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#define VIENNACL_COMPRESSED_compressed_compressed_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/compressed_compressed_matrix.hpp
@brief Implementation of the compressed_compressed_matrix class (CSR format with a relatively small number of nonzero rows)
*/
#include <vector>
#include <list>
#include <map>
#include "viennacl/forwards.h"
#include "viennacl/vector.hpp"
#include "viennacl/linalg/sparse_matrix_operations.hpp"
#include "viennacl/tools/tools.hpp"
#include "viennacl/tools/entry_proxy.hpp"
namespace viennacl
{
namespace detail
{
template <typename CPU_MATRIX, typename SCALARTYPE>
void copy_impl(const CPU_MATRIX & cpu_matrix,
compressed_compressed_matrix<SCALARTYPE> & gpu_matrix,
vcl_size_t nonzero_rows,
vcl_size_t nonzeros)
{
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") );
viennacl::backend::typesafe_host_array<unsigned int> row_buffer(gpu_matrix.handle1(), nonzero_rows + 1);
viennacl::backend::typesafe_host_array<unsigned int> row_indices(gpu_matrix.handle3(), nonzero_rows);
viennacl::backend::typesafe_host_array<unsigned int> col_buffer(gpu_matrix.handle2(), nonzeros);
std::vector<SCALARTYPE> elements(nonzeros);
vcl_size_t row_index = 0;
vcl_size_t data_index = 0;
for (typename CPU_MATRIX::const_iterator1 row_it = cpu_matrix.begin1();
row_it != cpu_matrix.end1();
++row_it)
{
bool row_empty = true;
for (typename CPU_MATRIX::const_iterator2 col_it = row_it.begin();
col_it != row_it.end();
++col_it)
{
SCALARTYPE entry = *col_it;
if (entry != SCALARTYPE(0))
{
if (row_empty)
{
assert(row_index < nonzero_rows && bool("Provided count of nonzero rows exceeded!"));
row_empty = false;
row_buffer.set(row_index, data_index);
row_indices.set(row_index, col_it.index1());
++row_index;
}
col_buffer.set(data_index, col_it.index2());
elements[data_index] = entry;
++data_index;
}
}
}
row_buffer.set(row_index, data_index);
gpu_matrix.set(row_buffer.get(),
row_indices.get(),
col_buffer.get(),
&elements[0],
cpu_matrix.size1(),
cpu_matrix.size2(),
nonzero_rows,
nonzeros);
}
}
//provide copy-operation:
/** @brief Copies a sparse matrix from the host to the OpenCL device (either GPU or multi-core CPU)
*
* There are some type requirements on the CPU_MATRIX type (fulfilled by e.g. boost::numeric::ublas):
* - .size1() returns the number of rows
* - .size2() returns the number of columns
* - const_iterator1 is a type definition for an iterator along increasing row indices
* - const_iterator2 is a type definition for an iterator along increasing columns indices
* - The const_iterator1 type provides an iterator of type const_iterator2 via members .begin() and .end() that iterates along column indices in the current row.
* - The types const_iterator1 and const_iterator2 provide members functions .index1() and .index2() that return the current row and column indices respectively.
* - Dereferenciation of an object of type const_iterator2 returns the entry.
*
* @param cpu_matrix A sparse matrix on the host.
