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https://github.com/OPM/opm-simulators.git
synced 2025-02-25 18:55:30 -06:00
clang format
This commit is contained in:
Tobias Meyer Andersen
parent
2b9c81fe09
commit
82ff782d5f
@ -25,9 +25,9 @@
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#include <opm/simulators/linalg/cuistl/CuDILU.hpp>
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#include <opm/simulators/linalg/cuistl/CuSparseMatrix.hpp>
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#include <opm/simulators/linalg/cuistl/CuVector.hpp>
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#include <opm/simulators/linalg/cuistl/detail/cuda_safe_call.hpp>
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#include <opm/simulators/linalg/cuistl/detail/cusparse_matrix_operations.hpp>
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#include <opm/simulators/linalg/cuistl/detail/safe_conversion.hpp>
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#include <opm/simulators/linalg/cuistl/detail/cuda_safe_call.hpp>
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#include <opm/simulators/linalg/matrixblock.hh>
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#include <vector>
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@ -65,7 +65,9 @@ createNaturalToReordered(Opm::SparseTable<size_t> levelSets)
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template <class M, class field_type, class GPUM>
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void
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createReorderedMatrix(const M& naturalMatrix, std::vector<int> reorderedToNatural, std::unique_ptr<GPUM>& reorderedGpuMat)
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createReorderedMatrix(const M& naturalMatrix,
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std::vector<int> reorderedToNatural,
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std::unique_ptr<GPUM>& reorderedGpuMat)
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{
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M reorderedMatrix(naturalMatrix.N(), naturalMatrix.N(), naturalMatrix.nonzeroes(), M::row_wise);
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for (auto dstRowIt = reorderedMatrix.createbegin(); dstRowIt != reorderedMatrix.createend(); ++dstRowIt) {
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@ -81,22 +83,26 @@ createReorderedMatrix(const M& naturalMatrix, std::vector<int> reorderedToNatura
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template <class M, class field_type, class GPUM>
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void
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extractLowerAndUpperMatrices(const M& naturalMatrix, std::vector<int> reorderedToNatural, std::unique_ptr<GPUM>& lower, std::unique_ptr<GPUM>& upper)
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extractLowerAndUpperMatrices(const M& naturalMatrix,
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std::vector<int> reorderedToNatural,
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std::unique_ptr<GPUM>& lower,
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std::unique_ptr<GPUM>& upper)
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{
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const size_t new_nnz = (naturalMatrix.nonzeroes() - naturalMatrix.N()) / 2;
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M reorderedLower(naturalMatrix.N(), naturalMatrix.N(), new_nnz, M::row_wise);
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M reorderedUpper(naturalMatrix.N(), naturalMatrix.N(), new_nnz, M::row_wise);
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for (auto lowerIt = reorderedLower.createbegin(), upperIt = reorderedUpper.createbegin(); lowerIt != reorderedLower.createend(); ++lowerIt, ++upperIt) {
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for (auto lowerIt = reorderedLower.createbegin(), upperIt = reorderedUpper.createbegin();
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lowerIt != reorderedLower.createend();
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++lowerIt, ++upperIt) {
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auto srcRow = naturalMatrix.begin() + reorderedToNatural[lowerIt.index()];
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for (auto elem = srcRow->begin(); elem != srcRow->end(); ++elem) {
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if (elem.index() < srcRow.index()) { // add index to lower matrix if under the diagonal
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lowerIt.insert(elem.index());
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}
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else if (elem.index() > srcRow.index()){ // add element to upper matrix if above the diagonal
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} else if (elem.index() > srcRow.index()) { // add element to upper matrix if above the diagonal
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upperIt.insert(elem.index());
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}
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}
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@ -146,10 +152,11 @@ CuDILU<M, X, Y, l>::CuDILU(const M& A, bool split_matrix)
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A.nonzeroes()));
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if (m_split_matrix) {
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m_gpuMatrixReorderedDiag.emplace(CuVector<field_type>(blocksize_ * blocksize_ * m_cpuMatrix.N()));
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extractLowerAndUpperMatrices<M, field_type, CuSparseMatrix<field_type>>(m_cpuMatrix, m_reorderedToNatural, m_gpuMatrixReorderedLower, m_gpuMatrixReorderedUpper);
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}
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else{
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createReorderedMatrix<M, field_type, CuSparseMatrix<field_type>>(m_cpuMatrix, m_reorderedToNatural, m_gpuMatrixReordered);
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extractLowerAndUpperMatrices<M, field_type, CuSparseMatrix<field_type>>(
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m_cpuMatrix, m_reorderedToNatural, m_gpuMatrixReorderedLower, m_gpuMatrixReorderedUpper);
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} else {
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createReorderedMatrix<M, field_type, CuSparseMatrix<field_type>>(
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m_cpuMatrix, m_reorderedToNatural, m_gpuMatrixReordered);
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}
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computeDiagAndMoveReorderedData();
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}
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@ -171,7 +178,8 @@ CuDILU<M, X, Y, l>::apply(X& v, const Y& d)
