Commit 1996cd77 authored by Lukas Cejka's avatar Lukas Cejka
Browse files

Implement changes: 

	Added getType test error location, commented out its tests.
	Added visual representation of used matrices in tests.
	Added working and correct version of addElement test.
	Fixed vectorProduct test for Host, CUDA throws illegal memory
access.
	Fixed performSORIteration for Host, CUDA throws segmentation
fault.
	Fixed load/save test to not use a path.
	Added test for print function.
	Reformatted code.
	Added messages thrown for CUDA errors into tests, so that the
whole test doesn't crash after those errors.
	Updated TODO.
parent 6b4f0df3

src/UnitTests/Matrices/SparseMatrixTest.h

+373 −181
Original line number Diff line number Diff line
@@ -11,45 +11,46 @@ 
// TODO

/*

 * getType()                        ::HOW?  How to test this for each format? edit string how?

 *      Found the mistake for Cuda instead of Devices::Cuda. Incorrect String in src/TNL/Devices/Cuda.cpp

 * getTypeVirtual()                 ::TEST? This just calls getType().

 * getSerializationType()           ::TEST? This just calls HostType::getType().

 * getSerializationTypeVirtual()    ::TEST? This just calls getSerializationType().

 * setDimensions()                      ::DONE

 * setCompressedRowLengths()            ::DONE

 * getRowLength()                   ::USED! In test_SetCompressedRowLengths() to verify the test itself.

 * getRowLengthFast()               ::TEST? How to test __cuda_callable__?

 * getRowLengthFast()               ::TEST? How to test __cuda_callable__? ONLY TEST ON CPU FOR NOW

 * setLike()                            ::DONE

 * reset()                              ::DONE

 * setElementFast()                 ::TEST? How to test __cuda_callable__?

 * setElementFast()                 ::TEST? How to test __cuda_callable__? ONLY TEST ON CPU FOR NOW

 * setElement()                         ::DONE

 * addElementFast()                 ::TEST? How to test __cuda_callable__?

 * addElement()                     ::HOW?  How to use the thisElementMultiplicator? Does it need testing?

 * setRowFast()                     ::TEST? How to test __cuda_callable__?

 * addElementFast()                 ::TEST? How to test __cuda_callable__? ONLY TEST ON CPU FOR NOW

 * addElement()                         ::DONE

 * setRowFast()                     ::TEST? How to test __cuda_callable__? ONLY TEST ON CPU FOR NOW

 * setRow()                             ::DONE

 * addRowFast()                     ::TEST? How to test __cuda_callable__?

 * addRow()                         ::NOT IMPLEMENTED! Implement? Is it supposed to add an extra row to the matrix or arr elements of a row to another row in the matrix?

 * getElementFast()                 ::TEST? How to test __cuda_callable__?

 * addRowFast()                     ::TEST? How to test __cuda_callable__? ONLY TEST ON CPU FOR NOW

 * addRow()                         ::NOT IMPLEMENTED! This calls addRowFast() which isn't implemented. Implement? Is it supposed to add an extra row to the matrix or add elements of a row to another row in the matrix?

 * getElementFast()                 ::TEST? How to test __cuda_callable__? ONLY TEST ON CPU FOR NOW

 * getElement()                     ::USED! In test_SetElement(), test_AddElement() and test_setRow() to verify the test itself.

 * getRowFast()                     ::TEST? How to test __cuda_callable__?

 * MatrixRow getRow()               ::TEST? How to test __cuda_callable__?

 * ConstMatrixRow getRow()          ::TEST? How to test __cuda_callable__?

 * rowVectorProduct()               ::TEST? How to test __cuda_callable__?

 * vectorProduct()                  ::HOW? Throwing errors in CSR_impl.h (779) no instance matches the arguments when using int arrays. When tried using Vector_impl.h index out of bounds or CUDA illegal memory access

 * getRowFast()                     ::TEST? How to test __cuda_callable__? ONLY TEST ON CPU FOR NOW

 * MatrixRow getRow()               ::TEST? How to test __cuda_callable__? ONLY TEST ON CPU FOR NOW

 * ConstMatrixRow getRow()          ::TEST? How to test __cuda_callable__? ONLY TEST ON CPU FOR NOW

 * rowVectorProduct()               ::TEST? How to test __cuda_callable__? ONLY TEST ON CPU FOR NOW

 * vectorProduct()                  ::HOW? Throwing abort CUDA illegal memory access errors.

