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Implement mixed precision GPU ILU0

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This commit is contained in:
Tobias Meyer Andersen 2024-09-30 12:03:01 +02:00
parent 9c74c4d638
commit 55c20dbddd
8 changed files with 291 additions and 103 deletions
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8
opm/simulators/linalg/PreconditionerFactory_impl.hpp
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@ -361,9 +361,10 @@ struct StandardPreconditioners {
F::addCreator("OPMGPUILU0", [](const O& op, [[maybe_unused]] const P& prm, const std::function<V()>&, std::size_t, const C& comm) {
const bool split_matrix = prm.get<bool>("split_matrix", true);
const bool tune_gpu_kernels = prm.get<bool>("tune_gpu_kernels", true);
const bool store_factorization_as_float = prm.get<bool>("store_factorization_as_float", false);
using field_type = typename V::field_type;
using OpmGpuILU0 = typename gpuistl::OpmGpuILU0<M, gpuistl::GpuVector<field_type>, gpuistl::GpuVector<field_type>>;
auto gpuilu0 = std::make_shared<OpmGpuILU0>(op.getmat(), split_matrix, tune_gpu_kernels);
auto gpuilu0 = std::make_shared<OpmGpuILU0>(op.getmat(), split_matrix, tune_gpu_kernels, store_factorization_as_float);
auto adapted = std::make_shared<gpuistl::PreconditionerAdapter<V, V, OpmGpuILU0>>(gpuilu0);
auto wrapped = std::make_shared<gpuistl::GpuBlockPreconditioner<V, V, Comm>>(adapted, comm);
@ -619,10 +620,12 @@ struct StandardPreconditioners<Operator, Dune::Amg::SequentialInformation> {
F::addCreator("OPMGPUILU0", [](const O& op, [[maybe_unused]] const P& prm, const std::function<V()>&, std::size_t) {
const bool split_matrix = prm.get<bool>("split_matrix", true);
const bool tune_gpu_kernels = prm.get<bool>("tune_gpu_kernels", true);
const bool store_factorization_as_float = prm.get<bool>("store_factorization_as_float", false);
using field_type = typename V::field_type;
using GPUILU0 = typename gpuistl::OpmGpuILU0<M, gpuistl::GpuVector<field_type>, gpuistl::GpuVector<field_type>>;
return std::make_shared<gpuistl::PreconditionerAdapter<V, V, GPUILU0>>(std::make_shared<GPUILU0>(op.getmat(), split_matrix, tune_gpu_kernels));
return std::make_shared<gpuistl::PreconditionerAdapter<V, V, GPUILU0>>(std::make_shared<GPUILU0>(op.getmat(), split_matrix, tune_gpu_kernels, store_factorization_as_float));
});
F::addCreator("GPUDILU", [](const O& op, [[maybe_unused]] const P& prm, const std::function<V()>&, std::size_t) {
@ -636,6 +639,7 @@ struct StandardPreconditioners<Operator, Dune::Amg::SequentialInformation> {
F::addCreator("GPUDILUFloat", [](const O& op, [[maybe_unused]] const P& prm, const std::function<V()>&, std::size_t) {
const bool split_matrix = prm.get<bool>("split_matrix", true);
const bool tune_gpu_kernels = prm.get<bool>("tune_gpu_kernels", true);
using block_type = typename V::block_type;
using VTo = Dune::BlockVector<Dune::FieldVector<float, block_type::dimension>>;
using matrix_type_to = typename Dune::BCRSMatrix<Dune::FieldMatrix<float, block_type::dimension, block_type::dimension>>;

15
opm/simulators/linalg/gpuistl/GpuSparseMatrix.cpp
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@ -81,6 +81,21 @@ GpuSparseMatrix<T>::GpuSparseMatrix(const T* nonZeroElements,
}
}
template <class T>
GpuSparseMatrix<T>::GpuSparseMatrix(const GpuVector<int> rowIndices,
const GpuVector<int> columnIndices,
size_t blockSize)
: m_nonZeroElements(columnIndices.dim() * blockSize * blockSize)
, m_columnIndices(columnIndices)
, m_rowIndices(rowIndices)
, m_numberOfNonzeroBlocks(detail::to_int(columnIndices.dim()))
, m_numberOfRows(detail::to_int(rowIndices.dim()-1))
, m_blockSize(detail::to_int(blockSize))
, m_matrixDescription(detail::createMatrixDescription())
, m_cusparseHandle(detail::CuSparseHandle::getInstance())
{
}
template <class T>
GpuSparseMatrix<T>::~GpuSparseMatrix()
{

14
opm/simulators/linalg/gpuistl/GpuSparseMatrix.hpp
View File

@ -67,6 +67,20 @@ public:
size_t blockSize,
size_t numberOfRows);
//! Create a sparse matrix by copying the sparsity structure of another matrix, not filling in the values
//!
//! \note Prefer to use the constructor taking a const reference to a matrix instead.
//!
//! \param[in] rowIndices the row indices of the non-zero elements
//! \param[in] columnIndices the column indices of the non-zero elements
//! \param[in] blockSize size of each block matrix (typically 3)
//!
//! \note We assume numberOfNonzeroBlocks, blockSize and numberOfRows all are representable as int due to
//! restrictions in the current version of cusparse. This might change in future versions.
