diff --git a/src/TNL/Matrices/Dense.hpp b/src/TNL/Matrices/Dense.hpp
index 49e218c779a3c80339436fff3adada73619466df..c4deeb6fa4834ab6192887d125d1dc7be7f3618f 100644
--- a/src/TNL/Matrices/Dense.hpp
+++ b/src/TNL/Matrices/Dense.hpp
@@ -78,7 +78,7 @@ getSerializationType()
{
return String( "Matrices::Dense< " ) +
TNL::getSerializationType< RealType >() + ", [any_device], " +
- TNL::getSerializationType< IndexType >() +
+ TNL::getSerializationType< IndexType >() + ", " +
( RowMajorOrder ? "true" : "false" ) + ", [any_allocator] >";
}
diff --git a/src/UnitTests/Matrices/DenseMatrixTest.h b/src/UnitTests/Matrices/DenseMatrixTest.h
index 8c3132caf8f348f310154bbd5d0ff1305f139c7e..183783ea3ef20cd778d66fd6e1674bea5e34dfb0 100644
--- a/src/UnitTests/Matrices/DenseMatrixTest.h
+++ b/src/UnitTests/Matrices/DenseMatrixTest.h
@@ -31,24 +31,16 @@ static const char* TEST_FILE_NAME = "test_DenseMatrixTest.tnl";
#include <gtest/gtest.h>
-template< typename MatrixHostFloat, typename MatrixHostInt >
-void host_test_GetType()
+void test_GetSerializationType()
{
- MatrixHostFloat mtrxHostFloat;
- MatrixHostInt mtrxHostInt;
-
- EXPECT_EQ( mtrxHostFloat.getType(), TNL::String( "Matrices::Dense< float, Devices::Host, int >" ) );
- EXPECT_EQ( mtrxHostInt.getType(), TNL::String( "Matrices::Dense< int, Devices::Host, int >" ) );
-}
-
-template< typename MatrixCudaFloat, typename MatrixCudaInt >
-void cuda_test_GetType()
-{
- MatrixCudaFloat mtrxCudaFloat;
- MatrixCudaInt mtrxCudaInt;
-
- EXPECT_EQ( mtrxCudaFloat.getType(), TNL::String( "Matrices::Dense< float, Devices::Cuda, int >" ) );
- EXPECT_EQ( mtrxCudaInt.getType(), TNL::String( "Matrices::Dense< int, Devices::Cuda, int >" ) );
+ EXPECT_EQ( ( TNL::Matrices::Dense< float, TNL::Devices::Host, int, true >::getSerializationType() ), TNL::String( "Matrices::Dense< float, [any_device], int, true, [any_allocator] >" ) );
+ EXPECT_EQ( ( TNL::Matrices::Dense< int, TNL::Devices::Host, int, true >::getSerializationType() ), TNL::String( "Matrices::Dense< int, [any_device], int, true, [any_allocator] >" ) );
+ EXPECT_EQ( ( TNL::Matrices::Dense< float, TNL::Devices::Cuda, int, true >::getSerializationType() ), TNL::String( "Matrices::Dense< float, [any_device], int, true, [any_allocator] >" ) );
+ EXPECT_EQ( ( TNL::Matrices::Dense< int, TNL::Devices::Cuda, int, true >::getSerializationType() ), TNL::String( "Matrices::Dense< int, [any_device], int, true, [any_allocator] >" ) );
+ EXPECT_EQ( ( TNL::Matrices::Dense< float, TNL::Devices::Host, int, false >::getSerializationType() ), TNL::String( "Matrices::Dense< float, [any_device], int, false, [any_allocator] >" ) );
+ EXPECT_EQ( ( TNL::Matrices::Dense< int, TNL::Devices::Host, int, false >::getSerializationType() ), TNL::String( "Matrices::Dense< int, [any_device], int, false, [any_allocator] >" ) );
+ EXPECT_EQ( ( TNL::Matrices::Dense< float, TNL::Devices::Cuda, int, false >::getSerializationType() ), TNL::String( "Matrices::Dense< float, [any_device], int, false, [any_allocator] >" ) );
+ EXPECT_EQ( ( TNL::Matrices::Dense< int, TNL::Devices::Cuda, int, false >::getSerializationType() ), TNL::String( "Matrices::Dense< int, [any_device], int, false, [any_allocator] >" ) );
}
template< typename Matrix >
@@ -1377,6 +1369,11 @@ using MatrixTypes = ::testing::Types
TYPED_TEST_SUITE( MatrixTest, MatrixTypes );
+TYPED_TEST( MatrixTest, getSerializationType )
+{
+ test_GetSerializationType();
+}
+
TYPED_TEST( MatrixTest, setDimensionsTest )
{
using MatrixType = typename TestFixture::MatrixType;
diff --git a/src/UnitTests/Matrices/TridiagonalMatrixTest.cpp b/src/UnitTests/Matrices/TridiagonalMatrixTest.cpp
new file mode 100644
index 0000000000000000000000000000000000000000..a56349360a8ba6667dfb567c64a6f4fa0d7ff48f
--- /dev/null
+++ b/src/UnitTests/Matrices/TridiagonalMatrixTest.cpp
@@ -0,0 +1,11 @@
+/***************************************************************************
+ DenseMatrixTest.cpp - description
+ -------------------
+ begin : Nov 10, 2018
+ copyright : (C) 2018 by Tomas Oberhuber et al.
+ email : tomas.oberhuber@fjfi.cvut.cz
+ ***************************************************************************/
+
+/* See Copyright Notice in tnl/Copyright */
+
+#include "DenseMatrixTest.h"
\ No newline at end of file
diff --git a/src/UnitTests/Matrices/TridiagonalMatrixTest.cu b/src/UnitTests/Matrices/TridiagonalMatrixTest.cu
new file mode 100644
index 0000000000000000000000000000000000000000..11d45efdb5f30ecc236e8086d9149f8f35b8c0d3
--- /dev/null
+++ b/src/UnitTests/Matrices/TridiagonalMatrixTest.cu
@@ -0,0 +1,11 @@
+/***************************************************************************
+ DenseMatrixTest.cu - description
+ -------------------
+ begin : Nov 10, 2018
+ copyright : (C) 2018 by Tomas Oberhuber et al.
+ email : tomas.oberhuber@fjfi.cvut.cz
+ ***************************************************************************/
+
+/* See Copyright Notice in tnl/Copyright */
+
+#include "DenseMatrixTest.h"
\ No newline at end of file
diff --git a/src/UnitTests/Matrices/TridiagonalMatrixTest.h b/src/UnitTests/Matrices/TridiagonalMatrixTest.h
new file mode 100644
index 0000000000000000000000000000000000000000..8c3132caf8f348f310154bbd5d0ff1305f139c7e
--- /dev/null
+++ b/src/UnitTests/Matrices/TridiagonalMatrixTest.h
@@ -0,0 +1,1614 @@
+/***************************************************************************
+ DenseMatrixTest.h - description
+ -------------------
+ begin : Nov 10, 2018
+ copyright : (C) 2018 by Tomas Oberhuber et al.
+ email : tomas.oberhuber@fjfi.cvut.cz
+ ***************************************************************************/
+
+/* See Copyright Notice in tnl/Copyright */
+
+#include <TNL/Devices/Host.h>
+#include <TNL/Matrices/Matrix.h>
+#include <TNL/Matrices/Dense.h>
+#include <TNL/Containers/Array.h>
+
+#include <TNL/Containers/Vector.h>
+#include <TNL/Containers/VectorView.h>
+#include <TNL/Math.h>
+#include <iostream>
+
+using Dense_host_float = TNL::Matrices::Dense< float, TNL::Devices::Host, int >;
+using Dense_host_int = TNL::Matrices::Dense< int, TNL::Devices::Host, int >;
+
+using Dense_cuda_float = TNL::Matrices::Dense< float, TNL::Devices::Cuda, int >;
+using Dense_cuda_int = TNL::Matrices::Dense< int, TNL::Devices::Cuda, int >;
+
+static const char* TEST_FILE_NAME = "test_DenseMatrixTest.tnl";
+
+#ifdef HAVE_GTEST
+#include <type_traits>
+
+#include <gtest/gtest.h>
+
+template< typename MatrixHostFloat, typename MatrixHostInt >
+void host_test_GetType()
+{
+ MatrixHostFloat mtrxHostFloat;
+ MatrixHostInt mtrxHostInt;
+
+ EXPECT_EQ( mtrxHostFloat.getType(), TNL::String( "Matrices::Dense< float, Devices::Host, int >" ) );
+ EXPECT_EQ( mtrxHostInt.getType(), TNL::String( "Matrices::Dense< int, Devices::Host, int >" ) );
+}
+
+template< typename MatrixCudaFloat, typename MatrixCudaInt >
+void cuda_test_GetType()
+{
+ MatrixCudaFloat mtrxCudaFloat;
+ MatrixCudaInt mtrxCudaInt;
+
+ EXPECT_EQ( mtrxCudaFloat.getType(), TNL::String( "Matrices::Dense< float, Devices::Cuda, int >" ) );
+ EXPECT_EQ( mtrxCudaInt.getType(), TNL::String( "Matrices::Dense< int, Devices::Cuda, int >" ) );
+}
+
+template< typename Matrix >
+void test_SetDimensions()
+{
+ using RealType = typename Matrix::RealType;
+ using DeviceType = typename Matrix::DeviceType;
+ using IndexType = typename Matrix::IndexType;
+
+ const IndexType rows = 9;
+ const IndexType cols = 8;
+
+ Matrix m;
+ m.setDimensions( rows, cols );
+
+ EXPECT_EQ( m.getRows(), 9 );
+ EXPECT_EQ( m.getColumns(), 8 );
+}
+
+template< typename Matrix1, typename Matrix2 >
+void test_SetLike()
+{
+ using RealType = typename Matrix1::RealType;
+ using DeviceType = typename Matrix1::DeviceType;
+ using IndexType = typename Matrix1::IndexType;
+
+ const IndexType rows = 8;
+ const IndexType cols = 7;
+
+ Matrix1 m1;
+ m1.reset();
+ m1.setDimensions( rows + 1, cols + 2 );
+
+ Matrix2 m2;
+ m2.reset();
+ m2.setDimensions( rows, cols );
+
+ m1.setLike( m2 );
+
+ EXPECT_EQ( m1.getRows(), m2.getRows() );
+ EXPECT_EQ( m1.getColumns(), m2.getColumns() );
+}
+
+template< typename Matrix >
+void test_GetCompressedRowLengths()
+{
+ using RealType = typename Matrix::RealType;
+ using DeviceType = typename Matrix::DeviceType;
+ using IndexType = typename Matrix::IndexType;
+
+ const IndexType rows = 10;
+ const IndexType cols = 11;
+
+ Matrix m( rows, cols );
+
+ // Insert values into the rows.