* @param gpu_matrix A compressed_compressed_matrix from ViennaCL
*/
template <typename CPU_MATRIX, typename SCALARTYPE>
void copy(const CPU_MATRIX & cpu_matrix,
compressed_compressed_matrix<SCALARTYPE> & gpu_matrix )
{
//std::cout << "copy for (" << cpu_matrix.size1() << ", " << cpu_matrix.size2() << ", " << cpu_matrix.nnz() << ")" << std::endl;
if ( cpu_matrix.size1() > 0 && cpu_matrix.size2() > 0 )
{
//determine nonzero rows and total nonzeros:
vcl_size_t num_entries = 0;
vcl_size_t nonzero_rows = 0;
for (typename CPU_MATRIX::const_iterator1 row_it = cpu_matrix.begin1();
row_it != cpu_matrix.end1();
++row_it)
{
bool row_empty = true;
for (typename CPU_MATRIX::const_iterator2 col_it = row_it.begin();
col_it != row_it.end();
++col_it)
{
if (*col_it != SCALARTYPE(0))
{
++num_entries;
if (row_empty)
{
row_empty = false;
++nonzero_rows;
}
}
}
}
if (num_entries == 0) //we copy an empty matrix
num_entries = 1;
//set up matrix entries:
detail::copy_impl(cpu_matrix, gpu_matrix, nonzero_rows, num_entries);
}
}
//adapted for std::vector< std::map < > > argument:
/** @brief Copies a sparse square matrix in the std::vector< std::map < > > format to an OpenCL device. Use viennacl::tools::sparse_matrix_adapter for non-square matrices.
*
* @param cpu_matrix A sparse square matrix on the host using STL types
* @param gpu_matrix A compressed_compressed_matrix from ViennaCL
*/
template <typename SizeType, typename SCALARTYPE>
void copy(const std::vector< std::map<SizeType, SCALARTYPE> > & cpu_matrix,
compressed_compressed_matrix<SCALARTYPE> & gpu_matrix )
{
vcl_size_t nonzero_rows = 0;
vcl_size_t nonzeros = 0;
vcl_size_t max_col = 0;
for (vcl_size_t i=0; i<cpu_matrix.size(); ++i)
{
if (cpu_matrix[i].size() > 0)
++nonzero_rows;
nonzeros += cpu_matrix[i].size();
if (cpu_matrix[i].size() > 0)
max_col = std::max<vcl_size_t>(max_col, (cpu_matrix[i].rbegin())->first);
}
viennacl::detail::copy_impl(tools::const_sparse_matrix_adapter<SCALARTYPE, SizeType>(cpu_matrix, cpu_matrix.size(), max_col + 1),
gpu_matrix,
nonzero_rows,
nonzeros);
}
//
// gpu to cpu:
//
/** @brief Copies a sparse matrix from the OpenCL device (either GPU or multi-core CPU) to the host.
*
* There are two type requirements on the CPU_MATRIX type (fulfilled by e.g. boost::numeric::ublas):
* - resize(rows, cols) A resize function to bring the matrix into the correct size
* - operator(i,j) Write new entries via the parenthesis operator
*
* @param gpu_matrix A compressed_compressed_matrix from ViennaCL
* @param cpu_matrix A sparse matrix on the host.
*/
template <typename CPU_MATRIX, typename SCALARTYPE>
void copy(const compressed_compressed_matrix<SCALARTYPE> & gpu_matrix,
CPU_MATRIX & cpu_matrix )
{
assert( (cpu_matrix.size1() == gpu_matrix.size1()) && bool("Size mismatch") );
assert( (cpu_matrix.size2() == gpu_matrix.size2()) && bool("Size mismatch") );
if ( gpu_matrix.size1() > 0 && gpu_matrix.size2() > 0 )
{
//get raw data from memory:
viennacl::backend::typesafe_host_array<unsigned int> row_buffer(gpu_matrix.handle1(), gpu_matrix.nnz1() + 1);
viennacl::backend::typesafe_host_array<unsigned int> row_indices(gpu_matrix.handle1(), gpu_matrix.nnz1());
viennacl::backend::typesafe_host_array<unsigned int> col_buffer(gpu_matrix.handle2(), gpu_matrix.nnz());
std::vector<SCALARTYPE> elements(gpu_matrix.nnz());
//std::cout << "GPU->CPU, nonzeros: " << gpu_matrix.nnz() << std::endl;
viennacl::backend::memory_read(gpu_matrix.handle1(), 0, row_buffer.raw_size(), row_buffer.get());
viennacl::backend::memory_read(gpu_matrix.handle3(), 0, row_indices.raw_size(), row_indices.get());
viennacl::backend::memory_read(gpu_matrix.handle2(), 0, col_buffer.raw_size(), col_buffer.get());
viennacl::backend::memory_read(gpu_matrix.handle(), 0, sizeof(SCALARTYPE)* gpu_matrix.nnz(), &(elements[0]));
//fill the cpu_matrix:
vcl_size_t data_index = 0;
for (vcl_size_t i = 1; i < row_buffer.size(); ++i)
{
while (data_index < row_buffer[i])
{
if (col_buffer[data_index] >= gpu_matrix.size2())
{
std::cerr << "ViennaCL encountered invalid data at colbuffer[" << data_index << "]: " << col_buffer[data_index] << std::endl;
return;
}
if (elements[data_index] != static_cast<SCALARTYPE>(0.0))
cpu_matrix(row_indices[i-1], col_buffer[data_index]) = elements[data_index];
++data_index;
}
}
}
}
/** @brief Copies a sparse matrix from an OpenCL device to the host. The host type is the std::vector< std::map < > > format .