 * addMatrix()                      ::NOT IMPLEMENTED!

 * getTransposition()               ::NOT IMPLMENETED!

 * performSORIteration()            ::HOW? What does this do? Ax=b but splitting A into D(:=Diagonal) L(:=Lower Triangular) U(=Upper Triangular) matrices. What is the omega(relaxation/residual factor??)? https://en.wikipedia.org/wiki/Successive_over-relaxation

 * performSORIteration()            ::HOW? Throws segmentation fault CUDA.

 * operator=()                      ::HOW? What is this supposed to enable? Overloading operators?

 * save( File& file)                ::USED! In save( String& fileName )

 * load( File& file )               ::USED! In load( String& fileName )

 * save( String& fileName )             ::DONE

 * load( String& fileName )             ::DONE

 * print()

 * setCudaKernelType()

 * getCudaKernelType()              ::TEST? How to test __cuda_callable__?

 * setCudaWarpSize()

 * getCudaWarpSize()

 * setHybridModeSplit()

 * getHybridModeSplit()             ::TEST? How to test __cuda_callable__?

 * print()                              ::DONE

 * setCudaKernelType()              ::NOT SUPPOSED TO TEST! via notes from 8.11.2018 supervisor meeting.

 * getCudaKernelType()              ::NOT SUPPOSED TO TEST! via notes from 8.11.2018 supervisor meeting.

 * setCudaWarpSize()                ::NOT SUPPOSED TO TEST! via notes from 8.11.2018 supervisor meeting.

 * getCudaWarpSize()                ::NOT SUPPOSED TO TEST! via notes from 8.11.2018 supervisor meeting.

 * setHybridModeSplit()             ::NOT SUPPOSED TO TEST! via notes from 8.11.2018 supervisor meeting.

 * getHybridModeSplit()             ::NOT SUPPOSED TO TEST! via notes from 8.11.2018 supervisor meeting.

 * spmvCudaVectorized()             ::TEST? How to test __device__?

 * vectorProductCuda()              ::TEST? How to test __device__?

 */
@@ -59,6 +60,7 @@ 
 * For every function, EXPECT_EQ needs to be done, even for zeros in matrices.

 * Figure out __cuda_callable_. When trying to call __cuda_callable__ functions

 *      a segmentation fault (core dumped) is thrown.

 *  ==>__cuda_callable__ works only for CPU at the moment. (for loops vs thread kernel assignment)

 */
@@ -68,6 +70,7 @@ 


#include <TNL/Containers/VectorView.h>

#include <TNL/Math.h>

#include <iostream>



using CSR_host_float = TNL::Matrices::CSR< float, TNL::Devices::Host, int >;

using CSR_host_int = TNL::Matrices::CSR< int, TNL::Devices::Host, int >;
@@ -98,8 +101,8 @@ void cuda_test_GetType() 
    MatrixCudaFloat mtrxCudaFloat;

    MatrixCudaInt mtrxCudaInt;



    EXPECT_EQ( mtrxCudaFloat.getType(), TNL::String( "Matrices::CSR< float, Cuda >" ) );

    EXPECT_EQ( mtrxCudaInt.getType(), TNL::String( "Matrices::CSR< int, Cuda >" ) );

    EXPECT_EQ( mtrxCudaFloat.getType(), TNL::String( "Matrices::CSR< float, Cuda >" ) );    // This is mistakenly labeled in /src/TNL/Devices/Cuda.cpp

    EXPECT_EQ( mtrxCudaInt.getType(), TNL::String( "Matrices::CSR< int, Cuda >" ) );        // Should be Devices::Cuda

}



template< typename Matrix >
@@ -168,6 +171,15 @@ void test_SetLike() 
template< typename Matrix >

void test_Reset()