GpuSparseMatrix(const GpuVector<int> rowIndices,
const GpuVector<int> columnIndices,
size_t blockSize);
/**
* We don't want to be able to copy this for now (too much hassle in copying the cusparse resources)
*/

92
opm/simulators/linalg/gpuistl/OpmGpuILU0.cpp
View File

@ -41,7 +41,7 @@ namespace Opm::gpuistl
{
template <class M, class X, class Y, int l>
OpmGpuILU0<M, X, Y, l>::OpmGpuILU0(const M& A, bool splitMatrix, bool tuneKernels)
OpmGpuILU0<M, X, Y, l>::OpmGpuILU0(const M& A, bool splitMatrix, bool tuneKernels, bool storeFactorizationAsFloat)
: m_cpuMatrix(A)
, m_levelSets(Opm::getMatrixRowColoring(m_cpuMatrix, Opm::ColoringType::LOWER))
, m_reorderedToNatural(detail::createReorderedToNatural(m_levelSets))
@ -49,11 +49,14 @@ OpmGpuILU0<M, X, Y, l>::OpmGpuILU0(const M& A, bool splitMatrix, bool tuneKernel
, m_gpuMatrix(GpuSparseMatrix<field_type>::fromMatrix(m_cpuMatrix, true))
, m_gpuMatrixReorderedLower(nullptr)
, m_gpuMatrixReorderedUpper(nullptr)
, m_gpuMatrixReorderedLowerFloat(nullptr)
, m_gpuMatrixReorderedUpperFloat(nullptr)
, m_gpuNaturalToReorder(m_naturalToReordered)
, m_gpuReorderToNatural(m_reorderedToNatural)
, m_gpuDInv(m_gpuMatrix.N() * m_gpuMatrix.blockSize() * m_gpuMatrix.blockSize())
, m_splitMatrix(splitMatrix)
, m_tuneThreadBlockSizes(tuneKernels)
, m_storeFactorizationAsFloat(storeFactorizationAsFloat)
{
// TODO: Should in some way verify that this matrix is symmetric, only do it debug mode?
// Some sanity check
@ -71,6 +74,7 @@ OpmGpuILU0<M, X, Y, l>::OpmGpuILU0(const M& A, bool splitMatrix, bool tuneKernel
fmt::format("CuSparse matrix not same number of non zeroes as DUNE matrix. {} vs {}. ",
m_gpuMatrix.nonzeroes(),
A.nonzeroes()));
if (m_splitMatrix) {
m_gpuMatrixReorderedDiag.emplace(GpuVector<field_type>(blocksize_ * blocksize_ * m_cpuMatrix.N()));
std::tie(m_gpuMatrixReorderedLower, m_gpuMatrixReorderedUpper)
@ -80,6 +84,15 @@ OpmGpuILU0<M, X, Y, l>::OpmGpuILU0(const M& A, bool splitMatrix, bool tuneKernel
m_gpuReorderedLU = detail::createReorderedMatrix<M, field_type, GpuSparseMatrix<field_type>>(
m_cpuMatrix, m_reorderedToNatural);
}
if (m_storeFactorizationAsFloat){
assert(m_splitMatrix && "Mixed precision GpuILU0 is currently only supported when using split_matrix=true");
// initialize mixed precision datastructures
m_gpuMatrixReorderedLowerFloat = std::unique_ptr<FloatMat>(new auto(FloatMat(m_gpuMatrixReorderedLower->getRowIndices(), m_gpuMatrixReorderedLower->getColumnIndices(), blocksize_)));
m_gpuMatrixReorderedUpperFloat = std::unique_ptr<FloatMat>(new auto(FloatMat(m_gpuMatrixReorderedUpper->getRowIndices(), m_gpuMatrixReorderedUpper->getColumnIndices(), blocksize_)));
m_gpuMatrixReorderedDiagFloat.emplace(GpuVector<float>(m_gpuMatrix.N() * m_gpuMatrix.blockSize() * m_gpuMatrix.blockSize()));
}
LUFactorizeAndMoveData(m_moveThreadBlockSize, m_ILU0FactorizationThreadBlockSize);
if (m_tuneThreadBlockSizes) {
@ -107,20 +120,37 @@ template <class M, class X, class Y, int l>
void
OpmGpuILU0<M, X, Y, l>::apply(X& v, const Y& d, int lowerSolveThreadBlockSize, int upperSolveThreadBlockSize)
{
// perform a lower solve and then an upper solve to apply the approximate inverse using ILU factorization
// for the lower and upper solve we have some if's that determine which underlying implementation to use
int levelStartIdx = 0;
for (int level = 0; level < m_levelSets.size(); ++level) {
const int numOfRowsInLevel = m_levelSets[level].size();
if (m_splitMatrix) {
detail::ILU0::solveLowerLevelSetSplit<field_type, blocksize_>(
m_gpuMatrixReorderedLower->getNonZeroValues().data(),
m_gpuMatrixReorderedLower->getRowIndices().data(),
m_gpuMatrixReorderedLower->getColumnIndices().data(),
m_gpuReorderToNatural.data(),
levelStartIdx,
numOfRowsInLevel,
d.data(),
v.data(),
lowerSolveThreadBlockSize);
if (m_storeFactorizationAsFloat){
detail::ILU0::solveLowerLevelSetSplit<blocksize_, field_type, float>(
m_gpuMatrixReorderedLowerFloat->getNonZeroValues().data(),
m_gpuMatrixReorderedLowerFloat->getRowIndices().data(),
m_gpuMatrixReorderedLowerFloat->getColumnIndices().data(),