+ RealType value = 1;
+
+ for( IndexType i = 0; i < 3; i++ ) // 0th row
+ m.setElement( 0, i, value++ );
+
+ for( IndexType i = 0; i < 3; i++ ) // 1st row
+ m.setElement( 1, i, value++ );
+
+ for( IndexType i = 0; i < 1; i++ ) // 2nd row
+ m.setElement( 2, i, value++ );
+
+ for( IndexType i = 0; i < 2; i++ ) // 3rd row
+ m.setElement( 3, i, value++ );
+
+ for( IndexType i = 0; i < 3; i++ ) // 4th row
+ m.setElement( 4, i, value++ );
+
+ for( IndexType i = 0; i < 4; i++ ) // 5th row
+ m.setElement( 5, i, value++ );
+
+ for( IndexType i = 0; i < 5; i++ ) // 6th row
+ m.setElement( 6, i, value++ );
+
+ for( IndexType i = 0; i < 6; i++ ) // 7th row
+ m.setElement( 7, i, value++ );
+
+ for( IndexType i = 0; i < 7; i++ ) // 8th row
+ m.setElement( 8, i, value++ );
+
+ for( IndexType i = 0; i < 8; i++ ) // 9th row
+ m.setElement( 9, i, value++ );
+
+ typename Matrix::CompressedRowLengthsVector rowLengths;
+ rowLengths = 0;
+ m.getCompressedRowLengths( rowLengths );
+ typename Matrix::CompressedRowLengthsVector correctRowLengths{ 3, 3, 1, 2, 3, 4, 5, 6, 7, 8 };
+ EXPECT_EQ( rowLengths, correctRowLengths );
+}
+
+template< typename Matrix >
+void test_GetRowLength()
+{
+ using RealType = typename Matrix::RealType;
+ using DeviceType = typename Matrix::DeviceType;
+ using IndexType = typename Matrix::IndexType;
+
+ const IndexType rows = 8;
+ const IndexType cols = 7;
+
+ Matrix m;
+ m.reset();
+ m.setDimensions( rows, cols );
+
+ EXPECT_EQ( m.getRowLength( 0 ), 7 );
+ EXPECT_EQ( m.getRowLength( 1 ), 7 );
+ EXPECT_EQ( m.getRowLength( 2 ), 7 );
+ EXPECT_EQ( m.getRowLength( 3 ), 7 );
+ EXPECT_EQ( m.getRowLength( 4 ), 7 );
+ EXPECT_EQ( m.getRowLength( 5 ), 7 );
+ EXPECT_EQ( m.getRowLength( 6 ), 7 );
+ EXPECT_EQ( m.getRowLength( 7 ), 7 );
+}
+
+template< typename Matrix >
+void test_GetNumberOfMatrixElements()
+{
+ using RealType = typename Matrix::RealType;
+ using DeviceType = typename Matrix::DeviceType;
+ using IndexType = typename Matrix::IndexType;
+
+ const IndexType rows = 7;
+ const IndexType cols = 6;
+
+ Matrix m;
+ m.reset();
+ m.setDimensions( rows, cols );
+
+ EXPECT_EQ( m.getNumberOfMatrixElements(), 42 );
+}
+
+template< typename Matrix >
+void test_GetNumberOfNonzeroMatrixElements()
+{
+ using RealType = typename Matrix::RealType;
+ using DeviceType = typename Matrix::DeviceType;
+ using IndexType = typename Matrix::IndexType;
+
+/*
+ * Sets up the following 7x6 dense matrix:
+ *
+ * / 0 2 3 4 5 6 \
+ * | 7 8 9 10 11 12 |
+ * | 13 14 15 16 17 18 |
+ * | 19 20 21 22 23 24 |
+ * | 25 26 27 28 29 30 |
+ * | 31 32 33 34 35 36 |
+ * \ 37 38 39 40 41 0 /
+ */
+ const IndexType rows = 7;
+ const IndexType cols = 6;
+
+ Matrix m;
+ m.reset();
+ m.setDimensions( rows, cols );
+
+ RealType value = 1;
+ for( IndexType i = 0; i < rows; i++ )
+ for( IndexType j = 0; j < cols; j++ )
+ m.setElement( i, j, value++ );
+
+ m.setElement( 0, 0, 0); // Set the first element of the diagonal to 0.
+ m.setElement( 6, 5, 0); // Set the last element of the diagonal to 0.
+
+ EXPECT_EQ( m.getNumberOfNonzeroMatrixElements(), 40 );
+}
+
+template< typename Matrix >
+void test_Reset()
+{
+ using RealType = typename Matrix::RealType;
+ using DeviceType = typename Matrix::DeviceType;
+ using IndexType = typename Matrix::IndexType;
+
+/*
+ * Sets up the following 5x4 dense matrix:
+ *
+ * / 0 0 0 0 \
+ * | 0 0 0 0 |
+ * | 0 0 0 0 |
+ * | 0 0 0 0 |
+ * \ 0 0 0 0 /
+ */
+ const IndexType rows = 5;
+ const IndexType cols = 4;
+
+ Matrix m;
+ m.setDimensions( rows, cols );
+
+ m.reset();
+
+ EXPECT_EQ( m.getRows(), 0 );
+ EXPECT_EQ( m.getColumns(), 0 );
+}
+
+template< typename Matrix >
+void test_SetValue()
+{
+ using RealType = typename Matrix::RealType;
+ using DeviceType = typename Matrix::DeviceType;
+ using IndexType = typename Matrix::IndexType;
+/*
+ * Sets up the following 7x6 dense matrix:
+ *
+ * / 1 2 3 4 5 6 \
+ * | 7 8 9 10 11 12 |
+ * | 13 14 15 16 17 18 |
+ * | 19 20 21 22 23 24 |
+ * | 25 26 27 28 29 30 |
+ * | 31 32 33 34 35 36 |
+ * \ 37 38 39 40 41 42 /
+ */
+ const IndexType rows = 7;
+ const IndexType cols = 6;
+
+ Matrix m;
+ m.reset();
+ m.setDimensions( rows, cols );
+
+ RealType value = 1;
+ for( IndexType i = 0; i < rows; i++ )
+ for( IndexType j = 0; j < cols; j++ )
+ m.setElement( i, j, value++ );
+
+ EXPECT_EQ( m.getElement( 0, 0 ), 1 );
+ EXPECT_EQ( m.getElement( 0, 1 ), 2 );
+ EXPECT_EQ( m.getElement( 0, 2 ), 3 );
+ EXPECT_EQ( m.getElement( 0, 3 ), 4 );
+ EXPECT_EQ( m.getElement( 0, 4 ), 5 );
+ EXPECT_EQ( m.getElement( 0, 5 ), 6 );
+
+ EXPECT_EQ( m.getElement( 1, 0 ), 7 );
+ EXPECT_EQ( m.getElement( 1, 1 ), 8 );
+ EXPECT_EQ( m.getElement( 1, 2 ), 9 );
+ EXPECT_EQ( m.getElement( 1, 3 ), 10 );
+ EXPECT_EQ( m.getElement( 1, 4 ), 11 );
+ EXPECT_EQ( m.getElement( 1, 5 ), 12 );
+
+ EXPECT_EQ( m.getElement( 2, 0 ), 13 );
+ EXPECT_EQ( m.getElement( 2, 1 ), 14 );
+ EXPECT_EQ( m.getElement( 2, 2 ), 15 );
+ EXPECT_EQ( m.getElement( 2, 3 ), 16 );
+ EXPECT_EQ( m.getElement( 2, 4 ), 17 );
+ EXPECT_EQ( m.getElement( 2, 5 ), 18 );
+
+ EXPECT_EQ( m.getElement( 3, 0 ), 19 );
+ EXPECT_EQ( m.getElement( 3, 1 ), 20 );
+ EXPECT_EQ( m.getElement( 3, 2 ), 21 );
+ EXPECT_EQ( m.getElement( 3, 3 ), 22 );
+ EXPECT_EQ( m.getElement( 3, 4 ), 23 );
+ EXPECT_EQ( m.getElement( 3, 5 ), 24 );
+
+ EXPECT_EQ( m.getElement( 4, 0 ), 25 );
+ EXPECT_EQ( m.getElement( 4, 1 ), 26 );
+ EXPECT_EQ( m.getElement( 4, 2 ), 27 );
+ EXPECT_EQ( m.getElement( 4, 3 ), 28 );
+ EXPECT_EQ( m.getElement( 4, 4 ), 29 );
+ EXPECT_EQ( m.getElement( 4, 5 ), 30 );
+
+ EXPECT_EQ( m.getElement( 5, 0 ), 31 );
+ EXPECT_EQ( m.getElement( 5, 1 ), 32 );
+ EXPECT_EQ( m.getElement( 5, 2 ), 33 );
+ EXPECT_EQ( m.getElement( 5, 3 ), 34 );
+ EXPECT_EQ( m.getElement( 5, 4 ), 35 );
+ EXPECT_EQ( m.getElement( 5, 5 ), 36 );