*
* @param gpu_matrix A compressed_compressed_matrix from ViennaCL
* @param cpu_matrix A sparse matrix on the host.
*/
template <typename SCALARTYPE>
void copy(const compressed_compressed_matrix<SCALARTYPE> & gpu_matrix,
std::vector< std::map<unsigned int, SCALARTYPE> > & cpu_matrix)
{
tools::sparse_matrix_adapter<SCALARTYPE> temp(cpu_matrix, cpu_matrix.size(), cpu_matrix.size());
copy(gpu_matrix, temp);
}
//////////////////////// compressed_compressed_matrix //////////////////////////
/** @brief A sparse square matrix in compressed sparse rows format optimized for the case that only a few rows carry nonzero entries.
*
* The difference to the 'standard' CSR format is that there is an additional array 'row_indices' so that the i-th set of indices in the CSR-layout refers to row_indices[i].
*
* @tparam SCALARTYPE The floating point type (either float or double, checked at compile time)
* @tparam ALIGNMENT The internal memory size for the entries in each row is given by (size()/ALIGNMENT + 1) * ALIGNMENT. ALIGNMENT must be a power of two. Best values or usually 4, 8 or 16, higher values are usually a waste of memory.
*/
template<class SCALARTYPE>
class compressed_compressed_matrix
{
public:
typedef viennacl::backend::mem_handle handle_type;
typedef scalar<typename viennacl::tools::CHECK_SCALAR_TEMPLATE_ARGUMENT<SCALARTYPE>::ResultType> value_type;
typedef vcl_size_t size_type;
/** @brief Default construction of a compressed matrix. No memory is allocated */
compressed_compressed_matrix() : rows_(0), cols_(0), nonzero_rows_(0), nonzeros_(0) {}
/** @brief Construction of a compressed matrix with the supplied number of rows and columns. If the number of nonzeros is positive, memory is allocated
*
* @param rows Number of rows
* @param cols Number of columns
* @param nonzero_rows Optional number of nonzero rows for memory preallocation
* @param nonzeros Optional number of nonzeros for memory preallocation
* @param ctx Context in which to create the matrix. Uses the default context if omitted
*/
explicit compressed_compressed_matrix(vcl_size_t rows, vcl_size_t cols, vcl_size_t nonzero_rows = 0, vcl_size_t nonzeros = 0, viennacl::context ctx = viennacl::context())
: rows_(rows), cols_(cols), nonzero_rows_(nonzero_rows), nonzeros_(nonzeros)
{
row_buffer_.switch_active_handle_id(ctx.memory_type());
row_indices_.switch_active_handle_id(ctx.memory_type());
col_buffer_.switch_active_handle_id(ctx.memory_type());
elements_.switch_active_handle_id(ctx.memory_type());
#ifdef VIENNACL_WITH_OPENCL
if (ctx.memory_type() == OPENCL_MEMORY)
{
row_buffer_.opencl_handle().context(ctx.opencl_context());
row_indices_.opencl_handle().context(ctx.opencl_context());
col_buffer_.opencl_handle().context(ctx.opencl_context());
elements_.opencl_handle().context(ctx.opencl_context());
}
#endif
if (rows > 0)
{
viennacl::backend::memory_create(row_buffer_, viennacl::backend::typesafe_host_array<unsigned int>().element_size() * (rows + 1), ctx);
}
if (nonzeros > 0)
{