m_gpuReorderToNatural.data(),
levelStartIdx,
numOfRowsInLevel,
d.data(),
v.data(),
lowerSolveThreadBlockSize);
}
else{
detail::ILU0::solveLowerLevelSetSplit<blocksize_, field_type, field_type>(
m_gpuMatrixReorderedLower->getNonZeroValues().data(),
m_gpuMatrixReorderedLower->getRowIndices().data(),
m_gpuMatrixReorderedLower->getColumnIndices().data(),
m_gpuReorderToNatural.data(),
levelStartIdx,
numOfRowsInLevel,
d.data(),
v.data(),
lowerSolveThreadBlockSize);
}
} else {
detail::ILU0::solveLowerLevelSet<field_type, blocksize_>(m_gpuReorderedLU->getNonZeroValues().data(),
m_gpuReorderedLU->getRowIndices().data(),
@ -140,16 +170,30 @@ OpmGpuILU0<M, X, Y, l>::apply(X& v, const Y& d, int lowerSolveThreadBlockSize, i
const int numOfRowsInLevel = m_levelSets[level].size();
levelStartIdx -= numOfRowsInLevel;
if (m_splitMatrix) {
detail::ILU0::solveUpperLevelSetSplit<field_type, blocksize_>(
m_gpuMatrixReorderedUpper->getNonZeroValues().data(),
m_gpuMatrixReorderedUpper->getRowIndices().data(),
m_gpuMatrixReorderedUpper->getColumnIndices().data(),
m_gpuReorderToNatural.data(),
levelStartIdx,
numOfRowsInLevel,
m_gpuMatrixReorderedDiag.value().data(),
v.data(),
upperSolveThreadBlockSize);
if (m_storeFactorizationAsFloat) {
detail::ILU0::solveUpperLevelSetSplit<blocksize_, field_type, float>(
m_gpuMatrixReorderedUpperFloat->getNonZeroValues().data(),
m_gpuMatrixReorderedUpperFloat->getRowIndices().data(),
m_gpuMatrixReorderedUpperFloat->getColumnIndices().data(),
m_gpuReorderToNatural.data(),
levelStartIdx,
numOfRowsInLevel,
m_gpuMatrixReorderedDiagFloat.value().data(),
v.data(),
upperSolveThreadBlockSize);
}
else{
detail::ILU0::solveUpperLevelSetSplit<blocksize_, field_type, field_type>(
m_gpuMatrixReorderedUpper->getNonZeroValues().data(),
m_gpuMatrixReorderedUpper->getRowIndices().data(),
m_gpuMatrixReorderedUpper->getColumnIndices().data(),
m_gpuReorderToNatural.data(),
levelStartIdx,
numOfRowsInLevel,
m_gpuMatrixReorderedDiag.value().data(),
v.data(),
upperSolveThreadBlockSize);
}
} else {
detail::ILU0::solveUpperLevelSet<field_type, blocksize_>(m_gpuReorderedLU->getNonZeroValues().data(),
m_gpuReorderedLU->getRowIndices().data(),
@ -227,7 +271,7 @@ OpmGpuILU0<M, X, Y, l>::LUFactorizeAndMoveData(int moveThreadBlockSize, int fact
const int numOfRowsInLevel = m_levelSets[level].size();
if (m_splitMatrix) {
detail::ILU0::LUFactorizationSplit<field_type, blocksize_>(
detail::ILU0::LUFactorizationSplit<blocksize_, field_type, float>(
m_gpuMatrixReorderedLower->getNonZeroValues().data(),
m_gpuMatrixReorderedLower->getRowIndices().data(),
m_gpuMatrixReorderedLower->getColumnIndices().data(),
@ -235,10 +279,14 @@ OpmGpuILU0<M, X, Y, l>::LUFactorizeAndMoveData(int moveThreadBlockSize, int fact
m_gpuMatrixReorderedUpper->getRowIndices().data(),
m_gpuMatrixReorderedUpper->getColumnIndices().data(),
m_gpuMatrixReorderedDiag.value().data(),
m_storeFactorizationAsFloat ? m_gpuMatrixReorderedLowerFloat->getNonZeroValues().data() : nullptr,
m_storeFactorizationAsFloat ? m_gpuMatrixReorderedUpperFloat->getNonZeroValues().data() : nullptr,
m_storeFactorizationAsFloat ? m_gpuMatrixReorderedDiagFloat.value().data() : nullptr,
m_gpuReorderToNatural.data(),
m_gpuNaturalToReorder.data(),
levelStartIdx,
numOfRowsInLevel,
m_storeFactorizationAsFloat,
factorizationThreadBlockSize);
} else {

21
opm/simulators/linalg/gpuistl/OpmGpuILU0.hpp
View File

@ -54,7 +54,9 @@ public:
//! \brief The field type of the preconditioner.
using field_type = typename X::field_type;
//! \brief The GPU matrix type
using CuMat = GpuSparseMatrix<field_type>;
using GpuMat = GpuSparseMatrix<field_type>;
//! \brief The Float matrix type for mixed precision
using FloatMat = GpuSparseMatrix<float>;
//! \brief Constructor.
//!
@ -62,7 +64,7 @@ public:
//! \param A The matrix to operate on.
//! \param w The relaxation factor.
//!
explicit OpmGpuILU0(const M& A, bool splitMatrix, bool tuneKernels);
explicit OpmGpuILU0(const M& A, bool splitMatrix, bool tuneKernels, bool storeFactorizationAsFloat);
//! \brief Prepare the preconditioner.
