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trunk/escript/src/DataLazy.cpp revision 2082 by caltinay, Fri Nov 21 01:46:05 2008 UTC trunk/escriptcore/src/DataLazy.cpp revision 6057 by jfenwick, Thu Mar 10 06:00:58 2016 UTC
# Line 1  Line 1 
1    
2  /*******************************************************  /*****************************************************************************
3  *  *
4  * Copyright (c) 2003-2008 by University of Queensland  * Copyright (c) 2003-2016 by The University of Queensland
5  * Earth Systems Science Computational Center (ESSCC)  * http://www.uq.edu.au
 * http://www.uq.edu.au/esscc  
6  *  *
7  * Primary Business: Queensland, Australia  * Primary Business: Queensland, Australia
8  * Licensed under the Open Software License version 3.0  * Licensed under the Open Software License version 3.0
9  * http://www.opensource.org/licenses/osl-3.0.php  * http://www.opensource.org/licenses/osl-3.0.php
10  *  *
11  *******************************************************/  * Development until 2012 by Earth Systems Science Computational Center (ESSCC)
12    * Development 2012-2013 by School of Earth Sciences
13    * Development from 2014 by Centre for Geoscience Computing (GeoComp)
14    *
15    *****************************************************************************/
16    
17  #include "DataLazy.h"  #include "DataLazy.h"
18    #include "Data.h"
19    #include "DataTypes.h"
20    #include "EscriptParams.h"
21    #include "FunctionSpace.h"
22    #include "UnaryFuncs.h"    // for escript::fsign
23    #include "Utils.h"
24    #include "DataMaths.h"
25    
26  #ifdef USE_NETCDF  #ifdef USE_NETCDF
27  #include <netcdfcpp.h>  #include <netcdfcpp.h>
28  #endif  #endif
29  #ifdef PASO_MPI  
30  #include <mpi.h>  #include <iomanip> // for some fancy formatting in debug
31  #endif  
32  #ifdef _OPENMP  using namespace escript::DataTypes;
33  #include <omp.h>  
34  #endif  #define NO_ARG
35  #include "FunctionSpace.h"  
36  #include "DataTypes.h"  // #define LAZYDEBUG(X) if (privdebug){X;}
37  #include "Data.h"  #define LAZYDEBUG(X)
38  #include "UnaryFuncs.h"     // for escript::fsign  namespace
39  #include "Utils.h"  {
40    bool privdebug=false;
41    
42    #define ENABLEDEBUG privdebug=true;
43    #define DISABLEDEBUG privdebug=false;
44    }
45    
46    // #define SIZELIMIT if ((m_height>escript::escriptParams.getTOO_MANY_LEVELS()) || (m_children>escript::escriptParams.getTOO_MANY_NODES())) {cerr << "\n!!!!!!! SIZE LIMIT EXCEEDED " << m_children << ";" << m_height << endl << toString() << endl;resolveToIdentity();}
47    
48    // #define SIZELIMIT if ((m_height>escript::escriptParams.getTOO_MANY_LEVELS()) || (m_children>escript::escriptParams.getTOO_MANY_NODES())) {cerr << "SIZE LIMIT EXCEEDED " << m_height << endl;resolveToIdentity();}
49    
50    
51    #define SIZELIMIT if (m_height>escript::escriptParams.getTOO_MANY_LEVELS())  {if (escript::escriptParams.getLAZY_VERBOSE()){cerr << "SIZE LIMIT EXCEEDED height=" << m_height << endl;}resolveToIdentity();}
52    
53  /*  /*
54  How does DataLazy work?  How does DataLazy work?
# Line 39  A special operation, IDENTITY, stores an Line 61  A special operation, IDENTITY, stores an
61  This means that all "internal" nodes in the structure are instances of DataLazy.  This means that all "internal" nodes in the structure are instances of DataLazy.
62    
63  Each operation has a string representation as well as an opgroup - eg G_IDENTITY, G_BINARY, ...  Each operation has a string representation as well as an opgroup - eg G_IDENTITY, G_BINARY, ...
64  Note that IDENITY is not considered a unary operation.  Note that IDENTITY is not considered a unary operation.
65    
66  I am avoiding calling the structure formed a tree because it is not guaranteed to be one (eg c=a+a).  I am avoiding calling the structure formed a tree because it is not guaranteed to be one (eg c=a+a).
67  It must however form a DAG (directed acyclic graph).  It must however form a DAG (directed acyclic graph).
# Line 47  I will refer to individual DataLazy obje Line 69  I will refer to individual DataLazy obje
69    
70  Each node also stores:  Each node also stores:
71  - m_readytype \in {'E','T','C','?'} ~ indicates what sort of DataReady would be produced if the expression was  - m_readytype \in {'E','T','C','?'} ~ indicates what sort of DataReady would be produced if the expression was
72      evaluated.          evaluated.
73  - m_buffsrequired ~ the larged number of samples which would need to be kept simultaneously in order to  - m_buffsrequired ~ the large number of samples which would need to be kept simultaneously in order to
74      evaluate the expression.          evaluate the expression.
75  - m_samplesize ~ the number of doubles stored in a sample.  - m_samplesize ~ the number of doubles stored in a sample.
76    
77  When a new node is created, the above values are computed based on the values in the child nodes.  When a new node is created, the above values are computed based on the values in the child nodes.
# Line 70  The convention that I use, is that the r Line 92  The convention that I use, is that the r
92  For expressions which evaluate to Constant or Tagged, there is a different evaluation method.  For expressions which evaluate to Constant or Tagged, there is a different evaluation method.
93  The collapse method invokes the (non-lazy) operations on the Data class to evaluate the expression.  The collapse method invokes the (non-lazy) operations on the Data class to evaluate the expression.
94    
95  To add a new operator you need to do the following (plus anything I might have forgotten):  To add a new operator you need to do the following (plus anything I might have forgotten - adding a new group for example):
96  1) Add to the ES_optype.  1) Add to the ES_optype.
97  2) determine what opgroup your operation belongs to (X)  2) determine what opgroup your operation belongs to (X)
98  3) add a string for the op to the end of ES_opstrings  3) add a string for the op to the end of ES_opstrings
# Line 90  namespace escript Line 112  namespace escript
112  namespace  namespace
113  {  {
114    
115    
116    // enabling this will print out when ever the maximum stacksize used by resolve increases
117    // it assumes _OPENMP is also in use
118    //#define LAZY_STACK_PROF
119    
120    
121    
122    #ifndef _OPENMP
123      #ifdef LAZY_STACK_PROF
124      #undef LAZY_STACK_PROF
125      #endif
126    #endif
127    
128    
129    #ifdef LAZY_STACK_PROF
130    std::vector<void*> stackstart(getNumberOfThreads());
131    std::vector<void*> stackend(getNumberOfThreads());
132    size_t maxstackuse=0;
133    #endif
134    
135  enum ES_opgroup  enum ES_opgroup
136  {  {
137     G_UNKNOWN,     G_UNKNOWN,
138     G_IDENTITY,     G_IDENTITY,
139     G_BINARY,        // pointwise operations with two arguments     G_BINARY,            // pointwise operations with two arguments
140     G_UNARY,     // pointwise operations with one argument     G_UNARY,             // pointwise operations with one argument
141     G_NP1OUT,        // non-pointwise op with one output     G_UNARY_P,           // pointwise operations with one argument, requiring a parameter
142     G_TENSORPROD     // general tensor product     G_NP1OUT,            // non-pointwise op with one output
143       G_NP1OUT_P,          // non-pointwise op with one output requiring a parameter
144       G_TENSORPROD,        // general tensor product
145       G_NP1OUT_2P,         // non-pointwise op with one output requiring two params
146       G_REDUCTION,         // non-pointwise unary op with a scalar output
147       G_CONDEVAL
148  };  };
149    
150    
151    
152    
153  string ES_opstrings[]={"UNKNOWN","IDENTITY","+","-","*","/","^",  string ES_opstrings[]={"UNKNOWN","IDENTITY","+","-","*","/","^",
154              "sin","cos","tan",                          "sin","cos","tan",
155              "asin","acos","atan","sinh","cosh","tanh","erf",                          "asin","acos","atan","sinh","cosh","tanh","erf",
156              "asinh","acosh","atanh",                          "asinh","acosh","atanh",
157              "log10","log","sign","abs","neg","pos","exp","sqrt",                          "log10","log","sign","abs","neg","pos","exp","sqrt",
158              "1/","where>0","where<0","where>=0","where<=0",                          "1/","where>0","where<0","where>=0","where<=0", "where<>0","where=0",
159              "symmetric","nonsymmetric",                          "symmetric","nonsymmetric",
160              "prod"};                          "prod",
161  int ES_opcount=36;                          "transpose", "trace",
162                            "swapaxes",
163                            "minval", "maxval",
164                            "condEval"};
165    int ES_opcount=44;
166  ES_opgroup opgroups[]={G_UNKNOWN,G_IDENTITY,G_BINARY,G_BINARY,G_BINARY,G_BINARY, G_BINARY,  ES_opgroup opgroups[]={G_UNKNOWN,G_IDENTITY,G_BINARY,G_BINARY,G_BINARY,G_BINARY, G_BINARY,
167              G_UNARY,G_UNARY,G_UNARY, //10                          G_UNARY,G_UNARY,G_UNARY, //10
168              G_UNARY,G_UNARY,G_UNARY,G_UNARY,G_UNARY,G_UNARY,G_UNARY,    // 17                          G_UNARY,G_UNARY,G_UNARY,G_UNARY,G_UNARY,G_UNARY,G_UNARY,        // 17
169              G_UNARY,G_UNARY,G_UNARY,                    // 20                          G_UNARY,G_UNARY,G_UNARY,                                        // 20
170              G_UNARY,G_UNARY,G_UNARY,G_UNARY,G_UNARY,G_UNARY,G_UNARY,G_UNARY,        // 28                          G_UNARY,G_UNARY,G_UNARY,G_UNARY,G_UNARY,G_UNARY,G_UNARY,G_UNARY,        // 28
171              G_UNARY,G_UNARY,G_UNARY,G_UNARY,G_UNARY,            // 33                          G_UNARY,G_UNARY,G_UNARY,G_UNARY,G_UNARY, G_UNARY_P, G_UNARY_P,          // 35
172              G_NP1OUT,G_NP1OUT,                          G_NP1OUT,G_NP1OUT,
173              G_TENSORPROD};                          G_TENSORPROD,
174                            G_NP1OUT_P, G_NP1OUT_P,
175                            G_NP1OUT_2P,
176                            G_REDUCTION, G_REDUCTION,
177                            G_CONDEVAL};
178  inline  inline
179  ES_opgroup  ES_opgroup
180  getOpgroup(ES_optype op)  getOpgroup(ES_optype op)
# Line 131  getOpgroup(ES_optype op) Line 186  getOpgroup(ES_optype op)
186  FunctionSpace  FunctionSpace
187  resultFS(DataAbstract_ptr left, DataAbstract_ptr right, ES_optype op)  resultFS(DataAbstract_ptr left, DataAbstract_ptr right, ES_optype op)
188  {  {
189      // perhaps this should call interpolate and throw or something?          // perhaps this should call interpolate and throw or something?
