Commit 742581b2 authored by Jakub Klinkovský's avatar Jakub Klinkovský
Browse files

Splitting benchmarks of vector operations into separate header

parent da855c19

tests/benchmarks/benchmarks.h

+2 −0
Original line number Diff line number Diff line
@@ -9,6 +9,8 @@ namespace tnl 
namespace benchmarks

{



const double oneGB = 1024.0 * 1024.0 * 1024.0;



// TODO: add data member for error message

struct BenchmarkError {};

tests/benchmarks/tnl-cuda-benchmarks.h

+2 −139
Original line number Diff line number Diff line
@@ -19,13 +19,12 @@ 
#define TNLCUDBENCHMARKS_H_



#include <tnlConfig.h>

#include <core/vectors/tnlVector.h>

#include <core/tnlList.h>

#include <matrices/tnlSlicedEllpackMatrix.h>

#include <matrices/tnlEllpackMatrix.h>

#include <matrices/tnlCSRMatrix.h>



#include "benchmarks.h"

#include "vector-operations.h"



using namespace tnl::benchmarks;
@@ -37,7 +36,6 @@ using namespace tnl::benchmarks; 
template< typename Real, typename Device, typename Index >

using SlicedEllpackMatrix = tnlSlicedEllpackMatrix< Real, Device, Index >;



const double oneGB = 1024.0 * 1024.0 * 1024.0;





// TODO:
@@ -199,9 +197,6 @@ int main( int argc, char* argv[] ) 
#ifdef HAVE_CUDA

   

   typedef double Real;

   typedef tnlVector< Real, tnlHost > HostVector;

   typedef tnlVector< Real, tnlCuda > CudaVector;



   

   /****

    * The first argument of this program is the size od data set to be reduced.
@@ -217,139 +212,7 @@ int main( int argc, char* argv[] ) 
   if( argc > 3 )

      elementsPerRow = atoi( argv[ 3 ] );

   

   

   double datasetSize = ( double ) ( loops * size ) * sizeof( Real ) / oneGB;

   

   HostVector hostVector, hostVector2;

   CudaVector deviceVector, deviceVector2;

   hostVector.setSize( size );

   if( ! deviceVector.setSize( size ) )

      return EXIT_FAILURE;

   hostVector2.setLike( hostVector );

   if( ! deviceVector2.setLike( deviceVector ) )

      return EXIT_FAILURE;



   Real resultHost, resultDevice;





   // check functions

   auto compare1 = [&]() {

      return hostVector == deviceVector;

   };

   auto compare2 = [&]() {

      return hostVector2 == deviceVector2;

   };

   auto compare12 = [&]() {

      return compare1() && compare2();

   };

   auto compareScalars = [&]() {

      return resultHost == resultDevice;

   };



   // reset functions

   auto reset1 = [&]() {

      hostVector.setValue( 1.0 );

      deviceVector.setValue( 1.0 );

   };

   auto reset2 = [&]() {

      hostVector2.setValue( 1.0 );

      deviceVector2.setValue( 1.0 );

   };

   auto reset12 = [&]() {

      reset1();

      reset2();

   };





   reset12();



   cout << "Benchmarking CPU-GPU memory transfer:" << endl;

   auto copyAssign = [&]() {

      deviceVector = hostVector;

   };

   cout << "  ";

   benchmarkSingle( loops, datasetSize, copyAssign, compare1, reset1 );

    



   cout << "Benchmarking vector addition:" << endl;

   auto addVectorHost = [&]() {

      hostVector.addVector( hostVector2 );

   };

   auto addVectorCuda = [&]() {

      deviceVector.addVector( deviceVector2 );

      // TODO: synchronization should be part of addVector

      cudaThreadSynchronize();

   };

   benchmarkCuda( loops, 3 * datasetSize, addVectorHost, addVectorCuda, compare1, reset1 );





   cout << "Benchmarking scalar product:" << endl;

   auto scalarProductHost = [&]() {

      resultHost = hostVector.scalarProduct( hostVector2 );

