diff --git a/src/TNL/Matrices/Dense.h b/src/TNL/Matrices/Dense.h
index 757fa4eae4fcc34d5fa0e8752dd8f658aaf66daa..9c05297d152f6809a140edbbe302dcf3e394f0e2 100644
--- a/src/TNL/Matrices/Dense.h
+++ b/src/TNL/Matrices/Dense.h
@@ -34,7 +34,8 @@ class Dense : public Matrix< Real, Device, Index >
using RealType = Real;
using DeviceType = Device;
using IndexType = Index;
- using BaseType = Matrix< Real, Device, Index >;
+ using RealAllocatorType = RealAllocator;
+ using BaseType = Matrix< Real, Device, Index, RealAllocator >;
using ValuesType = typename BaseType::ValuesVector;
using ValuesViewType = typename ValuesType::ViewType;
using SegmentsType = Containers::Segments::Ellpack< DeviceType, IndexType, typename Allocators::Default< Device >::template Allocator< IndexType >, RowMajorOrder, 1 >;
diff --git a/src/TNL/Matrices/Dense.hpp b/src/TNL/Matrices/Dense.hpp
index 7517c6b0eaadd31a1242f88ac2371ed1b304c2a2..7a6c4becc383e4398813b1c8d4b1645ad973ad7a 100644
--- a/src/TNL/Matrices/Dense.hpp
+++ b/src/TNL/Matrices/Dense.hpp
@@ -377,7 +377,6 @@ forRows( IndexType first, IndexType last, Function& function ) const
return true;
};
this->segments.forSegments( first, last, f );
-
}
template< typename Real,
@@ -392,11 +391,10 @@ forRows( IndexType first, IndexType last, Function& function )
{
auto values_view = this->values.getView();
auto f = [=] __cuda_callable__ ( IndexType rowIdx, IndexType columnIdx, IndexType globalIdx ) mutable -> bool {
- function( rowIdx, columnIdx, values_view[ globalIdx ] );
+ function( rowIdx, columnIdx, globalIdx, values_view[ globalIdx ] );
return true;
};
this->segments.forSegments( first, last, f );
-
}
template< typename Real,
@@ -959,6 +957,50 @@ void Dense< Real, Device, Index, RowMajorOrder, RealAllocator >::performSORItera
x[ row ] = ( 1.0 - omega ) * x[ row ] + omega / diagonalValue * ( b[ row ] - sum );
}
+/*template< typename Real,
+ typename Device,
+ typename Index,
+ bool RowMajorOrder,
+ typename RealAllocator >
+Dense< Real, Device, Index, RowMajorOrder, RealAllocator >&
+Dense< Real, Device, Index, RowMajorOrder, RealAllocator >::
+operator=( const Dense< Real, Device, Index, RowMajorOrder, RealAllocator >& matrix )
+{
+ const IndexType bufferRowsCount( 128 );
+ const IndexType columns = this->getColumns();
+ const size_t bufferSize = bufferRowsCount * columns;
+ Containers::Vector< RealType, Device, IndexType, RealAllocatorType > sourceValuesBuffer( bufferSize );
+ Containers::Vector< RealType, DeviceType, IndexType, RealAllocatorType > destinationValuesBuffer( bufferSize );
+ auto sourceValuesBuffer_view = sourceValuesBuffer.getView();
+ auto destinationValuesBuffer_view = destinationValuesBuffer.getView();
+
+ IndexType baseRow( 0 );
+ const IndexType rowsCount = this->getRows();
+ while( baseRow < rowsCount )
+ {
+ const IndexType lastRow = min( baseRow + bufferRowsCount, rowsCount );
+
+ ////
+ // Copy matrix elements into buffer
+ auto f1 = [=] __cuda_callable__ ( Index rowIdx, Index columnIdx, Index globalIdx, const Real& value ) mutable {
+ const IndexType bufferIdx = ( rowIdx - baseRow ) * columns + columnIdx;
+ sourceValuesBuffer_view[ bufferIdx ] = value;
+ };
+ matrix.forRows( baseRow, lastRow, f1 );
+ destinationValuesBuffer = sourceValuesBuffer;
+
+ ////
+ // Copy buffer to this matrix
+ auto f2 = [=] __cuda_callable__ ( IndexType rowIdx, IndexType columnIdx, IndexType globalIdx, RealType& value ) mutable {
+ const IndexType bufferIdx = ( rowIdx - baseRow ) * columns + columnIdx;
+ value = destinationValuesBuffer_view[ bufferIdx ];
+ };
+ this->forRows( baseRow, lastRow, f2 );
+ baseRow += bufferRowsCount;
+ }
+ return *this;
+}*/
+
template< typename Real,
typename Device,
typename Index,
@@ -969,15 +1011,57 @@ Dense< Real, Device, Index, RowMajorOrder, RealAllocator >&
Dense< Real, Device, Index, RowMajorOrder, RealAllocator >::
operator=( const Dense< Real_, Device_, Index_, RowMajorOrder_, RealAllocator_ >& matrix )
{
+ this->setLike( matrix );
if( RowMajorOrder == RowMajorOrder_ )