{

/*

 * Sets up the following 5x4 sparse matrix:

 *

 *    /  0  0  0  0 \

 *    |  0  0  0  0 |

 *    |  0  0  0  0 |

 *    |  0  0  0  0 |

 *    \  0  0  0  0 /

 */

    const int rows = 5;

    const int cols = 4;
@@ -183,6 +195,15 @@ void test_Reset() 
template< typename Matrix >

void test_SetElement()

{

/*

 * Sets up the following 5x5 sparse matrix:

 *

 *    /  1  0  0  0  0 \

 *    |  0  2  0  0  0 |

 *    |  0  0  3  0  0 |

 *    |  0  0  0  4  0 |

 *    \  0  0  0  0  5 /

 */

    const int rows = 5;

    const int cols = 5;
@@ -232,6 +253,16 @@ void test_SetElement() 
template< typename Matrix >

void test_AddElement()

{

/*

 * Sets up the following 6x5 sparse matrix:

 *

 *    /  1  2  3  0  0 \

 *    |  0  4  5  6  0 |

 *    |  0  0  7  8  9 |

 *    | 10  0  0  0  0 |

 *    |  0 11  0  0  0 |

 *    \  0  0  0 12  0 /

 */

    const int rows = 6;

    const int cols = 5;
@@ -244,51 +275,136 @@ void test_AddElement() 
    m.setCompressedRowLengths( rowLengths );

    

    int value = 1;

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

        m.addElement( i, 0, value++, 0.0 );

    for( int i = 0; i < cols - 2; i++ )     // 0th row

        m.setElement( 0, i, value++ );

    

    m.addElement( 0, 4, 1, 0.0 );

    for( int i = 1; i < cols - 1; i++ )     // 1st row

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

        

    for( int i = 2; i < cols; i++ )         // 2nd row

        m.setElement( 2, i, value++ );

        

        m.setElement( 3, 0, value++ );      // 3rd row

        

        m.setElement( 4, 1, value++ );      // 4th row

    

        m.setElement( 5, 3, value++ );      // 5th row

    

    // Check the set elements

    EXPECT_EQ( m.getElement( 0, 0 ),  1 );

    EXPECT_EQ( m.getElement( 0, 1 ), 0 );

    EXPECT_EQ( m.getElement( 0, 2 ), 0 );

    EXPECT_EQ( m.getElement( 0, 1 ),  2 );

    EXPECT_EQ( m.getElement( 0, 2 ),  3 );

    EXPECT_EQ( m.getElement( 0, 3 ),  0 );

    EXPECT_EQ( m.getElement( 0, 4 ), 1 );

    EXPECT_EQ( m.getElement( 0, 4 ),  0 );

    

    EXPECT_EQ( m.getElement( 1, 0 ), 2 );

    EXPECT_EQ( m.getElement( 1, 1 ), 0 );

    EXPECT_EQ( m.getElement( 1, 2 ), 0 );

    EXPECT_EQ( m.getElement( 1, 3 ), 0 );

    EXPECT_EQ( m.getElement( 1, 0 ),  0 );

    EXPECT_EQ( m.getElement( 1, 1 ),  4 );

    EXPECT_EQ( m.getElement( 1, 2 ),  5 );

    EXPECT_EQ( m.getElement( 1, 3 ),  6 );

    EXPECT_EQ( m.getElement( 1, 4 ),  0 );

    

    EXPECT_EQ( m.getElement( 2, 0 ), 3 );

    EXPECT_EQ( m.getElement( 2, 0 ),  0 );

    EXPECT_EQ( m.getElement( 2, 1 ),  0 );

    EXPECT_EQ( m.getElement( 2, 2 ), 0 );

    EXPECT_EQ( m.getElement( 2, 3 ), 0 );

    EXPECT_EQ( m.getElement( 2, 4 ), 0 );

    EXPECT_EQ( m.getElement( 2, 2 ),  7 );

    EXPECT_EQ( m.getElement( 2, 3 ),  8 );

    EXPECT_EQ( m.getElement( 2, 4 ),  9 );

    

    EXPECT_EQ( m.getElement( 3, 0 ), 4 );