190      // maybe we need an interpolate node -          // maybe we need an interpolate node -
191      // that way, if interpolate is required in any other op we can just throw a          // that way, if interpolate is required in any other op we can just throw a
192      // programming error exception.          // programming error exception.
193    
194    FunctionSpace l=left->getFunctionSpace();    FunctionSpace l=left->getFunctionSpace();
195    FunctionSpace r=right->getFunctionSpace();    FunctionSpace r=right->getFunctionSpace();
196    if (l!=r)    if (l!=r)
197    {    {
198      if (r.probeInterpolation(l))      signed char res=r.getDomain()->preferredInterpolationOnDomain(r.getTypeCode(), l.getTypeCode());
199        if (res==1)
200      {      {
201      return l;          return l;
202      }      }
203      if (l.probeInterpolation(r))      if (res==-1)
204      {      {
205      return r;          return r;
206      }      }
207      throw DataException("Cannot interpolate between the FunctionSpaces given for operation "+opToString(op)+".");      throw DataException("Cannot interpolate between the FunctionSpaces given for operation "+opToString(op)+".");
208    }    }
# Line 158  resultFS(DataAbstract_ptr left, DataAbst Line 214  resultFS(DataAbstract_ptr left, DataAbst
214  DataTypes::ShapeType  DataTypes::ShapeType
215  resultShape(DataAbstract_ptr left, DataAbstract_ptr right, ES_optype op)  resultShape(DataAbstract_ptr left, DataAbstract_ptr right, ES_optype op)
216  {  {
217      if (left->getShape()!=right->getShape())          if (left->getShape()!=right->getShape())
218      {          {
219        if ((getOpgroup(op)!=G_BINARY) && (getOpgroup(op)!=G_NP1OUT))            if ((getOpgroup(op)!=G_BINARY) && (getOpgroup(op)!=G_NP1OUT))
220        {            {
221          throw DataException("Shapes not the name - shapes must match for (point)binary operations.");                  throw DataException("Shapes not the name - shapes must match for (point)binary operations.");
222        }            }
223        if (left->getRank()==0)   // we need to allow scalar * anything  
224        {            if (left->getRank()==0)       // we need to allow scalar * anything
225          return right->getShape();            {
226        }                  return right->getShape();
227        if (right->getRank()==0)            }
228        {            if (right->getRank()==0)
229          return left->getShape();            {
230        }                  return left->getShape();
231        throw DataException("Shapes not the same - arguments must have matching shapes (or be scalars) for (point)binary operations on lazy data.");            }
232      }            throw DataException("Shapes not the same - arguments must have matching shapes (or be scalars) for (point)binary operations on lazy data.");
233      return left->getShape();          }
234            return left->getShape();
235    }
236    
237    // return the shape for "op left"
238    
239    DataTypes::ShapeType
240    resultShape(DataAbstract_ptr left, ES_optype op, int axis_offset)
241    {
242            switch(op)
243            {
244            case TRANS:
245               {                    // for the scoping of variables
246                    const DataTypes::ShapeType& s=left->getShape();
247                    DataTypes::ShapeType sh;
248                    int rank=left->getRank();
249                    if (axis_offset<0 || axis_offset>rank)
250                    {
251                stringstream e;
252                e << "Error - Data::transpose must have 0 <= axis_offset <= rank=" << rank;
253                throw DataException(e.str());
254            }
255            for (int i=0; i<rank; i++)
256                    {
257                       int index = (axis_offset+i)%rank;
258               sh.push_back(s[index]); // Append to new shape
259            }
260                    return sh;
261               }
262            break;
263            case TRACE:
264               {
265                    int rank=left->getRank();
266                    if (rank<2)
267                    {
268                       throw DataException("Trace can only be computed for objects with rank 2 or greater.");
269                    }
270                    if ((axis_offset>rank-2) || (axis_offset<0))
271                    {
272                       throw DataException("Trace: axis offset must lie between 0 and rank-2 inclusive.");
273                    }
274                    if (rank==2)
275                    {
276                       return DataTypes::scalarShape;
277                    }
278                    else if (rank==3)
279                    {
280                       DataTypes::ShapeType sh;
281                       if (axis_offset==0)
282                       {
283                            sh.push_back(left->getShape()[2]);
284                       }
285                       else         // offset==1
286                       {
287                            sh.push_back(left->getShape()[0]);
288                       }
289                       return sh;
290                    }
291                    else if (rank==4)
292                    {
293                       DataTypes::ShapeType sh;
294                       const DataTypes::ShapeType& s=left->getShape();
295                       if (axis_offset==0)
296                       {
297                            sh.push_back(s[2]);
298                            sh.push_back(s[3]);
299                       }
300                       else if (axis_offset==1)
301                       {
302                            sh.push_back(s[0]);
303                            sh.push_back(s[3]);
304                       }
305                       else         // offset==2
306                       {
307                            sh.push_back(s[0]);
308                            sh.push_back(s[1]);
309                       }
310                       return sh;
311                    }
312                    else            // unknown rank
313                    {
314                       throw DataException("Error - Data::trace can only be calculated for rank 2, 3 or 4 object.");
315                    }
316               }
317            break;
318            default:
319            throw DataException("Programmer error - resultShape(left,op) can't compute shapes for operator "+opToString(op)+".");
320            }
321    }
322    
323    DataTypes::ShapeType
324    SwapShape(DataAbstract_ptr left, const int axis0, const int axis1)
325    {
326         // This code taken from the Data.cpp swapaxes() method
327         // Some of the checks are probably redundant here
328         int axis0_tmp,axis1_tmp;
329         const DataTypes::ShapeType& s=left->getShape();
330         DataTypes::ShapeType out_shape;
331         // Here's the equivalent of python s_out=s[axis_offset:]+s[:axis_offset]
332         // which goes thru all shape vector elements starting with axis_offset (at index=rank wrap around to 0)
333         int rank=left->getRank();
334         if (rank<2) {
335            throw DataException("Error - Data::swapaxes argument must have at least rank 2.");
336         }
337         if (axis0<0 || axis0>rank-1) {
338            stringstream e;
339            e << "Error - Data::swapaxes: axis0 must be between 0 and rank-1=" << (rank-1);
340            throw DataException(e.str());
341         }
342         if (axis1<0 || axis1>rank-1) {
343            stringstream e;
344            e << "Error - Data::swapaxes: axis1 must be between 0 and rank-1=" << (rank-1);
345            throw DataException(e.str());
346         }
347         if (axis0 == axis1) {
348             throw DataException("Error - Data::swapaxes: axis indices must be different.");
349         }
350         if (axis0 > axis1) {
351             axis0_tmp=axis1;
352             axis1_tmp=axis0;
353         } else {
354             axis0_tmp=axis0;
355             axis1_tmp=axis1;
356         }
357         for (int i=0; i<rank; i++) {
358           if (i == axis0_tmp) {
359              out_shape.push_back(s[axis1_tmp]);
360           } else if (i == axis1_tmp) {
361              out_shape.push_back(s[axis0_tmp]);
362           } else {
363              out_shape.push_back(s[i]);
364           }
365         }
366        return out_shape;
367  }  }
368    
369    
370  // determine the output shape for the general tensor product operation  // determine the output shape for the general tensor product operation
371  // the additional parameters return information required later for the product  // the additional parameters return information required later for the product
372  // the majority of this code is copy pasted from C_General_Tensor_Product  // the majority of this code is copy pasted from C_General_Tensor_Product
373  DataTypes::ShapeType  DataTypes::ShapeType
374  GTPShape(DataAbstract_ptr left, DataAbstract_ptr right, int axis_offset, int transpose, int& SL, int& SM, int& SR)  GTPShape(DataAbstract_ptr left, DataAbstract_ptr right, int axis_offset, int transpose, int& SL, int& SM, int& SR)
375  {  {
376                
377    // Get rank and shape of inputs    // Get rank and shape of inputs
378    int rank0 = left->getRank();    int rank0 = left->getRank();
379    int rank1 = right->getRank();    int rank1 = right->getRank();
# Line 192  GTPShape(DataAbstract_ptr left, DataAbst Line 382  GTPShape(DataAbstract_ptr left, DataAbst
382    
383    // Prepare for the loops of the product and verify compatibility of shapes    // Prepare for the loops of the product and verify compatibility of shapes
384    int start0=0, start1=0;    int start0=0, start1=0;
385    if (transpose == 0)       {}    if (transpose == 0)           {}
386    else if (transpose == 1)  { start0 = axis_offset; }    else if (transpose == 1)      { start0 = axis_offset; }
387    else if (transpose == 2)  { start1 = rank1-axis_offset; }    else if (transpose == 2)      { start1 = rank1-axis_offset; }
388    else              { throw DataException("DataLazy GeneralTensorProduct Constructor: Error - transpose should be 0, 1 or 2"); }    else                          { throw DataException("DataLazy GeneralTensorProduct Constructor: Error - transpose should be 0, 1 or 2"); }
389    
390      if (rank0<axis_offset)
391      {
392            throw DataException("DataLazy GeneralTensorProduct Constructor: Error - rank of left < axisoffset");
393      }
394    
395    // Adjust the shapes for transpose    // Adjust the shapes for transpose
396    DataTypes::ShapeType tmpShape0(rank0);    // pre-sizing the vectors rather    DataTypes::ShapeType tmpShape0(rank0);        // pre-sizing the vectors rather
397    DataTypes::ShapeType tmpShape1(rank1);    // than using push_back    DataTypes::ShapeType tmpShape1(rank1);        // than using push_back
398    for (int i=0; i<rank0; i++)   { tmpShape0[i]=shape0[(i+start0)%rank0]; }    for (int i=0; i<rank0; i++)   { tmpShape0[i]=shape0[(i+start0)%rank0]; }
399    for (int i=0; i<rank1; i++)   { tmpShape1[i]=shape1[(i+start1)%rank1]; }    for (int i=0; i<rank1; i++)   { tmpShape1[i]=shape1[(i+start1)%rank1]; }
400    
401    // Prepare for the loops of the product    // Prepare for the loops of the product
402    SL=1, SM=1, SR=1;    SL=1, SM=1, SR=1;
403    for (int i=0; i<rank0-axis_offset; i++)   {    for (int i=0; i<rank0-axis_offset; i++)       {
404      SL *= tmpShape0[i];      SL *= tmpShape0[i];
405    }    }
406    for (int i=rank0-axis_offset; i<rank0; i++)   {    for (int i=rank0-axis_offset; i<rank0; i++)   {
407      if (tmpShape0[i] != tmpShape1[i-(rank0-axis_offset)]) {      if (tmpShape0[i] != tmpShape1[i-(rank0-axis_offset)]) {
408        throw DataException("C_GeneralTensorProduct: Error - incompatible shapes");        throw DataException("C_GeneralTensorProduct: Error - incompatible shapes");
409      }      }
410      SM *= tmpShape0[i];      SM *= tmpShape0[i];
411    }    }
412    for (int i=axis_offset; i<rank1; i++)     {    for (int i=axis_offset; i<rank1; i++)         {
413      SR *= tmpShape1[i];      SR *= tmpShape1[i];
414    }    }
415    
416    // Define the shape of the output (rank of shape is the sum of the loop ranges below)    // Define the shape of the output (rank of shape is the sum of the loop ranges below)
417    DataTypes::ShapeType shape2(rank0+rank1-2*axis_offset);      DataTypes::ShapeType shape2(rank0+rank1-2*axis_offset);      
418    {         // block to limit the scope of out_index    {                     // block to limit the scope of out_index
419       int out_index=0;       int out_index=0;
420       for (int i=0; i<rank0-axis_offset; i++, ++out_index) { shape2[out_index]=tmpShape0[i]; } // First part of arg_0_Z       for (int i=0; i<rank0-axis_offset; i++, ++out_index) { shape2[out_index]=tmpShape0[i]; } // First part of arg_0_Z
421       for (int i=axis_offset; i<rank1; i++, ++out_index)   { shape2[out_index]=tmpShape1[i]; } // Last part of arg_1_Z       for (int i=axis_offset; i<rank1; i++, ++out_index)   { shape2[out_index]=tmpShape1[i]; } // Last part of arg_1_Z
422    }    }
   return shape2;  
 }  
423    
424      if (shape2.size()>ESCRIPT_MAX_DATA_RANK)
425      {
426         ostringstream os;
427         os << "C_GeneralTensorProduct: Error - Attempt to create a rank " << shape2.size() << " object. The maximum rank is " << ESCRIPT_MAX_DATA_RANK << ".";
428         throw DataException(os.str());
429      }
430    
431  // determine the number of points in the result of "left op right"    return shape2;
 // note that determining the resultLength for G_TENSORPROD is more complex and will not be processed here  
 // size_t  
 // resultLength(DataAbstract_ptr left, DataAbstract_ptr right, ES_optype op)  
 // {  
 //    switch (getOpgroup(op))  
 //    {  
 //    case G_BINARY: return left->getLength();  
 //    case G_UNARY: return left->getLength();  
 //    case G_NP1OUT: return left->getLength();  
 //    default:  
 //  throw DataException("Programmer Error - attempt to getLength() for operator "+opToString(op)+".");  
 //    }  
 // }  
   
 // determine the number of samples requires to evaluate an expression combining left and right  
 // NP1OUT needs an extra buffer because we can't write the answers over the top of the input.  
