/[escript]/branches/arrexp_trunk2098/escript/src/DataVector.cpp
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Revision 2119 - (show annotations)
Tue Dec 2 06:06:04 2008 UTC (11 years, 10 months ago) by jfenwick
File size: 7312 byte(s)
Branch commit.
Threading Wrapped Array through the code.
1
2 /*******************************************************
3 *
4 * Copyright (c) 2003-2008 by University of Queensland
5 * Earth Systems Science Computational Center (ESSCC)
6 * http://www.uq.edu.au/esscc
7 *
8 * Primary Business: Queensland, Australia
9 * Licensed under the Open Software License version 3.0
10 * http://www.opensource.org/licenses/osl-3.0.php
11 *
12 *******************************************************/
13
14
15 #include "DataVector.h"
16
17 #include "Taipan.h"
18 #include "DataException.h"
19 #include <boost/python/extract.hpp>
20 #include "DataTypes.h"
21 #include "WrappedArray.h"
22
23 #include <cassert>
24
25 using namespace std;
26 using namespace escript;
27 using namespace boost::python;
28
29 namespace escript {
30
31 Taipan arrayManager;
32
33 void releaseUnusedMemory()
34 {
35 arrayManager.release_unused_arrays();
36 }
37
38
39 DataVector::DataVector() :
40 m_size(0),
41 m_dim(0),
42 m_N(0),
43 m_array_data(0)
44 {
45 }
46
47 DataVector::DataVector(const DataVector& other) :
48 m_size(other.m_size),
49 m_dim(other.m_dim),
50 m_N(other.m_N),
51 m_array_data(0)
52 {
53 m_array_data = arrayManager.new_array(m_dim,m_N);
54 int i;
55 #pragma omp parallel for private(i) schedule(static)
56 for (i=0; i<m_size; i++) {
57 m_array_data[i] = other.m_array_data[i];
58 }
59 }
60
61 DataVector::DataVector(const DataVector::size_type size,
62 const DataVector::value_type val,
63 const DataVector::size_type blockSize) :
64 m_size(size),
65 m_dim(blockSize),
66 m_array_data(0)
67 {
68 resize(size, val, blockSize);
69 }
70
71 DataVector::~DataVector()
72 {
73 // dispose of data array
74 if (m_array_data!=0) {
75 arrayManager.delete_array(m_array_data);
76 }
77
78 // clear data members
79 m_size = -1;
80 m_dim = -1;
81 m_N = -1;
82 m_array_data = 0;
83 }
84
85 void
86 DataVector::resize(const DataVector::size_type newSize,
87 const DataVector::value_type newValue,
88 const DataVector::size_type newBlockSize)
89 {
90 assert(m_size >= 0);
91
92 if ( newBlockSize == 0) {
93 throw DataException("DataVector: invalid blockSize specified (newBlockSize)");
94 }
95
96 if ( (newSize % newBlockSize) != 0) {
97 throw DataException("DataVector: invalid blockSize specified");
98 }
99
100 if (m_array_data!=0) {
101 arrayManager.delete_array(m_array_data);
102 }
103
104 m_size = newSize;
105 m_dim = newBlockSize;
106 m_N = newSize / newBlockSize;
107 m_array_data = arrayManager.new_array(m_dim,m_N);
108
109 int i;
110 #pragma omp parallel for private(i) schedule(static)
111 for (i=0; i<m_size; i++) {
112 m_array_data[i] = newValue;
113 }
114 }
115
116 DataVector&
117 DataVector::operator=(const DataVector& other)
118 {
119 assert(m_size >= 0);
120
121 if (m_array_data!=0) {
122 arrayManager.delete_array(m_array_data);
123 }
124
125 m_size = other.m_size;
126 m_dim = other.m_dim;
127 m_N = other.m_N;
128
129 m_array_data = arrayManager.new_array(m_dim,m_N);
130 int i;
131 #pragma omp parallel for private(i) schedule(static)
132 for (i=0; i<m_size; i++) {
133 m_array_data[i] = other.m_array_data[i];
134 }
135
136 return *this;
137 }
138
139 bool
140 DataVector::operator==(const DataVector& other) const
141 {
142 assert(m_size >= 0);
143
144 if (m_size!=other.m_size) {
145 return false;
146 }
147 if (m_dim!=other.m_dim) {
148 return false;
149 }
150 if (m_N!=other.m_N) {
151 return false;
152 }
153 for (int i=0; i<m_size; i++) {
154 if (m_array_data[i] != other.m_array_data[i]) {
155 return false;
156 }
157 }
158 return true;
159 }
160
161 bool
162 DataVector::operator!=(const DataVector& other) const
163 {
