/usr/include/viennacl/hyb_matrix.hpp is in libviennacl-dev 1.5.2-2.
This file is owned by root:root, with mode 0o644.
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#define VIENNACL_HYB_MATRIX_HPP_
/* =========================================================================
Copyright (c) 2010-2014, Institute for Microelectronics,
Institute for Analysis and Scientific Computing,
TU Wien.
Portions of this software are copyright by UChicago Argonne, LLC.
-----------------
ViennaCL - The Vienna Computing Library
-----------------
Project Head: Karl Rupp rupp@iue.tuwien.ac.at
(A list of authors and contributors can be found in the PDF manual)
License: MIT (X11), see file LICENSE in the base directory
============================================================================= */
/** @file viennacl/hyb_matrix.hpp
@brief Implementation of the hyb_matrix class
Contributed by Volodymyr Kysenko.
*/
#include "viennacl/forwards.h"
#include "viennacl/vector.hpp"
#include "viennacl/tools/tools.hpp"
#include "viennacl/linalg/sparse_matrix_operations.hpp"
namespace viennacl
{
/** @brief Sparse matrix class using a hybrid format composed of the ELL and CSR format for storing the nonzeros. */
template<typename SCALARTYPE, unsigned int ALIGNMENT /* see forwards.h for default argument */>
class hyb_matrix
{
public:
typedef viennacl::backend::mem_handle handle_type;
typedef scalar<typename viennacl::tools::CHECK_SCALAR_TEMPLATE_ARGUMENT<SCALARTYPE>::ResultType> value_type;
hyb_matrix() : csr_threshold_(SCALARTYPE(0.8)), rows_(0), cols_(0) {}
hyb_matrix(viennacl::context ctx) : csr_threshold_(SCALARTYPE(0.8)), rows_(0), cols_(0)
{
ell_coords_.switch_active_handle_id(ctx.memory_type());
ell_elements_.switch_active_handle_id(ctx.memory_type());
csr_rows_.switch_active_handle_id(ctx.memory_type());
csr_cols_.switch_active_handle_id(ctx.memory_type());
csr_elements_.switch_active_handle_id(ctx.memory_type());
#ifdef VIENNACL_WITH_OPENCL
if (ctx.memory_type() == OPENCL_MEMORY)
{
ell_coords_.opencl_handle().context(ctx.opencl_context());
ell_elements_.opencl_handle().context(ctx.opencl_context());
csr_rows_.opencl_handle().context(ctx.opencl_context());
csr_cols_.opencl_handle().context(ctx.opencl_context());
csr_elements_.opencl_handle().context(ctx.opencl_context());
}
#endif
}
SCALARTYPE csr_threshold() const { return csr_threshold_; }
void csr_threshold(SCALARTYPE thr) { csr_threshold_ = thr; }
vcl_size_t internal_size1() const { return viennacl::tools::align_to_multiple<vcl_size_t>(rows_, ALIGNMENT); }
vcl_size_t internal_size2() const { return viennacl::tools::align_to_multiple<vcl_size_t>(cols_, ALIGNMENT); }
vcl_size_t size1() const { return rows_; }
vcl_size_t size2() const { return cols_; }
vcl_size_t internal_ellnnz() const {return viennacl::tools::align_to_multiple<vcl_size_t>(ellnnz_, ALIGNMENT); }
vcl_size_t ell_nnz() const { return ellnnz_; }
vcl_size_t csr_nnz() const { return csrnnz_; }
const handle_type & handle() const { return ell_elements_; }
const handle_type & handle2() const { return ell_coords_; }
const handle_type & handle3() const { return csr_rows_; }
const handle_type & handle4() const { return csr_cols_; }
const handle_type & handle5() const { return csr_elements_; }
public:
