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