    EXPECT_EQ( m.getElement( 3, 0 ), 10 );

    EXPECT_EQ( m.getElement( 3, 1 ),  0 );

    EXPECT_EQ( m.getElement( 3, 2 ),  0 );

    EXPECT_EQ( m.getElement( 3, 3 ),  0 );

    EXPECT_EQ( m.getElement( 3, 4 ),  0 );

    

    EXPECT_EQ( m.getElement( 4, 0 ), 5 );

    EXPECT_EQ( m.getElement( 4, 1 ), 0 );

    EXPECT_EQ( m.getElement( 4, 0 ),  0 );

    EXPECT_EQ( m.getElement( 4, 1 ), 11 );

    EXPECT_EQ( m.getElement( 4, 2 ),  0 );

    EXPECT_EQ( m.getElement( 4, 3 ),  0 );

    EXPECT_EQ( m.getElement( 4, 4 ),  0 );

    

    EXPECT_EQ( m.getElement( 5, 0 ), 6 );

    EXPECT_EQ( m.getElement( 5, 0 ),  0 );

    EXPECT_EQ( m.getElement( 5, 1 ),  0 );

    EXPECT_EQ( m.getElement( 5, 2 ),  0 );

    EXPECT_EQ( m.getElement( 5, 3 ), 0 );

    EXPECT_EQ( m.getElement( 5, 3 ), 12 );

    EXPECT_EQ( m.getElement( 5, 4 ),  0 );

    

    // 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  0  0 \

 *    |  0 12 15 18  0 |

 *    |  0  0 21 24 27 |

 *    | 30 11 12  0  0 |

 *    |  0 35 14 15  0 |

 *    \  0  0 16 41 18 /

 */

    int newValue = 1;

    for( int i = 0; i < cols - 2; i++ )         // 0th row

        m.addElement( 0, i, newValue++, 2.0 );

    

    for( int i = 1; i < cols - 1; i++ )         // 1st row

        m.addElement( 1, i, newValue++, 2.0 );

        

    for( int i = 2; i < cols; i++ )             // 2nd row

        m.addElement( 2, i, newValue++, 2.0 );

        

    for( int i = 0; i < cols - 2; i++ )         // 3rd row

        m.addElement( 3, i, newValue++, 2.0 );

    

    for( int i = 1; i < cols - 1; i++ )         // 4th row

        m.addElement( 4, i, newValue++, 2.0 );

    

    for( int i = 2; i < cols; i++ )             // 5th row

        m.addElement( 5, i, newValue++, 2.0 );

    

    

    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 ),  0 );

    EXPECT_EQ( m.getElement( 0, 4 ),  0 );

    

    EXPECT_EQ( m.getElement( 1, 0 ),  0 );

    EXPECT_EQ( m.getElement( 1, 1 ), 12 );

    EXPECT_EQ( m.getElement( 1, 2 ), 15 );

    EXPECT_EQ( m.getElement( 1, 3 ), 18 );

    EXPECT_EQ( m.getElement( 1, 4 ),  0 );

    

    EXPECT_EQ( m.getElement( 2, 0 ),  0 );

    EXPECT_EQ( m.getElement( 2, 1 ),  0 );

    EXPECT_EQ( m.getElement( 2, 2 ), 21 );

    EXPECT_EQ( m.getElement( 2, 3 ), 24 );

    EXPECT_EQ( m.getElement( 2, 4 ), 27 );

    

    EXPECT_EQ( m.getElement( 3, 0 ), 30 );

    EXPECT_EQ( m.getElement( 3, 1 ), 11 );

    EXPECT_EQ( m.getElement( 3, 2 ), 12 );

    EXPECT_EQ( m.getElement( 3, 3 ),  0 );

    EXPECT_EQ( m.getElement( 3, 4 ),  0 );

    

    EXPECT_EQ( m.getElement( 4, 0 ),  0 );

    EXPECT_EQ( m.getElement( 4, 1 ), 35 );

    EXPECT_EQ( m.getElement( 4, 2 ), 14 );

    EXPECT_EQ( m.getElement( 4, 3 ), 15 );

