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Commit 733401dc authored by Tomáš Oberhuber's avatar Tomáš Oberhuber Committed by Tomáš Oberhuber
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Fixed tridiagonal to sparse matrix assignment. Debugging multidiagonal to sparse matrix assignment.

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src/TNL/Matrices/MultidiagonalMatrixRowView.hpp
+ 1
− 1
View file @ 733401dc
......@@ -30,7 +30,7 @@ auto
MultidiagonalMatrixRowView< ValuesView, Indexer, DiagonalsShiftsView >::
getSize() const -> IndexType
{
return indexer.getRowSize();
return diagonalsShifts.getSize();//indexer.getRowSize( rowIdx );
}
template< typename ValuesView, typename Indexer, typename DiagonalsShiftsView >
......
src/TNL/Matrices/MultidiagonalMatrixView.hpp
+ 2
− 1
View file @ 733401dc
......@@ -393,12 +393,13 @@ forRows( IndexType first, IndexType last, Function& function ) const
const IndexType diagonalsCount = this->diagonalsShifts.getSize();
const IndexType columns = this->getColumns();
const auto indexer = this->indexer;
bool compute( true );
auto f = [=] __cuda_callable__ ( IndexType rowIdx ) mutable {
for( IndexType localIdx = 0; localIdx < diagonalsCount; localIdx++ )
{
const IndexType columnIdx = rowIdx + diagonalsShifts_view[ localIdx ];
if( columnIdx >= 0 && columnIdx < columns )
function( rowIdx, localIdx, columnIdx, values_view[ indexer.getGlobalIndex( rowIdx, localIdx ) ] );
function( rowIdx, localIdx, columnIdx, values_view[ indexer.getGlobalIndex( rowIdx, localIdx ) ], compute );
}
};
Algorithms::ParallelFor< DeviceType >::exec( first, last, f );
......
src/TNL/Matrices/SparseMatrix.hpp
+ 1
− 2
View file @ 733401dc
......@@ -682,7 +682,6 @@ operator=( const RHSMatrix& matrix )
Containers::Vector< IndexType, DeviceType, IndexType > rowLocalIndexes( matrix.getRows() );
rowLocalIndexes = 0;
// TODO: use getConstView when it works
const auto matrixView = const_cast< RHSMatrix& >( matrix ).getView();
const IndexType paddingIndex = this->getPaddingIndex();
......@@ -697,7 +696,7 @@ operator=( const RHSMatrix& matrix )
auto f = [=] __cuda_callable__ ( RHSIndexType rowIdx, RHSIndexType localIdx_, RHSIndexType columnIndex, const RHSRealType& value, bool& compute ) mutable {
RealType inValue( 0.0 );
IndexType localIdx( rowLocalIndexes_view[ rowIdx ] );
if( columnIndex != paddingIndex )
if( value != 0.0 && columnIndex != paddingIndex )
{
IndexType thisGlobalIdx = segments_view.getGlobalIndex( rowIdx, localIdx++ );
columns_view[ thisGlobalIdx ] = columnIndex;
......