 // The same goes for G_TENSORPROD  
 int  
 calcBuffs(const DataLazy_ptr& left, const DataLazy_ptr& right, ES_optype op)  
 {  
    switch(getOpgroup(op))  
    {  
    case G_IDENTITY: return 1;  
    case G_BINARY: return max(left->getBuffsRequired(),right->getBuffsRequired()+1);  
    case G_UNARY: return max(left->getBuffsRequired(),1);  
    case G_NP1OUT: return 1+max(left->getBuffsRequired(),1);  
    case G_TENSORPROD: return 1+max(left->getBuffsRequired(),right->getBuffsRequired()+1);  
    default:  
     throw DataException("Programmer Error - attempt to calcBuffs() for operator "+opToString(op)+".");  
    }  
432  }  }
433    
434    }       // end anonymous namespace
 }   // end anonymous namespace  
435    
436    
437    
# Line 279  opToString(ES_optype op) Line 446  opToString(ES_optype op)
446    return ES_opstrings[op];    return ES_opstrings[op];
447  }  }
448    
449    void DataLazy::LazyNodeSetup()
450    {
451    #ifdef _OPENMP
452        int numthreads=omp_get_max_threads();
453        m_samples.resize(numthreads*m_samplesize);
454        m_sampleids=new int[numthreads];
455        for (int i=0;i<numthreads;++i)
456        {
457            m_sampleids[i]=-1;  
458        }
459    #else
460        m_samples.resize(m_samplesize);
461        m_sampleids=new int[1];
462        m_sampleids[0]=-1;
463    #endif  // _OPENMP
464    }
465    
466    
467    // Creates an identity node
468  DataLazy::DataLazy(DataAbstract_ptr p)  DataLazy::DataLazy(DataAbstract_ptr p)
469      : parent(p->getFunctionSpace(),p->getShape()),          : parent(p->getFunctionSpace(),p->getShape())
470      m_op(IDENTITY),          ,m_sampleids(0),
471      m_axis_offset(0),          m_samples(1)
     m_transpose(0),  
     m_SL(0), m_SM(0), m_SR(0)  
472  {  {
473     if (p->isLazy())     if (p->isLazy())
474     {     {
475      // I don't want identity of Lazy.          // I don't want identity of Lazy.
476      // Question: Why would that be so bad?          // Question: Why would that be so bad?
477      // Answer: We assume that the child of ID is something we can call getVector on          // Answer: We assume that the child of ID is something we can call getVector on
478      throw DataException("Programmer error - attempt to create identity from a DataLazy.");          throw DataException("Programmer error - attempt to create identity from a DataLazy.");
479     }     }
480     else     else
481     {     {
482      m_id=dynamic_pointer_cast<DataReady>(p);          p->makeLazyShared();
483      if(p->isConstant()) {m_readytype='C';}          DataReady_ptr dr=dynamic_pointer_cast<DataReady>(p);
484      else if(p->isExpanded()) {m_readytype='E';}          makeIdentity(dr);
485      else if (p->isTagged()) {m_readytype='T';}  LAZYDEBUG(cout << "Wrapping " << dr.get() << " id=" << m_id.get() << endl;)
     else {throw DataException("Unknown DataReady instance in DataLazy constructor.");}  
486     }     }
487     m_buffsRequired=1;  LAZYDEBUG(cout << "(1)Lazy created with " << m_samplesize << endl;)
    m_samplesize=getNumDPPSample()*getNoValues();  
    m_maxsamplesize=m_samplesize;  
 cout << "(1)Lazy created with " << m_samplesize << endl;  
488  }  }
489    
   
   
   
490  DataLazy::DataLazy(DataAbstract_ptr left, ES_optype op)  DataLazy::DataLazy(DataAbstract_ptr left, ES_optype op)
491      : parent(left->getFunctionSpace(),left->getShape()),          : parent(left->getFunctionSpace(),(getOpgroup(op)!=G_REDUCTION)?left->getShape():DataTypes::scalarShape),
492      m_op(op),          m_op(op),
493      m_axis_offset(0),          m_axis_offset(0),
494      m_transpose(0),          m_transpose(0),
495      m_SL(0), m_SM(0), m_SR(0)          m_SL(0), m_SM(0), m_SR(0)
496  {  {
497     if ((getOpgroup(op)!=G_UNARY) && (getOpgroup(op)!=G_NP1OUT))     if ((getOpgroup(op)!=G_UNARY) && (getOpgroup(op)!=G_NP1OUT) && (getOpgroup(op)!=G_REDUCTION))
498     {     {
499      throw DataException("Programmer error - constructor DataLazy(left, op) will only process UNARY operations.");          throw DataException("Programmer error - constructor DataLazy(left, op) will only process UNARY operations.");
500     }     }
501    
502     DataLazy_ptr lleft;     DataLazy_ptr lleft;
503     if (!left->isLazy())     if (!left->isLazy())
504     {     {
505      lleft=DataLazy_ptr(new DataLazy(left));          lleft=DataLazy_ptr(new DataLazy(left));
506     }     }
507     else     else
508     {     {
509      lleft=dynamic_pointer_cast<DataLazy>(left);          lleft=dynamic_pointer_cast<DataLazy>(left);
510     }     }
511     m_readytype=lleft->m_readytype;     m_readytype=lleft->m_readytype;
512     m_left=lleft;     m_left=lleft;
    m_buffsRequired=calcBuffs(m_left, m_right,m_op); // yeah m_right will be null at this point  
513     m_samplesize=getNumDPPSample()*getNoValues();     m_samplesize=getNumDPPSample()*getNoValues();
514     m_maxsamplesize=max(m_samplesize,m_left->getMaxSampleSize());     m_children=m_left->m_children+1;
515       m_height=m_left->m_height+1;
516       LazyNodeSetup();
517       SIZELIMIT
518  }  }
519    
520    
521  // In this constructor we need to consider interpolation  // In this constructor we need to consider interpolation
522  DataLazy::DataLazy(DataAbstract_ptr left, DataAbstract_ptr right, ES_optype op)  DataLazy::DataLazy(DataAbstract_ptr left, DataAbstract_ptr right, ES_optype op)
523      : parent(resultFS(left,right,op), resultShape(left,right,op)),          : parent(resultFS(left,right,op), resultShape(left,right,op)),
524      m_op(op),          m_op(op),
525      m_SL(0), m_SM(0), m_SR(0)          m_SL(0), m_SM(0), m_SR(0)
526  {  {
527    LAZYDEBUG(cout << "Forming operator with " << left.get() << " " << right.get() << endl;)
528     if ((getOpgroup(op)!=G_BINARY))     if ((getOpgroup(op)!=G_BINARY))
529     {     {
530      throw DataException("Programmer error - constructor DataLazy(left, right, op) will only process BINARY operations.");          throw DataException("Programmer error - constructor DataLazy(left, right, op) will only process BINARY operations.");
531     }     }
532    
533     if (getFunctionSpace()!=left->getFunctionSpace())    // left needs to be interpolated     if (getFunctionSpace()!=left->getFunctionSpace())    // left needs to be interpolated
534     {     {
535      FunctionSpace fs=getFunctionSpace();          FunctionSpace fs=getFunctionSpace();
536      Data ltemp(left);          Data ltemp(left);
537      Data tmp(ltemp,fs);          Data tmp(ltemp,fs);
538      left=tmp.borrowDataPtr();          left=tmp.borrowDataPtr();
539     }     }
540     if (getFunctionSpace()!=right->getFunctionSpace())   // right needs to be interpolated     if (getFunctionSpace()!=right->getFunctionSpace())   // right needs to be interpolated
541     {     {
542      Data tmp(Data(right),getFunctionSpace());          Data tmp(Data(right),getFunctionSpace());
543      right=tmp.borrowDataPtr();          right=tmp.borrowDataPtr();
544    LAZYDEBUG(cout << "Right interpolation required " << right.get() << endl;)
545     }     }
546     left->operandCheck(*right);     left->operandCheck(*right);
547    
548     if (left->isLazy())          // the children need to be DataLazy. Wrap them in IDENTITY if required     if (left->isLazy())                  // the children need to be DataLazy. Wrap them in IDENTITY if required
549     {     {
550      m_left=dynamic_pointer_cast<DataLazy>(left);          m_left=dynamic_pointer_cast<DataLazy>(left);
551    LAZYDEBUG(cout << "Left is " << m_left->toString() << endl;)
552     }     }
553     else     else
554     {     {
555      m_left=DataLazy_ptr(new DataLazy(left));          m_left=DataLazy_ptr(new DataLazy(left));
556    LAZYDEBUG(cout << "Left " << left.get() << " wrapped " << m_left->m_id.get() << endl;)
557     }     }
558     if (right->isLazy())     if (right->isLazy())
559     {     {
560      m_right=dynamic_pointer_cast<DataLazy>(right);          m_right=dynamic_pointer_cast<DataLazy>(right);
561    LAZYDEBUG(cout << "Right is " << m_right->toString() << endl;)
562     }     }
563     else     else
564     {     {
565      m_right=DataLazy_ptr(new DataLazy(right));          m_right=DataLazy_ptr(new DataLazy(right));
566    LAZYDEBUG(cout << "Right " << right.get() << " wrapped " << m_right->m_id.get() << endl;)
567     }     }
568     char lt=m_left->m_readytype;     char lt=m_left->m_readytype;
569     char rt=m_right->m_readytype;     char rt=m_right->m_readytype;
570     if (lt=='E' || rt=='E')     if (lt=='E' || rt=='E')
571     {     {
572      m_readytype='E';          m_readytype='E';
573     }     }
574     else if (lt=='T' || rt=='T')     else if (lt=='T' || rt=='T')
575     {     {
576      m_readytype='T';          m_readytype='T';
577     }     }
578     else     else
579     {     {
580      m_readytype='C';          m_readytype='C';
581     }     }
582     m_samplesize=getNumDPPSample()*getNoValues();     m_samplesize=getNumDPPSample()*getNoValues();
583     m_maxsamplesize=max(max(m_samplesize,m_right->getMaxSampleSize()),m_left->getMaxSampleSize());       m_children=m_left->m_children+m_right->m_children+2;
584     m_buffsRequired=calcBuffs(m_left, m_right,m_op);     m_height=max(m_left->m_height,m_right->m_height)+1;
585  cout << "(3)Lazy created with " << m_samplesize << endl;     LazyNodeSetup();
586       SIZELIMIT
587    LAZYDEBUG(cout << "(3)Lazy created with " << m_samplesize << endl;)
588  }  }
589    
590  DataLazy::DataLazy(DataAbstract_ptr left, DataAbstract_ptr right, ES_optype op, int axis_offset, int transpose)  DataLazy::DataLazy(DataAbstract_ptr left, DataAbstract_ptr right, ES_optype op, int axis_offset, int transpose)
