Commit 2da7e995 authored by Tomáš Oberhuber's avatar Tomáš Oberhuber
Browse files

Merge branch 'TO/matrices' into 'develop' 

Improving SpMV benchmark with benchmark results.

See merge request !51
parents ab65956e a147f47a

src/Benchmarks/SpMV/SpmvBenchmarkResult.h

0 → 100644
+52 −0
Original line number Diff line number Diff line 
/***************************************************************************

                          SpmvBenchmarkResult.h  -  description

                             -------------------

    begin                : Mar 5, 2020

    copyright            : (C) 2020 by Tomas Oberhuber

    email                : tomas.oberhuber@fjfi.cvut.cz

 ***************************************************************************/



/* See Copyright Notice in tnl/Copyright */



#pragma once



#include "../Benchmarks.h"



namespace TNL {

namespace Benchmarks {



template< typename Real = double,

          typename Index = int >

struct SpmvBenchmarkResult

: public BenchmarkResult

{

   using RealType = Real;

   using IndexType = Index;

   using HostVector = Containers::Vector< Real, Devices::Host, Index >;

   using CudaVector = Containers::Vector< Real, Devices::Cuda, Index >;



   SpmvBenchmarkResult( CudaVector& cudaResult, HostVector& hostResult, CudaVector& cusparseResult )

   : hostResult( hostResult ), cudaResult( cudaResult), cusparseResult( cusparseResult ){};



   virtual HeaderElements getTableHeader() const override

   {

      return HeaderElements({"time", "stddev", "stddev/time", "speedup", "Host.Diff.Max", "Host.Diff.L2", "Cusparse.Diff.Max", "Cusparse.Diff.L2"});

   }



   virtual RowElements getRowElements() const override

   {

      HostVector cudaCopy, cusparseCopy, a, b;

      cudaCopy = cudaResult;

      cusparseCopy = cusparseResult;

      a = cudaCopy - hostResult;

      b = cudaCopy - cusparseCopy;

      return RowElements({ time, stddev, stddev/time, speedup, max( abs( a ) ), lpNorm( a, 2.0 ), max( abs( b ) ), lpNorm( b, 2.0 ) });

   }



   HostVector &hostResult;



   CudaVector &cudaResult, &cusparseResult;

};

   

} //namespace Benchmarks

} //namespace TNL

src/Benchmarks/SpMV/spmv.h

+102 −132
Original line number Diff line number Diff line
@@ -15,6 +15,7 @@ 
#pragma once



#include "../Benchmarks.h"

#include "SpmvBenchmarkResult.h"



#include <TNL/Pointers/DevicePointer.h>

#include <TNL/Matrices/Legacy/CSR.h>
@@ -114,8 +115,8 @@ benchmarkSpMV( Benchmark& benchmark, 
               bool verboseMR )

{

   // Setup CSR for cuSPARSE. It will compared to the format given as a template parameter to this function

    typedef Matrices::Legacy::CSR< Real, Devices::Host, int > CSR_HostMatrix;

    typedef Matrices::Legacy::CSR< Real, Devices::Cuda, int > CSR_DeviceMatrix;

   using CSR_HostMatrix = Matrices::Legacy::CSR< Real, Devices::Host, int >;

   using CSR_DeviceMatrix = Matrices::Legacy::CSR< Real, Devices::Cuda, int >;



   CSR_HostMatrix CSRhostMatrix;

   CSR_DeviceMatrix CSRdeviceMatrix;
@@ -148,7 +149,7 @@ benchmarkSpMV( Benchmark& benchmark, 
   HostMatrix hostMatrix;

   DeviceMatrix deviceMatrix;

   HostVector hostVector, hostVector2;

    CudaVector deviceVector, deviceVector2;

   CudaVector deviceVector, deviceVector2, cusparseVector;



   // Load the format

   MatrixReader< HostMatrix >::readMtxFile( inputFileName, hostMatrix, verboseMR );
@@ -171,20 +172,26 @@ benchmarkSpMV( Benchmark& benchmark, 
   deviceMatrix = hostMatrix;

   deviceVector.setSize( hostMatrix.getColumns() );

   deviceVector2.setSize( hostMatrix.getRows() );

   cusparseVector.setSize( hostMatrix.getRows() );

#endif



   // reset function

    auto reset = [&]() {

       hostVector.setValue( 1.0 );

       hostVector2.setValue( 0.0 );

   auto resetHostVectors = [&]() {

      hostVector = 1.0;

      hostVector2 = 0.0;

   };

#ifdef HAVE_CUDA

       deviceVector.setValue( 1.0 );

       deviceVector2.setValue( 0.0 );

 #endif

   auto resetCudaVectors = [&]() {

      deviceVector = 1.0;

      deviceVector2 = 0.0;

   };

   auto resetCusparseVectors = [&]() {

      deviceVector = 1.0;

      cusparseVector == 0.0;

   };

 #endif



   const int elements = hostMatrix.getNumberOfNonzeroMatrixElements();



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



    // compute functions
@@ -197,12 +204,12 @@ benchmarkSpMV( Benchmark& benchmark, 
   };



   auto spmvCusparse = [&]() {

        cusparseCSR.vectorProduct( deviceVector, deviceVector2 );

       cusparseCSR.vectorProduct( deviceVector, cusparseVector );

   };

#endif



   benchmark.setOperation( datasetSize );

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

   benchmark.time< Devices::Host >( resetHostVectors, "CPU", spmvHost );



   // Initialize the host vector to be compared.

   //  (The values in hostVector2 will be reset when spmvCuda starts)
@@ -214,7 +221,7 @@ benchmarkSpMV( Benchmark& benchmark, 
   resultHostVector2 = hostVector2;



#ifdef HAVE_CUDA

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

   benchmark.time< Devices::Cuda >( resetCudaVectors, "GPU", spmvCuda );



   // Initialize the device vector to be compared.

   //  (The values in deviceVector2 will be reset when spmvCusparse starts)
@@ -235,46 +242,9 @@ benchmarkSpMV( Benchmark& benchmark, 
         { "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;

   SpmvBenchmarkResult< Real, int > benchmarkResult( deviceVector2, hostVector2, cusparseVector );

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



    // 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;