Removed legacy CSR from the ILU0 preconditioner (b40fbbbe) · Commits · TNL / tnl-dev

src/TNL/Solvers/Linear/Preconditioners/ILU0.h

+3 −3

Original line number	Diff line number	Diff line
		@@ -20,7 +20,6 @@
		#include <TNL/Exceptions/NotImplementedError.h>

		#if defined(HAVE_CUDA) && defined(HAVE_CUSPARSE)
		#include <Benchmarks/SpMV/ReferenceFormats/Legacy/CSR.h>
		#include <cusparse.h>
		#endif

		@@ -77,7 +76,8 @@ public:

		protected:
		// The factors L and U are stored separately and the rows of U are reversed.
		Matrices::SparseMatrix< RealType, DeviceType, IndexType, Matrices::GeneralMatrix, Algorithms::Segments::CSRDefault > L, U;
		using CSR = Matrices::SparseMatrix< RealType, DeviceType, IndexType, Matrices::GeneralMatrix, Algorithms::Segments::CSRScalar >;
		CSR L, U;

		// Specialized methods to distinguish between normal and distributed matrices
		// in the implementation.
		@@ -136,7 +136,7 @@ public:
		protected:

		#if defined(HAVE_CUDA) && defined(HAVE_CUSPARSE)
		using CSR = Benchmarks::SpMV::ReferenceFormats::Legacy::CSR< RealType, DeviceType, IndexType >;
		using CSR = Matrices::SparseMatrix< RealType, DeviceType, IndexType, Matrices::GeneralMatrix, Algorithms::Segments::CSRScalar >;
		Pointers::UniquePointer< CSR > A, L, U;
		Containers::Vector< RealType, DeviceType, IndexType > y;

src/TNL/Solvers/Linear/Preconditioners/ILU0_impl.h

+11 −11

Original line number	Diff line number	Diff line
		@@ -203,17 +203,17 @@ update( const MatrixPointer& matrixPointer )
		int pBufferSize_A, pBufferSize_L, pBufferSize_U;
		cusparseDcsrilu02_bufferSize( handle, N, nnz_A, descr_A,
		A->getValues().getData(),
		A->getRowPointers().getData(),
		A->getSegments().getOffsets().getData(),
		A->getColumnIndexes().getData(),
		info_A, &pBufferSize_A );
		cusparseDcsrsv2_bufferSize( handle, trans_L, N, nnz_L, descr_L,
		L->getValues().getData(),
		L->getRowPointers().getData(),
		L->getSegments().getOffsets().getData(),
		L->getColumnIndexes().getData(),
		info_L, &pBufferSize_L );
		cusparseDcsrsv2_bufferSize( handle, trans_U, N, nnz_U, descr_U,
		U->getValues().getData(),
		U->getRowPointers().getData(),
		U->getSegments().getOffsets().getData(),
		U->getColumnIndexes().getData(),
		info_U, &pBufferSize_U );
		TNL_CHECK_CUDA_DEVICE;
		@@ -223,7 +223,7 @@ update( const MatrixPointer& matrixPointer )
		// Symbolic analysis of the incomplete LU decomposition
		cusparseDcsrilu02_analysis( handle, N, nnz_A, descr_A,
		A->getValues().getData(),
		A->getRowPointers().getData(),
		A->getSegments().getOffsets().getData(),
		A->getColumnIndexes().getData(),
		info_A, policy_A, pBuffer.getData() );
		int structural_zero;
		@@ -240,19 +240,19 @@ update( const MatrixPointer& matrixPointer )
		// pattern as L (U), so we can do the analysis for csrsv2 on the matrix A.
		// cusparseDcsrsv2_analysis( handle, trans_L, N, nnz_A, descr_L,
		// A->getValues().getData(),
		// A->getRowPointers().getData(),
		// A->getSegments().getOffsets().getData(),
		// A->getColumnIndexes().getData(),
		// info_L, policy_L, pBuffer.getData() );
		// cusparseDcsrsv2_analysis( handle, trans_U, N, nnz_A, descr_U,
		// A->getValues().getData(),
		// A->getRowPointers().getData(),
		// A->getSegments().getOffsets().getData(),
		// A->getColumnIndexes().getData(),
		// info_U, policy_U, pBuffer.getData() );

		// Numerical incomplete LU decomposition
		cusparseDcsrilu02( handle, N, nnz_A, descr_A,
		A->getValues().getData(),
		A->getRowPointers().getData(),
		A->getSegments().getOffsets().getData(),
		A->getColumnIndexes().getData(),
		info_A, policy_A, pBuffer.getData() );
		int numerical_zero;
		@@ -269,12 +269,12 @@ update( const MatrixPointer& matrixPointer )
		// Analysis for the triangular solves for L and U
		cusparseDcsrsv2_analysis( handle, trans_L, N, nnz_L, descr_L,
		L->getValues().getData(),
		L->getRowPointers().getData(),
		L->getSegments().getOffsets().getData(),
		L->getColumnIndexes().getData(),
		info_L, policy_L, pBuffer.getData() );
		cusparseDcsrsv2_analysis( handle, trans_U, N, nnz_U, descr_U,
		U->getValues().getData(),
		U->getRowPointers().getData(),
		U->getSegments().getOffsets().getData(),
		U->getColumnIndexes().getData(),
		info_U, policy_U, pBuffer.getData() );
		TNL_CHECK_CUDA_DEVICE;
		@@ -386,7 +386,7 @@ solve( ConstVectorViewType b, VectorViewType x ) const
		// Step 1: solve y from Ly = b
		cusparseDcsrsv2_solve( handle, trans_L, N, nnz_L, &alpha, descr_L,
		L->getValues().getData(),
		L->getRowPointers().getData(),
		L->getSegments().getOffsets().getData(),
		L->getColumnIndexes().getData(),
		info_L,
		b.getData(),
		@@ -396,7 +396,7 @@ solve( ConstVectorViewType b, VectorViewType x ) const
		// Step 2: solve x from Ux = y
		cusparseDcsrsv2_solve( handle, trans_U, N, nnz_U, &alpha, descr_U,
		U->getValues().getData(),
		U->getRowPointers().getData(),
		U->getSegments().getOffsets().getData(),
		U->getColumnIndexes().getData(),
		info_U,
		y.getData(),