591      : parent(resultFS(left,right,op), GTPShape(left,right, axis_offset, transpose, m_SL,m_SM, m_SR)),          : parent(resultFS(left,right,op), GTPShape(left,right, axis_offset, transpose, m_SL,m_SM, m_SR)),
592      m_op(op),          m_op(op),
593      m_axis_offset(axis_offset),          m_axis_offset(axis_offset),
594      m_transpose(transpose)          m_transpose(transpose)
595  {  {
596     if ((getOpgroup(op)!=G_TENSORPROD))     if ((getOpgroup(op)!=G_TENSORPROD))
597     {     {
598      throw DataException("Programmer error - constructor DataLazy(left, right, op, ax, tr) will only process BINARY operations which require parameters.");          throw DataException("Programmer error - constructor DataLazy(left, right, op, ax, tr) will only process BINARY operations which require parameters.");
599     }     }
600     if ((transpose>2) || (transpose<0))     if ((transpose>2) || (transpose<0))
601     {     {
602      throw DataException("DataLazy GeneralTensorProduct constructor: Error - transpose should be 0, 1 or 2");          throw DataException("DataLazy GeneralTensorProduct constructor: Error - transpose should be 0, 1 or 2");
603     }     }
604     if (getFunctionSpace()!=left->getFunctionSpace())    // left needs to be interpolated     if (getFunctionSpace()!=left->getFunctionSpace())    // left needs to be interpolated
605     {     {
606      FunctionSpace fs=getFunctionSpace();          FunctionSpace fs=getFunctionSpace();
607      Data ltemp(left);          Data ltemp(left);
608      Data tmp(ltemp,fs);          Data tmp(ltemp,fs);
609      left=tmp.borrowDataPtr();          left=tmp.borrowDataPtr();
610     }     }
611     if (getFunctionSpace()!=right->getFunctionSpace())   // right needs to be interpolated     if (getFunctionSpace()!=right->getFunctionSpace())   // right needs to be interpolated
612     {     {
613      Data tmp(Data(right),getFunctionSpace());          Data tmp(Data(right),getFunctionSpace());
614      right=tmp.borrowDataPtr();          right=tmp.borrowDataPtr();
615     }     }
616     left->operandCheck(*right);  //    left->operandCheck(*right);
617    
618     if (left->isLazy())          // the children need to be DataLazy. Wrap them in IDENTITY if required     if (left->isLazy())                  // the children need to be DataLazy. Wrap them in IDENTITY if required
619     {     {
620      m_left=dynamic_pointer_cast<DataLazy>(left);          m_left=dynamic_pointer_cast<DataLazy>(left);
621     }     }
622     else     else
623     {     {
624      m_left=DataLazy_ptr(new DataLazy(left));          m_left=DataLazy_ptr(new DataLazy(left));
625     }     }
626     if (right->isLazy())     if (right->isLazy())
627     {     {
628      m_right=dynamic_pointer_cast<DataLazy>(right);          m_right=dynamic_pointer_cast<DataLazy>(right);
629     }     }
630     else     else
631     {     {
632      m_right=DataLazy_ptr(new DataLazy(right));          m_right=DataLazy_ptr(new DataLazy(right));
633     }     }
634     char lt=m_left->m_readytype;     char lt=m_left->m_readytype;
635     char rt=m_right->m_readytype;     char rt=m_right->m_readytype;
636     if (lt=='E' || rt=='E')     if (lt=='E' || rt=='E')
637     {     {
638      m_readytype='E';          m_readytype='E';
639     }     }
640     else if (lt=='T' || rt=='T')     else if (lt=='T' || rt=='T')
641     {     {
642      m_readytype='T';          m_readytype='T';
643     }     }
644     else     else
645     {     {
646      m_readytype='C';          m_readytype='C';
647     }     }
648     m_samplesize=getNumDPPSample()*getNoValues();     m_samplesize=getNumDPPSample()*getNoValues();
649     m_maxsamplesize=max(max(m_samplesize,m_right->getMaxSampleSize()),m_left->getMaxSampleSize());       m_children=m_left->m_children+m_right->m_children+2;
650     m_buffsRequired=calcBuffs(m_left, m_right,m_op);     m_height=max(m_left->m_height,m_right->m_height)+1;
651  cout << "(4)Lazy created with " << m_samplesize << endl;     LazyNodeSetup();
652       SIZELIMIT
653    LAZYDEBUG(cout << "(4)Lazy created with " << m_samplesize << endl;)
654  }  }
655    
656    
657  DataLazy::~DataLazy()  DataLazy::DataLazy(DataAbstract_ptr left, ES_optype op, int axis_offset)
658            : parent(left->getFunctionSpace(), resultShape(left,op, axis_offset)),
659            m_op(op),
660            m_axis_offset(axis_offset),
661            m_transpose(0),
662            m_tol(0)
663    {
664       if ((getOpgroup(op)!=G_NP1OUT_P))
665       {
666            throw DataException("Programmer error - constructor DataLazy(left, op, ax) will only process UNARY operations which require parameters.");
667       }
668       DataLazy_ptr lleft;
669       if (!left->isLazy())
670       {
671            lleft=DataLazy_ptr(new DataLazy(left));
672       }
673       else
674       {
675            lleft=dynamic_pointer_cast<DataLazy>(left);
676       }
677       m_readytype=lleft->m_readytype;
678       m_left=lleft;
679       m_samplesize=getNumDPPSample()*getNoValues();
680       m_children=m_left->m_children+1;
681       m_height=m_left->m_height+1;
682       LazyNodeSetup();
683       SIZELIMIT
684    LAZYDEBUG(cout << "(5)Lazy created with " << m_samplesize << endl;)
685    }
686    
687    DataLazy::DataLazy(DataAbstract_ptr left, ES_optype op, double tol)
688            : parent(left->getFunctionSpace(), left->getShape()),
689            m_op(op),
690            m_axis_offset(0),
691            m_transpose(0),
692            m_tol(tol)
693    {
694       if ((getOpgroup(op)!=G_UNARY_P))
695       {
696            throw DataException("Programmer error - constructor DataLazy(left, op, tol) will only process UNARY operations which require parameters.");
697       }
698       DataLazy_ptr lleft;
699       if (!left->isLazy())
700       {
701            lleft=DataLazy_ptr(new DataLazy(left));
702       }
703       else
704       {
705            lleft=dynamic_pointer_cast<DataLazy>(left);
706       }
707       m_readytype=lleft->m_readytype;
708       m_left=lleft;
709       m_samplesize=getNumDPPSample()*getNoValues();
710       m_children=m_left->m_children+1;
711       m_height=m_left->m_height+1;
712       LazyNodeSetup();
713       SIZELIMIT
714    LAZYDEBUG(cout << "(6)Lazy created with " << m_samplesize << endl;)
715    }
716    
717    
718    DataLazy::DataLazy(DataAbstract_ptr left, ES_optype op, const int axis0, const int axis1)
719            : parent(left->getFunctionSpace(), SwapShape(left,axis0,axis1)),
720            m_op(op),
721            m_axis_offset(axis0),
722            m_transpose(axis1),
723            m_tol(0)
724  {  {
725       if ((getOpgroup(op)!=G_NP1OUT_2P))
726       {
727            throw DataException("Programmer error - constructor DataLazy(left, op, tol) will only process UNARY operations which require two integer parameters.");
728       }
729       DataLazy_ptr lleft;
730       if (!left->isLazy())
731       {
732            lleft=DataLazy_ptr(new DataLazy(left));
733       }
734       else
735       {
736            lleft=dynamic_pointer_cast<DataLazy>(left);
737       }
738       m_readytype=lleft->m_readytype;
739       m_left=lleft;
740       m_samplesize=getNumDPPSample()*getNoValues();
741       m_children=m_left->m_children+1;
742       m_height=m_left->m_height+1;
743       LazyNodeSetup();
744       SIZELIMIT
745    LAZYDEBUG(cout << "(7)Lazy created with " << m_samplesize << endl;)
746  }  }
747    
748    
749  int  namespace
750  DataLazy::getBuffsRequired() const  {
751    
752        inline int max3(int a, int b, int c)
753        {
754            int t=(a>b?a:b);
755            return (t>c?t:c);
756    
757        }
758    }
759    
760    DataLazy::DataLazy(DataAbstract_ptr mask, DataAbstract_ptr left, DataAbstract_ptr right/*, double tol*/)
761            : parent(left->getFunctionSpace(), left->getShape()),
762            m_op(CONDEVAL),
763            m_axis_offset(0),
764            m_transpose(0),
765            m_tol(0)
766  {  {
767      return m_buffsRequired;  
768       DataLazy_ptr lmask;
769       DataLazy_ptr lleft;
770       DataLazy_ptr lright;
771       if (!mask->isLazy())
772       {
773            lmask=DataLazy_ptr(new DataLazy(mask));
774       }
775       else
776       {
777            lmask=dynamic_pointer_cast<DataLazy>(mask);
778       }
779       if (!left->isLazy())
780       {
781            lleft=DataLazy_ptr(new DataLazy(left));
782       }
783       else
784       {
785            lleft=dynamic_pointer_cast<DataLazy>(left);
786       }
787       if (!right->isLazy())
788       {
789            lright=DataLazy_ptr(new DataLazy(right));
790       }
791       else
792       {
793            lright=dynamic_pointer_cast<DataLazy>(right);
794       }
795       m_readytype=lmask->m_readytype;
796       if ((lleft->m_readytype!=lright->m_readytype) || (lmask->m_readytype!=lleft->m_readytype))
797       {
798            throw DataException("Programmer Error - condEval arguments must have the same readytype");
799       }
800       m_left=lleft;
801       m_right=lright;
802       m_mask=lmask;
803       m_samplesize=getNumDPPSample()*getNoValues();
804       m_children=m_left->m_children+m_right->m_children+m_mask->m_children+1;
805       m_height=max3(m_left->m_height,m_right->m_height,m_mask->m_height)+1;
806       LazyNodeSetup();
807       SIZELIMIT
808    LAZYDEBUG(cout << "(8)Lazy created with " << m_samplesize << endl;)
809  }  }
810    
811    
812  size_t  
813  DataLazy::getMaxSampleSize() const  DataLazy::~DataLazy()
814  {  {
815      return m_maxsamplesize;     delete[] m_sampleids;
816  }  }
817    
818    
819  /*  /*
820    \brief Evaluates the expression using methods on Data.    \brief Evaluates the expression using methods on Data.
