ViennaCL - The Vienna Computing Library  1.7.0
Free open-source GPU-accelerated linear algebra and solver library.
common.hpp
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1 #ifndef VIENNACL_LINALG_DETAIL_ILU_COMMON_HPP_
2 #define VIENNACL_LINALG_DETAIL_ILU_COMMON_HPP_
3 
4 /* =========================================================================
5  Copyright (c) 2010-2015, Institute for Microelectronics,
6  Institute for Analysis and Scientific Computing,
7  TU Wien.
8  Portions of this software are copyright by UChicago Argonne, LLC.
9 
10  -----------------
11  ViennaCL - The Vienna Computing Library
12  -----------------
13 
14  Project Head: Karl Rupp rupp@iue.tuwien.ac.at
15 
16  (A list of authors and contributors can be found in the manual)
17 
18  License: MIT (X11), see file LICENSE in the base directory
19 ============================================================================= */
20 
25 #include <vector>
26 #include <cmath>
27 #include <iostream>
28 #include <map>
29 #include <list>
30 
31 #include "viennacl/forwards.h"
32 #include "viennacl/tools/tools.hpp"
34 
37 
38 namespace viennacl
39 {
40 namespace linalg
41 {
42 namespace detail
43 {
44 
45 
46 //
47 // Level Scheduling Setup for ILU:
48 //
49 
50 template<typename NumericT, unsigned int AlignmentV>
52  viennacl::vector<NumericT> const & diagonal_LU,
53  std::list<viennacl::backend::mem_handle> & row_index_arrays,
54  std::list<viennacl::backend::mem_handle> & row_buffers,
55  std::list<viennacl::backend::mem_handle> & col_buffers,
56  std::list<viennacl::backend::mem_handle> & element_buffers,
57  std::list<vcl_size_t> & row_elimination_num_list,
58  bool setup_U)
59 {
60  NumericT const * diagonal_buf = viennacl::linalg::host_based::detail::extract_raw_pointer<NumericT>(diagonal_LU.handle());
61  NumericT const * elements = viennacl::linalg::host_based::detail::extract_raw_pointer<NumericT>(LU.handle());
62  unsigned int const * row_buffer = viennacl::linalg::host_based::detail::extract_raw_pointer<unsigned int>(LU.handle1());
63  unsigned int const * col_buffer = viennacl::linalg::host_based::detail::extract_raw_pointer<unsigned int>(LU.handle2());
64 
65  //
66  // Step 1: Determine row elimination order for each row and build up meta information about the number of entries taking part in each elimination step:
67  //
68  std::vector<vcl_size_t> row_elimination(LU.size1());
69  std::map<vcl_size_t, std::map<vcl_size_t, vcl_size_t> > row_entries_per_elimination_step;
70 
71  vcl_size_t max_elimination_runs = 0;
72  for (vcl_size_t row2 = 0; row2 < LU.size1(); ++row2)
73  {
74  vcl_size_t row = setup_U ? (LU.size1() - row2) - 1 : row2;
75 
76  vcl_size_t row_begin = row_buffer[row];
77  vcl_size_t row_end = row_buffer[row+1];
78  vcl_size_t elimination_index = 0; //Note: first run corresponds to elimination_index = 1 (otherwise, type issues with int <-> unsigned int would arise
79  for (vcl_size_t i = row_begin; i < row_end; ++i)
80  {
81  unsigned int col = col_buffer[i];
82  if ( (!setup_U && col < row) || (setup_U && col > row) )
83  {
84  elimination_index = std::max<vcl_size_t>(elimination_index, row_elimination[col]);
85  row_entries_per_elimination_step[row_elimination[col]][row] += 1;
86  }
87  }
88  row_elimination[row] = elimination_index + 1;
89  max_elimination_runs = std::max<vcl_size_t>(max_elimination_runs, elimination_index + 1);
90  }
91 
92  //std::cout << "Number of elimination runs: " << max_elimination_runs << std::endl;
93 
94  //
95  // Step 2: Build row-major elimination matrix for each elimination step
96  //
97 
