ScalarType diff(ublas::compressed_matrix<ScalarType> & cpu_matrix, VCL_MATRIX & gpu_matrix)
{
typedef ublas::compressed_matrix<ScalarType> CPU_MATRIX;
CPU_MATRIX from_gpu;
viennacl::backend::finish();
viennacl::copy(gpu_matrix, from_gpu);
ScalarType error = 0;
//step 1: compare all entries from cpu_matrix with gpu_matrix:
//std::cout << "Ublas matrix: " << std::endl;
for (typename CPU_MATRIX::const_iterator1 row_it = cpu_matrix.begin1();
row_it != cpu_matrix.end1();
++row_it)
{
//std::cout << "Row " << row_it.index1() << ": " << std::endl;
for (typename CPU_MATRIX::const_iterator2 col_it = row_it.begin();
col_it != row_it.end();
++col_it)
{
//std::cout << "(" << col_it.index2() << ", " << *col_it << std::endl;
ScalarType current_error = 0;
if ( std::max( std::fabs(cpu_matrix(col_it.index1(), col_it.index2())),
std::fabs(from_gpu(col_it.index1(), col_it.index2())) ) > 0 )
current_error = std::fabs(cpu_matrix(col_it.index1(), col_it.index2()) - from_gpu(col_it.index1(), col_it.index2()))
/ std::max( std::fabs(cpu_matrix(col_it.index1(), col_it.index2())),
std::fabs(from_gpu(col_it.index1(), col_it.index2())) );
if (current_error > error)
error = current_error;
}
}
//step 2: compare all entries from gpu_matrix with cpu_matrix (sparsity pattern might differ):
//std::cout << "ViennaCL matrix: " << std::endl;
for (typename CPU_MATRIX::const_iterator1 row_it = from_gpu.begin1();
row_it != from_gpu.end1();
++row_it)
{
//std::cout << "Row " << row_it.index1() << ": " << std::endl;
for (typename CPU_MATRIX::const_iterator2 col_it = row_it.begin();
col_it != row_it.end();
++col_it)
{
//std::cout << "(" << col_it.index2() << ", " << *col_it << std::endl;
ScalarType current_error = 0;
if ( std::max( std::fabs(cpu_matrix(col_it.index1(), col_it.index2())),
std::fabs(from_gpu(col_it.index1(), col_it.index2())) ) > 0 )
current_error = std::fabs(cpu_matrix(col_it.index1(), col_it.index2()) - from_gpu(col_it.index1(), col_it.index2()))
/ std::max( std::fabs(cpu_matrix(col_it.index1(), col_it.index2())),
std::fabs(from_gpu(col_it.index1(), col_it.index2())) );
if (current_error > error)
error = current_error;
}
}
return error;
}
ScalarType diff(ublas::compressed_matrix<ScalarType> & cpu_matrix, VCL_MATRIX & gpu_matrix)
{
typedef ublas::compressed_matrix<ScalarType> CPU_MATRIX;
CPU_MATRIX from_gpu;
copy(gpu_matrix, from_gpu);
ScalarType error = 0;
//step 1: compare all entries from cpu_matrix with gpu_matrix:
for (typename CPU_MATRIX::const_iterator1 row_it = cpu_matrix.begin1();
row_it != cpu_matrix.end1();
++row_it)
{
for (typename CPU_MATRIX::const_iterator2 col_it = row_it.begin();
col_it != row_it.end();
++col_it)
{
ScalarType current_error = 0;
if ( std::max( fabs(cpu_matrix(col_it.index1(), col_it.index2())),
fabs(from_gpu(col_it.index1(), col_it.index2())) ) > 0 )
current_error = fabs(cpu_matrix(col_it.index1(), col_it.index2()) - from_gpu(col_it.index1(), col_it.index2()))
/ std::max( fabs(cpu_matrix(col_it.index1(), col_it.index2())),
fabs(from_gpu(col_it.index1(), col_it.index2())) );
if (current_error > error)
error = current_error;
}
}
//step 2: compare all entries from gpu_matrix with cpu_matrix (sparsity pattern might differ):
for (typename CPU_MATRIX::const_iterator1 row_it = from_gpu.begin1();
row_it != from_gpu.end1();
++row_it)
{
for (typename CPU_MATRIX::const_iterator2 col_it = row_it.begin();
col_it != row_it.end();
++col_it)
{
ScalarType current_error = 0;
if ( std::max( fabs(cpu_matrix(col_it.index1(), col_it.index2())),
fabs(from_gpu(col_it.index1(), col_it.index2())) ) > 0 )
current_error = fabs(cpu_matrix(col_it.index1(), col_it.index2()) - from_gpu(col_it.index1(), col_it.index2()))
/ std::max( fabs(cpu_matrix(col_it.index1(), col_it.index2())),
fabs(from_gpu(col_it.index1(), col_it.index2())) );
if (current_error > error)
error = current_error;
}
}
return error;
}
