/*!\brief Estimating the necessary number of steps for each benchmark.
//
// \param run The parameters for the benchmark run.
// \return void
//
// This function estimates the necessary number of steps for the given benchmark based on the
// performance of the Blaze library.
*/
void estimateSteps( DenseRun& run )
{
using blazemark::real;
using blaze::rowVector;
using blaze::columnMajor;
const size_t N( run.getSize() );
blaze::StaticVector<real,6UL,rowVector> vec( 0.1 );
blaze::StaticMatrix<real,6UL,6UL,columnMajor> mat( 0.1 );
std::vector< blaze::StaticVector<real,6UL,rowVector> > a( N, vec ), b( N );
std::vector< blaze::StaticMatrix<real,6UL,6UL,columnMajor> > A( N, mat );
blaze::timing::WcTimer timer;
double wct( 0.0 );
size_t steps( 1UL );
while( true ) {
timer.start();
for( size_t step=0UL, i=0UL; step<steps; ++step, ++i ) {
if( i == N ) i = 0UL;
b[i] = a[i] * A[i];
}
timer.end();
wct = timer.last();
if( wct >= 0.2 ) break;
steps *= 2UL;
}
for( size_t i=0UL; i<N; ++i )
if( b[i][0] < real(0) )
std::cerr << " Line " << __LINE__ << ": ERROR detected!!!\n";
run.setSteps( blaze::max( 1UL, ( blazemark::runtime * steps ) / timer.last() ) );
}
/*!\brief Estimating the necessary number of steps for each benchmark.
//
// \param run The parameters for the benchmark run.
// \return void
//
// This function estimates the necessary number of steps for the given benchmark based on the
// performance of the Blaze library.
*/
void estimateSteps( SparseRun& run )
{
using blazemark::real;
using blaze::rowMajor;
using blaze::columnMajor;
const size_t N( run.getSize() );
const size_t F( run.getNonZeros() );
blaze::CompressedMatrix<real,columnMajor> A( N, N, N*F ), C( N, N );
blaze::CompressedMatrix<real,rowMajor> B( N, N, N*F );
blaze::timing::WcTimer timer;
double wct( 0.0 );
size_t steps( 1UL );
for( size_t j=0UL; j<N; ++j ) {
A.reserve( j, F );
blazemark::Indices indices( N, F );
for( blazemark::Indices::Iterator it=indices.begin(); it!=indices.end(); ++it ) {
A.append( *it, j, real(0.1) );
}
}
for( size_t i=0UL; i<N; ++i ) {
B.reserve( i, F );
blazemark::Indices indices( N, F );
for( blazemark::Indices::Iterator it=indices.begin(); it!=indices.end(); ++it ) {
B.append( i, *it, real(0.1) );
}
}
while( true ) {
timer.start();
for( size_t i=0UL; i<steps; ++i ) {
C = A + B;
}
timer.end();
wct = timer.last();
if( wct >= 0.2 ) break;
steps *= 2UL;
}
if( C.rows() != N )
std::cerr << " Line " << __LINE__ << ": ERROR detected!!!\n";
run.setSteps( blaze::max( 1UL, ( blazemark::runtime * steps ) / timer.last() ) );
}
/*!\brief Estimating the necessary number of steps for each benchmark.
//
// \param run The parameters for the benchmark run.
// \return void
//
// This function estimates the necessary number of steps for the given benchmark based on the
// performance of the Blaze library.
*/
void estimateSteps( SparseRun& run )
{
using blazemark::real;
using blaze::columnVector;
const size_t N( run.getSize() );
const size_t F( run.getNonZeros() );
blaze::CompressedVector<real,columnVector> a( N, F ), b( N, F ), c( N );
blaze::timing::WcTimer timer;
double wct( 0.0 );
size_t steps( 1UL );
{
blazemark::Indices indices( N, F );
for( blazemark::Indices::Iterator it=indices.begin(); it!=indices.end(); ++it ) {
a[*it] = real(0.1);
}
}
{
blazemark::Indices indices( N, F );
for( blazemark::Indices::Iterator it=indices.begin(); it!=indices.end(); ++it ) {
b[*it] = real(0.1);
}
}
while( true ) {
timer.start();
for( size_t i=0UL; i<steps; ++i ) {
c = a * b;
}
timer.end();
wct = timer.last();
if( wct >= 0.2 ) break;
steps *= 2UL;
}
if( c.size() != N )
std::cerr << " Line " << __LINE__ << ": ERROR detected!!!\n";
run.setSteps( blaze::max( 1UL, ( blazemark::runtime * steps ) / timer.last() ) );
}