    EXPECT_EQ( m.getElement( 4, 4 ),  0 );

    

    EXPECT_EQ( m.getElement( 5, 0 ),  0 );

    EXPECT_EQ( m.getElement( 5, 1 ),  0 );

    EXPECT_EQ( m.getElement( 5, 2 ), 16 );

    EXPECT_EQ( m.getElement( 5, 3 ), 41 );

    EXPECT_EQ( m.getElement( 5, 4 ), 18 );

}



template< typename Matrix >

void test_SetRow()

{

/*

 * Sets up the following 3x7 sparse matrix:

 *

 *    /  0  0  0  1  1  1  0 \

 *    |  2  2  2  0  0  0  0 |

 *    \  3  3  3  0  0  0  0 /

 */

    const int rows = 3;

    const int cols = 7;
@@ -354,7 +470,6 @@ void test_VectorProduct() 
 *    |  0  8  9 10 |

 *    \  0  0 11 12 /

 */

    bool testRan = false;

    const int m_rows = 5;

    const int m_cols = 4;
@@ -381,117 +496,106 @@ void test_VectorProduct() 
    for( int i = 2; i < m_cols; i++ )       // 4th row

        m.setElement( 4, i, value++ );

    

//                        #include <TNL/Containers/Vector.h>

//                        #include <TNL/Containers/VectorView.h>

//                    

//                        using namespace TNL;

//                        using namespace TNL::Containers;

//                        using namespace TNL::Containers::Algorithms;

//                        

//                        Vector< int, Devices::Host, int > inVector;

//                        inVector.setSize( 5 );

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

//                            inVector.setElement( i, 1 );

//                        

//                        Vector< int, Devices::Host, int > outVector;  

//                        outVector.setSize( 4 );                        // ERROR: out of bounds, if set to 3 or 4. CUDA illegal memory access when set to 5.

//                        for( int j = 0; j < outVector.getSize(); j++ )

//                            outVector.setElement( j, 0 );//outVector[ j ] = 0;

    #include <TNL/Containers/Vector.h>

    #include <TNL/Containers/VectorView.h>



//    const int inVector [ 5 ] = { 1, 1, 1, 1, 1 };

//    int outVector [ 4 ] = { 0, 0, 0, 0 };

//    

//    m.vectorProduct( inVector, outVector); // ERROR: This throws an error when Vector<> declarations are used.

//    testRan = true;    

//    EXPECT_EQ( outVector.getElement( 0 ),  6 );

//    EXPECT_EQ( outVector.getElement( 1 ), 16 );

//    EXPECT_EQ( outVector.getElement( 2 ), 30 );

//    EXPECT_EQ( outVector.getElement( 3 ), 26 );

    using namespace TNL;

    using namespace TNL::Containers;

    using namespace TNL::Containers::Algorithms;



    EXPECT_TRUE( testRan );

    std::cout << "TEST DID NOT RUN. NOT IMPLETENTED.\n";

    Vector< int, Devices::Host, int > inVector;

    inVector.setSize( 4 );

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

        inVector.setElement( i, 2 );



    Vector< int, Devices::Host, int > outVector;  

    outVector.setSize( 5 );

    for( int j = 0; j < outVector.getSize(); j++ )

        outVector.setElement( j, 0 );

 

    

    m.vectorProduct( inVector, outVector); // ERROR: This throws an error when Vector<> declarations are used.

   

    EXPECT_EQ( outVector.getElement( 0 ), 12 );

    EXPECT_EQ( outVector.getElement( 1 ),  8 );

    EXPECT_EQ( outVector.getElement( 2 ), 36 );

    EXPECT_EQ( outVector.getElement( 3 ), 54 );

    EXPECT_EQ( outVector.getElement( 4 ), 46 );

}



template< typename Matrix >

void test_PerformSORIteration()

{

/*

 * Sets up the following 5x4 sparse matrix:

 * Sets up the following 4x4 sparse matrix:

 *

 *    /  1  2  3  0 \

 *    |  0  4  0  5 |

 *    |  6  7  8  0 |

 *    \  0  9 10 11 /

 *    /  4  1  0  0 \

 *    |  1  4  1  0 |

 *    |  0  1  4  1 |

 *    \  0  0  1  4 /

 */

    bool testRan = false;

//    const int m_rows = 4;

//    const int m_cols = 4;

//    

//    Matrix m;

//    m.reset();

//    m.setDimensions( m_rows, m_cols );

//    typename Matrix::CompressedRowLengthsVector rowLengths;

//    rowLengths.setSize( m_rows );

//    rowLengths.setValue( 3 );

//    m.setCompressedRowLengths( rowLengths );

//    

//    int value = 1;

//    for( int i = 0; i < m_cols - 1; i++ )   // 0th row

//        m.setElement( 0, i, value++ );

//        

//        m.setElement( 1, 1, value++ );

//        m.setElement( 1, 3, value++ );      // 1st row

//        

//    for( int i = 0; i < m_cols - 1; i++ )   // 2nd row

//        m.setElement( 2, i, value++ );

//        

//    for( int i = 1; i < m_cols; i++ )       // 3rd row

//        m.setElement( 3, i, value++ );

//    

//    // Print out the Matrix

//    std::cout << "Matrix m: \n";

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

//    {

//        std::cout << "| ";

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

//            std::cout << m.getElement( i, j ) << " ";

//        std::cout << " |\n";

//    }

//    std::cout << std::endl;

//    

//    int bVector [ 4 ] = { 6, 9, 21, 30 };

//    int xVector [ 4 ] = { 1, 1, 1, 1 };

//    

//    m.performSORIteration( bVector, 0, xVector, 1);

//    m.performSORIteration( bVector, 1, xVector, 1);

//    m.performSORIteration( bVector, 2, xVector, 1);

//    m.performSORIteration( bVector, 3, xVector, 1);

//    

//    std::cout << "\n[ ";

//    for( int i = 0; i < 4; i++ )

//        std::cout << xVector[ i ] << " ";

//    std::cout << " ]\n";

//    

//    std::cout << "\n[ ";

//    for( int i = 0; i < 4; i++ )

//        std::cout << bVector[ i ] << " ";

//    std::cout << " ]\n";

//    

//    testRan = true;

//    EXPECT_EQ( xVector[ 0 ], 1 );

//    EXPECT_EQ( xVector[ 1 ], 1 );

//    EXPECT_EQ( xVector[ 2 ], 1 );

//    EXPECT_EQ( xVector[ 3 ], 1 );

    const int m_rows = 4;

    const int m_cols = 4;

    

    EXPECT_TRUE( testRan );

    std::cout << "TEST DID NOT RUN. NOT IMPLETENTED.\n";

    Matrix m;

    m.reset();

    m.setDimensions( m_rows, m_cols );

    typename Matrix::CompressedRowLengthsVector rowLengths;

    rowLengths.setSize( m_rows );

    rowLengths.setValue( 3 );

    m.setCompressedRowLengths( rowLengths );

    

    m.setElement( 0, 0, 4.0 );        // 0th row

    m.setElement( 0, 1, 1.0);

        

    m.setElement( 1, 0, 1.0 );        // 1st row

    m.setElement( 1, 1, 4.0 );

    m.setElement( 1, 2, 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, 2, 1.0 );        // 3rd row

    m.setElement( 3, 3, 4.0 );

    

    float bVector [ 4 ] = { 1.0, 1.0, 1.0, 1.0 };

    float xVector [ 4 ] = { 1.0, 1.0, 1.0, 1.0 };

    

    m.performSORIteration( bVector, 0, xVector, 1);

    

    EXPECT_EQ( xVector[ 0 ], 0.0 );

    EXPECT_EQ( xVector[ 1 ], 1.0 );

    EXPECT_EQ( xVector[ 2 ], 1.0 );

    EXPECT_EQ( xVector[ 3 ], 1.0 );

    

    m.performSORIteration( bVector, 1, xVector, 1);

    

    EXPECT_EQ( xVector[ 0 ], 0.0 );

    EXPECT_EQ( xVector[ 1 ], 0.0 );

