spmv.h

/***************************************************************************
                          spmv.h  -  description
                             -------------------
    begin                : Dec 30, 2018
    copyright            : (C) 2015 by Tomas Oberhuber et al.
    email                : tomas.oberhuber@fjfi.cvut.cz
 ***************************************************************************/

/* See Copyright Notice in tnl/Copyright */

// Implemented by: Lukas Cejka
//      Original implemented by J. Klinkovsky in Benchmarks/BLAS
//      This is an edited copy of Benchmarks/BLAS/spmv.h by: Lukas Cejka

#pragma once

#include "../Benchmarks.h"

#include <TNL/Pointers/DevicePointer.h>
#include <TNL/Matrices/CSR.h>
#include <TNL/Matrices/Ellpack.h>
#include <TNL/Matrices/SlicedEllpack.h>
#include <TNL/Matrices/ChunkedEllpack.h>
#include <TNL/Matrices/AdEllpack.h>
#include <TNL/Matrices/BiEllpack.h>

#include <TNL/Matrices/MatrixReader.h>

#include <TNL/Matrices/SparseMatrix.h>
#include <TNL/Matrices/MatrixType.h>
#include <TNL/Containers/Segments/CSR.h>
#include <TNL/Containers/Segments/Ellpack.h>
#include <TNL/Containers/Segments/SlicedEllpack.h>
using namespace TNL::Matrices;

#include "cusparseCSRMatrix.h"

namespace TNL {
namespace Benchmarks {

// Alias to match the number of template parameters with other formats
template< typename Real, typename Device, typename Index >
using SlicedEllpackAlias = Matrices::SlicedEllpack< Real, Device, Index >;

// Segments based sparse matrix aliases
template< typename Real, typename Device, typename Index >
using SparseMatrix_CSR = Matrices::SparseMatrix< Real, Device, Index, Matrices::GeneralMatrix, Containers::Segments::CSR >;

template< typename Device, typename Index, typename IndexAllocator >
using EllpackSegments = Containers::Segments::Ellpack< Device, Index, IndexAllocator >;

template< typename Real, typename Device, typename Index >
using SparseMatrix_Ellpack = Matrices::SparseMatrix< Real, Device, Index, Matrices::GeneralMatrix, EllpackSegments >;

template< typename Device, typename Index, typename IndexAllocator >
using SlicedEllpackSegments = Containers::Segments::SlicedEllpack< Device, Index, IndexAllocator >;

template< typename Real, typename Device, typename Index >
using SparseMatrix_SlicedEllpack = Matrices::SparseMatrix< Real, Device, Index, Matrices::GeneralMatrix, SlicedEllpackSegments >;

// Get the name (with extension) of input matrix file
std::string getMatrixFileName( const String& InputFileName )
{
    std::string fileName = InputFileName;

    const size_t last_slash_idx = fileName.find_last_of( "/\\" );
    if( std::string::npos != last_slash_idx )
        fileName.erase( 0, last_slash_idx + 1 );

    return fileName;
}

// Get only the name of the format from getType()
template< typename Matrix >
std::string getMatrixFormat( const Matrix& matrix )
{
    std::string mtrxFullType = getType( matrix );
    std::string mtrxType = mtrxFullType.substr( 0, mtrxFullType.find( "<" ) );
    std::string format = mtrxType.substr( mtrxType.find( ':' ) + 2 );

    return format;
}

template< typename Matrix >
std::string getFormatShort( const Matrix& matrix )
{
    std::string mtrxFullType = getType( matrix );
    std::string mtrxType = mtrxFullType.substr( 0, mtrxFullType.find( "<" ) );
    std::string format = mtrxType.substr( mtrxType.find( ':' ) + 2 );
    format = format.substr( format.find(':') + 2);
    format = format.substr( 0, 3 );

    return format;
}

// Print information about the matrix.
template< typename Matrix >
void printMatrixInfo( const Matrix& matrix,
                      std::ostream& str )
{
    str << "\n Format: " << getMatrixFormat( matrix ) << std::endl;
    str << " Rows: " << matrix.getRows() << std::endl;
    str << " Cols: " << matrix.getColumns() << std::endl;
    str << " Nonzero Elements: " << matrix.getNumberOfNonzeroMatrixElements() << std::endl;
}

template< typename Real,
          template< typename, typename, typename > class Matrix,
          template< typename, typename, typename, typename > class Vector = Containers::Vector >
void
benchmarkSpMV( Benchmark& benchmark,
               const String& inputFileName,
               bool verboseMR )
{
    // Setup CSR for cuSPARSE. It will compared to the format given as a template parameter to this function
    typedef Matrices::CSR< Real, Devices::Host, int > CSR_HostMatrix;
    typedef Matrices::CSR< Real, Devices::Cuda, int > CSR_DeviceMatrix;

