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Commit 742581b2 authored by Jakub Klinkovský's avatar Jakub Klinkovský
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Splitting benchmarks of vector operations into separate header

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tests/benchmarks/benchmarks.h
+ 2
− 0
View file @ 742581b2
......@@ -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
View file @ 742581b2
......@@ -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
View file @ 742581b2
#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
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