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for (int level = 0; level < m_levelSets.size(); ++level) {
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const int numOfRowsInLevel = m_levelSets[level].size();
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if (m_split_matrix) {
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detail::computeLowerSolveLevelSetSplit<field_type, blocksize_>(m_gpuMatrixReorderedLower->getNonZeroValues().data(),
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detail::computeLowerSolveLevelSetSplit<field_type, blocksize_>(
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m_gpuMatrixReorderedLower->getNonZeroValues().data(),
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m_gpuMatrixReorderedLower->getRowIndices().data(),
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m_gpuMatrixReorderedLower->getColumnIndices().data(),
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m_gpuReorderToNatural.data(),
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@ -180,9 +188,9 @@ CuDILU<M, X, Y, l>::apply(X& v, const Y& d)
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m_gpuDInv.data(),
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d.data(),
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v.data());
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}
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else{
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detail::computeLowerSolveLevelSet<field_type, blocksize_>(m_gpuMatrixReordered->getNonZeroValues().data(),
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} else {
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detail::computeLowerSolveLevelSet<field_type, blocksize_>(
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m_gpuMatrixReordered->getNonZeroValues().data(),
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m_gpuMatrixReordered->getRowIndices().data(),
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m_gpuMatrixReordered->getColumnIndices().data(),
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m_gpuReorderToNatural.data(),
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@ -201,7 +209,8 @@ CuDILU<M, X, Y, l>::apply(X& v, const Y& d)
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const int numOfRowsInLevel = m_levelSets[level].size();
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levelStartIdx -= numOfRowsInLevel;
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if (m_split_matrix) {
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detail::computeUpperSolveLevelSetSplit<field_type, blocksize_>(m_gpuMatrixReorderedUpper->getNonZeroValues().data(),
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detail::computeUpperSolveLevelSetSplit<field_type, blocksize_>(
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m_gpuMatrixReorderedUpper->getNonZeroValues().data(),
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m_gpuMatrixReorderedUpper->getRowIndices().data(),
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m_gpuMatrixReorderedUpper->getColumnIndices().data(),
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m_gpuReorderToNatural.data(),
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@ -209,9 +218,9 @@ CuDILU<M, X, Y, l>::apply(X& v, const Y& d)
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numOfRowsInLevel,
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m_gpuDInv.data(),
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v.data());
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}
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else{
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detail::computeUpperSolveLevelSet<field_type, blocksize_>(m_gpuMatrixReordered->getNonZeroValues().data(),
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} else {
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detail::computeUpperSolveLevelSet<field_type, blocksize_>(
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m_gpuMatrixReordered->getNonZeroValues().data(),
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m_gpuMatrixReordered->getRowIndices().data(),
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m_gpuMatrixReordered->getColumnIndices().data(),
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m_gpuReorderToNatural.data(),
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@ -255,7 +264,8 @@ CuDILU<M, X, Y, l>::computeDiagAndMoveReorderedData()
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OPM_TIMEBLOCK(prec_update);
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{
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if (m_split_matrix) {
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detail::copyMatDataToReorderedSplit<field_type, blocksize_>(m_gpuMatrix.getNonZeroValues().data(),
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detail::copyMatDataToReorderedSplit<field_type, blocksize_>(
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m_gpuMatrix.getNonZeroValues().data(),
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m_gpuMatrix.getRowIndices().data(),
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m_gpuMatrix.getColumnIndices().data(),
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m_gpuMatrixReorderedLower->getNonZeroValues().data(),
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@ -265,8 +275,7 @@ CuDILU<M, X, Y, l>::computeDiagAndMoveReorderedData()
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m_gpuMatrixReorderedDiag.value().data(),
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m_gpuNaturalToReorder.data(),
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m_gpuMatrixReorderedLower->N());
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}
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else{
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} else {
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detail::copyMatDataToReordered<field_type, blocksize_>(m_gpuMatrix.getNonZeroValues().data(),
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m_gpuMatrix.getRowIndices().data(),
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m_gpuMatrixReordered->getNonZeroValues().data(),
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@ -279,7 +288,8 @@ CuDILU<M, X, Y, l>::computeDiagAndMoveReorderedData()
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for (int level = 0; level < m_levelSets.size(); ++level) {
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const int numOfRowsInLevel = m_levelSets[level].size();
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if (m_split_matrix) {
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detail::computeDiluDiagonalSplit<field_type, blocksize_>(m_gpuMatrixReorderedLower->getNonZeroValues().data(),
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detail::computeDiluDiagonalSplit<field_type, blocksize_>(