//! \note Does nothing at the time being.
@ -120,11 +122,15 @@ private:
//! \brief converts from index in natural ordered structure to index reordered strucutre
std::vector<int> m_naturalToReordered;
//! \brief The A matrix stored on the gpu, and its reordred version
CuMat m_gpuMatrix;
std::unique_ptr<CuMat> m_gpuReorderedLU;
GpuMat m_gpuMatrix;
std::unique_ptr<GpuMat> m_gpuReorderedLU;
//! \brief If matrix splitting is enabled, then we store the lower and upper part separately
std::unique_ptr<CuMat> m_gpuMatrixReorderedLower;
std::unique_ptr<CuMat> m_gpuMatrixReorderedUpper;
std::unique_ptr<GpuMat> m_gpuMatrixReorderedLower;
std::unique_ptr<GpuMat> m_gpuMatrixReorderedUpper;
//! \brief If mixed precision is enabled, store a float matrix
std::unique_ptr<FloatMat> m_gpuMatrixReorderedLowerFloat;
std::unique_ptr<FloatMat> m_gpuMatrixReorderedUpperFloat;
std::optional<GpuVector<float>> m_gpuMatrixReorderedDiagFloat;
//! \brief If matrix splitting is enabled, we also store the diagonal separately
std::optional<GpuVector<field_type>> m_gpuMatrixReorderedDiag;
//! row conversion from natural to reordered matrix indices stored on the GPU
@ -137,6 +143,9 @@ private:
bool m_splitMatrix;
//! \brief Bool storing whether or not we will tune the threadblock sizes. Only used for AMD cards
bool m_tuneThreadBlockSizes;
//! \brief Bool storing whether or not we should store the ILU factorization in a float datastructure.
//! This uses a mixed precision preconditioner to trade numerical accuracy for memory transfer speed.
bool m_storeFactorizationAsFloat;
//! \brief variables storing the threadblocksizes to use if using the tuned sizes and AMD cards
//! The default value of -1 indicates that we have not calibrated and selected a value yet
int m_upperSolveThreadBlockSize = -1;

52
opm/simulators/linalg/gpuistl/detail/deviceBlockOperations.hpp
View File

@ -37,9 +37,9 @@ __device__ __forceinline__ void
invBlockOutOfPlace(const T* __restrict__ srcBlock, T* __restrict__ dstBlock)
{
if (blocksize == 1) {
dstBlock[0] = 1.0 / (srcBlock[0]);
dstBlock[0] = T(1.0) / (srcBlock[0]);
} else if (blocksize == 2) {
T detInv = 1.0 / (srcBlock[0] * srcBlock[3] - srcBlock[1] * srcBlock[2]);
T detInv = T(1.0) / (srcBlock[0] * srcBlock[3] - srcBlock[1] * srcBlock[2]);
dstBlock[0] = srcBlock[3] * detInv;
dstBlock[1] = -srcBlock[1] * detInv;
dstBlock[2] = -srcBlock[2] * detInv;
@ -53,7 +53,7 @@ invBlockOutOfPlace(const T* __restrict__ srcBlock, T* __restrict__ dstBlock)
T t12 = srcBlock[1] * srcBlock[6];
T t14 = srcBlock[2] * srcBlock[6];
T t17 = 1.0
T t17 = T(1.0)
/ (t4 * srcBlock[8] - t6 * srcBlock[7] - t8 * srcBlock[8] + t10 * srcBlock[7] + t12 * srcBlock[5]
- t14 * srcBlock[4]); // t17 is 1/determinant
@ -75,9 +75,9 @@ __device__ __forceinline__ void
invBlockInPlace(T* __restrict__ block)
{
if (blocksize == 1) {
block[0] = 1.0 / (block[0]);
block[0] = T(1.0) / (block[0]);
} else if (blocksize == 2) {
T detInv = 1.0 / (block[0] * block[3] - block[1] * block[2]);
T detInv = T(1.0) / (block[0] * block[3] - block[1] * block[2]);
T temp = block[0];
block[0] = block[3] * detInv;
@ -93,7 +93,7 @@ invBlockInPlace(T* __restrict__ block)
T t14 = block[2] * block[6];
T det = (t4 * block[8] - t6 * block[7] - t8 * block[8] + t10 * block[7] + t12 * block[5] - t14 * block[4]);
T t17 = T(1.0) / det;
T t17 = T(T(1.0)) / det;
T matrix01 = block[1];
T matrix00 = block[0];
@ -229,6 +229,46 @@ matrixSubtraction(T* A, T* B)
}
}
}
// TODO: possibly place somewhere else in this file
// TODO: consider merging with existing block operations
template<int blocksize, class ScalarInputType, class ScalarOutputType>
__device__ __forceinline__ void
moveBlock(const ScalarInputType* __restrict__ A, ScalarOutputType* __restrict__ B){
for (int i = 0; i < blocksize; ++i){
for (int j = 0; j < blocksize; ++j){
B[i * blocksize + j] = ScalarOutputType(A[i * blocksize + j]);
}
}
}
// TODO: possibly place somewhere else in this file
// TODO: consider merging with existing block operations
template <int blocksize, class MatrixScalar, class VectorScalar, class ResultScalar, class ComputeScalar>
__device__ __forceinline__ void
mvMixedGeneral(const MatrixScalar* A, const VectorScalar* b, ResultScalar* c)