+
+ EXPECT_EQ( m.getElement( 6, 0 ), 37 );
+ EXPECT_EQ( m.getElement( 6, 1 ), 38 );
+ EXPECT_EQ( m.getElement( 6, 2 ), 39 );
+ EXPECT_EQ( m.getElement( 6, 3 ), 40 );
+ EXPECT_EQ( m.getElement( 6, 4 ), 41 );
+ EXPECT_EQ( m.getElement( 6, 5 ), 42 );
+
+ // Set the values of all elements to a certain number
+ m.setValue( 42 );
+
+ EXPECT_EQ( m.getElement( 0, 0 ), 42 );
+ EXPECT_EQ( m.getElement( 0, 1 ), 42 );
+ EXPECT_EQ( m.getElement( 0, 2 ), 42 );
+ EXPECT_EQ( m.getElement( 0, 3 ), 42 );
+ EXPECT_EQ( m.getElement( 0, 4 ), 42 );
+ EXPECT_EQ( m.getElement( 0, 5 ), 42 );
+
+ EXPECT_EQ( m.getElement( 1, 0 ), 42 );
+ EXPECT_EQ( m.getElement( 1, 1 ), 42 );
+ EXPECT_EQ( m.getElement( 1, 2 ), 42 );
+ EXPECT_EQ( m.getElement( 1, 3 ), 42 );
+ EXPECT_EQ( m.getElement( 1, 4 ), 42 );
+ EXPECT_EQ( m.getElement( 1, 5 ), 42 );
+
+ EXPECT_EQ( m.getElement( 2, 0 ), 42 );
+ EXPECT_EQ( m.getElement( 2, 1 ), 42 );
+ EXPECT_EQ( m.getElement( 2, 2 ), 42 );
+ EXPECT_EQ( m.getElement( 2, 3 ), 42 );
+ EXPECT_EQ( m.getElement( 2, 4 ), 42 );
+ EXPECT_EQ( m.getElement( 2, 5 ), 42 );
+
+ EXPECT_EQ( m.getElement( 3, 0 ), 42 );
+ EXPECT_EQ( m.getElement( 3, 1 ), 42 );
+ EXPECT_EQ( m.getElement( 3, 2 ), 42 );
+ EXPECT_EQ( m.getElement( 3, 3 ), 42 );
+ EXPECT_EQ( m.getElement( 3, 4 ), 42 );
+ EXPECT_EQ( m.getElement( 3, 5 ), 42 );
+
+ EXPECT_EQ( m.getElement( 4, 0 ), 42 );
+ EXPECT_EQ( m.getElement( 4, 1 ), 42 );
+ EXPECT_EQ( m.getElement( 4, 2 ), 42 );
+ EXPECT_EQ( m.getElement( 4, 3 ), 42 );
+ EXPECT_EQ( m.getElement( 4, 4 ), 42 );
+ EXPECT_EQ( m.getElement( 4, 5 ), 42 );
+
+ EXPECT_EQ( m.getElement( 5, 0 ), 42 );
+ EXPECT_EQ( m.getElement( 5, 1 ), 42 );
+ EXPECT_EQ( m.getElement( 5, 2 ), 42 );
+ EXPECT_EQ( m.getElement( 5, 3 ), 42 );
+ EXPECT_EQ( m.getElement( 5, 4 ), 42 );
+ EXPECT_EQ( m.getElement( 5, 5 ), 42 );
+
+ EXPECT_EQ( m.getElement( 6, 0 ), 42 );
+ EXPECT_EQ( m.getElement( 6, 1 ), 42 );
+ EXPECT_EQ( m.getElement( 6, 2 ), 42 );
+ EXPECT_EQ( m.getElement( 6, 3 ), 42 );
+ EXPECT_EQ( m.getElement( 6, 4 ), 42 );
+ EXPECT_EQ( m.getElement( 6, 5 ), 42 );
+}
+
+template< typename Matrix >
+void test_SetElement()
+{
+ using RealType = typename Matrix::RealType;
+ using DeviceType = typename Matrix::DeviceType;
+ using IndexType = typename Matrix::IndexType;
+/*
+ * Sets up the following 5x5 dense matrix:
+ *
+ * / 1 2 3 4 5 \
+ * | 6 7 8 9 10 |
+ * | 11 12 13 14 15 |
+ * | 16 17 18 19 20 |
+ * \ 21 22 23 24 25 /
+ */
+ const IndexType rows = 5;
+ const IndexType cols = 5;
+
+ Matrix m;
+ m.reset();
+ m.setDimensions( rows, cols );
+
+ RealType value = 1;
+ for( IndexType i = 0; i < rows; i++ )
+ for( IndexType j = 0; j < cols; j++ )
+ m.setElement( i, j, value++ );
+
+ EXPECT_EQ( m.getElement( 0, 0 ), 1 );
+ EXPECT_EQ( m.getElement( 0, 1 ), 2 );
+ EXPECT_EQ( m.getElement( 0, 2 ), 3 );
+ EXPECT_EQ( m.getElement( 0, 3 ), 4 );
+ EXPECT_EQ( m.getElement( 0, 4 ), 5 );
+
+ EXPECT_EQ( m.getElement( 1, 0 ), 6 );
+ EXPECT_EQ( m.getElement( 1, 1 ), 7 );
+ EXPECT_EQ( m.getElement( 1, 2 ), 8 );
+ EXPECT_EQ( m.getElement( 1, 3 ), 9 );
+ EXPECT_EQ( m.getElement( 1, 4 ), 10 );
+
+ EXPECT_EQ( m.getElement( 2, 0 ), 11 );
+ EXPECT_EQ( m.getElement( 2, 1 ), 12 );
+ EXPECT_EQ( m.getElement( 2, 2 ), 13 );
+ EXPECT_EQ( m.getElement( 2, 3 ), 14 );
+ EXPECT_EQ( m.getElement( 2, 4 ), 15 );
+
+ EXPECT_EQ( m.getElement( 3, 0 ), 16 );
+ EXPECT_EQ( m.getElement( 3, 1 ), 17 );
+ EXPECT_EQ( m.getElement( 3, 2 ), 18 );
+ EXPECT_EQ( m.getElement( 3, 3 ), 19 );
+ EXPECT_EQ( m.getElement( 3, 4 ), 20 );
+
+ EXPECT_EQ( m.getElement( 4, 0 ), 21 );
+ EXPECT_EQ( m.getElement( 4, 1 ), 22 );
+ EXPECT_EQ( m.getElement( 4, 2 ), 23 );
+ EXPECT_EQ( m.getElement( 4, 3 ), 24 );
+ EXPECT_EQ( m.getElement( 4, 4 ), 25 );
+}
+
+template< typename Matrix >
+void test_AddElement()
+{
+ using RealType = typename Matrix::RealType;
+ using DeviceType = typename Matrix::DeviceType;
+ using IndexType = typename Matrix::IndexType;
+/*
+ * Sets up the following 6x5 dense matrix:
+ *
+ * / 1 2 3 4 5 \
+ * | 6 7 8 9 10 |
+ * | 11 12 13 14 15 |
+ * | 16 17 18 19 20 |
+ * | 21 22 23 24 25 |
+ * \ 26 27 28 29 30 /
+ */
+ const IndexType rows = 6;
+ const IndexType cols = 5;
+
+ Matrix m;
+ m.reset();
+ m.setDimensions( rows, cols );
+
+ RealType value = 1;
+ for( IndexType i = 0; i < rows; i++ )
+ for( IndexType j = 0; j < cols; j++ )
+ m.setElement( i, j, value++ );
+
+ // Check the added elements
+ EXPECT_EQ( m.getElement( 0, 0 ), 1 );
+ EXPECT_EQ( m.getElement( 0, 1 ), 2 );
+ EXPECT_EQ( m.getElement( 0, 2 ), 3 );
+ EXPECT_EQ( m.getElement( 0, 3 ), 4 );
+ EXPECT_EQ( m.getElement( 0, 4 ), 5 );
+
+ EXPECT_EQ( m.getElement( 1, 0 ), 6 );
+ EXPECT_EQ( m.getElement( 1, 1 ), 7 );
+ EXPECT_EQ( m.getElement( 1, 2 ), 8 );
+ EXPECT_EQ( m.getElement( 1, 3 ), 9 );
+ EXPECT_EQ( m.getElement( 1, 4 ), 10 );
+
+ EXPECT_EQ( m.getElement( 2, 0 ), 11 );
+ EXPECT_EQ( m.getElement( 2, 1 ), 12 );
+ EXPECT_EQ( m.getElement( 2, 2 ), 13 );
+ EXPECT_EQ( m.getElement( 2, 3 ), 14 );
+ EXPECT_EQ( m.getElement( 2, 4 ), 15 );
+
+ EXPECT_EQ( m.getElement( 3, 0 ), 16 );
+ EXPECT_EQ( m.getElement( 3, 1 ), 17 );
+ EXPECT_EQ( m.getElement( 3, 2 ), 18 );
+ EXPECT_EQ( m.getElement( 3, 3 ), 19 );
+ EXPECT_EQ( m.getElement( 3, 4 ), 20 );
+
+ EXPECT_EQ( m.getElement( 4, 0 ), 21 );
+ EXPECT_EQ( m.getElement( 4, 1 ), 22 );
+ EXPECT_EQ( m.getElement( 4, 2 ), 23 );
+ EXPECT_EQ( m.getElement( 4, 3 ), 24 );
+ EXPECT_EQ( m.getElement( 4, 4 ), 25 );
+
+ EXPECT_EQ( m.getElement( 5, 0 ), 26 );
+ EXPECT_EQ( m.getElement( 5, 1 ), 27 );
+ EXPECT_EQ( m.getElement( 5, 2 ), 28 );
+ EXPECT_EQ( m.getElement( 5, 3 ), 29 );
+ EXPECT_EQ( m.getElement( 5, 4 ), 30 );
+
+ // Add new elements to the old elements with a multiplying factor applied to the old elements.