#if defined(_MSC_VER) && _MSC_VER < 1500 //Visual Studio 2005 needs special treatment
template <typename CPU_MATRIX>
friend void copy(const CPU_MATRIX & cpu_matrix, hyb_matrix & gpu_matrix );
#else
template <typename CPU_MATRIX, typename T, unsigned int ALIGN>
friend void copy(const CPU_MATRIX & cpu_matrix, hyb_matrix<T, ALIGN> & gpu_matrix );
#endif
private:
SCALARTYPE csr_threshold_;
vcl_size_t rows_;
vcl_size_t cols_;
vcl_size_t ellnnz_;
vcl_size_t csrnnz_;
handle_type ell_coords_; // ell coords
handle_type ell_elements_; // ell elements
handle_type csr_rows_;
handle_type csr_cols_;
handle_type csr_elements_;
};
template <typename CPU_MATRIX, typename SCALARTYPE, unsigned int ALIGNMENT>
void copy(const CPU_MATRIX& cpu_matrix, hyb_matrix<SCALARTYPE, ALIGNMENT>& gpu_matrix )
{
assert( (gpu_matrix.size1() == 0 || viennacl::traits::size1(cpu_matrix) == gpu_matrix.size1()) && bool("Size mismatch") );
assert( (gpu_matrix.size2() == 0 || viennacl::traits::size2(cpu_matrix) == gpu_matrix.size2()) && bool("Size mismatch") );
if(cpu_matrix.size1() > 0 && cpu_matrix.size2() > 0)
{
//determine max capacity for row
vcl_size_t max_entries_per_row = 0;
std::vector<vcl_size_t> hist_entries(cpu_matrix.size1() + 1, 0);
for (typename CPU_MATRIX::const_iterator1 row_it = cpu_matrix.begin1(); row_it != cpu_matrix.end1(); ++row_it)
{
vcl_size_t num_entries = 0;
for (typename CPU_MATRIX::const_iterator2 col_it = row_it.begin(); col_it != row_it.end(); ++col_it)
{
++num_entries;
}
hist_entries[num_entries] += 1;
max_entries_per_row = std::max(max_entries_per_row, num_entries);
}
vcl_size_t sum = 0;
for(vcl_size_t ind = 0; ind <= max_entries_per_row; ind++)
{
sum += hist_entries[ind];
if(sum >= gpu_matrix.csr_threshold() * cpu_matrix.size1())
{
max_entries_per_row = ind;
break;
}
}
//setup GPU matrix
gpu_matrix.ellnnz_ = max_entries_per_row;
gpu_matrix.rows_ = cpu_matrix.size1();
gpu_matrix.cols_ = cpu_matrix.size2();
vcl_size_t nnz = gpu_matrix.internal_size1() * gpu_matrix.internal_ellnnz();
viennacl::backend::typesafe_host_array<unsigned int> ell_coords(gpu_matrix.ell_coords_, nnz);
viennacl::backend::typesafe_host_array<unsigned int> csr_rows(gpu_matrix.csr_rows_, cpu_matrix.size1() + 1);
std::vector<unsigned int> csr_cols;
std::vector<SCALARTYPE> ell_elements(nnz);
std::vector<SCALARTYPE> csr_elements;
vcl_size_t csr_index = 0;
for (typename CPU_MATRIX::const_iterator1 row_it = cpu_matrix.begin1(); row_it != cpu_matrix.end1(); ++row_it)
{
vcl_size_t data_index = 0;
csr_rows.set(row_it.index1(), csr_index);
for (typename CPU_MATRIX::const_iterator2 col_it = row_it.begin(); col_it != row_it.end(); ++col_it)
{
if(data_index < max_entries_per_row)
{
ell_coords.set(gpu_matrix.internal_size1() * data_index + col_it.index1(), col_it.index2());
ell_elements[gpu_matrix.internal_size1() * data_index + col_it.index1()] = *col_it;
}
else
{
csr_cols.push_back(static_cast<unsigned int>(col_it.index2()));
csr_elements.push_back(*col_it);
csr_index++;
}
data_index++;
}
}
if(csr_cols.empty())
{
csr_cols.push_back(0);
csr_elements.push_back(0);
}
csr_rows.set(csr_rows.size() - 1, csr_index);
gpu_matrix.csrnnz_ = csr_cols.size();
viennacl::backend::typesafe_host_array<unsigned int> csr_cols_for_gpu(gpu_matrix.csr_cols_, csr_cols.size());