164 return !(*this==other);
165 }
166
167
168 void
169 DataVector::copyFromArray(const WrappedArray& value)
170 {
171 using DataTypes::ValueType;
172 if (m_array_data!=0) {
173 arrayManager.delete_array(m_array_data);
174 }
175 DataTypes::ShapeType tempShape=value.getShape();
176 DataVector::size_type nelements=DataTypes::noValues(tempShape);
177
178 m_array_data = arrayManager.new_array(1,nelements);
179
180 int si=0,sj=0,sk=0,sl=0; // bounds for each dimension of the shape
181
182
183 if (value.getRank()==0) {
184 m_array_data[0]=value.getElt();
185 } else if (value.getRank()==1) {
186 si=tempShape[0];
187 for (ValueType::size_type i=0;i<si;i++) {
188 m_array_data[i]=value.getElt(i);
189 }
190 } else if (value.getRank()==2) {
191 si=tempShape[0];
192 sj=tempShape[1];
193 for (ValueType::size_type i=0;i<si;i++) {
194 for (ValueType::size_type j=0;j<sj;j++) {
195 m_array_data[DataTypes::getRelIndex(tempShape,i,j)]=value.getElt(i,j);
196 }
197 }
198 } else if (value.getRank()==3) {
199 si=tempShape[0];
200 sj=tempShape[1];
201 sk=tempShape[2];
202 for (ValueType::size_type i=0;i<si;i++) {
203 for (ValueType::size_type j=0;j<sj;j++) {
204 for (ValueType::size_type k=0;k<sk;k++) {
205 m_array_data[DataTypes::getRelIndex(tempShape,i,j,k)]=value.getElt(i,j,k);
206 }
207 }
208 }
209 } else if (value.getRank()==4) {
210 si=tempShape[0];
211 sj=tempShape[1];
212 sk=tempShape[2];
213 sl=tempShape[3];
214 for (ValueType::size_type i=0;i<si;i++) {
215 for (ValueType::size_type j=0;j<sj;j++) {
216 for (ValueType::size_type k=0;k<sk;k++) {
217 for (ValueType::size_type l=0;l<sl;l++) {
218 m_array_data[DataTypes::getRelIndex(tempShape,i,j,k,l)]=value.getElt(i,j,k,l);
219 }
220 }
221 }
222 }
223 }
224 m_size=nelements; // total amount of elements
225 m_dim=m_size; // elements per sample
226 m_N=1; // number of samples
227 }
228
229 void
230 DataVector::copyFromNumArray(const boost::python::numeric::array& value)
231 {
232 using DataTypes::ValueType;
233 if (m_array_data!=0) {
234 arrayManager.delete_array(m_array_data);
235 }
236
237
238 m_array_data = arrayManager.new_array(1,value.nelements());
239
240 int si=0,sj=0,sk=0,sl=0; // bounds for each dimension of the shape
241 DataTypes::ShapeType tempShape;
242 for (int i=0; i<value.getrank(); i++) {
243 tempShape.push_back(extract<int>(value.getshape()[i]));
244 }
245
246 if (value.getrank()==0) {
247 m_array_data[0]=extract<double>(value[value.getshape()]);
248 } else if (value.getrank()==1) {
249 si=tempShape[0];
250 for (ValueType::size_type i=0;i<si;i++) {
251 m_array_data[i]=extract<double>(value[i]);
252 }
253 } else if (value.getrank()==2) {
254 si=tempShape[0];
255 sj=tempShape[1];
256 for (ValueType::size_type i=0;i<si;i++) {
257 for (ValueType::size_type j=0;j<sj;j++) {
258 m_array_data[DataTypes::getRelIndex(tempShape,i,j)]=extract<double>(value[i][j]);
259 }
260 }
261 } else if (value.getrank()==3) {
262 si=tempShape[0];
263 sj=tempShape[1];
264 sk=tempShape[2];
265 for (ValueType::size_type i=0;i<si;i++) {
266 for (ValueType::size_type j=0;j<sj;j++) {
267 for (ValueType::size_type k=0;k<sk;k++) {
268 m_array_data[DataTypes::getRelIndex(tempShape,i,j,k)]=extract<double>(value[i][j][k]);
269 }
270 }
271 }
272 } else if (value.getrank()==4) {
273 si=tempShape[0];
274 sj=tempShape[1];
275 sk=tempShape[2];
276 sl=tempShape[3];
277 for (ValueType::size_type i=0;i<si;i++) {
278 for (ValueType::size_type j=0;j<sj;j++) {
279 for (ValueType::size_type k=0;k<sk;k++) {
280 for (ValueType::size_type l=0;l<sl;l++) {
281 m_array_data[DataTypes::getRelIndex(tempShape,i,j,k,l)]=extract<double>(value[i][j][k][l]);
282 }
283 }
284 }
285 }
286 }
287 m_size=value.nelements(); // total amount of elements
288 m_dim=m_size; // elements per sample
289 m_N=1; // number of samples
290 }
291
292
293
294 } // end of namespace

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