{
for (int i = 0; i < blocksize; ++i) {
c[i] = 0;
for (int j = 0; j < blocksize; ++j) {
c[i] += ResultScalar(ComputeScalar(A[i * blocksize + j]) * ComputeScalar(b[j]));
}
}
}
// TODO: possibly place somewhere else in this file
// TODO: consider merging with existing block operations
template <int blocksize, class MatrixScalar, class VectorScalar, class ResultScalar, class ComputeScalar>
__device__ __forceinline__ void
mmvMixedGeneral(const MatrixScalar* A, const VectorScalar* b, ResultScalar* c)
{
for (int i = 0; i < blocksize; ++i) {
for (int j = 0; j < blocksize; ++j) {
c[i] -= ResultScalar(ComputeScalar(A[i * blocksize + j]) * ComputeScalar(b[j]));
}
}
}
} // namespace
#endif

163
opm/simulators/linalg/gpuistl/detail/preconditionerKernels/ILU0Kernels.cu
View File

@ -120,17 +120,21 @@ namespace
}
}
template <class T, int blocksize>
__global__ void cuLUFactorizationSplit(T* reorderedLowerMat,
template <int blocksize, class InputScalar, class OutputScalar>
__global__ void cuLUFactorizationSplit(InputScalar* srcReorderedLowerMat,
int* lowerRowIndices,
int* lowerColIndices,
T* reorderedUpperMat,
InputScalar* srcReorderedUpperMat,
int* upperRowIndices,
int* upperColIndices,
T* diagonal,
InputScalar* srcDiagonal,
OutputScalar* dstReorderedLowerMat,
OutputScalar* dstReorderedUpperMat,
OutputScalar* dstDiagonal,
int* reorderedToNatural,
int* naturalToReordered,
const int startIdx,
const bool copyResultToOtherMatrix,
int rowsInLevelSet)
{
auto reorderedIdx = startIdx + blockDim.x * blockIdx.x + threadIdx.x;
@ -155,9 +159,12 @@ namespace
// the DUNE implementation is inplace, and I need to take care to make sure
// this out of place implementation is equivalent
mmOverlap<T, blocksize>(&reorderedLowerMat[ij * scalarsInBlock],
&diagonal[j * scalarsInBlock],
&reorderedLowerMat[ij * scalarsInBlock]);
mmOverlap<InputScalar, blocksize>(&srcReorderedLowerMat[ij * scalarsInBlock],
&srcDiagonal[j * scalarsInBlock],
&srcReorderedLowerMat[ij * scalarsInBlock]);
if (copyResultToOtherMatrix){
moveBlock<blocksize, InputScalar, OutputScalar>(&srcReorderedLowerMat[ij * scalarsInBlock], &dstReorderedLowerMat[ij * scalarsInBlock]);
}
// we have now accounted for an element under the diagonal and the diagonal element above it
// now iterate over the blocks that align in the
@ -172,40 +179,53 @@ namespace
// the same block index might be found in the lower part of the matrix
while (!(ik == endOfRowIUpper && ikState == POSITION_TYPE::ABOVE_DIAG) && jk != endOfRowJ) {
T* ikBlockPtr;
InputScalar* ikBlockPtr;
OutputScalar* dstIkBlockPtr;
int ikColumn;
if (ikState == POSITION_TYPE::UNDER_DIAG) {
ikBlockPtr = &reorderedLowerMat[ik * scalarsInBlock];
ikBlockPtr = &srcReorderedLowerMat[ik * scalarsInBlock];
if (copyResultToOtherMatrix)
dstIkBlockPtr = &dstReorderedLowerMat[ik * scalarsInBlock];
ikColumn = lowerColIndices[ik];
} else if (ikState == POSITION_TYPE::ON_DIAG) {
ikBlockPtr = &diagonal[reorderedIdx * scalarsInBlock];
ikBlockPtr = &srcDiagonal[reorderedIdx * scalarsInBlock];
if (copyResultToOtherMatrix)
dstIkBlockPtr = &dstDiagonal[reorderedIdx * scalarsInBlock];
ikColumn = naturalIdx;
} else { // ikState == POSITION_TYPE::ABOVE_DIAG
ikBlockPtr = &reorderedUpperMat[ik * scalarsInBlock];
ikBlockPtr = &srcReorderedUpperMat[ik * scalarsInBlock];
if (copyResultToOtherMatrix)
dstIkBlockPtr = &dstReorderedUpperMat[ik * scalarsInBlock];
ikColumn = upperColIndices[ik];
}
if (ikColumn == upperColIndices[jk]) {
// A_jk = A_ij * A_jk
T tmp[scalarsInBlock] = {0};
InputScalar tmp[scalarsInBlock] = {0};
mmNoOverlap<T, blocksize>(
&reorderedLowerMat[ij * scalarsInBlock], &reorderedUpperMat[jk * scalarsInBlock], tmp);
matrixSubtraction<T, blocksize>(ikBlockPtr, tmp);
mmNoOverlap<InputScalar, blocksize>(
&srcReorderedLowerMat[ij * scalarsInBlock], &srcReorderedUpperMat[jk * scalarsInBlock], tmp);
matrixSubtraction<InputScalar, blocksize>(ikBlockPtr, tmp);
incrementAcrossSplitStructure(ik, ikState, endOfRowILower, startOfRowIUpper);
;
++jk;
} else {
if (ikColumn < upperColIndices[jk]) {
incrementAcrossSplitStructure(ik, ikState, endOfRowILower, startOfRowIUpper);
;
} else {
++jk;
}
}
