void copy(const CPU_MATRIX & cpu_matrix,
compressed_matrix<SCALARTYPE, ALIGNMENT> & gpu_matrix )
{
if ( cpu_matrix.size1() > 0 && cpu_matrix.size2() > 0 )
{
gpu_matrix.resize(static_cast<unsigned int>(cpu_matrix.size1()), static_cast<unsigned int>(cpu_matrix.size2()), false);
//determine nonzeros:
long num_entries = 0;
for (typename CPU_MATRIX::const_iterator1 row_it = cpu_matrix.begin1();
row_it != cpu_matrix.end1();
++row_it)
{
unsigned int entries_per_row = 0;
for (typename CPU_MATRIX::const_iterator2 col_it = row_it.begin();
col_it != row_it.end();
++col_it)
{
++entries_per_row;
}
num_entries += viennacl::tools::roundUpToNextMultiple<unsigned int>(entries_per_row, ALIGNMENT);
}
//std::cout << "CPU->GPU, Number of entries: " << num_entries << std::endl;
//set up matrix entries:
std::vector<unsigned int> row_buffer(cpu_matrix.size1() + 1);
std::vector<unsigned int> col_buffer(num_entries);
std::vector<SCALARTYPE> elements(num_entries);
unsigned int row_index = 0;
unsigned int data_index = 0;
for (typename CPU_MATRIX::const_iterator1 row_it = cpu_matrix.begin1();
row_it != cpu_matrix.end1();
++row_it)
{
row_buffer[row_index] = data_index;
++row_index;
for (typename CPU_MATRIX::const_iterator2 col_it = row_it.begin();
col_it != row_it.end();
++col_it)
{
col_buffer[data_index] = static_cast<unsigned int>(col_it.index2());
elements[data_index] = *col_it;
++data_index;
}
data_index = viennacl::tools::roundUpToNextMultiple<unsigned int>(data_index, ALIGNMENT); //take care of alignment
}
row_buffer[row_index] = data_index;
/*gpu_matrix._row_buffer = viennacl::ocl::device().createMemory(CL_MEM_READ_WRITE, row_buffer);
gpu_matrix._col_buffer = viennacl::ocl::device().createMemory(CL_MEM_READ_WRITE, col_buffer);
gpu_matrix._elements = viennacl::ocl::device().createMemory(CL_MEM_READ_WRITE, elements);
gpu_matrix._nonzeros = num_entries;*/
gpu_matrix.set(&row_buffer[0], &col_buffer[0], &elements[0], static_cast<unsigned int>(cpu_matrix.size1()), num_entries);
}
}
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;
}