+/*
+ * The following setup results in the following 6x5 dense matrix:
+ *
+ * / 3 6 9 12 15 \
+ * | 18 21 24 27 30 |
+ * | 33 36 39 42 45 |
+ * | 48 51 54 57 60 |
+ * | 63 66 69 72 75 |
+ * \ 78 81 84 87 90 /
+ */
+ RealType newValue = 1;
+ RealType multiplicator = 2;
+ for( IndexType i = 0; i < rows; i++ )
+ for( IndexType j = 0; j < cols; j++ )
+ m.addElement( i, j, newValue++, multiplicator );
+
+ EXPECT_EQ( m.getElement( 0, 0 ), 3 );
+ EXPECT_EQ( m.getElement( 0, 1 ), 6 );
+ EXPECT_EQ( m.getElement( 0, 2 ), 9 );
+ EXPECT_EQ( m.getElement( 0, 3 ), 12 );
+ EXPECT_EQ( m.getElement( 0, 4 ), 15 );
+
+ EXPECT_EQ( m.getElement( 1, 0 ), 18 );
+ EXPECT_EQ( m.getElement( 1, 1 ), 21 );
+ EXPECT_EQ( m.getElement( 1, 2 ), 24 );
+ EXPECT_EQ( m.getElement( 1, 3 ), 27 );
+ EXPECT_EQ( m.getElement( 1, 4 ), 30 );
+
+ EXPECT_EQ( m.getElement( 2, 0 ), 33 );
+ EXPECT_EQ( m.getElement( 2, 1 ), 36 );
+ EXPECT_EQ( m.getElement( 2, 2 ), 39 );
+ EXPECT_EQ( m.getElement( 2, 3 ), 42 );
+ EXPECT_EQ( m.getElement( 2, 4 ), 45 );
+
+ EXPECT_EQ( m.getElement( 3, 0 ), 48 );
+ EXPECT_EQ( m.getElement( 3, 1 ), 51 );
+ EXPECT_EQ( m.getElement( 3, 2 ), 54 );
+ EXPECT_EQ( m.getElement( 3, 3 ), 57 );
+ EXPECT_EQ( m.getElement( 3, 4 ), 60 );
+
+ EXPECT_EQ( m.getElement( 4, 0 ), 63 );
+ EXPECT_EQ( m.getElement( 4, 1 ), 66 );
+ EXPECT_EQ( m.getElement( 4, 2 ), 69 );
+ EXPECT_EQ( m.getElement( 4, 3 ), 72 );
+ EXPECT_EQ( m.getElement( 4, 4 ), 75 );
+
+ EXPECT_EQ( m.getElement( 5, 0 ), 78 );
+ EXPECT_EQ( m.getElement( 5, 1 ), 81 );
+ EXPECT_EQ( m.getElement( 5, 2 ), 84 );
+ EXPECT_EQ( m.getElement( 5, 3 ), 87 );
+ EXPECT_EQ( m.getElement( 5, 4 ), 90 );
+}
+
+template< typename Matrix >
+void test_SetRow()
+{
+ using RealType = typename Matrix::RealType;
+ using DeviceType = typename Matrix::DeviceType;
+ using IndexType = typename Matrix::IndexType;
+
+ /*
+ * Sets up the following 3x7 dense matrix:
+ *
+ * / 1 2 3 4 5 6 7 \
+ * | 8 9 10 11 12 13 14 |
+ * \ 15 16 17 18 19 20 21 /
+ */
+ const IndexType rows = 3;
+ const IndexType cols = 7;
+
+ Matrix m;
+ m.reset();
+ m.setDimensions( rows, cols );
+
+ RealType value = 1;
+ for( IndexType i = 0; i < rows; i++ )
+ for( IndexType j = 0; j < cols; j++ )
+ m.setElement( i, j, value++ );
+
+ auto matrix_view = m.getView();
+ auto f = [=] __cuda_callable__ ( IndexType rowIdx ) mutable {
+ RealType values[ 3 ][ 5 ] {
+ { 11, 11, 11, 11, 11 },
+ { 22, 22, 22, 22, 22 },
+ { 33, 33, 33, 33, 33 } };
+ IndexType columnIndexes[ 3 ][ 5 ] {
+ { 0, 1, 2, 3, 4 },
+ { 0, 1, 2, 3, 4 },
+ { 2, 3, 4, 5, 6 } };
+ auto row = matrix_view.getRow( rowIdx );
+ for( IndexType i = 0; i < 5; i++ )
+ row.setElement( columnIndexes[ rowIdx ][ i ], values[ rowIdx ][ i ] );
+ };
+ TNL::Algorithms::ParallelFor< DeviceType >::exec( 0, 3, f );
+
+ EXPECT_EQ( m.getElement( 0, 0 ), 11 );
+ EXPECT_EQ( m.getElement( 0, 1 ), 11 );
+ EXPECT_EQ( m.getElement( 0, 2 ), 11 );
+ EXPECT_EQ( m.getElement( 0, 3 ), 11 );
+ EXPECT_EQ( m.getElement( 0, 4 ), 11 );
+ EXPECT_EQ( m.getElement( 0, 5 ), 6 );
+ EXPECT_EQ( m.getElement( 0, 6 ), 7 );
+
+ EXPECT_EQ( m.getElement( 1, 0 ), 22 );
+ EXPECT_EQ( m.getElement( 1, 1 ), 22 );
+ EXPECT_EQ( m.getElement( 1, 2 ), 22 );
+ EXPECT_EQ( m.getElement( 1, 3 ), 22 );
+ EXPECT_EQ( m.getElement( 1, 4 ), 22 );
+ EXPECT_EQ( m.getElement( 1, 5 ), 13 );
+ EXPECT_EQ( m.getElement( 1, 6 ), 14 );
+
+ EXPECT_EQ( m.getElement( 2, 0 ), 15 );
+ EXPECT_EQ( m.getElement( 2, 1 ), 16 );
+ EXPECT_EQ( m.getElement( 2, 2 ), 33 );
+ EXPECT_EQ( m.getElement( 2, 3 ), 33 );
+ EXPECT_EQ( m.getElement( 2, 4 ), 33 );
+ EXPECT_EQ( m.getElement( 2, 5 ), 33 );
+ EXPECT_EQ( m.getElement( 2, 6 ), 33 );
+}
+
+template< typename Matrix >
+void test_AddRow()
+{
+ using RealType = typename Matrix::RealType;
+ using DeviceType = typename Matrix::DeviceType;
+ using IndexType = typename Matrix::IndexType;
+ /*
+ * Sets up the following 6x5 dense matrix:
+ *
+ * / 1 2 3 4 5 \
+ * | 6 7 8 9 10 |
+ * | 11 12 13 14 15 |
+ * | 16 17 18 19 20 |
+ * | 21 22 23 24 25 |
+ * \ 26 27 28 29 30 /
+ */
+
+ const IndexType rows = 6;
+ const IndexType cols = 5;
+
+ Matrix m( rows, cols );
+
+ RealType value = 1;
+ for( IndexType i = 0; i < rows; i++ )
+ for( IndexType j = 0; j < cols; j++ )
+ m.setElement( i, j, value++ );
+
+ // Check the added elements
+ EXPECT_EQ( m.getElement( 0, 0 ), 1 );
+ EXPECT_EQ( m.getElement( 0, 1 ), 2 );
+ EXPECT_EQ( m.getElement( 0, 2 ), 3 );
+ EXPECT_EQ( m.getElement( 0, 3 ), 4 );
+ EXPECT_EQ( m.getElement( 0, 4 ), 5 );
+
+ EXPECT_EQ( m.getElement( 1, 0 ), 6 );
+ EXPECT_EQ( m.getElement( 1, 1 ), 7 );
+ EXPECT_EQ( m.getElement( 1, 2 ), 8 );
+ EXPECT_EQ( m.getElement( 1, 3 ), 9 );
+ EXPECT_EQ( m.getElement( 1, 4 ), 10 );
+
+ EXPECT_EQ( m.getElement( 2, 0 ), 11 );
+ EXPECT_EQ( m.getElement( 2, 1 ), 12 );
+ EXPECT_EQ( m.getElement( 2, 2 ), 13 );
+ EXPECT_EQ( m.getElement( 2, 3 ), 14 );
+ EXPECT_EQ( m.getElement( 2, 4 ), 15 );
+
+ EXPECT_EQ( m.getElement( 3, 0 ), 16 );
+ EXPECT_EQ( m.getElement( 3, 1 ), 17 );
+ EXPECT_EQ( m.getElement( 3, 2 ), 18 );
+ EXPECT_EQ( m.getElement( 3, 3 ), 19 );
+ EXPECT_EQ( m.getElement( 3, 4 ), 20 );
+
+ EXPECT_EQ( m.getElement( 4, 0 ), 21 );
+ EXPECT_EQ( m.getElement( 4, 1 ), 22 );
+ EXPECT_EQ( m.getElement( 4, 2 ), 23 );
+ EXPECT_EQ( m.getElement( 4, 3 ), 24 );
+ EXPECT_EQ( m.getElement( 4, 4 ), 25 );
+
+ EXPECT_EQ( m.getElement( 5, 0 ), 26 );
+ EXPECT_EQ( m.getElement( 5, 1 ), 27 );
+ EXPECT_EQ( m.getElement( 5, 2 ), 28 );
+ EXPECT_EQ( m.getElement( 5, 3 ), 29 );
+ EXPECT_EQ( m.getElement( 5, 4 ), 30 );
+
+ // Add new elements to the old elements with a multiplying factor applied to the old elements.