    EXPECT_EQ( xVector[ 2 ], 1.0 );

    EXPECT_EQ( xVector[ 3 ], 1.0 );

    

    m.performSORIteration( bVector, 2, xVector, 1);

    

    EXPECT_EQ( xVector[ 0 ], 0.0 );

    EXPECT_EQ( xVector[ 1 ], 0.0 );

    EXPECT_EQ( xVector[ 2 ], 0.0 );

    EXPECT_EQ( xVector[ 3 ], 1.0 );

    

    m.performSORIteration( bVector, 3, xVector, 1);

    

    EXPECT_EQ( xVector[ 0 ], 0.0 );

    EXPECT_EQ( xVector[ 1 ], 0.0 );

    EXPECT_EQ( xVector[ 2 ], 0.0 );

    EXPECT_EQ( xVector[ 3 ], 0.25 );

}



template< typename Matrix >

void test_SaveAndLoad()

{

/*

 * Sets up the following 5x4 sparse matrix:

 * Sets up the following 4x4 sparse matrix:

 *

 *    /  1  2  3  0 \

 *    |  0  4  0  5 |
@@ -522,7 +626,7 @@ void test_SaveAndLoad() 
    for( int i = 1; i < m_cols; i++ )       // 3rd row

        savedMatrix.setElement( 3, i, value++ );

        

    savedMatrix.save( "/home/lukas/m" );

    savedMatrix.save( "matrixFile" );

    

    Matrix loadedMatrix;

    loadedMatrix.reset();
@@ -533,7 +637,7 @@ void test_SaveAndLoad() 
    loadedMatrix.setCompressedRowLengths( rowLengths2 );

    

    

    loadedMatrix.load( "/home/lukas/m" );

    loadedMatrix.load( "matrixFile" );

    

    EXPECT_EQ( savedMatrix.getElement( 0, 0 ), loadedMatrix.getElement( 0, 0 ) );

    EXPECT_EQ( savedMatrix.getElement( 0, 1 ), loadedMatrix.getElement( 0, 1 ) );
@@ -555,28 +659,91 @@ void test_SaveAndLoad() 
    EXPECT_EQ( savedMatrix.getElement( 3, 2 ), loadedMatrix.getElement( 3, 2 ) );

    EXPECT_EQ( savedMatrix.getElement( 3, 3 ), loadedMatrix.getElement( 3, 3 ) );

    

    std::cout << "\nThis will create a file called 'matrixFile' (of the matrix created in the test function), in .../tnl-dev/Debug/bin/!\n\n";

}





TEST( SparseMatrixTest, CSR_GetTypeTest_Host )

template< typename Matrix >

void test_Print()

{

   host_test_GetType< CSR_host_float, CSR_host_int >();

}

/*

 * Sets up the following 5x4 sparse matrix:

 *

 *    /  1  2  3  0 \

 *    |  0  0  0  4 |

 *    |  5  6  7  0 |

 *    |  0  8  9 10 |

 *    \  0  0 11 12 /

 */

    const int m_rows = 5;

    const int m_cols = 4;

    

#ifdef HAVE_CUDA

TEST( SparseMatrixTest, CSR_GetTypeTest_Cuda )

{

   cuda_test_GetType< CSR_cuda_float, CSR_cuda_int >();

    Matrix m;

    m.reset();

    m.setDimensions( m_rows, m_cols );

    typename Matrix::CompressedRowLengthsVector rowLengths;

    rowLengths.setSize( m_rows );

    rowLengths.setValue( 3 );

    m.setCompressedRowLengths( rowLengths );

    

    int value = 1;

    for( int i = 0; i < m_cols - 1; i++ )   // 0th row

        m.setElement( 0, i, value++ );

    

        m.setElement( 1, 3, value++ );      // 1st row

        

    for( int i = 0; i < m_cols - 1; i++ )   // 2nd row

        m.setElement( 2, i, value++ );

        

    for( int i = 1; i < m_cols; i++ )       // 3rd row

        m.setElement( 3, i, value++ );

        

    for( int i = 2; i < m_cols; i++ )       // 4th row

        m.setElement( 4, i, value++ );