   };

   auto scalarProductCuda = [&]() {

      resultDevice = deviceVector.scalarProduct( deviceVector2 );

   };

   benchmarkCuda( loops, 2 * datasetSize, scalarProductHost, scalarProductCuda, compareScalars, voidFunc );



/* TODO

#ifdef HAVE_CUBLAS

   cout << "Benchmarking scalar product on GPU with Cublas: " << endl;

   cublasHandle_t handle;

   cublasCreate( &handle );

   timer.reset();

   timer.start();   

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

      cublasDdot( handle,

                  size,

                  deviceVector.getData(), 1,

                  deviceVector.getData(), 1,

                  &resultDevice );

   cudaThreadSynchronize();

   timer.stop();

   bandwidth = 2 * datasetSize / timer.getTime();

   cout << "bandwidth: " << bandwidth << " GB/sec, time: " << timer.getTime() << " sec." << endl;

#endif    

*/



   cout << "Benchmarking L2 norm: " << endl;

   auto l2normHost = [&]() {

      resultHost = hostVector.lpNorm( 2.0 );

   };

   auto l2normCuda = [&]() {

      resultDevice = deviceVector.lpNorm( 2.0 );

   };

   benchmarkCuda( loops, datasetSize, l2normHost, l2normCuda, compareScalars, voidFunc );





   /*

   cout << "Benchmarking prefix-sum:" << endl;

   timer.reset();

   timer.start();

   hostVector.computePrefixSum();

   timer.stop();

   timeHost = timer.getTime();

   bandwidth = 2 * datasetSize / loops / timer.getTime();

   cout << "  CPU: bandwidth: " << bandwidth << " GB/sec, time: " << timer.getTime() << " sec." << endl;

   

   timer.reset();

   timer.start();

   deviceVector.computePrefixSum();

   timer.stop();

   timeDevice = timer.getTime();

   bandwidth = 2 * datasetSize / loops / timer.getTime();

   cout << "  GPU: bandwidth: " << bandwidth << " GB/sec, time: " << timer.getTime() << " sec." << endl;

   cout << "  CPU/GPU speedup: " << timeHost / timeDevice << endl;



   HostVector auxHostVector;

   auxHostVector.setLike( deviceVector );

   auxHostVector = deviceVector;

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

      if( hostVector.getElement( i ) != auxHostVector.getElement( i ) )

      {

         cerr << "Error in prefix sum at position " << i << ":  " << hostVector.getElement( i ) << " != " << auxHostVector.getElement( i ) << endl;

      }

*/

   benchmarkVectorOperations< Real >( loops, size );



   benchmarkSpMV< Real, tnlEllpackMatrix >( loops, size, elementsPerRow );

   benchmarkSpMV< Real, SlicedEllpackMatrix >( loops, size, elementsPerRow );

tests/benchmarks/vector-operations.h

0 → 100644
+159 −0
Original line number Diff line number Diff line 
#pragma once



#include "benchmarks.h"



#include <core/vectors/tnlVector.h>



namespace tnl

{

namespace benchmarks

{



template< typename Real = double,

          typename Index = int >

bool

benchmarkVectorOperations( const int & loops,

                           const int & size )

{

    typedef tnlVector< Real, tnlHost, Index > HostVector;

    typedef tnlVector< Real, tnlCuda, Index > CudaVector;

    using namespace std;



    double datasetSize = ( double ) ( loops * size ) * sizeof( Real ) / oneGB;

    

    HostVector hostVector, hostVector2;

    CudaVector deviceVector, deviceVector2;

    hostVector.setSize( size );

    if( ! deviceVector.setSize( size ) )

        return false;

    hostVector2.setLike( hostVector );

    if( ! deviceVector2.setLike( deviceVector ) )

        return false;

 

    Real resultHost, resultDevice;

 

 

    // check functions

    auto compare1 = [&]() {

        return hostVector == deviceVector;

    };

    auto compare2 = [&]() {

        return hostVector2 == deviceVector2;

    };

    auto compare12 = [&]() {

        return compare1() && compare2();