+ /*
+ * The following setup results in the following 6x5 sparse matrix:
+ *
+ * / 3 6 9 12 15 \
+ * | 18 21 24 27 30 |
+ * | 33 36 39 42 45 |
+ * | 48 51 54 57 60 |
+ * | 63 66 69 72 75 |
+ * \ 78 81 84 87 90 /
+ */
+
+ auto matrix_view = m.getView();
+ auto f = [=] __cuda_callable__ ( IndexType rowIdx ) mutable {
+ RealType values[ 6 ][ 5 ] {
+ { 11, 11, 11, 11, 0 },
+ { 22, 22, 22, 22, 0 },
+ { 33, 33, 33, 33, 0 },
+ { 44, 44, 44, 44, 0 },
+ { 55, 55, 55, 55, 0 },
+ { 66, 66, 66, 66, 0 } };
+ auto row = matrix_view.getRow( rowIdx );
+ for( IndexType i = 0; i < 5; i++ )
+ {
+ RealType& val = row.getValue( i );
+ val = rowIdx * val + values[ rowIdx ][ i ];
+ }
+ };
+ TNL::Algorithms::ParallelFor< DeviceType >::exec( 0, 6, f );
+
+
+ EXPECT_EQ( m.getElement( 0, 0 ), 11 );
+ EXPECT_EQ( m.getElement( 0, 1 ), 11 );
+ EXPECT_EQ( m.getElement( 0, 2 ), 11 );
+ EXPECT_EQ( m.getElement( 0, 3 ), 11 );
+ EXPECT_EQ( m.getElement( 0, 4 ), 0 );
+
+ EXPECT_EQ( m.getElement( 1, 0 ), 28 );
+ EXPECT_EQ( m.getElement( 1, 1 ), 29 );
+ EXPECT_EQ( m.getElement( 1, 2 ), 30 );
+ EXPECT_EQ( m.getElement( 1, 3 ), 31 );
+ EXPECT_EQ( m.getElement( 1, 4 ), 10 );
+
+ EXPECT_EQ( m.getElement( 2, 0 ), 55 );
+ EXPECT_EQ( m.getElement( 2, 1 ), 57 );
+ EXPECT_EQ( m.getElement( 2, 2 ), 59 );
+ EXPECT_EQ( m.getElement( 2, 3 ), 61 );
+ EXPECT_EQ( m.getElement( 2, 4 ), 30 );
+
+ EXPECT_EQ( m.getElement( 3, 0 ), 92 );
+ EXPECT_EQ( m.getElement( 3, 1 ), 95 );
+ EXPECT_EQ( m.getElement( 3, 2 ), 98 );
+ EXPECT_EQ( m.getElement( 3, 3 ), 101 );
+ EXPECT_EQ( m.getElement( 3, 4 ), 60 );
+
+ EXPECT_EQ( m.getElement( 4, 0 ), 139 );
+ EXPECT_EQ( m.getElement( 4, 1 ), 143 );
+ EXPECT_EQ( m.getElement( 4, 2 ), 147 );
+ EXPECT_EQ( m.getElement( 4, 3 ), 151 );
+ EXPECT_EQ( m.getElement( 4, 4 ), 100 );
+
+ EXPECT_EQ( m.getElement( 5, 0 ), 196 );
+ EXPECT_EQ( m.getElement( 5, 1 ), 201 );
+ EXPECT_EQ( m.getElement( 5, 2 ), 206 );
+ EXPECT_EQ( m.getElement( 5, 3 ), 211 );
+ EXPECT_EQ( m.getElement( 5, 4 ), 150 );
+}
+
+template< typename Matrix >
+void test_VectorProduct()
+{
+ using RealType = typename Matrix::RealType;
+ using DeviceType = typename Matrix::DeviceType;
+ using IndexType = typename Matrix::IndexType;
+/*
+ * Sets up the following 5x4 dense matrix:
+ *
+ * / 1 2 3 4 \
+ * | 5 6 7 8 |
+ * | 9 10 11 12 |
+ * | 13 14 15 16 |
+ * \ 17 18 19 20 /
+ */
+ const IndexType rows = 5;
+ const IndexType cols = 4;
+
+ Matrix m;
+ m.reset();
+ m.setDimensions( rows, cols );
+
+ RealType value = 1;
+ for( IndexType i = 0; i < rows; i++ )
+ for( IndexType j = 0; j < cols; j++)
+ m.setElement( i, j, value++ );
+
+ using VectorType = TNL::Containers::Vector< RealType, DeviceType, IndexType >;
+
+ VectorType inVector;
+ inVector.setSize( 4 );
+ for( IndexType i = 0; i < inVector.getSize(); i++ )
+ inVector.setElement( i, 2 );
+
+ VectorType outVector;
+ outVector.setSize( 5 );
+ for( IndexType j = 0; j < outVector.getSize(); j++ )
+ outVector.setElement( j, 0 );
+
+
+ m.vectorProduct( inVector, outVector);
+
+ EXPECT_EQ( outVector.getElement( 0 ), 20 );
+ EXPECT_EQ( outVector.getElement( 1 ), 52 );
+ EXPECT_EQ( outVector.getElement( 2 ), 84 );
+ EXPECT_EQ( outVector.getElement( 3 ), 116 );
+ EXPECT_EQ( outVector.getElement( 4 ), 148 );
+}
+
+template< typename Matrix >
+void test_AddMatrix()
+{
+ using RealType = typename Matrix::RealType;
+ using DeviceType = typename Matrix::DeviceType;
+ using IndexType = typename Matrix::IndexType;
+/*
+ * Sets up the following 5x4 dense matrix:
+ *
+ * / 1 2 3 4 \
+ * | 5 6 7 8 |
+ * | 9 10 11 12 |
+ * | 13 14 15 16 |
+ * \ 17 18 19 20 /
+ */
+ const IndexType rows = 5;
+ const IndexType cols = 4;
+
+ Matrix m;
+ m.reset();
+ m.setDimensions( rows, cols );
+
+ RealType value = 1;
+ for( IndexType i = 0; i < rows; i++ )
+ for( IndexType j = 0; j < cols; j++)
+ m.setElement( i, j, value++ );
+
+/*
+ * Sets up the following 5x4 dense matrix:
+ *
+ * / 1 2 3 4 \
+ * | 5 6 7 8 |
+ * | 9 10 11 12 |
+ * | 13 14 15 16 |
+ * \ 17 18 19 20 /
+ */
+
+ Matrix m2;
+ m2.reset();
+ m2.setDimensions( rows, cols );
+
+ RealType newValue = 1;
+ for( IndexType i = 0; i < rows; i++ )
+ for( IndexType j = 0; j < cols; j++)
+ m2.setElement( i, j, newValue++ );
+
+ /*
+ * Sets up the following 5x4 dense matrix:
+ *
+ * / 1 2 3 4 \
+ * | 5 6 7 8 |
+ * | 9 10 11 12 |
+ * | 13 14 15 16 |
+ * \ 17 18 19 20 /
+ */
+
+ Matrix mResult;
+ mResult.reset();
+ mResult.setDimensions( rows, cols );
+
+ mResult = m;
+
+ RealType matrixMultiplicator = 2;
+ RealType thisMatrixMultiplicator = 1;
+
+ mResult.addMatrix( m2, matrixMultiplicator, thisMatrixMultiplicator );
+
+ EXPECT_EQ( mResult.getElement( 0, 0 ), matrixMultiplicator * m2.getElement( 0, 0 ) + thisMatrixMultiplicator * m.getElement( 0, 0 ) );
+ EXPECT_EQ( mResult.getElement( 0, 1 ), matrixMultiplicator * m2.getElement( 0, 1 ) + thisMatrixMultiplicator * m.getElement( 0, 1 ) );
+ EXPECT_EQ( mResult.getElement( 0, 2 ), matrixMultiplicator * m2.getElement( 0, 2 ) + thisMatrixMultiplicator * m.getElement( 0, 2 ) );
+ EXPECT_EQ( mResult.getElement( 0, 3 ), matrixMultiplicator * m2.getElement( 0, 3 ) + thisMatrixMultiplicator * m.getElement( 0, 3 ) );
+
+ EXPECT_EQ( mResult.getElement( 1, 0 ), matrixMultiplicator * m2.getElement( 1, 0 ) + thisMatrixMultiplicator * m.getElement( 1, 0 ) );
+ EXPECT_EQ( mResult.getElement( 1, 1 ), matrixMultiplicator * m2.getElement( 1, 1 ) + thisMatrixMultiplicator * m.getElement( 1, 1 ) );
+ EXPECT_EQ( mResult.getElement( 1, 2 ), matrixMultiplicator * m2.getElement( 1, 2 ) + thisMatrixMultiplicator * m.getElement( 1, 2 ) );
+ EXPECT_EQ( mResult.getElement( 1, 3 ), matrixMultiplicator * m2.getElement( 1, 3 ) + thisMatrixMultiplicator * m.getElement( 1, 3 ) );
+
+ EXPECT_EQ( mResult.getElement( 2, 0 ), matrixMultiplicator * m2.getElement( 2, 0 ) + thisMatrixMultiplicator * m.getElement( 2, 0 ) );
+ EXPECT_EQ( mResult.getElement( 2, 1 ), matrixMultiplicator * m2.getElement( 2, 1 ) + thisMatrixMultiplicator * m.getElement( 2, 1 ) );
+ EXPECT_EQ( mResult.getElement( 2, 2 ), matrixMultiplicator * m2.getElement( 2, 2 ) + thisMatrixMultiplicator * m.getElement( 2, 2 ) );
+ EXPECT_EQ( mResult.getElement( 2, 3 ), matrixMultiplicator * m2.getElement( 2, 3 ) + thisMatrixMultiplicator * m.getElement( 2, 3 ) );
+
+ EXPECT_EQ( mResult.getElement( 3, 0 ), matrixMultiplicator * m2.getElement( 3, 0 ) + thisMatrixMultiplicator * m.getElement( 3, 0 ) );
+ EXPECT_EQ( mResult.getElement( 3, 1 ), matrixMultiplicator * m2.getElement( 3, 1 ) + thisMatrixMultiplicator * m.getElement( 3, 1 ) );
+ EXPECT_EQ( mResult.getElement( 3, 2 ), matrixMultiplicator * m2.getElement( 3, 2 ) + thisMatrixMultiplicator * m.getElement( 3, 2 ) );
+ EXPECT_EQ( mResult.getElement( 3, 3 ), matrixMultiplicator * m2.getElement( 3, 3 ) + thisMatrixMultiplicator * m.getElement( 3, 3 ) );
+
+ EXPECT_EQ( mResult.getElement( 4, 0 ), matrixMultiplicator * m2.getElement( 4, 0 ) + thisMatrixMultiplicator * m.getElement( 4, 0 ) );
+ EXPECT_EQ( mResult.getElement( 4, 1 ), matrixMultiplicator * m2.getElement( 4, 1 ) + thisMatrixMultiplicator * m.getElement( 4, 1 ) );
+ EXPECT_EQ( mResult.getElement( 4, 2 ), matrixMultiplicator * m2.getElement( 4, 2 ) + thisMatrixMultiplicator * m.getElement( 4, 2 ) );
+ EXPECT_EQ( mResult.getElement( 4, 3 ), matrixMultiplicator * m2.getElement( 4, 3 ) + thisMatrixMultiplicator * m.getElement( 4, 3 ) );
+
+ EXPECT_EQ( mResult.getElement( 0, 0 ), 3 );
+ EXPECT_EQ( mResult.getElement( 0, 1 ), 6 );
+ EXPECT_EQ( mResult.getElement( 0, 2 ), 9 );
+ EXPECT_EQ( mResult.getElement( 0, 3 ), 12 );
+
+ EXPECT_EQ( mResult.getElement( 1, 0 ), 15 );
+ EXPECT_EQ( mResult.getElement( 1, 1 ), 18 );
+ EXPECT_EQ( mResult.getElement( 1, 2 ), 21 );