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m_gpuMatrixReorderedLower->getNonZeroValues().data(),
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m_gpuMatrixReorderedLower->getRowIndices().data(),
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m_gpuMatrixReorderedLower->getColumnIndices().data(),
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m_gpuMatrixReorderedUpper->getNonZeroValues().data(),
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@ -291,8 +301,7 @@ CuDILU<M, X, Y, l>::computeDiagAndMoveReorderedData()
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levelStartIdx,
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numOfRowsInLevel,
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m_gpuDInv.data());
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}
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else{
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} else {
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detail::computeDiluDiagonal<field_type, blocksize_>(m_gpuMatrixReordered->getNonZeroValues().data(),
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m_gpuMatrixReordered->getRowIndices().data(),
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m_gpuMatrixReordered->getColumnIndices().data(),
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@ -457,8 +457,16 @@ namespace
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}
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template <class T, int blocksize>
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__global__ void cuMoveDataToReorderedSplit(
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T* srcMatrix, int* srcRowIndices, int* srcColumnIndices, T* dstLowerMatrix, int* dstLowerRowIndices, T* dstUpperMatrix, int* dstUpperRowIndices, T* dstDiag, int* naturalToReordered, size_t numberOfRows)
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__global__ void cuMoveDataToReorderedSplit(T* srcMatrix,
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int* srcRowIndices,
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int* srcColumnIndices,
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T* dstLowerMatrix,
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int* dstLowerRowIndices,
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T* dstUpperMatrix,
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int* dstUpperRowIndices,
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T* dstDiag,
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int* naturalToReordered,
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size_t numberOfRows)
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{
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const auto srcRow = blockDim.x * blockIdx.x + threadIdx.x;
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if (srcRow < numberOfRows) {
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@ -478,13 +486,12 @@ namespace
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dstBlock = lowerBlock;
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++lowerBlock;
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dstBuffer = dstLowerMatrix;
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}
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else if (srcColumnIndices[srcBlock] > srcRow){ // we are writing a value to the upper triangular matrix
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} else if (srcColumnIndices[srcBlock]
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> srcRow) { // we are writing a value to the upper triangular matrix
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dstBlock = upperBlock;
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++upperBlock;
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dstBuffer = dstUpperMatrix;
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}
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else{ // we are writing a value to the diagonal
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} else { // we are writing a value to the diagonal
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dstBlock = dstRow;
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dstBuffer = dstDiag;
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}
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@ -511,14 +518,16 @@ namespace
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// Kernel here is the function object of the cuda kernel
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template <class Kernel>
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inline int getCudaRecomendedThreadBlockSize(Kernel k){
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inline int getCudaRecomendedThreadBlockSize(Kernel k)
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{
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int blockSize;
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int tmpGridSize;
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cudaOccupancyMaxPotentialBlockSize(&tmpGridSize, &blockSize, k, 0, 0);
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return blockSize;
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}
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inline int getNumberOfBlocks(int wantedThreads, int threadBlockSize){
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inline int getNumberOfBlocks(int wantedThreads, int threadBlockSize)
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{
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return (wantedThreads + threadBlockSize - 1) / threadBlockSize;
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}
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@ -648,8 +657,7 @@ computeDiluDiagonalSplit(T* reorderedLowerMat,
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if (blocksize <= 3) {
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int threadBlockSize = getCudaRecomendedThreadBlockSize(cuComputeLowerSolveLevelSetSplit<T, blocksize>);
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int nThreadBlocks = getNumberOfBlocks(rowsInLevelSet, threadBlockSize);
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cuComputeDiluDiagonalSplit<T, blocksize>
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<<<nThreadBlocks, threadBlockSize>>>(reorderedLowerMat,
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cuComputeDiluDiagonalSplit<T, blocksize><<<nThreadBlocks, threadBlockSize>>>(reorderedLowerMat,
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lowerRowIndices,
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lowerColIndices,
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reorderedUpperMat,
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@ -677,13 +685,29 @@ copyMatDataToReordered(
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template <class T, int blocksize>
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void
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copyMatDataToReorderedSplit(
