Commit 62e73b0d authored by Tomáš Oberhuber's avatar Tomáš Oberhuber
Browse files

Optimizing slow Legacy CSR unit test.

parent dca1b1ca

src/UnitTests/Matrices/Legacy/SparseMatrixTest.hpp

+42 −44
Original line number Diff line number Diff line
@@ -1393,45 +1393,36 @@ void test_VectorProductCSRAdaptive() 
   using IndexType = typename Matrix::IndexType;

   using VectorType = TNL::Containers::Vector< RealType, DeviceType, IndexType >;





   Matrix m;

   m.reset();

   IndexType m_rows = 100;

   IndexType m_cols = 100;

   //----------------- Test CSR Stream part ------------------

   Matrix m;

   m.setDimensions( m_rows, m_cols );

   typename Matrix::CompressedRowLengthsVector rowLengths(

   typename Matrix::CompressedRowLengthsVector rowLengths( 100, 100 );



   if( std::is_same< DeviceType, TNL::Devices::Cuda >::value )

   {

         100, 100, 100, 100, 100, 100, 100, 100, 100, 100,

         100, 100, 100, 100, 100, 100, 100, 100, 100, 100,

         100, 100, 100, 100, 100, 100, 100, 100, 100, 100,

         100, 100, 100, 100, 100, 100, 100, 100, 100, 100,

         100, 100, 100, 100, 100, 100, 100, 100, 100, 100,

         100, 100, 100, 100, 100, 100, 100, 100, 100, 100,

         100, 100, 100, 100, 100, 100, 100, 100, 100, 100,

         100, 100, 100, 100, 100, 100, 100, 100, 100, 100,

         100, 100, 100, 100, 100, 100, 100, 100, 100, 100,

         100, 100, 100, 100, 100, 100, 100, 100, 100, 100

      typedef typename Matrix::Self< RealType, TNL::Devices::Host, IndexType > HostMatrixType;

      typename HostMatrixType::CompressedRowLengthsVector rowLengths( 100, 100 );

      HostMatrixType hostMatrix;

      hostMatrix.setDimensions( m_rows, m_cols );

      hostMatrix.setCompressedRowLengths( rowLengths );

      for (int i = 0; i < m_rows; ++i)

         for (int j = 0; j < m_cols; ++j) 

            hostMatrix.setElement( i, j, i + 1 );

      m = hostMatrix;

   }

   );



   else

   {

      m.setCompressedRowLengths( rowLengths );



      for (int i = 0; i < m_rows; ++i)

         for (int j = 0; j < m_cols; ++j) 

            m.setElement( i, j, i + 1 );

   }





   VectorType inVector;

   inVector.setSize( m_rows );

   for( IndexType i = 0; i < inVector.getSize(); ++i )        

      inVector.setElement( i, 1 );



   VectorType outVector;  

   outVector.setSize( m_rows );

   for( IndexType i = 0; i < outVector.getSize(); ++i )

      outVector.setElement( i, 0 );

   

   VectorType inVector( m_rows, 1.0 );

   VectorType outVector( m_rows, 0.0 );

   m.vectorProduct( inVector, outVector);



   for (int i = 0; i < m_rows; ++i)
@@ -1447,20 +1438,27 @@ void test_VectorProductCSRAdaptive() 
   m.setDimensions( m_rows, m_cols );

   typename Matrix::CompressedRowLengthsVector rowLengths2({m_cols});



   if( std::is_same< DeviceType, TNL::Devices::Cuda >::value )

   {

      typedef typename Matrix::Self< RealType, TNL::Devices::Host, IndexType > HostMatrixType;

      typename HostMatrixType::CompressedRowLengthsVector rowLengths( {m_cols} );

      HostMatrixType hostMatrix;

      hostMatrix.setDimensions( m_rows, m_cols );

      hostMatrix.setCompressedRowLengths( rowLengths );

      for( int i = 0; i < m_cols; ++i )

         hostMatrix.setElement( 0, i, i );

      m = hostMatrix;

   }

   else

   {

      m.setCompressedRowLengths( rowLengths2 );



      for (int i = 0; i < m_cols; ++i) 

         m.setElement( 0, i, i );

   }



   VectorType inVector2;

   inVector2.setSize( m_cols );

   for( IndexType i = 0; i < inVector2.getSize(); i++ )

      inVector2.setElement( i, 2 );

   VectorType inVector2( m_cols, 2.0 );



   VectorType outVector2;

   outVector2.setSize( m_rows );

   for( IndexType i = 0; i < outVector2.getSize(); ++i )

      outVector2.setElement( i, 0 );

   VectorType outVector2( m_rows, 0.0 );



   m.vectorProduct(inVector2, outVector2);

   EXPECT_EQ( outVector2.getElement( 0 ), 8997000 );