+ EXPECT_EQ( mResult.getElement( 1, 3 ), 24 );
+
+ EXPECT_EQ( mResult.getElement( 2, 0 ), 27 );
+ EXPECT_EQ( mResult.getElement( 2, 1 ), 30 );
+ EXPECT_EQ( mResult.getElement( 2, 2 ), 33 );
+ EXPECT_EQ( mResult.getElement( 2, 3 ), 36 );
+
+ EXPECT_EQ( mResult.getElement( 3, 0 ), 39 );
+ EXPECT_EQ( mResult.getElement( 3, 1 ), 42 );
+ EXPECT_EQ( mResult.getElement( 3, 2 ), 45 );
+ EXPECT_EQ( mResult.getElement( 3, 3 ), 48 );
+
+ EXPECT_EQ( mResult.getElement( 4, 0 ), 51 );
+ EXPECT_EQ( mResult.getElement( 4, 1 ), 54 );
+ EXPECT_EQ( mResult.getElement( 4, 2 ), 57 );
+ EXPECT_EQ( mResult.getElement( 4, 3 ), 60 );
+}
+
+template< typename Matrix >
+void test_GetMatrixProduct()
+{
+ using RealType = typename Matrix::RealType;
+ using DeviceType = typename Matrix::DeviceType;
+ using IndexType = typename Matrix::IndexType;
+/*
+ * Sets up the following 5x4 dense matrix:
+ *
+ * / 1 2 3 4 \
+ * | 5 6 7 8 |
+ * | 9 10 11 12 |
+ * | 13 14 15 16 |
+ * \ 17 18 19 20 /
+ */
+ const IndexType leftRows = 5;
+ const IndexType leftCols = 4;
+
+ Matrix leftMatrix;
+ leftMatrix.reset();
+ leftMatrix.setDimensions( leftRows, leftCols );
+
+ RealType value = 1;
+ for( IndexType i = 0; i < leftRows; i++ )
+ for( IndexType j = 0; j < leftCols; j++)
+ leftMatrix.setElement( i, j, value++ );
+
+/*
+ * Sets up the following 4x5 dense matrix:
+ *
+ * / 1 2 3 4 5 \
+ * | 6 7 8 9 10 |
+ * | 11 12 13 14 15 |
+ * \ 16 17 18 19 20 /
+ */
+ const IndexType rightRows = 4;
+ const IndexType rightCols = 5;
+
+ Matrix rightMatrix;
+ rightMatrix.reset();
+ rightMatrix.setDimensions( rightRows, rightCols );
+
+ RealType newValue = 1;
+ for( IndexType i = 0; i < rightRows; i++ )
+ for( IndexType j = 0; j < rightCols; j++)
+ rightMatrix.setElement( i, j, newValue++ );
+
+/*
+ * Sets up the following 5x5 resulting dense matrix:
+ *
+ * / 0 0 0 0 \
+ * | 0 0 0 0 |
+ * | 0 0 0 0 |
+ * | 0 0 0 0 |
+ * \ 0 0 0 0 /
+ */
+
+ Matrix mResult;
+ mResult.reset();
+ mResult.setDimensions( leftRows, rightCols );
+ mResult.setValue( 0 );
+
+ RealType leftMatrixMultiplicator = 1;
+ RealType rightMatrixMultiplicator = 2;
+/*
+ * / 1 2 3 4 \ / 220 240 260 280 300 \
+ * | 5 6 7 8 | / 1 2 3 4 5 \ | 492 544 596 648 700 |
+ * 1 * | 9 10 11 12 | * 2 * | 6 7 8 9 10 | = | 764 848 932 1016 1100 |
+ * | 13 14 15 16 | | 11 12 13 14 15 | | 1036 1152 1268 1384 1500 |
+ * \ 17 18 19 20 / \ 16 17 18 19 20 / \ 1308 1456 1604 1752 1900 /
+ */
+
+ mResult.getMatrixProduct( leftMatrix, rightMatrix, leftMatrixMultiplicator, rightMatrixMultiplicator );
+
+ EXPECT_EQ( mResult.getElement( 0, 0 ), 220 );
+ EXPECT_EQ( mResult.getElement( 0, 1 ), 240 );
+ EXPECT_EQ( mResult.getElement( 0, 2 ), 260 );
+ EXPECT_EQ( mResult.getElement( 0, 3 ), 280 );
+ EXPECT_EQ( mResult.getElement( 0, 4 ), 300 );
+
+ EXPECT_EQ( mResult.getElement( 1, 0 ), 492 );
+ EXPECT_EQ( mResult.getElement( 1, 1 ), 544 );
+ EXPECT_EQ( mResult.getElement( 1, 2 ), 596 );
+ EXPECT_EQ( mResult.getElement( 1, 3 ), 648 );
+ EXPECT_EQ( mResult.getElement( 1, 4 ), 700 );
+
+ EXPECT_EQ( mResult.getElement( 2, 0 ), 764 );
+ EXPECT_EQ( mResult.getElement( 2, 1 ), 848 );
+ EXPECT_EQ( mResult.getElement( 2, 2 ), 932 );
+ EXPECT_EQ( mResult.getElement( 2, 3 ), 1016 );
+ EXPECT_EQ( mResult.getElement( 2, 4 ), 1100 );
+
+ EXPECT_EQ( mResult.getElement( 3, 0 ), 1036 );
+ EXPECT_EQ( mResult.getElement( 3, 1 ), 1152 );
+ EXPECT_EQ( mResult.getElement( 3, 2 ), 1268 );
+ EXPECT_EQ( mResult.getElement( 3, 3 ), 1384 );
+ EXPECT_EQ( mResult.getElement( 3, 4 ), 1500 );
+
+ EXPECT_EQ( mResult.getElement( 4, 0 ), 1308 );
+ EXPECT_EQ( mResult.getElement( 4, 1 ), 1456 );
+ EXPECT_EQ( mResult.getElement( 4, 2 ), 1604 );
+ EXPECT_EQ( mResult.getElement( 4, 3 ), 1752 );
+ EXPECT_EQ( mResult.getElement( 4, 4 ), 1900 );
+}
+
+template< typename Matrix >
+void test_GetTransposition()
+{
+ using RealType = typename Matrix::RealType;
+ using DeviceType = typename Matrix::DeviceType;
+ using IndexType = typename Matrix::IndexType;
+/*
+ * Sets up the following 3x2 dense matrix:
+ *
+ * / 1 2 \
+ * | 3 4 |
+ * \ 5 6 /
+ */
+ const IndexType rows = 3;
+ const IndexType cols = 2;
+
+ Matrix m;
+ m.reset();
+ m.setDimensions( rows, cols );
+
+ RealType value = 1;
+ for( IndexType i = 0; i < rows; i++ )
+ for( IndexType j = 0; j < cols; j++ )
+ m.setElement( i, j, value++ );
+
+ m.print( std::cout );
+
+/*
+ * Sets up the following 2x3 dense matrix:
+ *
+ * / 0 0 0 \
+ * \ 0 0 0 /
+ */
+ Matrix mTransposed;
+ mTransposed.reset();
+ mTransposed.setDimensions( cols, rows );
+
+ mTransposed.print( std::cout );
+
+ RealType matrixMultiplicator = 1;
+
+ mTransposed.getTransposition( m, matrixMultiplicator );
+
+ mTransposed.print( std::cout );
+
+/*
+ * Should result in the following 2x3 dense matrix:
+ *
+ * / 1 3 5 \
+ * \ 2 4 6 /
+ */
+
+ EXPECT_EQ( mTransposed.getElement( 0, 0 ), 1 );
+ EXPECT_EQ( mTransposed.getElement( 0, 1 ), 3 );
+ EXPECT_EQ( mTransposed.getElement( 0, 2 ), 5 );
+
+ EXPECT_EQ( mTransposed.getElement( 1, 0 ), 2 );
+ EXPECT_EQ( mTransposed.getElement( 1, 1 ), 4 );
+ EXPECT_EQ( mTransposed.getElement( 1, 2 ), 6 );
+}
+
+
+template< typename Matrix >
+void test_PerformSORIteration()
+{
+ using RealType = typename Matrix::RealType;
+ using DeviceType = typename Matrix::DeviceType;
+ using IndexType = typename Matrix::IndexType;
+/*
+ * Sets up the following 4x4 dense matrix:
+ *
+ * / 4 1 1 1 \
+ * | 1 4 1 1 |
+ * | 1 1 4 1 |
+ * \ 1 1 1 4 /
+ */
+ const IndexType rows = 4;
+ const IndexType cols = 4;
+
+ Matrix m;
+ m.reset();
+ m.setDimensions( rows, cols );
+
+ m.setElement( 0, 0, 4.0 ); // 0th row
+ m.setElement( 0, 1, 1.0 );
+ m.setElement( 0, 2, 1.0 );
+ m.setElement( 0, 3, 1.0 );
+
+ m.setElement( 1, 0, 1.0 ); // 1st row
+ m.setElement( 1, 1, 4.0 );
+ m.setElement( 1, 2, 1.0 );
+ m.setElement( 1, 3, 1.0 );
+
+ m.setElement( 2, 0, 1.0 );
+ m.setElement( 2, 1, 1.0 ); // 2nd row
+ m.setElement( 2, 2, 4.0 );
+ m.setElement( 2, 3, 1.0 );
+
+ m.setElement( 3, 0, 1.0 ); // 3rd row
+ m.setElement( 3, 1, 1.0 );
+ m.setElement( 3, 2, 1.0 );
+ m.setElement( 3, 3, 4.0 );
+
+ RealType bVector [ 4 ] = { 1.0, 1.0, 1.0, 1.0 };
+ RealType xVector [ 4 ] = { 1.0, 1.0, 1.0, 1.0 };
+
+ IndexType row = 0;
+ RealType omega = 1;
+
+ m.performSORIteration( bVector, row++, xVector, omega);
+
+ EXPECT_EQ( xVector[ 0 ], -0.5 );
+ EXPECT_EQ( xVector[ 1 ], 1.0 );
+ EXPECT_EQ( xVector[ 2 ], 1.0 );
+ EXPECT_EQ( xVector[ 3 ], 1.0 );
+
+ m.performSORIteration( bVector, row++, xVector, omega);
+
+ EXPECT_EQ( xVector[ 0 ], -0.5 );
+ EXPECT_EQ( xVector[ 1 ], -0.125 );
+ EXPECT_EQ( xVector[ 2 ], 1.0 );
+ EXPECT_EQ( xVector[ 3 ], 1.0 );
+
+ m.performSORIteration( bVector, row++, xVector, omega);
+
+ EXPECT_EQ( xVector[ 0 ], -0.5 );
+ EXPECT_EQ( xVector[ 1 ], -0.125 );
+ EXPECT_EQ( xVector[ 2 ], 0.15625 );
+ EXPECT_EQ( xVector[ 3 ], 1.0 );
+
+ m.performSORIteration( bVector, row++, xVector, omega);
+
+ EXPECT_EQ( xVector[ 0 ], -0.5 );
+ EXPECT_EQ( xVector[ 1 ], -0.125 );
+ EXPECT_EQ( xVector[ 2 ], 0.15625 );
+ EXPECT_EQ( xVector[ 3 ], 0.3671875 );
+}
+
+template< typename Matrix >
+void test_AssignmentOperator()
+{
+ 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()
+{
+ using RealType = typename Matrix::RealType;
+ using DeviceType = typename Matrix::DeviceType;
+ using IndexType = typename Matrix::IndexType;
+/*
+ * Sets up the following 4x4 dense matrix:
+ *
+ * / 1 2 3 4 \
+ * | 5 6 7 8 |
+ * | 9 10 11 12 |
+ * \ 13 14 15 16 /
+ */
+ const IndexType rows = 4;
+ const IndexType cols = 4;
+
+ Matrix savedMatrix;
+ savedMatrix.reset();
+ savedMatrix.setDimensions( rows, cols );
+
+ RealType value = 1;
+ for( IndexType i = 0; i < rows; i++ )
+ for( IndexType j = 0; j < cols; j++ )
+ savedMatrix.setElement( i, j, value++ );
