BOOST_AUTO_TEST_CASE_TEMPLATE( buildTest, T, test_types )
{
typedef T ValueType;
PartitionId size = comm->getSize();
int numRows = 3 * size;
int numCols = 5 * size;
scoped_array<ValueType> values( new ValueType[ numRows * numCols ] );
for ( IndexType i = 0; i < numRows; ++i)
{
for ( IndexType j = 0; j < numCols; ++j )
{
ValueType value = static_cast<ValueType> ( i * numCols + j + 1.0 );
values[ i * numCols + j ] = value;
}
}
DenseMatrix<ValueType> repM;
repM.setRawDenseData( numRows, numCols, values.get() );
for ( IndexType i = 0; i < numRows; ++i )
{
for ( IndexType j = 0; j<numCols; ++j )
{
Scalar value = repM.getValue( i , j );
Scalar expectedvalue = Scalar( static_cast<ValueType>(values[i * numCols + j]) );
LAMA_CHECK_SCALAR_SMALL( value - expectedvalue, ValueType, eps<ValueType>() );
}
}
shared_ptr<Distribution> dist( new BlockDistribution(numRows, comm) );
shared_ptr<Distribution> distCol( new BlockDistribution(numCols, comm) );
DenseMatrix<ValueType> distM( repM, dist, distCol );
for (IndexType i = 0; i<numRows; ++i)
{
for (IndexType j = 0; j<numCols; ++j)
{
Scalar value = distM.getValue( i, j );
Scalar expectedvalue = repM.getValue( i, j );
LAMA_CHECK_SCALAR_SMALL( value - expectedvalue , ValueType, eps<ValueType>() );
}
}
}
void cyclicDistTestImpl( const IndexType chunkSize, const IndexType n )
{
boost::scoped_array<float> values( new float[n * n] );
for ( IndexType i = 0; i < n; ++i )
{
for ( IndexType j = 0; j < n; ++j )
{
values[i * n + j] = static_cast<float>( i * n + j );
}
}
DenseMatrix<float> repMatrix;
repMatrix.setRawDenseData( n, n, values.get() );
boost::shared_ptr<Distribution> dist( new CyclicDistribution( n, chunkSize, comm ) );
DenseMatrix<float> distMatrix( repMatrix, dist, dist );
for ( IndexType i = 0; i < n; i++ )
{
for ( IndexType j = 0; j < n; j++ )
{
Scalar expectedvalue = repMatrix.getValue( i, j );
Scalar value = distMatrix.getValue( i, j );
Scalar diff = expectedvalue - value;
LAMA_CHECK_SCALAR_SMALL( diff, float, 1E-8 );
}
}
}
