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
|
// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2012 Désiré Nuentsa-Wakam <desire.nuentsa_wakam@inria.fr>
//
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
/*
* NOTE: This file is the modified version of [s,d,c,z]memory.c files in SuperLU
* -- SuperLU routine (version 3.1) --
* Univ. of California Berkeley, Xerox Palo Alto Research Center,
* and Lawrence Berkeley National Lab.
* August 1, 2008
*
* Copyright (c) 1994 by Xerox Corporation. All rights reserved.
*
* THIS MATERIAL IS PROVIDED AS IS, WITH ABSOLUTELY NO WARRANTY
* EXPRESSED OR IMPLIED. ANY USE IS AT YOUR OWN RISK.
*
* Permission is hereby granted to use or copy this program for any
* purpose, provided the above notices are retained on all copies.
* Permission to modify the code and to distribute modified code is
* granted, provided the above notices are retained, and a notice that
* the code was modified is included with the above copyright notice.
*/
#ifndef EIGEN_SPARSELU_MEMORY
#define EIGEN_SPARSELU_MEMORY
namespace Eigen {
namespace internal {
enum { LUNoMarker = 3 };
enum {emptyIdxLU = -1};
inline Index LUnumTempV(Index& m, Index& w, Index& t, Index& b)
{
return (std::max)(m, (t+b)*w);
}
template< typename Scalar>
inline Index LUTempSpace(Index&m, Index& w)
{
return (2*w + 4 + LUNoMarker) * m * sizeof(Index) + (w + 1) * m * sizeof(Scalar);
}
/**
* Expand the existing storage to accommodate more fill-ins
* \param vec Valid pointer to the vector to allocate or expand
* \param[in,out] length At input, contain the current length of the vector that is to be increased. At output, length of the newly allocated vector
* \param[in] nbElts Current number of elements in the factors
* \param keep_prev 1: use length and do not expand the vector; 0: compute new_len and expand
* \param[in,out] num_expansions Number of times the memory has been expanded
*/
template <typename Scalar, typename StorageIndex>
template <typename VectorType>
Index SparseLUImpl<Scalar,StorageIndex>::expand(VectorType& vec, Index& length, Index nbElts, Index keep_prev, Index& num_expansions)
{
float alpha = 1.5; // Ratio of the memory increase
Index new_len; // New size of the allocated memory
if(num_expansions == 0 || keep_prev)
new_len = length ; // First time allocate requested
else
new_len = (std::max)(length+1,Index(alpha * length));
VectorType old_vec; // Temporary vector to hold the previous values
if (nbElts > 0 )
old_vec = vec.segment(0,nbElts);
//Allocate or expand the current vector
#ifdef EIGEN_EXCEPTIONS
try
#endif
{
vec.resize(new_len);
}
#ifdef EIGEN_EXCEPTIONS
catch(std::bad_alloc& )
#else
if(!vec.size())
#endif
{
if (!num_expansions)
{
// First time to allocate from LUMemInit()
// Let LUMemInit() deals with it.
return -1;
}
if (keep_prev)
{
// In this case, the memory length should not not be reduced
return new_len;
}
else
{
// Reduce the size and increase again
Index tries = 0; // Number of attempts
do
{
alpha = (alpha + 1)/2;
new_len = (std::max)(length+1,Index(alpha * length));
#ifdef EIGEN_EXCEPTIONS
try
#endif
{
vec.resize(new_len);
}
#ifdef EIGEN_EXCEPTIONS
catch(std::bad_alloc& )
#else
if (!vec.size())
#endif
{
tries += 1;
if ( tries > 10) return new_len;
}
} while (!vec.size());
}
}
//Copy the previous values to the newly allocated space
if (nbElts > 0)
vec.segment(0, nbElts) = old_vec;
length = new_len;
if(num_expansions) ++num_expansions;
return 0;
}
/**
* \brief Allocate various working space for the numerical factorization phase.