98  //std::cout << "Elimination order: " << std::endl;
99  //for (vcl_size_t i=0; i<row_elimination.size(); ++i)
100  // std::cout << row_elimination[i] << ", ";
101  //std::cout << std::endl;
102 
103  //vcl_size_t summed_rows = 0;
104  for (vcl_size_t elimination_run = 1; elimination_run <= max_elimination_runs; ++elimination_run)
105  {
106  std::map<vcl_size_t, vcl_size_t> const & current_elimination_info = row_entries_per_elimination_step[elimination_run];
107 
108  // count cols and entries handled in this elimination step
109  vcl_size_t num_tainted_cols = current_elimination_info.size();
110  vcl_size_t num_entries = 0;
111 
112  for (std::map<vcl_size_t, vcl_size_t>::const_iterator it = current_elimination_info.begin();
113  it != current_elimination_info.end();
114  ++it)
115  num_entries += it->second;
116 
117  //std::cout << "num_entries: " << num_entries << std::endl;
118  //std::cout << "num_tainted_cols: " << num_tainted_cols << std::endl;
119 
120  if (num_tainted_cols > 0)
121  {
122  row_index_arrays.push_back(viennacl::backend::mem_handle());
123  viennacl::backend::switch_memory_context<unsigned int>(row_index_arrays.back(), viennacl::traits::context(LU));
124  viennacl::backend::typesafe_host_array<unsigned int> elim_row_index_array(row_index_arrays.back(), num_tainted_cols);
125 
126  row_buffers.push_back(viennacl::backend::mem_handle());
127  viennacl::backend::switch_memory_context<unsigned int>(row_buffers.back(), viennacl::traits::context(LU));
128  viennacl::backend::typesafe_host_array<unsigned int> elim_row_buffer(row_buffers.back(), num_tainted_cols + 1);
129 
130  col_buffers.push_back(viennacl::backend::mem_handle());
131  viennacl::backend::switch_memory_context<unsigned int>(col_buffers.back(), viennacl::traits::context(LU));
132  viennacl::backend::typesafe_host_array<unsigned int> elim_col_buffer(col_buffers.back(), num_entries);
133 
134  element_buffers.push_back(viennacl::backend::mem_handle());
135  viennacl::backend::switch_memory_context<NumericT>(element_buffers.back(), viennacl::traits::context(LU));
136  std::vector<NumericT> elim_elements_buffer(num_entries);
137 
138  row_elimination_num_list.push_back(num_tainted_cols);
139 
140  vcl_size_t k=0;
141  vcl_size_t nnz_index = 0;
142  elim_row_buffer.set(0, 0);
143 
144  for (std::map<vcl_size_t, vcl_size_t>::const_iterator it = current_elimination_info.begin();
145  it != current_elimination_info.end();
146  ++it)
147  {
148  //vcl_size_t col = setup_U ? (elimination_matrix.size() - it->first) - 1 : col2;
149  vcl_size_t row = it->first;
150  elim_row_index_array.set(k, row);
151 
152  vcl_size_t row_begin = row_buffer[row];
153  vcl_size_t row_end = row_buffer[row+1];
154  for (vcl_size_t i = row_begin; i < row_end; ++i)
155  {
156  unsigned int col = col_buffer[i];
157  if ( (!setup_U && col < row) || (setup_U && col > row) ) //entry of L/U
158  {
159  if (row_elimination[col] == elimination_run) // this entry is substituted in this run
160  {
161  elim_col_buffer.set(nnz_index, col);
162  elim_elements_buffer[nnz_index] = setup_U ? elements[i] / diagonal_buf[it->first] : elements[i];
163  ++nnz_index;
164  }
165  }
166  }
167 
168  elim_row_buffer.set(++k, nnz_index);
169  }
170 
171  //
172  // Wrap in memory_handles:
173  //
174  viennacl::backend::memory_create(row_index_arrays.back(), elim_row_index_array.raw_size(), viennacl::traits::context(row_index_arrays.back()), elim_row_index_array.get());
175  viennacl::backend::memory_create(row_buffers.back(), elim_row_buffer.raw_size(), viennacl::traits::context(row_buffers.back()), elim_row_buffer.get());