viennacl::backend::memory_create(col_buffer_, viennacl::backend::typesafe_host_array<unsigned int>().element_size() * nonzeros, ctx);
viennacl::backend::memory_create(elements_, sizeof(SCALARTYPE) * nonzeros, ctx);
}
}
/** @brief Construction of a compressed matrix with the supplied number of rows and columns. If the number of nonzeros is positive, memory is allocated
*
* @param rows Number of rows
* @param cols Number of columns
* @param ctx Context in which to create the matrix
*/
explicit compressed_compressed_matrix(vcl_size_t rows, vcl_size_t cols, viennacl::context ctx)
: rows_(rows), cols_(cols), nonzeros_(0)
{
row_buffer_.switch_active_handle_id(ctx.memory_type());
col_buffer_.switch_active_handle_id(ctx.memory_type());
elements_.switch_active_handle_id(ctx.memory_type());
#ifdef VIENNACL_WITH_OPENCL
if (ctx.memory_type() == OPENCL_MEMORY)
{
row_buffer_.opencl_handle().context(ctx.opencl_context());
col_buffer_.opencl_handle().context(ctx.opencl_context());
elements_.opencl_handle().context(ctx.opencl_context());
}
#endif
if (rows > 0)
{
viennacl::backend::memory_create(row_buffer_, viennacl::backend::typesafe_host_array<unsigned int>().element_size() * (rows + 1), ctx);
}
}
explicit compressed_compressed_matrix(viennacl::context ctx) : rows_(0), cols_(0), nonzero_rows_(0), nonzeros_(0)
{
row_buffer_.switch_active_handle_id(ctx.memory_type());
row_indices_.switch_active_handle_id(ctx.memory_type());
col_buffer_.switch_active_handle_id(ctx.memory_type());
elements_.switch_active_handle_id(ctx.memory_type());
#ifdef VIENNACL_WITH_OPENCL
if (ctx.memory_type() == OPENCL_MEMORY)
{
row_buffer_.opencl_handle().context(ctx.opencl_context());
row_indices_.opencl_handle().context(ctx.opencl_context());
col_buffer_.opencl_handle().context(ctx.opencl_context());
elements_.opencl_handle().context(ctx.opencl_context());
}
#endif
}
#ifdef VIENNACL_WITH_OPENCL
explicit compressed_compressed_matrix(cl_mem mem_row_buffer, cl_mem mem_row_indices, cl_mem mem_col_buffer, cl_mem mem_elements,
vcl_size_t rows, vcl_size_t cols, vcl_size_t nonzero_rows, vcl_size_t nonzeros) :
rows_(rows), cols_(cols), nonzero_rows_(nonzero_rows), nonzeros_(nonzeros)
{
row_buffer_.switch_active_handle_id(viennacl::OPENCL_MEMORY);
row_buffer_.opencl_handle() = mem_row_buffer;
row_buffer_.opencl_handle().inc(); //prevents that the user-provided memory is deleted once the matrix object is destroyed.
row_buffer_.raw_size(sizeof(cl_uint) * (nonzero_rows + 1));
row_indices_.switch_active_handle_id(viennacl::OPENCL_MEMORY);
row_indices_.opencl_handle() = mem_row_indices;
row_indices_.opencl_handle().inc(); //prevents that the user-provided memory is deleted once the matrix object is destroyed.
row_indices_.raw_size(sizeof(cl_uint) * nonzero_rows);
col_buffer_.switch_active_handle_id(viennacl::OPENCL_MEMORY);
col_buffer_.opencl_handle() = mem_col_buffer;
col_buffer_.opencl_handle().inc(); //prevents that the user-provided memory is deleted once the matrix object is destroyed.
