/usr/include/viennacl/ell_matrix.hpp is in libviennacl-dev 1.5.2-2.
This file is owned by root:root, with mode 0o644.
The actual contents of the file can be viewed below.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 | #ifndef VIENNACL_ELL_MATRIX_HPP_
#define VIENNACL_ELL_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/ell_matrix.hpp
@brief Implementation of the ell_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 the ELLPACK format for storing the nonzeros.
*
* This format works best for matrices where the number of nonzeros per row is mostly the same.
* Finite element and finite difference methods on nicely shaped domains often result in such a nonzero pattern.
* For a matrix
*
* (1 2 0 0 0)
* (2 3 4 0 0)
* (0 5 6 0 7)
* (0 0 8 9 0)
*
* the entries are layed out in chunks of size 3 as
* (1 2 5 8; 2 3 6 9; 0 4 7 0)
* Note that this is a 'transposed' representation in order to maximize coalesced memory access.
*/
template<typename SCALARTYPE, unsigned int ALIGNMENT /* see forwards.h for default argument */>
class ell_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;
ell_matrix() : rows_(0), cols_(0), maxnnz_(0) {}
ell_matrix(viennacl::context ctx) : rows_(0), cols_(0), maxnnz_(0)
{
coords_.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)
{
coords_.opencl_handle().context(ctx.opencl_context());
elements_.opencl_handle().context(ctx.opencl_context());
}
#endif
}
public:
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_maxnnz() const {return viennacl::tools::align_to_multiple<vcl_size_t>(maxnnz_, ALIGNMENT); }
vcl_size_t maxnnz() const { return maxnnz_; }
vcl_size_t nnz() const { return rows_ * maxnnz_; }
vcl_size_t internal_nnz() const { return internal_size1() * internal_maxnnz(); }
handle_type & handle() { return elements_; }
const handle_type & handle() const { return elements_; }
handle_type & handle2() { return coords_; }
const handle_type & handle2() const { return coords_; }
#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, ell_matrix & gpu_matrix );
#else
template <typename CPU_MATRIX, typename T, unsigned int ALIGN>
friend void copy(const CPU_MATRIX & cpu_matrix, ell_matrix<T, ALIGN> & gpu_matrix );
#endif
private:
vcl_size_t rows_;
vcl_size_t cols_;
vcl_size_t maxnnz_;
handle_type coords_;
handle_type elements_;
};
template <typename CPU_MATRIX, typename SCALARTYPE, unsigned int ALIGNMENT>
void copy(const CPU_MATRIX& cpu_matrix, ell_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;
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;
}
max_entries_per_row = std::max(max_entries_per_row, num_entries);
}
//setup GPU matrix
gpu_matrix.maxnnz_ = max_entries_per_row;
gpu_matrix.rows_ = cpu_matrix.size1();
gpu_matrix.cols_ = cpu_matrix.size2();
vcl_size_t nnz = gpu_matrix.internal_nnz();
viennacl::backend::typesafe_host_array<unsigned int> coords(gpu_matrix.handle2(), nnz);
std::vector<SCALARTYPE> elements(nnz, 0);
// std::cout << "ELL_MATRIX copy " << gpu_matrix.maxnnz_ << " " << gpu_matrix.rows_ << " " << gpu_matrix.cols_ << " "
// << gpu_matrix.internal_maxnnz() << "\n";
for (typename CPU_MATRIX::const_iterator1 row_it = cpu_matrix.begin1(); row_it != cpu_matrix.end1(); ++row_it)
{
vcl_size_t data_index = 0;
for (typename CPU_MATRIX::const_iterator2 col_it = row_it.begin(); col_it != row_it.end(); ++col_it)
{
coords.set(gpu_matrix.internal_size1() * data_index + col_it.index1(), col_it.index2());
elements[gpu_matrix.internal_size1() * data_index + col_it.index1()] = *col_it;
//std::cout << *col_it << "\n";
data_index++;
}
}
viennacl::backend::memory_create(gpu_matrix.handle2(), coords.raw_size(), traits::context(gpu_matrix.handle2()), coords.get());
viennacl::backend::memory_create(gpu_matrix.handle(), sizeof(SCALARTYPE) * elements.size(), traits::context(gpu_matrix.handle()), &(elements[0]));
}
}
template <typename CPU_MATRIX, typename SCALARTYPE, unsigned int ALIGNMENT>
void copy(const ell_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> elements(gpu_matrix.internal_nnz());
viennacl::backend::typesafe_host_array<unsigned int> coords(gpu_matrix.handle2(), gpu_matrix.internal_nnz());
viennacl::backend::memory_read(gpu_matrix.handle(), 0, sizeof(SCALARTYPE) * elements.size(), &(elements[0]));
viennacl::backend::memory_read(gpu_matrix.handle2(), 0, coords.raw_size(), coords.get());
for(vcl_size_t row = 0; row < gpu_matrix.size1(); row++)
{
for(vcl_size_t ind = 0; ind < gpu_matrix.internal_maxnnz(); ind++)
{
vcl_size_t offset = gpu_matrix.internal_size1() * ind + row;
if(elements[offset] == static_cast<SCALARTYPE>(0.0))
continue;
if(coords[offset] >= gpu_matrix.size2())
{
std::cerr << "ViennaCL encountered invalid data " << offset << " " << ind << " " << row << " " << coords[offset] << " " << gpu_matrix.size2() << std::endl;
return;
}
cpu_matrix(row, coords[offset]) = elements[offset];
}
}
}
}
//
// 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 ell_matrix<T, A>, const vector_base<T>, op_prod> >
{
static void apply(vector_base<T> & lhs, vector_expression<const ell_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 ell_matrix<T, A>, const vector_base<T>, op_prod> >
{
static void apply(vector_base<T> & lhs, vector_expression<const ell_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 ell_matrix<T, A>, const vector_base<T>, op_prod> >
{
static void apply(vector_base<T> & lhs, vector_expression<const ell_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 ell_matrix<T, A>, const vector_expression<const LHS, const RHS, OP>, op_prod> >
{
static void apply(vector_base<T> & lhs, vector_expression<const ell_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 ell_matrix<T, A>, const vector_expression<const LHS, const RHS, OP>, op_prod> >
{
static void apply(vector_base<T> & lhs, vector_expression<const ell_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 ell_matrix<T, A>, const vector_expression<const LHS, const RHS, OP>, op_prod> >
{
static void apply(vector_base<T> & lhs, vector_expression<const ell_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
|