176  viennacl::backend::memory_create(col_buffers.back(), elim_col_buffer.raw_size(), viennacl::traits::context(col_buffers.back()), elim_col_buffer.get());
177  viennacl::backend::memory_create(element_buffers.back(), sizeof(NumericT) * elim_elements_buffer.size(), viennacl::traits::context(element_buffers.back()), &(elim_elements_buffer[0]));
178  }
179 
180  // Print some info:
181  //std::cout << "Eliminated columns in run " << elimination_run << ": " << num_tainted_cols << " (tainted columns: " << num_tainted_cols << ")" << std::endl;
182  //summed_rows += eliminated_rows_in_run;
183  //if (eliminated_rows_in_run == 0)
184  // break;
185  }
186  //std::cout << "Eliminated rows: " << summed_rows << " out of " << row_elimination.size() << std::endl;
187 }
188 
189 
190 template<typename NumericT, unsigned int AlignmentV>
192  viennacl::vector<NumericT> const & diagonal_LU,
193  std::list<viennacl::backend::mem_handle> & row_index_arrays,
194  std::list<viennacl::backend::mem_handle> & row_buffers,
195  std::list<viennacl::backend::mem_handle> & col_buffers,
196  std::list<viennacl::backend::mem_handle> & element_buffers,
197  std::list<vcl_size_t> & row_elimination_num_list)
198 {
199  level_scheduling_setup_impl(LU, diagonal_LU, row_index_arrays, row_buffers, col_buffers, element_buffers, row_elimination_num_list, false);
200 }
201 
202 
203 //
204 // Multifrontal setup of U:
205 //
206 
207 template<typename NumericT, unsigned int AlignmentV>
209  viennacl::vector<NumericT> const & diagonal_LU,
210  std::list<viennacl::backend::mem_handle> & row_index_arrays,
211  std::list<viennacl::backend::mem_handle> & row_buffers,
212  std::list<viennacl::backend::mem_handle> & col_buffers,
213  std::list<viennacl::backend::mem_handle> & element_buffers,
214  std::list<vcl_size_t> & row_elimination_num_list)
215 {
216  level_scheduling_setup_impl(LU, diagonal_LU, row_index_arrays, row_buffers, col_buffers, element_buffers, row_elimination_num_list, true);
217 }
218 
219 
220 //
221 // Multifrontal substitution (both L and U). Will partly be moved to single_threaded/opencl/cuda implementations
222 //
223 template<typename NumericT>
225  std::list<viennacl::backend::mem_handle> const & row_index_arrays,
226  std::list<viennacl::backend::mem_handle> const & row_buffers,
227  std::list<viennacl::backend::mem_handle> const & col_buffers,
228  std::list<viennacl::backend::mem_handle> const & element_buffers,
229  std::list<vcl_size_t> const & row_elimination_num_list)
230 {
231  typedef typename std::list< viennacl::backend::mem_handle >::const_iterator ListIterator;
232  ListIterator row_index_array_it = row_index_arrays.begin();
233  ListIterator row_buffers_it = row_buffers.begin();
234  ListIterator col_buffers_it = col_buffers.begin();
235  ListIterator element_buffers_it = element_buffers.begin();
236  typename std::list< vcl_size_t>::const_iterator row_elimination_num_it = row_elimination_num_list.begin();
237  for (vcl_size_t i=0; i<row_index_arrays.size(); ++i)
238  {
239  viennacl::linalg::detail::level_scheduling_substitute(vec, *row_index_array_it, *row_buffers_it, *col_buffers_it, *element_buffers_it, *row_elimination_num_it);
240 
241  ++row_index_array_it;
242  ++row_buffers_it;
243  ++col_buffers_it;
244  ++element_buffers_it;
245  ++row_elimination_num_it;
246  }
247 }
248 
249 
250 
251 
252 
253 } // namespace detail
254 } // namespace linalg
255 } // namespace viennacl
256 
257 
258 
259 
260 #endif
261 
262 
263 
Helper class implementing an array on the host. Default case: No conversion necessary.