}
}
invBlockInPlace<T, blocksize>(&diagonal[reorderedIdx * scalarsInBlock]);
invBlockInPlace<InputScalar, blocksize>(&srcDiagonal[reorderedIdx * scalarsInBlock]);
if (copyResultToOtherMatrix){
moveBlock<blocksize, InputScalar, OutputScalar>(&srcDiagonal[reorderedIdx * scalarsInBlock], &dstDiagonal[reorderedIdx * scalarsInBlock]);
// also move all values above the diagonal on this row
for (int block = startOfRowIUpper; block < endOfRowIUpper; ++block) {
moveBlock<blocksize, InputScalar, OutputScalar>(&srcReorderedUpperMat[block * scalarsInBlock], &dstReorderedUpperMat[block * scalarsInBlock]);
}
}
}
}
@ -266,15 +286,15 @@ namespace
}
}
template <class T, int blocksize>
__global__ void cuSolveLowerLevelSetSplit(T* mat,
template <int blocksize, class LinearSolverScalar, class MatrixScalar>
__global__ void cuSolveLowerLevelSetSplit(MatrixScalar* mat,
int* rowIndices,
int* colIndices,
int* indexConversion,
int startIdx,
int rowsInLevelSet,
const T* d,
T* v)
const LinearSolverScalar* d,
LinearSolverScalar* v)
{
auto reorderedIdx = startIdx + (blockDim.x * blockIdx.x + threadIdx.x);
if (reorderedIdx < rowsInLevelSet + startIdx) {
@ -282,30 +302,31 @@ namespace
const size_t nnzIdxLim = rowIndices[reorderedIdx + 1];
const int naturalRowIdx = indexConversion[reorderedIdx];
T rhs[blocksize];
LinearSolverScalar rhs[blocksize];
for (int i = 0; i < blocksize; i++) {
rhs[i] = d[naturalRowIdx * blocksize + i];
}
for (int block = nnzIdx; block < nnzIdxLim; ++block) {
const int col = colIndices[block];
mmv<T, blocksize>(&mat[block * blocksize * blocksize], &v[col * blocksize], rhs);
mmvMixedGeneral<blocksize, MatrixScalar, LinearSolverScalar, LinearSolverScalar, LinearSolverScalar>(
&mat[block * blocksize * blocksize], &v[col * blocksize], rhs);
}
for (int i = 0; i < blocksize; ++i) {
v[naturalRowIdx * blocksize + i] = rhs[i];
v[naturalRowIdx * blocksize + i] = LinearSolverScalar(rhs[i]);
}
}
}
template <class T, int blocksize>
__global__ void cuSolveUpperLevelSetSplit(T* mat,
template <int blocksize, class LinearSolverScalar, class MatrixScalar>
__global__ void cuSolveUpperLevelSetSplit(MatrixScalar* mat,
int* rowIndices,
int* colIndices,
int* indexConversion,
int startIdx,
int rowsInLevelSet,
const T* dInv,
T* v)
const MatrixScalar* dInv,
LinearSolverScalar* v)
{
auto reorderedIdx = startIdx + (blockDim.x * blockIdx.x + threadIdx.x);
if (reorderedIdx < rowsInLevelSet + startIdx) {
@ -313,17 +334,17 @@ namespace
const size_t nnzIdxLim = rowIndices[reorderedIdx + 1];
const int naturalRowIdx = indexConversion[reorderedIdx];
T rhs[blocksize];
LinearSolverScalar rhs[blocksize];
for (int i = 0; i < blocksize; i++) {
rhs[i] = v[naturalRowIdx * blocksize + i];
}
for (int block = nnzIdx; block < nnzIdxLim; ++block) {
const int col = colIndices[block];
mmv<T, blocksize>(&mat[block * blocksize * blocksize], &v[col * blocksize], rhs);
mmvMixedGeneral<blocksize, MatrixScalar, LinearSolverScalar, LinearSolverScalar, LinearSolverScalar>(&mat[block * blocksize * blocksize], &v[col * blocksize], rhs);
}
mv<T, blocksize>(&dInv[reorderedIdx * blocksize * blocksize], rhs, &v[naturalRowIdx * blocksize]);
mvMixedGeneral<blocksize, MatrixScalar, LinearSolverScalar, LinearSolverScalar, LinearSolverScalar>(&dInv[reorderedIdx * blocksize * blocksize], rhs, &v[naturalRowIdx * blocksize]);
}
}
} // namespace
@ -365,41 +386,41 @@ solveUpperLevelSet(T* reorderedMat,
reorderedMat, rowIndices, colIndices, indexConversion, startIdx, rowsInLevelSet, v);
}
template <class T, int blocksize>
template <int blocksize, class LinearSolverScalar, class MatrixScalar>
void
solveLowerLevelSetSplit(T* reorderedMat,
solveLowerLevelSetSplit(MatrixScalar* reorderedMat,
int* rowIndices,
int* colIndices,
int* indexConversion,
int startIdx,
int rowsInLevelSet,
const T* d,
T* v,
const LinearSolverScalar* d,
LinearSolverScalar* v,
int thrBlockSize)
{
int threadBlockSize = ::Opm::gpuistl::detail::getCudaRecomendedThreadBlockSize(
cuSolveLowerLevelSetSplit<T, blocksize>, thrBlockSize);
cuSolveLowerLevelSetSplit<blocksize, LinearSolverScalar, MatrixScalar>, thrBlockSize);
int nThreadBlocks = ::Opm::gpuistl::detail::getNumberOfBlocks(rowsInLevelSet, threadBlockSize);