col_buffer_.raw_size(sizeof(cl_uint) * nonzeros);
elements_.switch_active_handle_id(viennacl::OPENCL_MEMORY);
elements_.opencl_handle() = mem_elements;
elements_.opencl_handle().inc(); //prevents that the user-provided memory is deleted once the matrix object is destroyed.
elements_.raw_size(sizeof(SCALARTYPE) * nonzeros);
}
#endif
/** @brief Assignment a compressed matrix from possibly another memory domain. */
compressed_compressed_matrix & operator=(compressed_compressed_matrix const & other)
{
assert( (rows_ == 0 || rows_ == other.size1()) && bool("Size mismatch") );
assert( (cols_ == 0 || cols_ == other.size2()) && bool("Size mismatch") );
rows_ = other.size1();
cols_ = other.size2();
nonzero_rows_ = other.nnz1();
nonzeros_ = other.nnz();
viennacl::backend::typesafe_memory_copy<unsigned int>(other.row_buffer_, row_buffer_);
viennacl::backend::typesafe_memory_copy<unsigned int>(other.row_indices_, row_indices_);
viennacl::backend::typesafe_memory_copy<unsigned int>(other.col_buffer_, col_buffer_);
viennacl::backend::typesafe_memory_copy<SCALARTYPE>(other.elements_, elements_);
return *this;
}
/** @brief Sets the row, column and value arrays of the compressed matrix
*
* @param row_jumper Pointer to an array holding the indices of the first element of each row (starting with zero). E.g. row_jumper[10] returns the index of the first entry of the 11th row. The array length is 'cols + 1'
* @param row_indices Array holding the indices of the nonzero rows
* @param col_buffer Pointer to an array holding the column index of each entry. The array length is 'nonzeros'
* @param elements Pointer to an array holding the entries of the sparse matrix. The array length is 'elements'
* @param rows Number of rows of the sparse matrix
* @param cols Number of columns of the sparse matrix
* @param nonzero_rows Number of nonzero rows
* @param nonzeros Total number of nonzero entries
*/
void set(const void * row_jumper,
const void * row_indices,
const void * col_buffer,
const SCALARTYPE * elements,
vcl_size_t rows,
vcl_size_t cols,
vcl_size_t nonzero_rows,
vcl_size_t nonzeros)
{
assert( (rows > 0) && bool("Error in compressed_compressed_matrix::set(): Number of rows must be larger than zero!"));
assert( (cols > 0) && bool("Error in compressed_compressed_matrix::set(): Number of columns must be larger than zero!"));
assert( (nonzero_rows > 0) && bool("Error in compressed_compressed_matrix::set(): Number of nonzero rows must be larger than zero!"));
assert( (nonzeros > 0) && bool("Error in compressed_compressed_matrix::set(): Number of nonzeros must be larger than zero!"));
//std::cout << "Setting memory: " << cols + 1 << ", " << nonzeros << std::endl;
viennacl::backend::memory_create(row_buffer_, viennacl::backend::typesafe_host_array<unsigned int>(row_buffer_).element_size() * (rows + 1), viennacl::traits::context(row_buffer_), row_jumper);
viennacl::backend::memory_create(row_indices_, viennacl::backend::typesafe_host_array<unsigned int>(row_indices_).element_size() * (rows + 1), viennacl::traits::context(row_indices_), row_indices);
viennacl::backend::memory_create(col_buffer_, viennacl::backend::typesafe_host_array<unsigned int>(col_buffer_).element_size() * nonzeros, viennacl::traits::context(col_buffer_), col_buffer);
viennacl::backend::memory_create(elements_, sizeof(SCALARTYPE) * nonzeros, viennacl::traits::context(elements_), elements);
nonzeros_ = nonzeros;
nonzero_rows_ = nonzero_rows;
rows_ = rows;
cols_ = cols;
}
/** @brief Returns the number of rows */
const vcl_size_t & size1() const { return rows_; }
/** @brief Returns the number of columns */
const vcl_size_t & size2() const { return cols_; }
/** @brief Returns the number of nonzero entries */
const vcl_size_t & nnz1() const { return nonzero_rows_; }
/** @brief Returns the number of nonzero entries */
const vcl_size_t & nnz() const { return nonzeros_; }