821    This does the work for the collapse method.    This does the work for the collapse method.
822    For reasons of efficiency do not call this method on DataExpanded nodes.    For reasons of efficiency do not call this method on DataExpanded nodes.
823  */  */
824  DataReady_ptr  DataReady_ptr
825  DataLazy::collapseToReady()  DataLazy::collapseToReady() const
826  {  {
827    if (m_readytype=='E')    if (m_readytype=='E')
828    { // this is more an efficiency concern than anything else    {     // this is more an efficiency concern than anything else
829      throw DataException("Programmer Error - do not use collapse on Expanded data.");      throw DataException("Programmer Error - do not use collapse on Expanded data.");
830    }    }
831    if (m_op==IDENTITY)    if (m_op==IDENTITY)
# Line 511  DataLazy::collapseToReady() Line 843  DataLazy::collapseToReady()
843    switch(m_op)    switch(m_op)
844    {    {
845      case ADD:      case ADD:
846      result=left+right;          result=left+right;
847      break;          break;
848      case SUB:            case SUB:          
849      result=left-right;          result=left-right;
850      break;          break;
851      case MUL:            case MUL:          
852      result=left*right;          result=left*right;
853      break;          break;
854      case DIV:            case DIV:          
855      result=left/right;          result=left/right;
856      break;          break;
857      case SIN:      case SIN:
858      result=left.sin();            result=left.sin();      
859      break;          break;
860      case COS:      case COS:
861      result=left.cos();          result=left.cos();
862      break;          break;
863      case TAN:      case TAN:
864      result=left.tan();          result=left.tan();
865      break;          break;
866      case ASIN:      case ASIN:
867      result=left.asin();          result=left.asin();
868      break;          break;
869      case ACOS:      case ACOS:
870      result=left.acos();          result=left.acos();
871      break;          break;
872      case ATAN:      case ATAN:
873      result=left.atan();          result=left.atan();
874      break;          break;
875      case SINH:      case SINH:
876      result=left.sinh();          result=left.sinh();
877      break;          break;
878      case COSH:      case COSH:
879      result=left.cosh();          result=left.cosh();
880      break;          break;
881      case TANH:      case TANH:
882      result=left.tanh();          result=left.tanh();
883      break;          break;
884      case ERF:      case ERF:
885      result=left.erf();          result=left.erf();
886      break;          break;
887     case ASINH:     case ASINH:
888      result=left.asinh();          result=left.asinh();
889      break;          break;
890     case ACOSH:     case ACOSH:
891      result=left.acosh();          result=left.acosh();
892      break;          break;
893     case ATANH:     case ATANH:
894      result=left.atanh();          result=left.atanh();
895      break;          break;
896      case LOG10:      case LOG10:
897      result=left.log10();          result=left.log10();
898      break;          break;
899      case LOG:      case LOG:
900      result=left.log();          result=left.log();
901      break;          break;
902      case SIGN:      case SIGN:
903      result=left.sign();          result=left.sign();
904      break;          break;
905      case ABS:      case ABS:
906      result=left.abs();          result=left.abs();
907      break;          break;
908      case NEG:      case NEG:
909      result=left.neg();          result=left.neg();
910      break;          break;
911      case POS:      case POS:
912      // it doesn't mean anything for delayed.          // it doesn't mean anything for delayed.
913      // it will just trigger a deep copy of the lazy object          // it will just trigger a deep copy of the lazy object
914      throw DataException("Programmer error - POS not supported for lazy data.");          throw DataException("Programmer error - POS not supported for lazy data.");
915      break;          break;
916      case EXP:      case EXP:
917      result=left.exp();          result=left.exp();
918      break;          break;
919      case SQRT:      case SQRT:
920      result=left.sqrt();          result=left.sqrt();
921      break;          break;
922      case RECIP:      case RECIP:
923      result=left.oneOver();          result=left.oneOver();
924      break;          break;
925      case GZ:      case GZ:
926      result=left.wherePositive();          result=left.wherePositive();
927      break;          break;
928      case LZ:      case LZ:
929      result=left.whereNegative();          result=left.whereNegative();
930      break;          break;
931      case GEZ:      case GEZ:
932      result=left.whereNonNegative();          result=left.whereNonNegative();
933      break;          break;
934      case LEZ:      case LEZ:
935      result=left.whereNonPositive();          result=left.whereNonPositive();
936      break;          break;
937        case NEZ:
938            result=left.whereNonZero(m_tol);
939            break;
940        case EZ:
941            result=left.whereZero(m_tol);
942            break;
943      case SYM:      case SYM:
944      result=left.symmetric();          result=left.symmetric();
945      break;          break;
946      case NSYM:      case NSYM:
947      result=left.nonsymmetric();          result=left.nonsymmetric();
948      break;          break;
949      case PROD:      case PROD:
950      result=C_GeneralTensorProduct(left,right,m_axis_offset, m_transpose);          result=C_GeneralTensorProduct(left,right,m_axis_offset, m_transpose);
951      break;          break;
952        case TRANS:
953            result=left.transpose(m_axis_offset);
954            break;
955        case TRACE:
956            result=left.trace(m_axis_offset);
957            break;
958        case SWAP:
959            result=left.swapaxes(m_axis_offset, m_transpose);
960            break;
961        case MINVAL:
962            result=left.minval();
963            break;
964        case MAXVAL:
965            result=left.minval();
966            break;
967      default:      default:
968      throw DataException("Programmer error - collapseToReady does not know how to resolve operator "+opToString(m_op)+".");          throw DataException("Programmer error - collapseToReady does not know how to resolve operator "+opToString(m_op)+".");
969    }    }
970    return result.borrowReadyPtr();    return result.borrowReadyPtr();
971  }  }
# Line 624  DataLazy::collapseToReady() Line 977  DataLazy::collapseToReady()
977     the purpose of using DataLazy in the first place).     the purpose of using DataLazy in the first place).
978  */  */
979  void  void
980  DataLazy::collapse()  DataLazy::collapse() const
981  {  {
982    if (m_op==IDENTITY)    if (m_op==IDENTITY)
983    {    {
984      return;          return;
985    }    }
986    if (m_readytype=='E')    if (m_readytype=='E')
987    { // this is more an efficiency concern than anything else    {     // this is more an efficiency concern than anything else
988      throw DataException("Programmer Error - do not use collapse on Expanded data.");      throw DataException("Programmer Error - do not use collapse on Expanded data.");
989    }    }
990    m_id=collapseToReady();    m_id=collapseToReady();
991    m_op=IDENTITY;    m_op=IDENTITY;
992  }  }
993    
994  /*  // The result will be stored in m_samples
995    \brief Compute the value of the expression (unary operation) for the given sample.  // The return value is a pointer to the DataVector, offset is the offset within the return value
996    \return Vector which stores the value of the subexpression for the given sample.  const DataTypes::RealVectorType*
997    \param v A vector to store intermediate results.  DataLazy::resolveNodeSample(int tid, int sampleNo, size_t& roffset) const
998    \param offset Index in v to begin storing results.  {
999    \param sampleNo Sample number to evaluate.  LAZYDEBUG(cout << "Resolve sample " << toString() << endl;)
1000    \param roffset (output parameter) the offset in the return vector where the result begins.          // collapse so we have a 'E' node or an IDENTITY for some other type
1001      if (m_readytype!='E' && m_op!=IDENTITY)
1002    The return value will be an existing vector so do not deallocate it.    {
1003    If the result is stored in v it should be stored at the offset given.          collapse();
1004    Everything from offset to the end of v should be considered available for this method to use.    }
1005  */    if (m_op==IDENTITY)  
1006  DataTypes::ValueType*    {
1007  DataLazy::resolveUnary(ValueType& v, size_t offset, int sampleNo, size_t& roffset) const      const RealVectorType& vec=m_id->getVectorRO();
1008        roffset=m_id->getPointOffset(sampleNo, 0);
1009    #ifdef LAZY_STACK_PROF
1010    int x;
1011    if (&x<stackend[omp_get_thread_num()])
1012    {
1013           stackend[omp_get_thread_num()]=&x;
1014    }
1015    #endif
1016        return &(vec);
1017      }
1018      if (m_readytype!='E')
1019      {
1020        throw DataException("Programmer Error - Collapse did not produce an expanded node.");
1021      }
1022      if (m_sampleids[tid]==sampleNo)
1023      {
1024            roffset=tid*m_samplesize;
1025            return &(m_samples);            // sample is already resolved
1026      }
1027      m_sampleids[tid]=sampleNo;
1028    
1029      switch (getOpgroup(m_op))
1030      {
1031      case G_UNARY:
1032      case G_UNARY_P: return resolveNodeUnary(tid, sampleNo, roffset);
1033      case G_BINARY: return resolveNodeBinary(tid, sampleNo, roffset);
1034      case G_NP1OUT: return resolveNodeNP1OUT(tid, sampleNo, roffset);
1035      case G_NP1OUT_P: return resolveNodeNP1OUT_P(tid, sampleNo, roffset);
1036      case G_TENSORPROD: return resolveNodeTProd(tid, sampleNo, roffset);
1037      case G_NP1OUT_2P: return resolveNodeNP1OUT_2P(tid, sampleNo, roffset);
1038      case G_REDUCTION: return resolveNodeReduction(tid, sampleNo, roffset);
1039      case G_CONDEVAL: return resolveNodeCondEval(tid, sampleNo, roffset);
1040      default:
1041        throw DataException("Programmer Error - resolveSample does not know how to process "+opToString(m_op)+".");
1042      }
1043    }
1044    
1045    const DataTypes::RealVectorType*
1046    DataLazy::resolveNodeUnary(int tid, int sampleNo, size_t& roffset) const
1047  {  {
1048      // we assume that any collapsing has been done before we get here          // we assume that any collapsing has been done before we get here
1049      // since we only have one argument we don't need to think about only          // since we only have one argument we don't need to think about only
1050      // processing single points.          // processing single points.