src/UnitTests/Matrices/SparseMatrixCopyTest.h
+ 94
− 23
View file @ 733401dc
......@@ -421,7 +421,6 @@ void testConversion()
checkAntiTriDiagMatrix( antiTriDiag1 );
Matrix2 antiTriDiag2;
//TNL::Matrices::copySparseMatrix( antiTriDiag2, antiTriDiag1 );
antiTriDiag2 = antiTriDiag1;
checkAntiTriDiagMatrix( antiTriDiag2 );
}
......@@ -433,7 +432,6 @@ void testConversion()
checkUnevenRowSizeMatrix( unevenRowSize1 );
Matrix2 unevenRowSize2;
//TNL::Matrices::copySparseMatrix( unevenRowSize2, unevenRowSize1 );
unevenRowSize2 = unevenRowSize1;
checkUnevenRowSizeMatrix( unevenRowSize2 );
}
......@@ -451,21 +449,19 @@ void tridiagonalMatrixAssignment()
const IndexType rows( 10 ), columns( 10 );
TridiagonalHost hostMatrix( rows, columns );
for( IndexType i = 0; i < columns; i++ )
for( IndexType j = TNL::max( 0, i - 1 ); j < TNL::min( rows, i + 2 ); j++ )
for( IndexType i = 0; i < rows; i++ )
for( IndexType j = TNL::max( 0, i - 1 ); j < TNL::min( columns, i + 2 ); j++ )
hostMatrix.setElement( i, j, i + j );
std::cerr << hostMatrix << std::endl;
Matrix matrix;
matrix = hostMatrix;
std::cerr << matrix << std::endl;
using RowCapacitiesType = typename Matrix::RowsCapacitiesType;
RowCapacitiesType rowCapacities;
matrix.getCompressedRowLengths( rowCapacities );
RowCapacitiesType exactRowLengths{ 0, 3, 3, 3, 3, 3, 3, 3, 3, 2 };
RowCapacitiesType exactRowLengths{ 1, 3, 3, 3, 3, 3, 3, 3, 3, 2 };
EXPECT_EQ( rowCapacities, exactRowLengths );
for( IndexType i = 0; i < columns; i++ )
for( IndexType j = 0; j < rows; j++ )
for( IndexType i = 0; i < rows; i++ )
for( IndexType j = 0; j < columns; j++ )
{
if( abs( i - j ) > 1 )
EXPECT_EQ( matrix.getElement( i, j ), 0.0 );
......@@ -476,15 +472,11 @@ void tridiagonalMatrixAssignment()
#ifdef HAVE_CUDA
TridiagonalCuda cudaMatrix( rows, columns );
cudaMatrix = hostMatrix;
/*for( IndexType i = 0; i < columns; i++ )
for( IndexType j = TNL::max( 0, i - 1 ); j < TNL::min( row, i + 1 ); j++ )
cudaMatrix.setElement( i, j, i + j );*/
matrix = cudaMatrix;
matrix.getCompressedRowLengths( rowCapacities );
EXPECT_EQ( rowCapacities, exactRowLengths );
for( IndexType i = 0; i < columns; i++ )
for( IndexType j = 0; j < rows; j++ )
for( IndexType i = 0; i < rows; i++ )
for( IndexType j = 0; j < columns; j++ )
{
if( abs( i - j ) > 1 )
EXPECT_EQ( matrix.getElement( i, j ), 0.0 );
......@@ -492,7 +484,58 @@ void tridiagonalMatrixAssignment()
EXPECT_EQ( matrix.getElement( i, j ), i + j );
}
#endif
}
template< typename Matrix >
void multidiagonalMatrixAssignment()
{
using RealType = typename Matrix::RealType;
using DeviceType = typename Matrix::DeviceType;
using IndexType = typename Matrix::IndexType;
using MultidiagonalHost = TNL::Matrices::Multidiagonal< RealType, TNL::Devices::Host, IndexType >;
using MultidiagonalCuda = TNL::Matrices::Multidiagonal< RealType, TNL::Devices::Cuda, IndexType >;
using DiagonalsShiftsType = typename MultidiagonalHost::DiagonalsShiftsType;
DiagonalsShiftsType diagonals{ -4, -2, 0, 1, 3, 5 };
const IndexType rows( 10 ), columns( 10 );
MultidiagonalHost hostMatrix( rows, columns, diagonals );
for( IndexType i = 0; i < rows; i++ )
for( IndexType j = 0; j < columns; j++ )