/** @brief Returns the OpenCL handle to the row index array */
const handle_type & handle1() const { return row_buffer_; }
/** @brief Returns the OpenCL handle to the column index array */
const handle_type & handle2() const { return col_buffer_; }
/** @brief Returns the OpenCL handle to the row index array */
const handle_type & handle3() const { return row_indices_; }
/** @brief Returns the OpenCL handle to the matrix entry array */
const handle_type & handle() const { return elements_; }
/** @brief Returns the OpenCL handle to the row index array */
handle_type & handle1() { return row_buffer_; }
/** @brief Returns the OpenCL handle to the column index array */
handle_type & handle2() { return col_buffer_; }
/** @brief Returns the OpenCL handle to the row index array */
handle_type & handle3() { return row_indices_; }
/** @brief Returns the OpenCL handle to the matrix entry array */
handle_type & handle() { return elements_; }
void switch_memory_context(viennacl::context new_ctx)
{
viennacl::backend::switch_memory_context<unsigned int>(row_buffer_, new_ctx);
viennacl::backend::switch_memory_context<unsigned int>(row_indices_, new_ctx);
viennacl::backend::switch_memory_context<unsigned int>(col_buffer_, new_ctx);
viennacl::backend::switch_memory_context<SCALARTYPE>(elements_, new_ctx);
}
viennacl::memory_types memory_context() const
{
return row_buffer_.get_active_handle_id();
}
private:
vcl_size_t rows_;
vcl_size_t cols_;
vcl_size_t nonzero_rows_;
vcl_size_t nonzeros_;
handle_type row_buffer_;
handle_type row_indices_;
handle_type col_buffer_;
handle_type elements_;
};
//
// Specify available operations:
//
/** \cond */
namespace linalg
{
namespace detail
{
// x = A * y
template <typename T>
struct op_executor<vector_base<T>, op_assign, vector_expression<const compressed_compressed_matrix<T>, const vector_base<T>, op_prod> >
{
static void apply(vector_base<T> & lhs, vector_expression<const compressed_compressed_matrix<T>, 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>
struct op_executor<vector_base<T>, op_inplace_add, vector_expression<const compressed_compressed_matrix<T>, const vector_base<T>, op_prod> >
{
static void apply(vector_base<T> & lhs, vector_expression<const compressed_compressed_matrix<T>, 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>
struct op_executor<vector_base<T>, op_inplace_sub, vector_expression<const compressed_compressed_matrix<T>, const vector_base<T>, op_prod> >
{
static void apply(vector_base<T> & lhs, vector_expression<const compressed_compressed_matrix<T>, 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, typename LHS, typename RHS, typename OP>
struct op_executor<vector_base<T>, op_assign, vector_expression<const compressed_compressed_matrix<T>, const vector_expression<const LHS, const RHS, OP>, op_prod> >
{
static void apply(vector_base<T> & lhs, vector_expression<const compressed_compressed_matrix<T>, const vector_expression<const LHS, const RHS, OP>, op_prod> const & rhs)
{
viennacl::vector<T> temp(rhs.rhs());
viennacl::linalg::prod_impl(rhs.lhs(), temp, lhs);
}
};
// x = A * vec_op
template <typename T, typename LHS, typename RHS, typename OP>
struct op_executor<vector_base<T>, op_inplace_add, vector_expression<const compressed_compressed_matrix<T>, vector_expression<const LHS, const RHS, OP>, op_prod> >
{
static void apply(vector_base<T> & lhs, vector_expression<const compressed_compressed_matrix<T>, 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, typename LHS, typename RHS, typename OP>
struct op_executor<vector_base<T>, op_inplace_sub, vector_expression<const compressed_compressed_matrix<T>, const vector_expression<const LHS, const RHS, OP>, op_prod> >
{
static void apply(vector_base<T> & lhs, vector_expression<const compressed_compressed_matrix<T>, 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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