cuSolveLowerLevelSetSplit<T, blocksize><<<nThreadBlocks, threadBlockSize>>>(
cuSolveLowerLevelSetSplit<blocksize, LinearSolverScalar, MatrixScalar><<<nThreadBlocks, threadBlockSize>>>(
reorderedMat, rowIndices, colIndices, indexConversion, startIdx, rowsInLevelSet, d, v);
}
// perform the upper solve for all rows in the same level set
template <class T, int blocksize>
template <int blocksize, class LinearSolverScalar, class MatrixScalar>
void
solveUpperLevelSetSplit(T* reorderedMat,
solveUpperLevelSetSplit(MatrixScalar* reorderedMat,
int* rowIndices,
int* colIndices,
int* indexConversion,
int startIdx,
int rowsInLevelSet,
const T* dInv,
T* v,
const MatrixScalar* dInv,
LinearSolverScalar* v,
int thrBlockSize)
{
int threadBlockSize = ::Opm::gpuistl::detail::getCudaRecomendedThreadBlockSize(
cuSolveUpperLevelSetSplit<T, blocksize>, thrBlockSize);
cuSolveUpperLevelSetSplit<blocksize, LinearSolverScalar, MatrixScalar>, thrBlockSize);
int nThreadBlocks = ::Opm::gpuistl::detail::getNumberOfBlocks(rowsInLevelSet, threadBlockSize);
cuSolveUpperLevelSetSplit<T, blocksize><<<nThreadBlocks, threadBlockSize>>>(
cuSolveUpperLevelSetSplit<blocksize, LinearSolverScalar, MatrixScalar><<<nThreadBlocks, threadBlockSize>>>(
reorderedMat, rowIndices, colIndices, indexConversion, startIdx, rowsInLevelSet, dInv, v);
}
@ -421,34 +442,42 @@ LUFactorization(T* srcMatrix,
srcMatrix, srcRowIndices, srcColumnIndices, naturalToReordered, reorderedToNatual, rowsInLevelSet, startIdx);
}
template <class T, int blocksize>
template <int blocksize, class InputScalar, class OutputScalar>
void
LUFactorizationSplit(T* reorderedLowerMat,
LUFactorizationSplit(InputScalar* srcReorderedLowerMat,
int* lowerRowIndices,
int* lowerColIndices,
T* reorderedUpperMat,
InputScalar* reorderedUpperMat,
int* upperRowIndices,
int* upperColIndices,
T* diagonal,
InputScalar* srcDiagonal,
OutputScalar* dstReorderedLowerMat,
OutputScalar* dstReorderedUpperMat,
OutputScalar* dstDiagonal,
int* reorderedToNatural,
int* naturalToReordered,
const int startIdx,
int rowsInLevelSet,
bool copyResultToOtherMatrix,
int thrBlockSize)
{
int threadBlockSize
= ::Opm::gpuistl::detail::getCudaRecomendedThreadBlockSize(cuLUFactorizationSplit<T, blocksize>, thrBlockSize);
= ::Opm::gpuistl::detail::getCudaRecomendedThreadBlockSize(cuLUFactorizationSplit<blocksize, InputScalar, OutputScalar>, thrBlockSize);
int nThreadBlocks = ::Opm::gpuistl::detail::getNumberOfBlocks(rowsInLevelSet, threadBlockSize);
cuLUFactorizationSplit<T, blocksize><<<nThreadBlocks, threadBlockSize>>>(reorderedLowerMat,
cuLUFactorizationSplit<blocksize, InputScalar, OutputScalar><<<nThreadBlocks, threadBlockSize>>>(srcReorderedLowerMat,
lowerRowIndices,
lowerColIndices,
reorderedUpperMat,
upperRowIndices,
upperColIndices,
diagonal,
srcDiagonal,
dstReorderedLowerMat,
dstReorderedUpperMat,
dstDiagonal,
reorderedToNatural,
naturalToReordered,
startIdx,
copyResultToOtherMatrix,
rowsInLevelSet);
}
@ -456,10 +485,12 @@ LUFactorizationSplit(T* reorderedLowerMat,
template void solveUpperLevelSet<T, blocksize>(T*, int*, int*, int*, int, int, T*, int); \
template void solveLowerLevelSet<T, blocksize>(T*, int*, int*, int*, int, int, const T*, T*, int); \
template void LUFactorization<T, blocksize>(T*, int*, int*, int*, int*, size_t, int, int); \
template void LUFactorizationSplit<T, blocksize>( \
T*, int*, int*, T*, int*, int*, T*, int*, int*, const int, int, int); \
template void solveUpperLevelSetSplit<T, blocksize>(T*, int*, int*, int*, int, int, const T*, T*, int); \
template void solveLowerLevelSetSplit<T, blocksize>(T*, int*, int*, int*, int, int, const T*, T*, int);
template void LUFactorizationSplit<blocksize, T, float>( \
T*, int*, int*, T*, int*, int*, T*, float*, float*, float*, int*, int*, const int, int, bool, int); \
template void LUFactorizationSplit<blocksize, T, double>( \
T*, int*, int*, T*, int*, int*, T*, double*, double*, double*, int*, int*, const int, int, bool, int);
// template void solveUpperLevelSetSplit<T, blocksize>(T*, int*, int*, int*, int, int, const T*, T*, int); \
// template void solveLowerLevelSetSplit<T, blocksize>(T*, int*, int*, int*, int, int, const T*, T*, int);