for (vcl_size_t i=0; i<csr_cols.size(); ++i)
csr_cols_for_gpu.set(i, csr_cols[i]);
viennacl::backend::memory_create(gpu_matrix.ell_coords_, ell_coords.raw_size(), traits::context(gpu_matrix.ell_coords_), ell_coords.get());
viennacl::backend::memory_create(gpu_matrix.ell_elements_, sizeof(SCALARTYPE) * ell_elements.size(), traits::context(gpu_matrix.ell_elements_), &(ell_elements[0]));
viennacl::backend::memory_create(gpu_matrix.csr_rows_, csr_rows.raw_size(), traits::context(gpu_matrix.csr_rows_), csr_rows.get());
viennacl::backend::memory_create(gpu_matrix.csr_cols_, csr_cols_for_gpu.raw_size(), traits::context(gpu_matrix.csr_cols_), csr_cols_for_gpu.get());
viennacl::backend::memory_create(gpu_matrix.csr_elements_, sizeof(SCALARTYPE) * csr_elements.size(), traits::context(gpu_matrix.csr_elements_), &(csr_elements[0]));
}
}
template <typename CPU_MATRIX, typename SCALARTYPE, unsigned int ALIGNMENT>
void copy(const hyb_matrix<SCALARTYPE, ALIGNMENT>& gpu_matrix, CPU_MATRIX& cpu_matrix)
{
assert( (viennacl::traits::size1(cpu_matrix) == gpu_matrix.size1()) && bool("Size mismatch") );
assert( (viennacl::traits::size2(cpu_matrix) == gpu_matrix.size2()) && bool("Size mismatch") );
if(gpu_matrix.size1() > 0 && gpu_matrix.size2() > 0)
{
std::vector<SCALARTYPE> ell_elements(gpu_matrix.internal_size1() * gpu_matrix.internal_ellnnz());
viennacl::backend::typesafe_host_array<unsigned int> ell_coords(gpu_matrix.handle2(), gpu_matrix.internal_size1() * gpu_matrix.internal_ellnnz());
std::vector<SCALARTYPE> csr_elements(gpu_matrix.csr_nnz());
viennacl::backend::typesafe_host_array<unsigned int> csr_rows(gpu_matrix.handle3(), gpu_matrix.size1() + 1);
viennacl::backend::typesafe_host_array<unsigned int> csr_cols(gpu_matrix.handle4(), gpu_matrix.csr_nnz());
viennacl::backend::memory_read(gpu_matrix.handle(), 0, sizeof(SCALARTYPE) * ell_elements.size(), &(ell_elements[0]));
viennacl::backend::memory_read(gpu_matrix.handle2(), 0, ell_coords.raw_size(), ell_coords.get());
viennacl::backend::memory_read(gpu_matrix.handle3(), 0, csr_rows.raw_size(), csr_rows.get());
viennacl::backend::memory_read(gpu_matrix.handle4(), 0, csr_cols.raw_size(), csr_cols.get());
viennacl::backend::memory_read(gpu_matrix.handle5(), 0, sizeof(SCALARTYPE) * csr_elements.size(), &(csr_elements[0]));
for(vcl_size_t row = 0; row < gpu_matrix.size1(); row++)
{
for(vcl_size_t ind = 0; ind < gpu_matrix.internal_ellnnz(); ind++)
{
vcl_size_t offset = gpu_matrix.internal_size1() * ind + row;
if(ell_elements[offset] == static_cast<SCALARTYPE>(0.0))
{
continue;
}
if(ell_coords[offset] >= gpu_matrix.size2())
{
std::cerr << "ViennaCL encountered invalid data " << offset << " " << ind << " " << row << " " << ell_coords[offset] << " " << gpu_matrix.size2() << std::endl;
return;
}
cpu_matrix(row, ell_coords[offset]) = ell_elements[offset];
}
for(vcl_size_t ind = csr_rows[row]; ind < csr_rows[row+1]; ind++)
{
if(csr_elements[ind] == static_cast<SCALARTYPE>(0.0))
{
continue;
}
if(csr_cols[ind] >= gpu_matrix.size2())
{
std::cerr << "ViennaCL encountered invalid data " << std::endl;
return;
}
cpu_matrix(row, csr_cols[ind]) = csr_elements[ind];
}
}
}
}
//
// Specify available operations:
//
/** \cond */
namespace linalg
{
namespace detail