Definition: util.hpp:92
const vcl_size_t & size1() const
Returns the number of rows.
Various little tools used here and there in ViennaCL.
This file provides the forward declarations for the main types used within ViennaCL.
void level_scheduling_setup_U(viennacl::compressed_matrix< NumericT, AlignmentV > const &LU, viennacl::vector< NumericT > const &diagonal_LU, std::list< viennacl::backend::mem_handle > &row_index_arrays, std::list< viennacl::backend::mem_handle > &row_buffers, std::list< viennacl::backend::mem_handle > &col_buffers, std::list< viennacl::backend::mem_handle > &element_buffers, std::list< vcl_size_t > &row_elimination_num_list)
Definition: common.hpp:208
void level_scheduling_substitute(viennacl::vector< NumericT > &vec, std::list< viennacl::backend::mem_handle > const &row_index_arrays, std::list< viennacl::backend::mem_handle > const &row_buffers, std::list< viennacl::backend::mem_handle > const &col_buffers, std::list< viennacl::backend::mem_handle > const &element_buffers, std::list< vcl_size_t > const &row_elimination_num_list)
Definition: common.hpp:224
const handle_type & handle() const
Returns the OpenCL handle to the matrix entry array.
const handle_type & handle1() const
Returns the OpenCL handle to the row index array.
float NumericT
Definition: bisect.cpp:40
Main namespace in ViennaCL. Holds all the basic types such as vector, matrix, etc. and defines operations upon them.
Definition: cpu_ram.hpp:34
void level_scheduling_setup_impl(viennacl::compressed_matrix< NumericT, AlignmentV > const &LU, viennacl::vector< NumericT > const &diagonal_LU, std::list< viennacl::backend::mem_handle > &row_index_arrays, std::list< viennacl::backend::mem_handle > &row_buffers, std::list< viennacl::backend::mem_handle > &col_buffers, std::list< viennacl::backend::mem_handle > &element_buffers, std::list< vcl_size_t > &row_elimination_num_list, bool setup_U)
Definition: common.hpp:51
Definition: blas3.hpp:36
const handle_type & handle2() const
Returns the OpenCL handle to the column index array.
std::size_t vcl_size_t
Definition: forwards.h:75
Common routines for single-threaded or OpenMP-enabled execution on CPU.
vector_expression< const matrix_base< NumericT, F >, const unsigned int, op_row > row(const matrix_base< NumericT, F > &A, unsigned int i)
Definition: matrix.hpp:900
viennacl::context context(T const &t)
Returns an ID for the currently active memory domain of an object.
Definition: context.hpp:40
Main abstraction class for multiple memory domains. Represents a buffer in either main RAM...
Definition: mem_handle.hpp:89
void level_scheduling_setup_L(viennacl::compressed_matrix< NumericT, AlignmentV > const &LU, viennacl::vector< NumericT > const &diagonal_LU, std::list< viennacl::backend::mem_handle > &row_index_arrays, std::list< viennacl::backend::mem_handle > &row_buffers, std::list< viennacl::backend::mem_handle > &col_buffers, std::list< viennacl::backend::mem_handle > &element_buffers, std::list< vcl_size_t > &row_elimination_num_list)
Definition: common.hpp:191
A sparse square matrix in compressed sparse rows format.
void memory_create(mem_handle &handle, vcl_size_t size_in_bytes, viennacl::context const &ctx, const void *host_ptr=NULL)
Creates an array of the specified size. If the second argument is provided, the buffer is initialized...
Definition: memory.hpp:87
const handle_type & handle() const
Returns the memory handle.
Definition: vector_def.hpp:128
Implementations of miscellaneous operations.
Main interface routines for memory management.