INSTANTIATE_KERNEL_WRAPPERS(float, 1);
INSTANTIATE_KERNEL_WRAPPERS(float, 2);
@ -473,4 +504,26 @@ INSTANTIATE_KERNEL_WRAPPERS(double, 3);
INSTANTIATE_KERNEL_WRAPPERS(double, 4);
INSTANTIATE_KERNEL_WRAPPERS(double, 5);
INSTANTIATE_KERNEL_WRAPPERS(double, 6);
#define INSTANTIATE_MIXED_PRECISION_KERNEL_WRAPPERS(blocksize) \
/* double preconditioner */\
template void solveLowerLevelSetSplit<blocksize, double, double>(double*, int*, int*, int*, int, int, const double*, double*, int); \
/* float matrix, double compute preconditioner */\
template void solveLowerLevelSetSplit<blocksize, double, float>(float*, int*, int*, int*, int, int, const double*, double*, int); \
/* float preconditioner */\
template void solveLowerLevelSetSplit<blocksize, float, float>(float*, int*, int*, int*, int, int, const float*, float*, int); \
\
/* double preconditioner */\
template void solveUpperLevelSetSplit<blocksize, double, double>(double*, int*, int*, int*, int, int, const double*, double*, int); \
/* float matrix, double compute preconditioner */\
template void solveUpperLevelSetSplit<blocksize, double, float>(float*, int*, int*, int*, int, int, const float*, double*, int); \
/* float preconditioner */\
template void solveUpperLevelSetSplit<blocksize, float, float>(float*, int*, int*, int*, int, int, const float*, float*, int); \
INSTANTIATE_MIXED_PRECISION_KERNEL_WRAPPERS(1);
INSTANTIATE_MIXED_PRECISION_KERNEL_WRAPPERS(2);
INSTANTIATE_MIXED_PRECISION_KERNEL_WRAPPERS(3);
INSTANTIATE_MIXED_PRECISION_KERNEL_WRAPPERS(4);
INSTANTIATE_MIXED_PRECISION_KERNEL_WRAPPERS(5);
INSTANTIATE_MIXED_PRECISION_KERNEL_WRAPPERS(6);
} // namespace Opm::gpuistl::detail::ILU0

29
opm/simulators/linalg/gpuistl/detail/preconditionerKernels/ILU0Kernels.hpp
View File

@ -89,15 +89,15 @@ void solveLowerLevelSet(T* reorderedMat,
* solve
* @param threadBlockSize The number of threads per threadblock. Leave as -1 if no blocksize is already chosen
*/
template <class T, int blocksize>
void solveUpperLevelSetSplit(T* reorderedMat,
template <int blocksize, class LinearSolverScalar, class MatrixScalar>
void solveUpperLevelSetSplit(MatrixScalar* reorderedMat,
int* rowIndices,
int* colIndices,
int* indexConversion,
int startIdx,
int rowsInLevelSet,
const T* dInv,
T* v,
const MatrixScalar* dInv,
LinearSolverScalar* v,
int threadBlockSize);
/**
@ -117,15 +117,15 @@ void solveUpperLevelSetSplit(T* reorderedMat,
* solve
* @param threadBlockSize The number of threads per threadblock. Leave as -1 if no blocksize is already chosen
*/
template <class T, int blocksize>
void solveLowerLevelSetSplit(T* reorderedLowerMat,
template <int blocksize, class LinearSolverScalar, class MatrixScalar>
void solveLowerLevelSetSplit(MatrixScalar* reorderedLowerMat,
int* rowIndices,
int* colIndices,
int* indexConversion,
int startIdx,
int rowsInLevelSet,
const T* d,
T* v,
const LinearSolverScalar* d,
LinearSolverScalar* v,
int threadBlockSize);
/**
@ -155,6 +155,7 @@ void LUFactorization(T* reorderedMat,
int threadBlockSize);
/**
* TODO: update this doucmentation
* @brief Computes the ILU0 factorization in-place of a bcsr matrix stored in a split format (lower, diagonal and upper
* triangular part)
* @param [out] reorderedLowerMat pointer to GPU memory containing nonzerovalues of the strictly lower triangular sparse
@ -175,18 +176,22 @@ void LUFactorization(T* reorderedMat,
* function
* @param threadBlockSize The number of threads per threadblock. Leave as -1 if no blocksize is already chosen
*/
template <class T, int blocksize>
void LUFactorizationSplit(T* reorderedLowerMat,
template <int blocksize, class InputScalar, class OutputScalar>
void LUFactorizationSplit(InputScalar* srcReorderedLowerMat,
int* lowerRowIndices,
int* lowerColIndices,
T* reorderedUpperMat,
InputScalar* srcReorderedUpperMat,
int* upperRowIndices,
int* upperColIndices,
T* diagonal,
InputScalar* srcDiagonal,
OutputScalar* dstReorderedLowerMat,
OutputScalar* dstReorderedUpperMat,
OutputScalar* dstDiagonal,
int* reorderedToNatural,
int* naturalToReordered,
int startIdx,
int rowsInLevelSet,
bool copyResultToOtherMatrix,
int threadBlockSize);
} // namespace Opm::gpuistl::detail::ILU0