1051            // we will also know we won't get identity nodes
1052    if (m_readytype!='E')    if (m_readytype!='E')
1053    {    {
1054      throw DataException("Programmer error - resolveUnary should only be called on expanded Data.");      throw DataException("Programmer error - resolveUnary should only be called on expanded Data.");
1055    }    }
1056    const ValueType* vleft=m_left->resolveSample(v,offset,sampleNo,roffset);    if (m_op==IDENTITY)
1057    const double* left=&((*vleft)[roffset]);    {
1058    double* result=&(v[offset]);      throw DataException("Programmer error - resolveNodeUnary should not be called on identity nodes.");
1059    roffset=offset;    }
1060      const DataTypes::RealVectorType* leftres=m_left->resolveNodeSample(tid, sampleNo, roffset);
1061      const double* left=&((*leftres)[roffset]);
1062      roffset=m_samplesize*tid;
1063      double* result=&(m_samples[roffset]);
1064    switch (m_op)    switch (m_op)
1065    {    {
1066      case SIN:        case SIN:  
1067      tensor_unary_operation<double (*)(double)>(m_samplesize, left, result, ::sin);          tensor_unary_operation<double (*)(double)>(m_samplesize, left, result, ::sin);
1068      break;          break;
1069      case COS:      case COS:
1070      tensor_unary_operation<double (*)(double)>(m_samplesize, left, result, ::cos);          tensor_unary_operation<double (*)(double)>(m_samplesize, left, result, ::cos);
1071      break;          break;
1072      case TAN:      case TAN:
1073      tensor_unary_operation<double (*)(double)>(m_samplesize, left, result, ::tan);          tensor_unary_operation<double (*)(double)>(m_samplesize, left, result, ::tan);
1074      break;          break;
1075      case ASIN:      case ASIN:
1076      tensor_unary_operation<double (*)(double)>(m_samplesize, left, result, ::asin);          tensor_unary_operation<double (*)(double)>(m_samplesize, left, result, ::asin);
1077      break;          break;
1078      case ACOS:      case ACOS:
1079      tensor_unary_operation<double (*)(double)>(m_samplesize, left, result, ::acos);          tensor_unary_operation<double (*)(double)>(m_samplesize, left, result, ::acos);
1080      break;          break;
1081      case ATAN:      case ATAN:
1082      tensor_unary_operation<double (*)(double)>(m_samplesize, left, result, ::atan);          tensor_unary_operation<double (*)(double)>(m_samplesize, left, result, ::atan);
1083      break;          break;
1084      case SINH:      case SINH:
1085      tensor_unary_operation<double (*)(double)>(m_samplesize, left, result, ::sinh);          tensor_unary_operation<double (*)(double)>(m_samplesize, left, result, ::sinh);
1086      break;          break;
1087      case COSH:      case COSH:
1088      tensor_unary_operation<double (*)(double)>(m_samplesize, left, result, ::cosh);          tensor_unary_operation<double (*)(double)>(m_samplesize, left, result, ::cosh);
1089      break;          break;
1090      case TANH:      case TANH:
1091      tensor_unary_operation<double (*)(double)>(m_samplesize, left, result, ::tanh);          tensor_unary_operation<double (*)(double)>(m_samplesize, left, result, ::tanh);
1092      break;          break;
1093      case ERF:      case ERF:
1094  #if defined (_WIN32) && !defined(__INTEL_COMPILER)  #if defined (_WIN32) && !defined(__INTEL_COMPILER)
1095      throw DataException("Error - Data:: erf function is not supported on _WIN32 platforms.");          throw DataException("Error - Data:: erf function is not supported on _WIN32 platforms.");
1096  #else  #else
1097      tensor_unary_operation(m_samplesize, left, result, ::erf);          tensor_unary_operation(m_samplesize, left, result, ::erf);
1098      break;          break;
1099  #endif  #endif
1100     case ASINH:     case ASINH:
1101  #if defined (_WIN32) && !defined(__INTEL_COMPILER)  #if defined (_WIN32) && !defined(__INTEL_COMPILER)
1102      tensor_unary_operation(m_samplesize, left, result, escript::asinh_substitute);          tensor_unary_operation(m_samplesize, left, result, escript::asinh_substitute);
1103  #else  #else
1104      tensor_unary_operation(m_samplesize, left, result, ::asinh);          tensor_unary_operation(m_samplesize, left, result, ::asinh);
1105  #endif    #endif  
1106      break;          break;
1107     case ACOSH:     case ACOSH:
1108  #if defined (_WIN32) && !defined(__INTEL_COMPILER)  #if defined (_WIN32) && !defined(__INTEL_COMPILER)
1109      tensor_unary_operation(m_samplesize, left, result, escript::acosh_substitute);          tensor_unary_operation(m_samplesize, left, result, escript::acosh_substitute);
1110  #else  #else
1111      tensor_unary_operation(m_samplesize, left, result, ::acosh);          tensor_unary_operation(m_samplesize, left, result, ::acosh);
1112  #endif    #endif  
1113      break;          break;
1114     case ATANH:     case ATANH:
1115  #if defined (_WIN32) && !defined(__INTEL_COMPILER)  #if defined (_WIN32) && !defined(__INTEL_COMPILER)
1116      tensor_unary_operation(m_samplesize, left, result, escript::atanh_substitute);          tensor_unary_operation(m_samplesize, left, result, escript::atanh_substitute);
1117  #else  #else
1118      tensor_unary_operation(m_samplesize, left, result, ::atanh);          tensor_unary_operation(m_samplesize, left, result, ::atanh);
1119  #endif    #endif  
1120      break;          break;
1121      case LOG10:      case LOG10:
1122      tensor_unary_operation<double (*)(double)>(m_samplesize, left, result, ::log10);          tensor_unary_operation<double (*)(double)>(m_samplesize, left, result, ::log10);
1123      break;          break;
1124      case LOG:      case LOG:
1125      tensor_unary_operation<double (*)(double)>(m_samplesize, left, result, ::log);          tensor_unary_operation<double (*)(double)>(m_samplesize, left, result, ::log);
1126      break;          break;
1127      case SIGN:      case SIGN:
1128      tensor_unary_operation(m_samplesize, left, result, escript::fsign);          tensor_unary_operation(m_samplesize, left, result, escript::fsign);
1129      break;          break;
1130      case ABS:      case ABS:
1131      tensor_unary_operation<double (*)(double)>(m_samplesize, left, result, ::fabs);          tensor_unary_operation<double (*)(double)>(m_samplesize, left, result, ::fabs);
1132      break;          break;
1133      case NEG:      case NEG:
1134      tensor_unary_operation(m_samplesize, left, result, negate<double>());          tensor_unary_operation(m_samplesize, left, result, negate<double>());
1135      break;          break;
1136      case POS:      case POS:
1137      // it doesn't mean anything for delayed.          // it doesn't mean anything for delayed.
1138      // it will just trigger a deep copy of the lazy object          // it will just trigger a deep copy of the lazy object
1139      throw DataException("Programmer error - POS not supported for lazy data.");          throw DataException("Programmer error - POS not supported for lazy data.");
1140      break;          break;
1141      case EXP:      case EXP:
1142      tensor_unary_operation<double (*)(double)>(m_samplesize, left, result, ::exp);          tensor_unary_operation<double (*)(double)>(m_samplesize, left, result, ::exp);
1143      break;          break;
1144      case SQRT:      case SQRT:
1145      tensor_unary_operation<double (*)(double)>(m_samplesize, left, result, ::sqrt);          tensor_unary_operation<double (*)(double)>(m_samplesize, left, result, ::sqrt);
1146      break;          break;
1147      case RECIP:      case RECIP:
1148      tensor_unary_operation(m_samplesize, left, result, bind1st(divides<double>(),1.));          tensor_unary_operation(m_samplesize, left, result, bind1st(divides<double>(),1.));
1149      break;          break;
1150      case GZ:      case GZ:
1151      tensor_unary_operation(m_samplesize, left, result, bind2nd(greater<double>(),0.0));          tensor_unary_operation(m_samplesize, left, result, bind2nd(greater<double>(),0.0));
1152      break;          break;
1153      case LZ:      case LZ:
1154      tensor_unary_operation(m_samplesize, left, result, bind2nd(less<double>(),0.0));          tensor_unary_operation(m_samplesize, left, result, bind2nd(less<double>(),0.0));
1155      break;          break;
1156      case GEZ:      case GEZ:
1157      tensor_unary_operation(m_samplesize, left, result, bind2nd(greater_equal<double>(),0.0));          tensor_unary_operation(m_samplesize, left, result, bind2nd(greater_equal<double>(),0.0));
1158      break;          break;
1159      case LEZ:      case LEZ:
1160      tensor_unary_operation(m_samplesize, left, result, bind2nd(less_equal<double>(),0.0));          tensor_unary_operation(m_samplesize, left, result, bind2nd(less_equal<double>(),0.0));
1161      break;          break;
1162    // There are actually G_UNARY_P but I don't see a compelling reason to treat them differently
1163        case NEZ:
1164            tensor_unary_operation(m_samplesize, left, result, bind2nd(AbsGT(),m_tol));
1165            break;
1166        case EZ:
1167            tensor_unary_operation(m_samplesize, left, result, bind2nd(AbsLTE(),m_tol));
1168            break;
1169    
1170      default:      default:
1171      throw DataException("Programmer error - resolveUnary can not resolve operator "+opToString(m_op)+".");          throw DataException("Programmer error - resolveUnary can not resolve operator "+opToString(m_op)+".");
1172    }    }
1173    return &v;    return &(m_samples);
1174  }  }
1175    
1176    
1177  /*  const DataTypes::RealVectorType*
1178    \brief Compute the value of the expression (unary operation) for the given sample.  DataLazy::resolveNodeReduction(int tid, int sampleNo, size_t& roffset) const
   \return Vector which stores the value of the subexpression for the given sample.  