+
+ ASSERT_NO_THROW( savedMatrix.save( TEST_FILE_NAME ) );
+
+ Matrix loadedMatrix;
+ loadedMatrix.reset();
+ loadedMatrix.setDimensions( rows, cols );
+
+ ASSERT_NO_THROW( loadedMatrix.load( TEST_FILE_NAME ) );
+
+ EXPECT_EQ( savedMatrix.getElement( 0, 0 ), loadedMatrix.getElement( 0, 0 ) );
+ EXPECT_EQ( savedMatrix.getElement( 0, 1 ), loadedMatrix.getElement( 0, 1 ) );
+ EXPECT_EQ( savedMatrix.getElement( 0, 2 ), loadedMatrix.getElement( 0, 2 ) );
+ EXPECT_EQ( savedMatrix.getElement( 0, 3 ), loadedMatrix.getElement( 0, 3 ) );
+
+ EXPECT_EQ( savedMatrix.getElement( 1, 0 ), loadedMatrix.getElement( 1, 0 ) );
+ EXPECT_EQ( savedMatrix.getElement( 1, 1 ), loadedMatrix.getElement( 1, 1 ) );
+ EXPECT_EQ( savedMatrix.getElement( 1, 2 ), loadedMatrix.getElement( 1, 2 ) );
+ EXPECT_EQ( savedMatrix.getElement( 1, 3 ), loadedMatrix.getElement( 1, 3 ) );
+
+ EXPECT_EQ( savedMatrix.getElement( 2, 0 ), loadedMatrix.getElement( 2, 0 ) );
+ EXPECT_EQ( savedMatrix.getElement( 2, 1 ), loadedMatrix.getElement( 2, 1 ) );
+ EXPECT_EQ( savedMatrix.getElement( 2, 2 ), loadedMatrix.getElement( 2, 2 ) );
+ EXPECT_EQ( savedMatrix.getElement( 2, 3 ), loadedMatrix.getElement( 2, 3 ) );
+
+ EXPECT_EQ( savedMatrix.getElement( 3, 0 ), loadedMatrix.getElement( 3, 0 ) );
+ EXPECT_EQ( savedMatrix.getElement( 3, 1 ), loadedMatrix.getElement( 3, 1 ) );
+ EXPECT_EQ( savedMatrix.getElement( 3, 2 ), loadedMatrix.getElement( 3, 2 ) );
+ EXPECT_EQ( savedMatrix.getElement( 3, 3 ), loadedMatrix.getElement( 3, 3 ) );
+
+ EXPECT_EQ( savedMatrix.getElement( 0, 0 ), 1 );
+ EXPECT_EQ( savedMatrix.getElement( 0, 1 ), 2 );
+ EXPECT_EQ( savedMatrix.getElement( 0, 2 ), 3 );
+ EXPECT_EQ( savedMatrix.getElement( 0, 3 ), 4 );
+
+ EXPECT_EQ( savedMatrix.getElement( 1, 0 ), 5 );
+ EXPECT_EQ( savedMatrix.getElement( 1, 1 ), 6 );
+ EXPECT_EQ( savedMatrix.getElement( 1, 2 ), 7 );
+ EXPECT_EQ( savedMatrix.getElement( 1, 3 ), 8 );
+
+ EXPECT_EQ( savedMatrix.getElement( 2, 0 ), 9 );
+ EXPECT_EQ( savedMatrix.getElement( 2, 1 ), 10 );
+ EXPECT_EQ( savedMatrix.getElement( 2, 2 ), 11 );
+ EXPECT_EQ( savedMatrix.getElement( 2, 3 ), 12 );
+
+ EXPECT_EQ( savedMatrix.getElement( 3, 0 ), 13 );
+ EXPECT_EQ( savedMatrix.getElement( 3, 1 ), 14 );
+ EXPECT_EQ( savedMatrix.getElement( 3, 2 ), 15 );
+ EXPECT_EQ( savedMatrix.getElement( 3, 3 ), 16 );
+}
+
+template< typename Matrix >
+void test_Print()
+{
+ using RealType = typename Matrix::RealType;
+ using DeviceType = typename Matrix::DeviceType;
+ using IndexType = typename Matrix::IndexType;
+/*
+ * Sets up the following 5x4 sparse matrix:
+ *
+ * / 1 2 3 4 \
+ * | 5 6 7 8 |
+ * | 9 10 11 12 |
+ * | 13 14 15 16 |
+ * \ 17 18 19 20 /
+ */
+ const IndexType rows = 5;
+ const IndexType cols = 4;
+
+ Matrix m;
+ m.reset();
+ m.setDimensions( rows, cols );
+
+ RealType value = 1;
+ for( IndexType i = 0; i < rows; i++)
+ for( IndexType j = 0; j < cols; j++)
+ m.setElement( i, j, value++ );
+
+ #include <sstream>
+ std::stringstream printed;
+ std::stringstream couted;
+
+ //change the underlying buffer and save the old buffer
+ auto old_buf = std::cout.rdbuf(printed.rdbuf());
+
+ m.print( std::cout ); //all the std::cout goes to ss
+
+ std::cout.rdbuf(old_buf); //reset
+
+ couted << "Row: 0 -> Col:0->1 Col:1->2 Col:2->3 Col:3->4\t\n"
+ "Row: 1 -> Col:0->5 Col:1->6 Col:2->7 Col:3->8\t\n"
+ "Row: 2 -> Col:0->9 Col:1->10 Col:2->11 Col:3->12\t\n"
+ "Row: 3 -> Col:0->13 Col:1->14 Col:2->15 Col:3->16\t\n"
+ "Row: 4 -> Col:0->17 Col:1->18 Col:2->19 Col:3->20\t\n";
+
+ EXPECT_EQ( printed.str(), couted.str() );
+}
+
+// test fixture for typed tests
+template< typename Matrix >
+class MatrixTest : public ::testing::Test
+{
+protected:
+ using MatrixType = Matrix;
+};
+
+// types for which MatrixTest is instantiated
+using MatrixTypes = ::testing::Types
+<
+ TNL::Matrices::Dense< int, TNL::Devices::Host, short >,
+ TNL::Matrices::Dense< long, TNL::Devices::Host, short >,
+ TNL::Matrices::Dense< float, TNL::Devices::Host, short >,
+ TNL::Matrices::Dense< double, TNL::Devices::Host, short >,
+ TNL::Matrices::Dense< int, TNL::Devices::Host, int >,
+ TNL::Matrices::Dense< long, TNL::Devices::Host, int >,
+ TNL::Matrices::Dense< float, TNL::Devices::Host, int >,
+ TNL::Matrices::Dense< double, TNL::Devices::Host, int >,
+ TNL::Matrices::Dense< int, TNL::Devices::Host, long >,
+ TNL::Matrices::Dense< long, TNL::Devices::Host, long >,
+ TNL::Matrices::Dense< float, TNL::Devices::Host, long >,
+ TNL::Matrices::Dense< double, TNL::Devices::Host, long >
+#ifdef HAVE_CUDA
+ ,TNL::Matrices::Dense< int, TNL::Devices::Cuda, short >,
+ TNL::Matrices::Dense< long, TNL::Devices::Cuda, short >,
+ TNL::Matrices::Dense< float, TNL::Devices::Cuda, short >,
+ TNL::Matrices::Dense< double, TNL::Devices::Cuda, short >,
+ TNL::Matrices::Dense< int, TNL::Devices::Cuda, int >,
+ TNL::Matrices::Dense< long, TNL::Devices::Cuda, int >,
+ TNL::Matrices::Dense< float, TNL::Devices::Cuda, int >,
+ TNL::Matrices::Dense< double, TNL::Devices::Cuda, int >,
+ TNL::Matrices::Dense< int, TNL::Devices::Cuda, long >,
+ TNL::Matrices::Dense< long, TNL::Devices::Cuda, long >,
+ TNL::Matrices::Dense< float, TNL::Devices::Cuda, long >,
+ TNL::Matrices::Dense< double, TNL::Devices::Cuda, long >
+#endif
+>;
+
+TYPED_TEST_SUITE( MatrixTest, MatrixTypes );
+
+TYPED_TEST( MatrixTest, setDimensionsTest )
+{
+ using MatrixType = typename TestFixture::MatrixType;
+
+ test_SetDimensions< MatrixType >();
+}
+
+TYPED_TEST( MatrixTest, setLikeTest )
+{
+ using MatrixType = typename TestFixture::MatrixType;
+
+ test_SetLike< MatrixType, MatrixType >();
+}
+
+TYPED_TEST( MatrixTest, getRowLengthTest )
+{
+ using MatrixType = typename TestFixture::MatrixType;
+
+ test_GetRowLength< MatrixType >();
+}
+
+TYPED_TEST( MatrixTest, getNumberOfMatrixElementsTest )
+{
+ using MatrixType = typename TestFixture::MatrixType;
+
+ test_GetNumberOfMatrixElements< MatrixType >();
+}
+
+TYPED_TEST( MatrixTest, getNumberOfNonzeroMatrixElementsTest )
+{
+ using MatrixType = typename TestFixture::MatrixType;
+
+ test_GetNumberOfNonzeroMatrixElements< MatrixType >();
+}
+
+TYPED_TEST( MatrixTest, resetTest )
+{
+ using MatrixType = typename TestFixture::MatrixType;
+
+ test_Reset< MatrixType >();
+}
+
+TYPED_TEST( MatrixTest, setValueTest )
+{
+ using MatrixType = typename TestFixture::MatrixType;
+
+ test_SetValue< MatrixType >();
+}
+
+TYPED_TEST( MatrixTest, setElementTest )
+{
+ using MatrixType = typename TestFixture::MatrixType;
+
+ test_SetElement< MatrixType >();
+}
+
+TYPED_TEST( MatrixTest, addElementTest )
+{
+ using MatrixType = typename TestFixture::MatrixType;
+
+ test_AddElement< MatrixType >();
+}
+
+TYPED_TEST( MatrixTest, setRowTest )
+{
+ using MatrixType = typename TestFixture::MatrixType;
+
+ test_SetRow< MatrixType >();
+}
+
+TYPED_TEST( MatrixTest, addRowTest )
+{
+ using MatrixType = typename TestFixture::MatrixType;
+
+ test_AddRow< MatrixType >();
+}
+
+TYPED_TEST( MatrixTest, vectorProductTest )
+{
+ using MatrixType = typename TestFixture::MatrixType;
+
+ test_VectorProduct< MatrixType >();
+}
+
+TYPED_TEST( MatrixTest, addMatrixTest )
+{
+ using MatrixType = typename TestFixture::MatrixType;
+
+ test_AddMatrix< MatrixType >();
+}
+
+TYPED_TEST( MatrixTest, assignmentOperatorTest )
+{
+ using MatrixType = typename TestFixture::MatrixType;
+
+ test_AssignmentOperator< MatrixType >();
+}
+
+TYPED_TEST( MatrixTest, saveAndLoadTest )
+{
+ using MatrixType = typename TestFixture::MatrixType;
+
+ test_SaveAndLoad< MatrixType >();
+}
+
+TYPED_TEST( MatrixTest, printTest )
+{
+ using MatrixType = typename TestFixture::MatrixType;
+
+ test_Print< MatrixType >();
+}
+
+//// test_getType is not general enough yet. DO NOT TEST IT YET.