    CSR_HostMatrix CSRhostMatrix;
    CSR_DeviceMatrix CSRdeviceMatrix;

    // Read the matrix for CSR, to set up cuSPARSE
    MatrixReader< CSR_HostMatrix >::readMtxFile( inputFileName, CSRhostMatrix, verboseMR );

#ifdef HAVE_CUDA
    // cuSPARSE handle setup
    cusparseHandle_t cusparseHandle;
    cusparseCreate( &cusparseHandle );

    // cuSPARSE (in TNL's CSR) only works for device, copy the matrix from host to device
    CSRdeviceMatrix = CSRhostMatrix;

    // Delete the CSRhostMatrix, so it doesn't take up unnecessary space
    CSRhostMatrix.reset();

    // Initialize the cusparseCSR matrix.
    TNL::CusparseCSR< Real > cusparseCSR;
    cusparseCSR.init( CSRdeviceMatrix, &cusparseHandle );
#endif

    // Setup the format which is given as a template parameter to this function
    typedef Matrix< Real, Devices::Host, int > HostMatrix;
    typedef Matrix< Real, Devices::Cuda, int > DeviceMatrix;
    typedef Containers::Vector< Real, Devices::Host, int > HostVector;
    typedef Containers::Vector< Real, Devices::Cuda, int > CudaVector;

    HostMatrix hostMatrix;
    DeviceMatrix deviceMatrix;
    HostVector hostVector, hostVector2;
    CudaVector deviceVector, deviceVector2;

    // Load the format
    MatrixReader< HostMatrix >::readMtxFile( inputFileName, hostMatrix, verboseMR );


    // Setup MetaData here (not in tnl-benchmark-spmv.h, as done in Benchmarks/BLAS),
    //  because we need the matrix loaded first to get the rows and columns
    benchmark.setMetadataColumns( Benchmark::MetadataColumns({
          { "matrix name", convertToString( getMatrixFileName( inputFileName ) ) },
          { "non-zeros", convertToString( hostMatrix.getNumberOfNonzeroMatrixElements() ) },
          { "rows", convertToString( hostMatrix.getRows() ) },
          { "columns", convertToString( hostMatrix.getColumns() ) },
          { "matrix format", convertToString( getType( hostMatrix ) ) }
       } ));

    hostVector.setSize( hostMatrix.getColumns() );
    hostVector2.setSize( hostMatrix.getRows() );

#ifdef HAVE_CUDA
    deviceMatrix = hostMatrix;
    deviceVector.setSize( hostMatrix.getColumns() );
    deviceVector2.setSize( hostMatrix.getRows() );
#endif

    // reset function
    auto reset = [&]() {
       hostVector.setValue( 1.0 );
       hostVector2.setValue( 0.0 );
 #ifdef HAVE_CUDA
       deviceVector.setValue( 1.0 );
       deviceVector2.setValue( 0.0 );
 #endif
    };

    const int elements = hostMatrix.getNumberOfNonzeroMatrixElements();

    const double datasetSize = (double) elements * ( 2 * sizeof( Real ) + sizeof( int ) ) / oneGB;

    // compute functions
    auto spmvHost = [&]() {
       hostMatrix.vectorProduct( hostVector, hostVector2 );
    };
#ifdef HAVE_CUDA
    auto spmvCuda = [&]() {
       deviceMatrix.vectorProduct( deviceVector, deviceVector2 );
    };

    auto spmvCusparse = [&]() {
        cusparseCSR.vectorProduct( deviceVector, deviceVector2 );
    };
#endif

    benchmark.setOperation( datasetSize );
    benchmark.time< Devices::Host >( reset, "CPU", spmvHost );

    // Initialize the host vector to be compared.
    //  (The values in hostVector2 will be reset when spmvCuda starts)
    HostVector resultHostVector2;
    resultHostVector2.setSize( hostVector2.getSize() );
    resultHostVector2.setValue( 0.0 );

    // Copy the values
    resultHostVector2 = hostVector2;

#ifdef HAVE_CUDA
    benchmark.time< Devices::Cuda >( reset, "GPU", spmvCuda );

    // Initialize the device vector to be compared.
    //  (The values in deviceVector2 will be reset when spmvCusparse starts)
    HostVector resultDeviceVector2;
    resultDeviceVector2.setSize( deviceVector2.getSize() );
    resultDeviceVector2.setValue( 0.0 );

    resultDeviceVector2 = deviceVector2;
    
    // Setup cuSPARSE MetaData, since it has the same header as CSR,
    //  and therefore will not get its own headers (rows, cols, speedup etc.) in log.
    //      * Not setting this up causes (among other undiscovered errors) the speedup from CPU to GPU on the input format to be overwritten.
    benchmark.setMetadataColumns( Benchmark::MetadataColumns({
          { "matrix name", convertToString( getMatrixFileName( inputFileName ) ) },
          { "non-zeros", convertToString( hostMatrix.getNumberOfNonzeroMatrixElements() ) },
          { "rows", convertToString( hostMatrix.getRows() ) },
          { "columns", convertToString( hostMatrix.getColumns() ) },
          { "matrix format", convertToString( "CSR-cuSPARSE-" + getFormatShort( hostMatrix ) ) }
       } ));

    benchmark.time< Devices::Cuda >( reset, "GPU", spmvCusparse );