- {
- this->setLike( matrix );
this->values = matrix.getValues();
- }
else
{
-
+ if( std::is_same< DeviceType, Device_ >::value )
+ {
+ auto this_view = this->getView();
+ auto f = [=] __cuda_callable__ ( Index_ rowIdx, Index_ columnIdx, Index_ globalIdx, const Real_& value ) mutable {
+ this_view.getRow( rowIdx ).setElement( columnIdx, value );
+ };
+ matrix.forAllRows( f );
+ }
+ else
+ {
+ const IndexType bufferRowsCount( 128 );
+ const IndexType columns = this->getColumns();
+ const size_t bufferSize = bufferRowsCount * columns;
+ Containers::Vector< RealType, Device_, IndexType, RealAllocator_ > sourceValuesBuffer( bufferSize );
+ Containers::Vector< RealType, DeviceType, IndexType, RealAllocatorType > destinationValuesBuffer( bufferSize );
+ auto sourceValuesBuffer_view = sourceValuesBuffer.getView();
+ auto destinationValuesBuffer_view = destinationValuesBuffer.getView();
+
+ IndexType baseRow( 0 );
+ const IndexType rowsCount = this->getRows();
+ while( baseRow < rowsCount )
+ {
+ const IndexType lastRow = min( baseRow + bufferRowsCount, rowsCount );
+
+ ////
+ // Copy matrix elements into buffer
+ auto f1 = [=] __cuda_callable__ ( Index_ rowIdx, Index_ columnIdx, Index_ globalIdx, const Real_& value ) mutable {
+ const IndexType bufferIdx = ( rowIdx - baseRow ) * columns + columnIdx;
+ sourceValuesBuffer_view[ bufferIdx ] = value;
+ };
+ matrix.forRows( baseRow, lastRow, f1 );
+
+ destinationValuesBuffer = sourceValuesBuffer;
+
+ ////
+ // Copy buffer to this matrix
+ auto f2 = [=] __cuda_callable__ ( IndexType rowIdx, IndexType columnIdx, IndexType globalIdx, RealType& value ) mutable {
+ const IndexType bufferIdx = ( rowIdx - baseRow ) * columns + columnIdx;
+ value = destinationValuesBuffer_view[ bufferIdx ];
+ };
+ this->forRows( baseRow, lastRow, f2 );
+ baseRow += bufferRowsCount;
+ }
+ }
}
+ return *this;
}
template< typename Real,
diff --git a/src/UnitTests/Matrices/DenseMatrixTest.h b/src/UnitTests/Matrices/DenseMatrixTest.h
index 2ddd19c7af3fa4eab6941a3ef25b1dd777acf67b..686602ebd10a33d7f73185cabbd274fe09ec1669 100644
--- a/src/UnitTests/Matrices/DenseMatrixTest.h
+++ b/src/UnitTests/Matrices/DenseMatrixTest.h
@@ -1169,9 +1169,51 @@ void test_PerformSORIteration()
template< typename Matrix >
void test_AssignmentOperator()
{
- EXPECT_EQ( 1, 0 );
+ using RealType = typename Matrix::RealType;
+ using DeviceType = typename Matrix::DeviceType;
+ using IndexType = typename Matrix::IndexType;
+
+ using DenseHost = TNL::Matrices::Dense< RealType, TNL::Devices::Host, IndexType >;
+ using DenseCuda = TNL::Matrices::Dense< RealType, TNL::Devices::Cuda, IndexType >;
+
+ const IndexType rows( 10 ), columns( 10 );
+ DenseHost hostMatrix( rows, columns );
+ for( IndexType i = 0; i < columns; i++ )
+ for( IndexType j = 0; j <= i; j++ )
+ hostMatrix( i, j ) = i + j;
+
+ Matrix matrix( rows, columns );
+ matrix.getValues() = 0.0;
+ matrix = hostMatrix;
+ for( IndexType i = 0; i < columns; i++ )
+ for( IndexType j = 0; j < rows; j++ )
+ {
+ if( j > i )
+ EXPECT_EQ( matrix.getElement( i, j ), 0.0 );
+ else
+ EXPECT_EQ( matrix.getElement( i, j ), i + j );
+ }
+
+#ifdef HAVE_CUDA
+ DenseCuda cudaMatrix( rows, columns );
+ for( IndexType i = 0; i < columns; i++ )
+ for( IndexType j = 0; j <= i; j++ )
+ cudaMatrix.setElement( i, j, i + j );
+
+ matrix.getValues() = 0.0;
+ matrix = cudaMatrix;
+ for( IndexType i = 0; i < columns; i++ )
+ for( IndexType j = 0; j < rows; j++ )
+ {
+ if( j > i )
+ EXPECT_EQ( matrix.getElement( i, j ), 0.0 );
+ else
+ EXPECT_EQ( matrix.getElement( i, j ), i + j );
+ }
+#endif
}
+
template< typename Matrix >
void test_SaveAndLoad()
{
@@ -1426,6 +1468,13 @@ TYPED_TEST( MatrixTest, addMatrixTest )
test_AddMatrix< MatrixType >();
}
+TYPED_TEST( MatrixTest, assignmentOperatorTest )
+{
+ using MatrixType = typename TestFixture::MatrixType;
+
+ test_AssignmentOperator< MatrixType >();
+}
+
TYPED_TEST( MatrixTest, saveAndLoadTest )
{
using MatrixType = typename TestFixture::MatrixType;