* \param m number of rows of the input matrix
* \param n number of columns
* \param annz number of initial nonzeros in the matrix
* \param lwork if lwork=-1, this routine returns an estimated size of the required memory
* \param glu persistent data to facilitate multiple factors : will be deleted later ??
* \param fillratio estimated ratio of fill in the factors
* \param panel_size Size of a panel
* \return an estimated size of the required memory if lwork = -1; otherwise, return the size of actually allocated memory when allocation failed, and 0 on success
* \note Unlike SuperLU, this routine does not support successive factorization with the same pattern and the same row permutation
*/
template <typename Scalar, typename StorageIndex>
Index SparseLUImpl<Scalar,StorageIndex>::memInit(Index m, Index n, Index annz, Index lwork, Index fillratio, Index panel_size, GlobalLU_t& glu)
{
Index& num_expansions = glu.num_expansions; //No memory expansions so far
num_expansions = 0;
glu.nzumax = glu.nzlumax = (std::min)(fillratio * (annz+1) / n, m) * n; // estimated number of nonzeros in U
glu.nzlmax = (std::max)(Index(4), fillratio) * (annz+1) / 4; // estimated nnz in L factor
// Return the estimated size to the user if necessary
Index tempSpace;
tempSpace = (2*panel_size + 4 + LUNoMarker) * m * sizeof(Index) + (panel_size + 1) * m * sizeof(Scalar);
if (lwork == emptyIdxLU)
{
Index estimated_size;
estimated_size = (5 * n + 5) * sizeof(Index) + tempSpace
+ (glu.nzlmax + glu.nzumax) * sizeof(Index) + (glu.nzlumax+glu.nzumax) * sizeof(Scalar) + n;
return estimated_size;
}
// Setup the required space
// First allocate Integer pointers for L\U factors
glu.xsup.resize(n+1);
glu.supno.resize(n+1);
glu.xlsub.resize(n+1);
glu.xlusup.resize(n+1);
glu.xusub.resize(n+1);
// Reserve memory for L/U factors
do
{
if( (expand<ScalarVector>(glu.lusup, glu.nzlumax, 0, 0, num_expansions)<0)
|| (expand<ScalarVector>(glu.ucol, glu.nzumax, 0, 0, num_expansions)<0)
|| (expand<IndexVector> (glu.lsub, glu.nzlmax, 0, 0, num_expansions)<0)
|| (expand<IndexVector> (glu.usub, glu.nzumax, 0, 1, num_expansions)<0) )
{
//Reduce the estimated size and retry
glu.nzlumax /= 2;
glu.nzumax /= 2;
glu.nzlmax /= 2;
if (glu.nzlumax < annz ) return glu.nzlumax;
}
} while (!glu.lusup.size() || !glu.ucol.size() || !glu.lsub.size() || !glu.usub.size());
++num_expansions;
return 0;
} // end LuMemInit
/**
* \brief Expand the existing storage
* \param vec vector to expand
* \param[in,out] maxlen On input, previous size of vec (Number of elements to copy ). on output, new size
* \param nbElts current number of elements in the vector.
* \param memtype Type of the element to expand
* \param num_expansions Number of expansions
* \return 0 on success, > 0 size of the memory allocated so far
*/
template <typename Scalar, typename StorageIndex>
template <typename VectorType>
Index SparseLUImpl<Scalar,StorageIndex>::memXpand(VectorType& vec, Index& maxlen, Index nbElts, MemType memtype, Index& num_expansions)
{
Index failed_size;
if (memtype == USUB)
failed_size = this->expand<VectorType>(vec, maxlen, nbElts, 1, num_expansions);
else
failed_size = this->expand<VectorType>(vec, maxlen, nbElts, 0, num_expansions);
if (failed_size)
return failed_size;
return 0 ;
}
} // end namespace internal
} // end namespace Eigen
#endif // EIGEN_SPARSELU_MEMORY
|