+
+//TEST( DenseMatrixTest, Dense_GetTypeTest_Host )
+//{
+// host_test_GetType< Dense_host_float, Dense_host_int >();
+//}
+//
+//#ifdef HAVE_CUDA
+//TEST( DenseMatrixTest, Dense_GetTypeTest_Cuda )
+//{
+// cuda_test_GetType< Dense_cuda_float, Dense_cuda_int >();
+//}
+//#endif
+
+/*TEST( DenseMatrixTest, Dense_getMatrixProductTest_Host )
+{
+ bool testRan = false;
+ EXPECT_TRUE( testRan );
+ std::cout << "\nTEST DID NOT RUN. NOT WORKING.\n\n";
+ std::cout << "If launched on CPU, this test will not build, but will print the following message: \n";
+ std::cout << " /home/lukas/tnl-dev/src/TNL/Matrices/Dense_impl.h(609): error: no instance of function template \"TNL::Matrices::DenseMatrixProductKernel\" matches the argument list\n";
+ std::cout << " argument types are: (TNL::Matrices::Dense<int, TNL::Devices::Host, int> *, Dense_host_int *, Dense_host_int *, const int, const int, int, int)\n";
+ std::cout << " detected during:\n";
+ std::cout << " instantiation of \"void TNL::Matrices::Dense<Real, Device, Index>::getMatrixProduct(const Matrix1 &, const Matrix2 &, const TNL::Matrices::Dense<Real, Device, Index>::RealType &, const TNL::Matrices::Dense<Real, Device, Index>::RealType &) [with Real=int, Device=TNL::Devices::Host, Index=int, Matrix1=Dense_host_int, Matrix2=Dense_host_int, tileDim=32]\"\n";
+ std::cout << " /home/lukas/tnl-dev/src/UnitTests/Matrices/DenseMatrixTest.h(901): here\n";
+ std::cout << " instantiation of \"void test_GetMatrixProduct<Matrix>() [with Matrix=Dense_host_int]\"\n";
+ std::cout << " /home/lukas/tnl-dev/src/UnitTests/Matrices/DenseMatrixTest.h(1315): here\n\n";
+}
+
+#ifdef HAVE_CUDA
+TEST( DenseMatrixTest, Dense_getMatrixProductTest_Cuda )
+{
+ bool testRan = false;
+ EXPECT_TRUE( testRan );
+ std::cout << "\nTEST DID NOT RUN. NOT WORKING.\n\n";
+ std::cout << "If launched on GPU, this test will not build, but will print the following message: \n";
+ std::cout << " /home/lukas/tnl-dev/src/TNL/Matrices/Dense_impl.h(510): error: identifier \"tnlCudaMin\" is undefined\n";
+ std::cout << " detected during:\n";
+ std::cout << " instantiation of \"void TNL::Matrices::DenseMatrixProductKernel<Real,Index,Matrix1,Matrix2,tileDim,tileRowBlockSize>(TNL::Matrices::Dense<Real, TNL::Devices::Cuda, Index> *, const Matrix1 *, const Matrix2 *, Real, Real, Index, Index) [with Real=int, Index=int, Matrix1=Dense_cuda_int, Matrix2=Dense_cuda_int, tileDim=32, tileRowBlockSize=8]\"\n";
+ std::cout << " instantiation of \"void TNL::Matrices::Dense<Real, Device, Index>::getMatrixProduct(const Matrix1 &, const Matrix2 &, const TNL::Matrices::Dense<Real, Device, Index>::RealType &, const TNL::Matrices::Dense<Real, Device, Index>::RealType &) [with Real=int, Device=TNL::Devices::Cuda, Index=int, Matrix1=Dense_cuda_int, Matrix2=Dense_cuda_int, tileDim=32]\"\n";
+ std::cout << " /home/lukas/tnl-dev/src/UnitTests/Matrices/DenseMatrixTest.h(901): here\n";
+ std::cout << " instantiation of \"void test_GetMatrixProduct<Matrix>() [with Matrix=Dense_cuda_int]\"\n";
+ std::cout << " /home/lukas/tnl-dev/src/UnitTests/Matrices/DenseMatrixTest.h(1332): here\n\n";
+}
+#endif
+
+TEST( DenseMatrixTest, Dense_getTranspositionTest_Host )
+{
+// test_GetTransposition< Dense_host_int >();
+ bool testRan = false;
+ EXPECT_TRUE( testRan );
+ std::cout << "\nTEST DID NOT RUN. NOT WORKING.\n\n";
+ std::cout << "If launched on CPU, this test will not build, but will print the following message: \n";
+ std::cout << " /home/lukas/tnl-dev/src/TNL/Matrices/Dense_impl.h(836): error: no instance of function template \"TNL::Matrices::DenseTranspositionAlignedKernel\" matches the argument list\n";
+ std::cout << " argument types are: (TNL::Matrices::Dense<int, TNL::Devices::Host, int> *, Dense_host_int *, const int, int, int)\n";
+ std::cout << " detected during:\n";
+ std::cout << " instantiation of \"void TNL::Matrices::Dense<Real, Device, Index>::getTransposition(const Matrix &, const TNL::Matrices::Dense<Real, Device, Index>::RealType &) [with Real=int, Device=TNL::Devices::Host, Index=int, Matrix=Dense_host_int, tileDim=32]\"\n";
+ std::cout << " /home/lukas/tnl-dev/src/UnitTests/Matrices/DenseMatrixTest.h(977): here\n";
+ std::cout << " instantiation of \"void test_GetTransposition<Matrix>() [with Matrix=Dense_host_int]\"\n";
+ std::cout << " /home/lukas/tnl-dev/src/UnitTests/Matrices/DenseMatrixTest.h(1420): here\n\n";
+ std::cout << "AND this message: \n";
+ std::cout << " /home/lukas/tnl-dev/src/TNL/Matrices/Dense_impl.h(852): error: no instance of function template \"TNL::Matrices::DenseTranspositionNonAlignedKernel\" matches the argument list\n";
+ std::cout << " argument types are: (TNL::Matrices::Dense<int, TNL::Devices::Host, int> *, Dense_host_int *, const int, int, int)\n";
+ std::cout << " detected during:\n";
+ std::cout << " instantiation of \"void TNL::Matrices::Dense<Real, Device, Index>::getTransposition(const Matrix &, const TNL::Matrices::Dense<Real, Device, Index>::RealType &) [with Real=int, Device=TNL::Devices::Host, Index=int, Matrix=Dense_host_int, tileDim=32]\"\n";
+ std::cout << " /home/lukas/tnl-dev/src/UnitTests/Matrices/DenseMatrixTest.h(977): here\n";
+ std::cout << " instantiation of \"void test_GetTransposition<Matrix>() [with Matrix=Dense_host_int]\"\n";
+ std::cout << " /home/lukas/tnl-dev/src/UnitTests/Matrices/DenseMatrixTest.h(1420): here\n\n";
+}
+
+#ifdef HAVE_CUDA
+TEST( DenseMatrixTest, Dense_getTranspositionTest_Cuda )
+{
+// test_GetTransposition< Dense_cuda_int >();
+ bool testRan = false;
+ EXPECT_TRUE( testRan );
+ std::cout << "\nTEST DID NOT RUN. NOT WORKING.\n\n";
+ std::cout << "If launched on GPU, this test throws the following message: \n";
+ std::cout << " Assertion 'row >= 0 && row < this->getRows() && column >= 0 && column < this->getColumns()' failed !!!\n";
+ std::cout << " File: /home/lukas/tnl-dev/src/TNL/Matrices/Dense_impl.h \n";
+ std::cout << " Line: 329 \n";
+ std::cout << " Diagnostics: Not supported with CUDA.\n";
+ std::cout << " Assertion 'row >= 0 && row < this->getRows() && column >= 0 && column < this->getColumns()' failed !!! \n";
+ std::cout << " File: /home/lukas/tnl-dev/src/TNL/Matrices/Dense_impl.h \n";
+ std::cout << " Line: 329 \n";
+ std::cout << " Diagnostics: Not supported with CUDA.\n";
+ std::cout << " Assertion 'row >= 0 && row < this->getRows() && column >= 0 && column < this->getColumns()' failed !!! \n";
+ std::cout << " File: /home/lukas/tnl-dev/src/TNL/Matrices/Dense_impl.h \n";
+ std::cout << " Line: 329 \n";
+ std::cout << " Diagnostics: Not supported with CUDA.\n";
+ std::cout << " Assertion 'row >= 0 && row < this->getRows() && column >= 0 && column < this->getColumns()' failed !!! \n";
+ std::cout << " File: /home/lukas/tnl-dev/src/TNL/Matrices/Dense_impl.h \n";
+ std::cout << " Line: 329 \n";
+ std::cout << " Diagnostics: Not supported with CUDA.\n";
+ std::cout << " terminate called after throwing an instance of 'TNL::Exceptions::CudaRuntimeError'\n";
+ std::cout << " what(): CUDA ERROR 4 (cudaErrorLaunchFailure): unspecified launch failure.\n";
+ std::cout << " Source: line 57 in /home/lukas/tnl-dev/src/TNL/Containers/Algorithms/ArrayOperationsCuda_impl.h: unspecified launch failure\n";
+ std::cout << " [1] 4003 abort (core dumped) ./DenseMatrixTest-dbg\n";
+}
+#endif
+
+TEST( DenseMatrixTest, Dense_performSORIterationTest_Host )
+{
+ test_PerformSORIteration< Dense_host_float >();
+}
+
+#ifdef HAVE_CUDA
+TEST( DenseMatrixTest, Dense_performSORIterationTest_Cuda )
+{
+// test_PerformSORIteration< Dense_cuda_float >();
+ bool testRan = false;
+ EXPECT_TRUE( testRan );
+ std::cout << "\nTEST DID NOT RUN. NOT WORKING.\n\n";
+ std::cout << "If launched, this test throws the following message: \n";
+ std::cout << " [1] 6992 segmentation fault (core dumped) ./SparseMatrixTest-dbg\n\n";
+ std::cout << "\n THIS IS NOT IMPLEMENTED FOR CUDA YET!!\n\n";
+}
+#endif
+ * */
+
+#endif // HAVE_GTEST
+
+#include "../main.h"