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T* srcMatrix, int* srcRowIndices, int* srcColumnIndices, T* dstLowerMatrix, int* dstLowerRowIndices, T* dstUpperMatrix, int* dstUpperRowIndices, T* dstDiag, int* naturalToReordered, size_t numberOfRows)
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copyMatDataToReorderedSplit(T* srcMatrix,
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int* srcRowIndices,
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int* srcColumnIndices,
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T* dstLowerMatrix,
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int* dstLowerRowIndices,
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T* dstUpperMatrix,
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int* dstUpperRowIndices,
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T* dstDiag,
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int* naturalToReordered,
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size_t numberOfRows)
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{
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int threadBlockSize = getCudaRecomendedThreadBlockSize(cuComputeLowerSolveLevelSetSplit<T, blocksize>);
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int nThreadBlocks = getNumberOfBlocks(numberOfRows, threadBlockSize);
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cuMoveDataToReorderedSplit<T, blocksize><<<nThreadBlocks, threadBlockSize>>>(
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srcMatrix, srcRowIndices, srcColumnIndices, dstLowerMatrix, dstLowerRowIndices, dstUpperMatrix, dstUpperRowIndices, dstDiag, naturalToReordered, numberOfRows);
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cuMoveDataToReorderedSplit<T, blocksize><<<nThreadBlocks, threadBlockSize>>>(srcMatrix,
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srcRowIndices,
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srcColumnIndices,
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dstLowerMatrix,
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dstLowerRowIndices,
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dstUpperMatrix,
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dstUpperRowIndices,
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dstDiag,
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naturalToReordered,
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numberOfRows);
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}
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#define INSTANTIATE_KERNEL_WRAPPERS(T, blocksize) \
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@ -691,7 +715,8 @@ copyMatDataToReorderedSplit(
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template void copyMatDataToReordered<T, blocksize>(T*, int*, T*, int*, int*, size_t); \
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template void copyMatDataToReorderedSplit<T, blocksize>(T*, int*, int*, T*, int*, T*, int*, T*, int*, size_t); \
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template void computeDiluDiagonal<T, blocksize>(T*, int*, int*, int*, int*, const int, int, T*); \
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template void computeDiluDiagonalSplit<T, blocksize>(T*, int*, int*, T*, int*, int*, T*, int*, int*, const int, int, T*);\
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template void computeDiluDiagonalSplit<T, blocksize>( \
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T*, int*, int*, T*, int*, int*, T*, int*, int*, const int, int, T*); \
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template void computeUpperSolveLevelSet<T, blocksize>(T*, int*, int*, int*, int, int, const T*, T*); \
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template void computeLowerSolveLevelSet<T, blocksize>(T*, int*, int*, int*, int, int, const T*, const T*, T*); \
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template void computeUpperSolveLevelSetSplit<T, blocksize>(T*, int*, int*, int*, int, int, const T*, T*); \
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@ -24,12 +24,12 @@
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#include <dune/common/fmatrix.hh>
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#include <dune/istl/bcrsmatrix.hh>
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#include <memory>
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#include <opm/simulators/linalg/DILU.hpp>
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#include <opm/simulators/linalg/cuistl/CuDILU.hpp>
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#include <opm/simulators/linalg/cuistl/CuSparseMatrix.hpp>
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#include <opm/simulators/linalg/cuistl/CuVector.hpp>
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#include <opm/simulators/linalg/cuistl/detail/cuda_safe_call.hpp>
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#include <opm/simulators/linalg/cuistl/detail/cusparse_matrix_operations.hpp>
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#include <opm/simulators/linalg/DILU.hpp>
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#include <random>
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#include <vector>
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@ -47,7 +47,9 @@ using CuFloatingPointVec = Opm::cuistl::CuVector<T>;
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using CuDilu1x1 = Opm::cuistl::CuDILU<Sp1x1BlockMatrix, CuFloatingPointVec, CuFloatingPointVec>;
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using CuDilu2x2 = Opm::cuistl::CuDILU<Sp2x2BlockMatrix, CuFloatingPointVec, CuFloatingPointVec>;
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Sp1x1BlockMatrix get1x1BlockTestMatrix(){
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Sp1x1BlockMatrix
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get1x1BlockTestMatrix()
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{
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/*
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matA:
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1 2 0 3 0 0
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@ -132,7 +134,9 @@ Sp1x1BlockMatrix get1x1BlockTestMatrix(){
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return matA;
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}
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Sp2x2BlockMatrix get2x2BlockTestMatrix(){
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Sp2x2BlockMatrix
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get2x2BlockTestMatrix()
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{
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/*
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matA:
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1 2 0 3 0 0
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