Update of matrix forRows examples. (4f594e30) · Commits · TNL / tnl-dev

Documentation/Examples/Matrices/DenseMatrix/DenseMatrixExample_forRows.cpp

+21 −4

Original line number	Diff line number	Diff line
		@@ -9,17 +9,34 @@ void forRowsExample()
		{
		using MatrixType = TNL::Matrices::DenseMatrix< double, Device >;
		using RowView = typename MatrixType::RowView;
		MatrixType matrix( 5, 5 );
		const int size = 5;
		MatrixType matrix( size, size );

		/***
		* Set the matrix elements.
		*/
		auto f = [=] __cuda_callable__ ( RowView& row ) mutable {
		const int& rowIdx = row.getRowIndex();
		row.setValue( rowIdx, 10 * ( rowIdx + 1 ) );
		if( rowIdx > 0 )
		row.setValue( rowIdx - 1, -1.0 );
		row.setValue( rowIdx, rowIdx + 1.0 );
		if( rowIdx < size - 1 )
		row.setValue( rowIdx + 1, -1.0 );
		};
		matrix.forAllRows( f );
		std::cout << matrix << std::endl;

		/***
		* Set the matrix elements.
		* Now divide each matrix row by its largest element with use of iterators.
		*/
		matrix.forAllRows( f );
		matrix.forAllRows( [=] __cuda_callable__ ( RowView& row ) mutable {
		double largest = std::numeric_limits< double >::lowest();
		for( auto element : row )
		largest = TNL::max( largest, element.value() );
		for( auto element : row )
		element.value() /= largest;
		} );
		std::cout << "Divide each matrix row by its largest element... " << std::endl;
		std::cout << matrix << std::endl;
		}

+1 −1

Original line number	Diff line number	Diff line
		@@ -10,7 +10,7 @@ void forElementsExample()
		auto matrixView = matrix.getView();

		auto f = [=] __cuda_callable__ ( int rowIdx, int columnIdx, int globalIdx, double& value, bool& compute ) {
		if( rowIdx < columnIdx )
		if( columnIdx > rowIdx )
		compute = false;
		else
		value = rowIdx + columnIdx;

+21 −4

Original line number	Diff line number	Diff line
		@@ -9,18 +9,35 @@ void forRowsExample()
		{
		using MatrixType = TNL::Matrices::DenseMatrix< double, Device >;
		using RowView = typename MatrixType::RowView;
		MatrixType matrix( 5, 5 );
		const int size = 5;
		MatrixType matrix( size, size );
		auto view = matrix.getView();

		/***
		* Set the matrix elements.
		*/
		auto f = [=] __cuda_callable__ ( RowView& row ) mutable {
		const int& rowIdx = row.getRowIndex();
		row.setValue( rowIdx, 10 * ( rowIdx + 1 ) );
		if( rowIdx > 0 )
		row.setValue( rowIdx - 1, -1.0 );
		row.setValue( rowIdx, rowIdx + 1.0 );
		if( rowIdx < size - 1 )
		row.setValue( rowIdx + 1, -1.0 );
		};
		view.forAllRows( f );
		std::cout << matrix << std::endl;

		/***
		* Set the matrix elements.
		* Now divide each matrix row by its largest element - with the use of iterators.
		*/
		view.forAllRows( f );
		view.forAllRows( [=] __cuda_callable__ ( RowView& row ) mutable {
		double largest = std::numeric_limits< double >::lowest();
		for( auto element : row )
		largest = TNL::max( largest, element.value() );
		for( auto element : row )
		element.value() /= largest;
		} );
		std::cout << "Divide each matrix row by its largest element... " << std::endl;
		std::cout << matrix << std::endl;
		}

+7 −2

Original line number	Diff line number	Diff line
		@@ -7,7 +7,8 @@
		template< typename Device >
		void getRowExample()
		{
		TNL::Matrices::DenseMatrix< double, Device > matrix( 5, 5 );
		const int size = 5;
		TNL::Matrices::DenseMatrix< double, Device > matrix( size, size );

		/***
		* Create dense matrix view which can be captured by the following lambda
		@@ -17,7 +18,11 @@ void getRowExample()

		auto f = [=] __cuda_callable__ ( int rowIdx ) mutable {
		auto row = matrixView.getRow( rowIdx );
		row.setValue( rowIdx, 10 * ( rowIdx + 1 ) );
		if( rowIdx > 0 )
		row.setValue( rowIdx - 1, -1.0 );
		row.setValue( rowIdx, rowIdx + 1.0 );
		if( rowIdx < size - 1 )
		row.setValue( rowIdx + 1, -1.0 );
		};

		/***

+16 −1

Original line number	Diff line number	Diff line
		@@ -62,11 +62,12 @@ void forRowsExample()
		auto matrix = TNL::Matrices::LambdaMatrixFactory< double, Device, int >::create(
		matrixSize, matrixSize, matrixElements, rowLengths );
		using MatrixType = decltype( matrix );
		using RowView = typename MatrixType::RowView;

		TNL::Matrices::DenseMatrix< double, Device > denseMatrix( matrixSize, matrixSize );
		denseMatrix.setValue( 0.0 );
		auto dense_view = denseMatrix.getView();
		auto f = [=] __cuda_callable__ ( const typename MatrixType::RowView& row ) mutable {
		auto f = [=] __cuda_callable__ ( const RowView& row ) mutable {
		auto dense_row = dense_view.getRow( row.getRowIndex() );
		for( int localIdx = 0; localIdx < row.getSize(); localIdx++ )
		dense_row.setValue( row.getColumnIndex( localIdx ), row.getValue( localIdx ) );
		@@ -75,6 +76,20 @@ void forRowsExample()

		std::cout << "Laplace operator lambda matrix: " << std::endl << matrix << std::endl;
		std::cout << "Laplace operator dense matrix: " << std::endl << denseMatrix << std::endl;

		/***
		* Compute sum of elements in each row and store it into a vector.
		*/
		TNL::Containers::Vector< double, Device > sum_vector( matrixSize );
		auto sum_view = sum_vector.getView();
		matrix.forAllRows( [=] __cuda_callable__ ( const RowView& row ) mutable {
		double sum( 0.0 );
		for( auto element : row )
		sum += TNL::abs( element.value() );
		sum_view[ row.getRowIndex() ] = sum;
		} );

		std::cout << "Sums in matrix rows = " << sum_vector << std::endl;
		}

		int main( int argc, char* argv[] )