   \param v A vector to store intermediate results.  
   \param offset Index in v to begin storing results.  
   \param sampleNo Sample number to evaluate.  
   \param roffset (output parameter) the offset in the return vector where the result begins.  
   
   The return value will be an existing vector so do not deallocate it.  
   If the result is stored in v it should be stored at the offset given.  
   Everything from offset to the end of v should be considered available for this method to use.  
 */  
 DataTypes::ValueType*  
 DataLazy::resolveNP1OUT(ValueType& v, size_t offset, int sampleNo, size_t& roffset) const  
1179  {  {
1180      // we assume that any collapsing has been done before we get here          // we assume that any collapsing has been done before we get here
1181      // since we only have one argument we don't need to think about only          // since we only have one argument we don't need to think about only
1182      // processing single points.          // processing single points.
1183            // we will also know we won't get identity nodes
1184    if (m_readytype!='E')    if (m_readytype!='E')
1185    {    {
1186      throw DataException("Programmer error - resolveNP1OUT should only be called on expanded Data.");      throw DataException("Programmer error - resolveUnary should only be called on expanded Data.");
1187    }    }
1188      // since we can't write the result over the input, we need a result offset further along    if (m_op==IDENTITY)
1189    size_t subroffset=roffset+m_samplesize;    {
1190    const ValueType* vleft=m_left->resolveSample(v,offset,sampleNo,subroffset);      throw DataException("Programmer error - resolveNodeUnary should not be called on identity nodes.");
1191    roffset=offset;    }
1192      size_t loffset=0;
1193      const DataTypes::RealVectorType* leftres=m_left->resolveNodeSample(tid, sampleNo, loffset);
1194    
1195      roffset=m_samplesize*tid;
1196      unsigned int ndpps=getNumDPPSample();
1197      unsigned int psize=DataTypes::noValues(m_left->getShape());
1198      double* result=&(m_samples[roffset]);
1199      switch (m_op)
1200      {
1201        case MINVAL:
1202            {
1203              for (unsigned int z=0;z<ndpps;++z)
1204              {
1205                FMin op;
1206                *result=DataMaths::reductionOp(*leftres, m_left->getShape(), loffset, op, numeric_limits<double>::max());
1207                loffset+=psize;
1208                result++;
1209              }
1210            }
1211            break;
1212        case MAXVAL:
1213            {
1214              for (unsigned int z=0;z<ndpps;++z)
1215              {
1216              FMax op;
1217              *result=DataMaths::reductionOp(*leftres, m_left->getShape(), loffset, op, numeric_limits<double>::max()*-1);
1218              loffset+=psize;
1219              result++;
1220              }
1221            }
1222            break;
1223        default:
1224            throw DataException("Programmer error - resolveUnary can not resolve operator "+opToString(m_op)+".");
1225      }
1226      return &(m_samples);
1227    }
1228    
1229    const DataTypes::RealVectorType*
1230    DataLazy::resolveNodeNP1OUT(int tid, int sampleNo, size_t& roffset) const
1231    {
1232            // we assume that any collapsing has been done before we get here
1233            // since we only have one argument we don't need to think about only
1234            // processing single points.
1235      if (m_readytype!='E')
1236      {
1237        throw DataException("Programmer error - resolveNodeNP1OUT should only be called on expanded Data.");
1238      }
1239      if (m_op==IDENTITY)
1240      {
1241        throw DataException("Programmer error - resolveNodeNP1OUT should not be called on identity nodes.");
1242      }
1243      size_t subroffset;
1244      const RealVectorType* leftres=m_left->resolveNodeSample(tid, sampleNo, subroffset);
1245      roffset=m_samplesize*tid;
1246      size_t loop=0;
1247      size_t numsteps=(m_readytype=='E')?getNumDPPSample():1;
1248      size_t step=getNoValues();
1249      size_t offset=roffset;
1250    switch (m_op)    switch (m_op)
1251    {    {
1252      case SYM:      case SYM:
1253      DataMaths::symmetric(*vleft,m_left->getShape(),subroffset, v, getShape(), offset);          for (loop=0;loop<numsteps;++loop)
1254      break;          {
1255                DataMaths::symmetric(*leftres,m_left->getShape(),subroffset, m_samples, getShape(), offset);
1256                subroffset+=step;
1257                offset+=step;
1258            }
1259            break;
1260      case NSYM:      case NSYM:
1261      DataMaths::nonsymmetric(*vleft,m_left->getShape(),subroffset, v, getShape(), offset);          for (loop=0;loop<numsteps;++loop)
1262      break;          {
1263                DataMaths::nonsymmetric(*leftres,m_left->getShape(),subroffset, m_samples, getShape(), offset);
1264                subroffset+=step;
1265                offset+=step;
1266            }
1267            break;
1268        default:
1269            throw DataException("Programmer error - resolveNP1OUT can not resolve operator "+opToString(m_op)+".");
1270      }
1271      return &m_samples;
1272    }
1273    
1274    const DataTypes::RealVectorType*
1275    DataLazy::resolveNodeNP1OUT_P(int tid, int sampleNo, size_t& roffset) const
1276    {
1277            // we assume that any collapsing has been done before we get here
1278            // since we only have one argument we don't need to think about only
1279            // processing single points.
1280      if (m_readytype!='E')
1281      {
1282        throw DataException("Programmer error - resolveNodeNP1OUT_P should only be called on expanded Data.");
1283      }
1284      if (m_op==IDENTITY)
1285      {
1286        throw DataException("Programmer error - resolveNodeNP1OUT_P should not be called on identity nodes.");
1287      }
1288      size_t subroffset;
1289      size_t offset;
1290      const RealVectorType* leftres=m_left->resolveNodeSample(tid, sampleNo, subroffset);
1291      roffset=m_samplesize*tid;
1292      offset=roffset;
1293      size_t loop=0;
1294      size_t numsteps=(m_readytype=='E')?getNumDPPSample():1;
1295      size_t outstep=getNoValues();
1296      size_t instep=m_left->getNoValues();
1297      switch (m_op)
1298      {
1299        case TRACE:
1300            for (loop=0;loop<numsteps;++loop)
1301            {
1302                DataMaths::trace(*leftres,m_left->getShape(),subroffset, m_samples ,getShape(),offset,m_axis_offset);
1303                subroffset+=instep;
1304                offset+=outstep;
1305            }
1306            break;
1307        case TRANS:
1308            for (loop=0;loop<numsteps;++loop)
1309            {
1310                DataMaths::transpose(*leftres,m_left->getShape(),subroffset, m_samples, getShape(),offset,m_axis_offset);
1311                subroffset+=instep;
1312                offset+=outstep;
1313            }
1314            break;
1315      default:      default:
1316      throw DataException("Programmer error - resolveNP1OUT can not resolve operator "+opToString(m_op)+".");          throw DataException("Programmer error - resolveNP1OUTP can not resolve operator "+opToString(m_op)+".");
1317    }    }
1318    return &v;    return &m_samples;
1319  }  }
1320    
1321    
1322    const DataTypes::RealVectorType*
1323    DataLazy::resolveNodeNP1OUT_2P(int tid, int sampleNo, size_t& roffset) const
1324    {
1325      if (m_readytype!='E')
1326      {
1327        throw DataException("Programmer error - resolveNodeNP1OUT_2P should only be called on expanded Data.");
1328      }
1329      if (m_op==IDENTITY)
1330      {
1331        throw DataException("Programmer error - resolveNodeNP1OUT_2P should not be called on identity nodes.");
1332      }
1333      size_t subroffset;
1334      size_t offset;
1335      const RealVectorType* leftres=m_left->resolveNodeSample(tid, sampleNo, subroffset);
1336      roffset=m_samplesize*tid;
1337      offset=roffset;
1338      size_t loop=0;
1339      size_t numsteps=(m_readytype=='E')?getNumDPPSample():1;
1340      size_t outstep=getNoValues();
1341      size_t instep=m_left->getNoValues();
1342      switch (m_op)
1343      {
1344        case SWAP:
1345            for (loop=0;loop<numsteps;++loop)
1346            {
1347                DataMaths::swapaxes(*leftres,m_left->getShape(),subroffset, m_samples, getShape(),offset, m_axis_offset, m_transpose);
1348                subroffset+=instep;
1349                offset+=outstep;
1350            }
1351            break;
1352        default:
1353            throw DataException("Programmer error - resolveNodeNP1OUT2P can not resolve operator "+opToString(m_op)+".");
1354      }
1355      return &m_samples;
1356    }
1357    
1358    const DataTypes::RealVectorType*
1359    DataLazy::resolveNodeCondEval(int tid, int sampleNo, size_t& roffset) const
1360    {
1361      if (m_readytype!='E')
1362      {
1363        throw DataException("Programmer error - resolveNodeCondEval should only be called on expanded Data.");
1364      }
1365      if (m_op!=CONDEVAL)
1366      {
1367        throw DataException("Programmer error - resolveNodeCondEval should only be called on CONDEVAL nodes.");
1368      }
1369      size_t subroffset;
1370    
1371  #define PROC_OP(TYPE,X)                               \    const RealVectorType* maskres=m_mask->resolveNodeSample(tid, sampleNo, subroffset);
1372      for (int i=0;i<steps;++i,resultp+=resultStep) \    const RealVectorType* srcres=0;
1373      { \    if ((*maskres)[subroffset]>0)
1374         tensor_binary_operation< TYPE >(chunksize, &((*left)[lroffset]), &((*right)[rroffset]), resultp, X); \    {
1375         lroffset+=leftStep; \          srcres=m_left->resolveNodeSample(tid, sampleNo, subroffset);
1376         rroffset+=rightStep; \    }
1377      }    else
1378      {
1379            srcres=m_right->resolveNodeSample(tid, sampleNo, subroffset);
1380      }
1381    
1382      // Now we need to copy the result
1383    
1384      roffset=m_samplesize*tid;
1385      for (int i=0;i<m_samplesize;++i)
1386      {
1387            m_samples[roffset+i]=(*srcres)[subroffset+i];  
1388      }
1389    
1390      return &m_samples;
1391    }
1392    
 /*  
   \brief Compute the value of the expression (binary operation) for the given sample.  
   \return Vector which stores the value of the subexpression for the given sample.  
   \param v A vector to store intermediate results.  
   \param offset Index in v to begin storing results.  
   \param sampleNo Sample number to evaluate.  
   \param roffset (output parameter) the offset in the return vector where the result begins.  
   
   The return value will be an existing vector so do not deallocate it.  
