContribs contains all WellContributions, to apply them separately, instead of adding them to matrix A /// \param[inout] res summary of solver result void solve_system(WellContributions& wellContribs, BdaResult &res); public: /// Construct a cusparseSolver /// \param[in] linear_solver_verbosity verbosity of cusparseSolver /// \param[in] maxit maximum number of iterations for cusparseSolver /// \param[in] tolerance required relative tolerance for cusparseSolver /// \param[in] deviceID the device to be used cusparseSolverBackend(int linear_solver_verbosity, int maxit, double tolerance, unsigned int deviceID); /// Destroy a cusparseSolver, and free memory ~cusparseSolverBackend(); /// Solve linear system, A*x = b, matrix A must be in blocked-CSR format /// \param[in] N number of rows, divide by dim to get number of blockrows /// \param[in] nnz number of nonzeroes, divide by dim*dim to get number of blocks /// \param[in] dim size of block /// \param[in] vals array of nonzeroes, each block is stored row-wise and contiguous, contains nnz values /// \param[in] rows array of rowPointers, contains N/dim+1 values /// \param[in] cols array of columnIndices, contains nnz values /// \param[in] b input vector, contains N values /// \param[in] wellContribs contains all WellContributions, to apply them separately, instead of adding them to matrix A /// \param[inout] res summary of solver result /// \return status code SolverStatus solve_system(int N, int nnz, int dim, double *vals, int *rows, int *cols, double *b, WellContributions& wellContribs, BdaResult &res) override; /// Get resulting vector x after linear solve, also includes post processing if necessary /// \param[inout] x resulting x vector, caller must guarantee that x points to a valid array void get_result(double *x) override; }; // end class cusparseSolverBackend } // namespace bda #endif