    

    // This is from: https://stackoverflow.com/questions/5193173/getting-cout-output-to-a-stdstring

    #include <sstream>

    std::stringstream printed;

    std::stringstream couted;

    

    // This is from: https://stackoverflow.com/questions/19485536/redirect-output-of-an-function-printing-to-console-to-string

    //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

    

    //printed << printed.str() << std::endl;

    couted << "Row: 0 ->  Col:0->1	 Col:1->2	 Col:2->3\t\n"

               "Row: 1 ->  Col:3->4\t\n"

               "Row: 2 ->  Col:0->5	 Col:1->6	 Col:2->7\t\n"

               "Row: 3 ->  Col:1->8	 Col:2->9	 Col:3->10\t\n"

               "Row: 4 ->  Col:2->11	 Col:3->12\t\n";

    

    EXPECT_EQ( printed.str(), couted.str() );

}

#endif



TEST( SparseMatrixTest, CSR_SetDimensionsTest_Host )

//// test_getType is not general enough yet. DO NOT TEST IT YET.



//TEST( SparseMatrixTest, CSR_GetTypeTest_Host )

//{

//   host_test_GetType< CSR_host_float, CSR_host_int >();

//}

//

//#ifdef HAVE_CUDA

//TEST( SparseMatrixTest, CSR_GetTypeTest_Cuda )

//{

//   cuda_test_GetType< CSR_cuda_float, CSR_cuda_int >();

//}

//#endif



TEST( SparseMatrixTest, CSR_setDimensionsTest_Host )

{

   test_SetDimensions< CSR_host_int >();

}



#ifdef HAVE_CUDA

TEST( SparseMatrixTest, CSR_SetDimensionsTest_Cuda )

TEST( SparseMatrixTest, CSR_setDimensionsTest_Cuda )

{

   test_SetDimensions< CSR_cuda_int >();

}
@@ -662,19 +829,32 @@ TEST( SparseMatrixTest, CSR_vectorProductTest_Host ) 
#ifdef HAVE_CUDA

TEST( SparseMatrixTest, CSR_vectorProductTest_Cuda )

{

    test_VectorProduct< CSR_cuda_int >();

//    test_VectorProduct< CSR_cuda_int >();

    bool testRan = false;

    EXPECT_TRUE( testRan );

    std::cout << "\nTEST DID NOT RUN. NOT WOKRING.\n\n";

    std::cout << "If launched, this test throws the following message: \n";

    std::cout << "      terminate called after throwing an instance of 'TNL::Exceptions::CudaRuntimeError'\n";

    std::cout << "        what():  CUDA ERROR 77 (cudaErrorIllegalAddress): an illegal memory access was encountered.\n";

    std::cout << "      Source: line 57 in /home/lukas/tnl-dev/src/TNL/Containers/Algorithms/ArrayOperationsCuda_impl.h: an illegal memory access was encountered\n";

    std::cout << "      [1]    7238 abort (core dumped)  ./SparseMatrixTest-dbg\n\n";

}

#endif



TEST( SparseMatrixTest, CSR_perforSORIterationTest_Host )

{

    test_PerformSORIteration< CSR_host_int >();

    test_PerformSORIteration< CSR_host_float >();

}



#ifdef HAVE_CUDA

TEST( SparseMatrixTest, CSR_perforSORIterationTest_Cuda )

{

    test_PerformSORIteration< CSR_cuda_int >();

//    test_PerformSORIteration< CSR_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]    16958 segmentation fault (core dumped)  ./SparseMatrixTest-dbg\n\n";

}

#endif
@@ -690,6 +870,18 @@ TEST( SparseMatrixTest, CSR_saveAndLoadTest_Cuda ) 
}

#endif



TEST( SparseMatrixTest, CSR_printTest_Host )

{

    test_Print< CSR_host_int >();

}



#ifdef HAVE_CUDA

TEST( SparseMatrixTest, CSR_printTest_Cuda )

{

    test_Print< CSR_cuda_int >();

}

#endif



#endif



#include "../GtestMissingError.h"