if( diagonals.containsValue( i - j ) )
hostMatrix.setElement( i, j, i + j );
Matrix matrix;
matrix = hostMatrix;
using RowCapacitiesType = typename Matrix::RowsCapacitiesType;
RowCapacitiesType rowCapacities;
matrix.getCompressedRowLengths( rowCapacities );
RowCapacitiesType exactRowLengths{ 1, 3, 3, 3, 3, 3, 3, 3, 3, 2 };
EXPECT_EQ( rowCapacities, exactRowLengths );
for( IndexType i = 0; i < rows; i++ )
for( IndexType j = 0; j < columns; j++ )
{
if( diagonals.containsValue( i - j ) )
EXPECT_EQ( matrix.getElement( i, j ), 0.0 );
else
EXPECT_EQ( matrix.getElement( i, j ), i + j );
}
#ifdef HAVE_CUDA
MultidiagonalCuda cudaMatrix( rows, columns, diagonals );
cudaMatrix = hostMatrix;
matrix = cudaMatrix;
matrix.getCompressedRowLengths( rowCapacities );
EXPECT_EQ( rowCapacities, exactRowLengths );
for( IndexType i = 0; i < rows; i++ )
for( IndexType j = 0; j < columns; j++ )
{
if( diagonals.containsValue( i - j ) > 1 )
EXPECT_EQ( matrix.getElement( i, j ), 0.0 );
else
EXPECT_EQ( matrix.getElement( i, j ), i + j );
}
#endif
}
template< typename Matrix >
......@@ -530,10 +573,6 @@ void denseMatrixAssignment()
#ifdef HAVE_CUDA
DenseCuda cudaMatrix( rows, columns );
cudaMatrix = hostMatrix;
/*for( IndexType i = 0; i < columns; i++ )
for( IndexType j = 0; j <= i; j++ )
cudaMatrix.setElement( i, j, i + j );*/
matrix = cudaMatrix;
matrix.getCompressedRowLengths( rowCapacities );
EXPECT_EQ( rowCapacities, exactRowLengths );
......@@ -547,7 +586,7 @@ void denseMatrixAssignment()
}
#endif
}
/*
TEST( SparseMatrixCopyTest, CSR_HostToHost )
{
testCopyAssignment< CSR_host, CSR_host >();
......@@ -616,8 +655,8 @@ TEST( SparseMatrixCopyTest, SlicedEllpack_CudaToCuda )
}
#endif
// test conversion between formats
////
// Test of conversion between formats
TEST( SparseMatrixCopyTest, CSR_to_Ellpack_host )
{
testConversion< CSR_host, E_host >();
......@@ -679,7 +718,6 @@ TEST( SparseMatrixCopyTest, SlicedEllpack_to_Ellpack_cuda )
testConversion< SE_cuda, E_cuda >();
}
#endif
*/
////
// Tridiagonal matrix assignment test
......@@ -715,8 +753,41 @@ TEST( SparseMatrixCopyTest, TridiagonalMatrixAssignment_to_SlicedEllpack_cuda )
}
#endif // HAVE_CUDA
////
// Multidiagonal matrix assignment test
TEST( SparseMatrixCopyTest, MultidiagonalMatrixAssignment_to_CSR_host )
{
multidiagonalMatrixAssignment< CSR_host >();
}
TEST( SparseMatrixCopyTest, MultidiagonalMatrixAssignment_to_Ellpack_host )
{
multidiagonalMatrixAssignment< E_host >();
}
TEST( SparseMatrixCopyTest, MultidiagonalMatrixAssignment_to_SlicedEllpack_host )
{
multidiagonalMatrixAssignment< SE_host >();
}
#ifdef HAVE_CUDA
TEST( SparseMatrixCopyTest, MultidiagonalMatrixAssignment_to_CSR_cuda )
{
multidiagonalMatrixAssignment< CSR_cuda >();
}
TEST( SparseMatrixCopyTest, MultidiagonalMatrixAssignment_to_Ellpack_cuda )
{
multidiagonalMatrixAssignment< E_cuda >();
}
TEST( SparseMatrixCopyTest, MultidiagonalMatrixAssignment_to_SlicedEllpack_cuda )
{
multidiagonalMatrixAssignment< SE_cuda >();
}
#endif // HAVE_CUDA
////
// Dense matrix assignment test
TEST( SparseMatrixCopyTest, DenseMatrixAssignment_to_CSR_host )
{
......
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