    HostVector resultcuSPARSEDeviceVector2;
    resultcuSPARSEDeviceVector2.setSize( deviceVector2.getSize() );
    resultcuSPARSEDeviceVector2.setValue( 0.0 );

    resultcuSPARSEDeviceVector2 = deviceVector2;

    // Difference between GPU (current format) and GPU-cuSPARSE results
    //Real cuSparseDifferenceAbsMax = resultDeviceVector2.differenceAbsMax( resultcuSPARSEDeviceVector2 );
    Real cuSparseDifferenceAbsMax = max( abs( resultDeviceVector2 - resultcuSPARSEDeviceVector2 ) );
    //Real cuSparseDifferenceLpNorm = resultDeviceVector2.differenceLpNorm( resultcuSPARSEDeviceVector2, 1 );
    Real cuSparseDifferenceLpNorm = lpNorm( resultDeviceVector2 - resultcuSPARSEDeviceVector2, 1 );

    std::string GPUxGPUcuSparse_resultDifferenceAbsMax = "GPUxGPUcuSPARSE differenceAbsMax = " + std::to_string( cuSparseDifferenceAbsMax );
    std::string GPUxGPUcuSparse_resultDifferenceLpNorm = "GPUxGPUcuSPARSE differenceLpNorm = " + std::to_string( cuSparseDifferenceLpNorm );

    char *GPUcuSparse_absMax = &GPUxGPUcuSparse_resultDifferenceAbsMax[ 0u ];
    char *GPUcuSparse_lpNorm = &GPUxGPUcuSparse_resultDifferenceLpNorm[ 0u ];


    // Difference between CPU and GPU results for the current format
    //Real differenceAbsMax = resultHostVector2.differenceAbsMax( resultDeviceVector2 );
    Real differenceAbsMax = max( abs( resultHostVector2 - resultDeviceVector2 ) );
    //Real differenceLpNorm = resultHostVector2.differenceLpNorm( resultDeviceVector2, 1 );
    Real differenceLpNorm = lpNorm( resultHostVector2 - resultDeviceVector2, 1 );

    std::string CPUxGPU_resultDifferenceAbsMax = "CPUxGPU differenceAbsMax = " + std::to_string( differenceAbsMax );
    std::string CPUxGPU_resultDifferenceLpNorm = "CPUxGPU differenceLpNorm = " + std::to_string( differenceLpNorm );

    char *CPUxGPU_absMax = &CPUxGPU_resultDifferenceAbsMax[ 0u ];
    char *CPUxGPU_lpNorm = &CPUxGPU_resultDifferenceLpNorm[ 0u ];

    // Print result differences of CPU and GPU of current format
    std::cout << CPUxGPU_absMax << std::endl;
    std::cout << CPUxGPU_lpNorm << std::endl;

    // Print result differences of GPU of current format and GPU with cuSPARSE.
    std::cout << GPUcuSparse_absMax << std::endl;
    std::cout << GPUcuSparse_lpNorm << std::endl;
 #endif

    std::cout << std::endl;
}

template< typename Real = double,
          typename Index = int >
void
benchmarkSpmvSynthetic( Benchmark& benchmark,
                        const String& inputFileName,
                        bool verboseMR )
{
   benchmarkSpMV< Real, Matrices::CSR >( benchmark, inputFileName, verboseMR );
   benchmarkSpMV< Real, SparseMatrix_CSR >( benchmark, inputFileName, verboseMR );
   
   benchmarkSpMV< Real, Matrices::Ellpack >( benchmark, inputFileName, verboseMR );
   benchmarkSpMV< Real, SparseMatrix_Ellpack >( benchmark, inputFileName, verboseMR );
   
   benchmarkSpMV< Real, SlicedEllpackAlias >( benchmark, inputFileName, verboseMR );
   benchmarkSpMV< Real, SparseMatrix_SlicedEllpack >( benchmark, inputFileName, verboseMR );
   //benchmarkSpMV< Real, Matrices::ChunkedEllpack >( benchmark, inputFileName, verboseMR );

   ////
   // Segments based sparse matrices
   
   
   //

   // AdEllpack is broken
   // benchmarkSpMV< Real, Matrices::AdEllpack >( benchmark, inputFileName, verboseMR );
   //benchmarkSpMV< Real, Matrices::BiEllpack >( benchmark, inputFileName, verboseMR );
}

} // namespace Benchmarks
} // namespace TNL