    };

    auto compareScalars = [&]() {

        return resultHost == resultDevice;

    };

 

    // reset functions

    auto reset1 = [&]() {

        hostVector.setValue( 1.0 );

        deviceVector.setValue( 1.0 );

    };

    auto reset2 = [&]() {

        hostVector2.setValue( 1.0 );

        deviceVector2.setValue( 1.0 );

    };

    auto reset12 = [&]() {

        reset1();

        reset2();

    };

 

 

    reset12();

 

    cout << "Benchmarking CPU-GPU memory transfer:" << endl;

    auto copyAssign = [&]() {

        deviceVector = hostVector;

    };

    cout << "  ";

    benchmarkSingle( loops, datasetSize, copyAssign, compare1, reset1 );

     

 

    cout << "Benchmarking vector addition:" << endl;

    auto addVectorHost = [&]() {

        hostVector.addVector( hostVector2 );

    };

    auto addVectorCuda = [&]() {

        deviceVector.addVector( deviceVector2 );

        // TODO: synchronization should be part of addVector

        cudaThreadSynchronize();

    };

    benchmarkCuda( loops, 3 * datasetSize, addVectorHost, addVectorCuda, compare1, reset1 );

 

 

    cout << "Benchmarking scalar product:" << endl;

    auto scalarProductHost = [&]() {

        resultHost = hostVector.scalarProduct( hostVector2 );

    };

    auto scalarProductCuda = [&]() {

        resultDevice = deviceVector.scalarProduct( deviceVector2 );

    };

    benchmarkCuda( loops, 2 * datasetSize, scalarProductHost, scalarProductCuda, compareScalars, voidFunc );



/* TODO

#ifdef HAVE_CUBLAS

   cout << "Benchmarking scalar product on GPU with Cublas: " << endl;

   cublasHandle_t handle;

   cublasCreate( &handle );

   timer.reset();

   timer.start();   

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

      cublasDdot( handle,

                  size,

                  deviceVector.getData(), 1,

                  deviceVector.getData(), 1,

                  &resultDevice );

   cudaThreadSynchronize();

   timer.stop();

   bandwidth = 2 * datasetSize / timer.getTime();

   cout << "bandwidth: " << bandwidth << " GB/sec, time: " << timer.getTime() << " sec." << endl;

#endif    

*/



    cout << "Benchmarking L2 norm: " << endl;

    auto l2normHost = [&]() {

        resultHost = hostVector.lpNorm( 2.0 );

    };

    auto l2normCuda = [&]() {

        resultDevice = deviceVector.lpNorm( 2.0 );

    };

    benchmarkCuda( loops, datasetSize, l2normHost, l2normCuda, compareScalars, voidFunc );

 

 

    /*

    cout << "Benchmarking prefix-sum:" << endl;

    timer.reset();

    timer.start();

    hostVector.computePrefixSum();

    timer.stop();

    timeHost = timer.getTime();

    bandwidth = 2 * datasetSize / loops / timer.getTime();

    cout << "  CPU: bandwidth: " << bandwidth << " GB/sec, time: " << timer.getTime() << " sec." << endl;

    

    timer.reset();

    timer.start();

    deviceVector.computePrefixSum();

    timer.stop();

    timeDevice = timer.getTime();

    bandwidth = 2 * datasetSize / loops / timer.getTime();

    cout << "  GPU: bandwidth: " << bandwidth << " GB/sec, time: " << timer.getTime() << " sec." << endl;

    cout << "  CPU/GPU speedup: " << timeHost / timeDevice << endl;

 

    HostVector auxHostVector;

    auxHostVector.setLike( deviceVector );

    auxHostVector = deviceVector;

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

       if( hostVector.getElement( i ) != auxHostVector.getElement( i ) )

       {

          cerr << "Error in prefix sum at position " << i << ":  " << hostVector.getElement( i ) << " != " << auxHostVector.getElement( i ) << endl;

       }

    */



    return true;

}



} // namespace benchmarks

} // namespace tnl