{
// x = A * y
template <typename T, unsigned int A>
struct op_executor<vector_base<T>, op_assign, vector_expression<const hyb_matrix<T, A>, const vector_base<T>, op_prod> >
{
static void apply(vector_base<T> & lhs, vector_expression<const hyb_matrix<T, A>, const vector_base<T>, op_prod> const & rhs)
{
// check for the special case x = A * x
if (viennacl::traits::handle(lhs) == viennacl::traits::handle(rhs.rhs()))
{
viennacl::vector<T> temp(lhs);
viennacl::linalg::prod_impl(rhs.lhs(), rhs.rhs(), temp);
lhs = temp;
}
else
viennacl::linalg::prod_impl(rhs.lhs(), rhs.rhs(), lhs);
}
};
template <typename T, unsigned int A>
struct op_executor<vector_base<T>, op_inplace_add, vector_expression<const hyb_matrix<T, A>, const vector_base<T>, op_prod> >
{
static void apply(vector_base<T> & lhs, vector_expression<const hyb_matrix<T, A>, const vector_base<T>, op_prod> const & rhs)
{
viennacl::vector<T> temp(lhs);
viennacl::linalg::prod_impl(rhs.lhs(), rhs.rhs(), temp);
lhs += temp;
}
};
template <typename T, unsigned int A>
struct op_executor<vector_base<T>, op_inplace_sub, vector_expression<const hyb_matrix<T, A>, const vector_base<T>, op_prod> >
{
static void apply(vector_base<T> & lhs, vector_expression<const hyb_matrix<T, A>, const vector_base<T>, op_prod> const & rhs)
{
viennacl::vector<T> temp(lhs);
viennacl::linalg::prod_impl(rhs.lhs(), rhs.rhs(), temp);
lhs -= temp;
}
};
// x = A * vec_op
template <typename T, unsigned int A, typename LHS, typename RHS, typename OP>
struct op_executor<vector_base<T>, op_assign, vector_expression<const hyb_matrix<T, A>, const vector_expression<const LHS, const RHS, OP>, op_prod> >
{
static void apply(vector_base<T> & lhs, vector_expression<const hyb_matrix<T, A>, const vector_expression<const LHS, const RHS, OP>, op_prod> const & rhs)
{
viennacl::vector<T> temp(rhs.rhs(), viennacl::traits::context(rhs));
viennacl::linalg::prod_impl(rhs.lhs(), temp, lhs);
}
};
// x = A * vec_op
template <typename T, unsigned int A, typename LHS, typename RHS, typename OP>
struct op_executor<vector_base<T>, op_inplace_add, vector_expression<const hyb_matrix<T, A>, const vector_expression<const LHS, const RHS, OP>, op_prod> >
{
static void apply(vector_base<T> & lhs, vector_expression<const hyb_matrix<T, A>, const vector_expression<const LHS, const RHS, OP>, op_prod> const & rhs)
{
viennacl::vector<T> temp(rhs.rhs(), viennacl::traits::context(rhs));
viennacl::vector<T> temp_result(lhs);
viennacl::linalg::prod_impl(rhs.lhs(), temp, temp_result);
lhs += temp_result;
}
};
// x = A * vec_op
template <typename T, unsigned int A, typename LHS, typename RHS, typename OP>
struct op_executor<vector_base<T>, op_inplace_sub, vector_expression<const hyb_matrix<T, A>, const vector_expression<const LHS, const RHS, OP>, op_prod> >
{
static void apply(vector_base<T> & lhs, vector_expression<const hyb_matrix<T, A>, const vector_expression<const LHS, const RHS, OP>, op_prod> const & rhs)
{
viennacl::vector<T> temp(rhs.rhs(), viennacl::traits::context(rhs));
viennacl::vector<T> temp_result(lhs);
viennacl::linalg::prod_impl(rhs.lhs(), temp, temp_result);
lhs -= temp_result;
}
};
} // namespace detail
} // namespace linalg
/** \endcond */
}
#endif
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