   If the result is stored in v it should be stored at the offset given.  
   Everything from offset to the end of v should be considered available for this method to use.  
 */  
1393  // This method assumes that any subexpressions which evaluate to Constant or Tagged Data  // This method assumes that any subexpressions which evaluate to Constant or Tagged Data
1394  // have already been collapsed to IDENTITY. So we must have at least one expanded child.  // have already been collapsed to IDENTITY. So we must have at least one expanded child.
1395  // If both children are expanded, then we can process them in a single operation (we treat  // If both children are expanded, then we can process them in a single operation (we treat
# Line 842  DataLazy::resolveNP1OUT(ValueType& v, si Line 1399  DataLazy::resolveNP1OUT(ValueType& v, si
1399  // There is an additional complication when scalar operations are considered.  // There is an additional complication when scalar operations are considered.
1400  // For example, 2+Vector.  // For example, 2+Vector.
1401  // In this case each double within the point is treated individually  // In this case each double within the point is treated individually
1402  DataTypes::ValueType*  const DataTypes::RealVectorType*
1403  DataLazy::resolveBinary(ValueType& v,  size_t offset, int sampleNo, size_t& roffset) const  DataLazy::resolveNodeBinary(int tid, int sampleNo, size_t& roffset) const
1404  {  {
1405  cout << "Resolve binary: " << toString() << endl;  LAZYDEBUG(cout << "Resolve binary: " << toString() << endl;)
1406    
1407    size_t lroffset=0, rroffset=0;    // offsets in the left and right result vectors    size_t lroffset=0, rroffset=0;        // offsets in the left and right result vectors
1408      // first work out which of the children are expanded          // first work out which of the children are expanded
1409    bool leftExp=(m_left->m_readytype=='E');    bool leftExp=(m_left->m_readytype=='E');
1410    bool rightExp=(m_right->m_readytype=='E');    bool rightExp=(m_right->m_readytype=='E');
1411    bool bigloops=((leftExp && rightExp) || (!leftExp && !rightExp)); // is processing in single step?    if (!leftExp && !rightExp)
1412    int steps=(bigloops?1:getNumDPPSample());    {
1413    size_t chunksize=(bigloops? m_samplesize : getNoValues());    // if bigloops, pretend the whole sample is a datapoint          throw DataException("Programmer Error - please use collapse if neither argument has type 'E'.");
1414    if (m_left->getRank()!=m_right->getRank())    // need to deal with scalar * ? ops    }
1415    {    bool leftScalar=(m_left->getRank()==0);
1416      EsysAssert((m_left->getRank()==0) || (m_right->getRank()==0), "Error - Ranks must match unless one is 0.");    bool rightScalar=(m_right->getRank()==0);
1417      steps=getNumDPPSample()*max(m_left->getNoValues(),m_right->getNoValues());    if ((m_left->getRank()!=m_right->getRank()) && (!leftScalar && !rightScalar))
1418      chunksize=1;    // for scalar    {
1419    }              throw DataException("resolveBinary - ranks of arguments must match unless one of them is scalar.");
1420    int leftStep=((leftExp && !rightExp)? m_right->getNoValues() : 0);    }
1421    int rightStep=((rightExp && !leftExp)? m_left->getNoValues() : 0);    size_t leftsize=m_left->getNoValues();
1422    int resultStep=max(leftStep,rightStep);   // only one (at most) should be !=0    size_t rightsize=m_right->getNoValues();
1423      // Get the values of sub-expressions    size_t chunksize=1;                   // how many doubles will be processed in one go
1424    const ValueType* left=m_left->resolveSample(v,offset,sampleNo,lroffset);    int leftstep=0;               // how far should the left offset advance after each step
1425    const ValueType* right=m_right->resolveSample(v,offset+m_samplesize,sampleNo,rroffset); // Note    int rightstep=0;
1426      // the right child starts further along.    int numsteps=0;               // total number of steps for the inner loop
1427    double* resultp=&(v[offset]);     // results are stored at the vector offset we recieved    int oleftstep=0;      // the o variables refer to the outer loop
1428      int orightstep=0;     // The outer loop is only required in cases where there is an extended scalar
1429      int onumsteps=1;
1430      
1431      bool LES=(leftExp && leftScalar);     // Left is an expanded scalar
1432      bool RES=(rightExp && rightScalar);
1433      bool LS=(!leftExp && leftScalar);     // left is a single scalar
1434      bool RS=(!rightExp && rightScalar);
1435      bool LN=(!leftExp && !leftScalar);    // left is a single non-scalar
1436      bool RN=(!rightExp && !rightScalar);
1437      bool LEN=(leftExp && !leftScalar);    // left is an expanded non-scalar
1438      bool REN=(rightExp && !rightScalar);
1439    
1440      if ((LES && RES) || (LEN && REN))     // both are Expanded scalars or both are expanded non-scalars
1441      {
1442            chunksize=m_left->getNumDPPSample()*leftsize;
1443            leftstep=0;
1444            rightstep=0;
1445            numsteps=1;
1446      }
1447      else if (LES || RES)
1448      {
1449            chunksize=1;
1450            if (LES)                // left is an expanded scalar
1451            {
1452                    if (RS)
1453                    {
1454                       leftstep=1;
1455                       rightstep=0;
1456                       numsteps=m_left->getNumDPPSample();
1457                    }
1458                    else            // RN or REN
1459                    {
1460                       leftstep=0;
1461                       oleftstep=1;
1462                       rightstep=1;
1463                       orightstep=(RN ? -(int)rightsize : 0);
1464                       numsteps=rightsize;
1465                       onumsteps=m_left->getNumDPPSample();
1466                    }
1467            }
1468            else            // right is an expanded scalar
1469            {
1470                    if (LS)
1471                    {
1472                       rightstep=1;
1473                       leftstep=0;
1474                       numsteps=m_right->getNumDPPSample();
1475                    }
1476                    else
1477                    {
1478                       rightstep=0;
1479                       orightstep=1;
1480                       leftstep=1;
1481                       oleftstep=(LN ? -(int)leftsize : 0);
1482                       numsteps=leftsize;
1483                       onumsteps=m_right->getNumDPPSample();
1484                    }
1485            }
1486      }
1487      else  // this leaves (LEN, RS), (LEN, RN) and their transposes
1488      {
1489            if (LEN)        // and Right will be a single value
1490            {
1491                    chunksize=rightsize;
1492                    leftstep=rightsize;
1493                    rightstep=0;
1494                    numsteps=m_left->getNumDPPSample();
1495                    if (RS)
1496                    {
1497                       numsteps*=leftsize;
1498                    }
1499            }
1500            else    // REN
1501            {
1502                    chunksize=leftsize;
1503                    rightstep=leftsize;
1504                    leftstep=0;
1505                    numsteps=m_right->getNumDPPSample();
1506                    if (LS)
1507                    {
1508                       numsteps*=rightsize;
1509                    }
1510            }
1511      }
1512    
1513      int resultStep=max(leftstep,rightstep);       // only one (at most) should be !=0
1514            // Get the values of sub-expressions
1515      const RealVectorType* left=m_left->resolveNodeSample(tid,sampleNo,lroffset);      
1516      const RealVectorType* right=m_right->resolveNodeSample(tid,sampleNo,rroffset);
1517    LAZYDEBUG(cout << "Post sub calls in " << toString() << endl;)
1518    LAZYDEBUG(cout << "shapes=" << DataTypes::shapeToString(m_left->getShape()) << "," << DataTypes::shapeToString(m_right->getShape()) << endl;)
1519    LAZYDEBUG(cout << "chunksize=" << chunksize << endl << "leftstep=" << leftstep << " rightstep=" << rightstep;)
1520    LAZYDEBUG(cout << " numsteps=" << numsteps << endl << "oleftstep=" << oleftstep << " orightstep=" << orightstep;)
1521    LAZYDEBUG(cout << "onumsteps=" << onumsteps << endl;)
1522    LAZYDEBUG(cout << " DPPS=" << m_left->getNumDPPSample() << "," <<m_right->getNumDPPSample() << endl;)
1523    LAZYDEBUG(cout << "" << LS << RS << LN << RN << LES << RES <<LEN << REN <<   endl;)
1524    
1525    LAZYDEBUG(cout << "Left res["<< lroffset<< "]=" << (*left)[lroffset] << endl;)
1526    LAZYDEBUG(cout << "Right res["<< rroffset<< "]=" << (*right)[rroffset] << endl;)
1527    
1528    
1529      roffset=m_samplesize*tid;
1530      double* resultp=&(m_samples[roffset]);                // results are stored at the vector offset we received
1531    switch(m_op)    switch(m_op)
1532    {    {
1533      case ADD:      case ADD:
1534          PROC_OP(NO_ARG,plus<double>());          //PROC_OP(NO_ARG,plus<double>());
1535      break;        DataMaths::binaryOpVectorLazyHelper<real_t, real_t, real_t>(resultp,
1536                 &(*left)[0],
1537                 &(*right)[0],
1538                 chunksize,
1539                 onumsteps,
1540                 numsteps,
1541                 resultStep,
1542                 leftstep,
1543                 rightstep,
1544                 oleftstep,
1545                 orightstep,
1546                 lroffset,
1547                 rroffset,
1548                 escript::ESFunction::PLUSF);  
1549            break;
1550      case SUB:      case SUB:
1551      PROC_OP(NO_ARG,minus<double>());        DataMaths::binaryOpVectorLazyHelper<real_t, real_t, real_t>(resultp,
1552      break;               &(*left)[0],
1553                 &(*right)[0],
1554                 chunksize,
1555                 onumsteps,
1556                 numsteps,
1557                 resultStep,
1558                 leftstep,
1559                 rightstep,
1560                 oleftstep,
1561                 orightstep,
1562                 lroffset,
1563                 rroffset,
1564                 escript::ESFunction::MINUSF);        
1565            //PROC_OP(NO_ARG,minus<double>());
1566            break;
1567      case MUL:      case MUL:
1568      PROC_OP(NO_ARG,multiplies<double>());          //PROC_OP(NO_ARG,multiplies<double>());
1569      break;        DataMaths::binaryOpVectorLazyHelper<real_t, real_t, real_t>(resultp,
1570                 &(*left)[0],
1571                 &(*right)[0],
1572                 chunksize,
1573                 onumsteps,
1574                 numsteps,
1575                 resultStep,
1576                 leftstep,
1577                 rightstep,
1578                 oleftstep,
1579                 orightstep,
1580                 lroffset,
1581                 rroffset,
1582                 escript::ESFunction::MULTIPLIESF);      
1583            break;
1584      case DIV:      case DIV:
1585      PROC_OP(NO_ARG,divides<double>());          //PROC_OP(NO_ARG,divides<double>());
1586      break;        DataMaths::binaryOpVectorLazyHelper<real_t, real_t, real_t>(resultp,
1587                 &(*left)[0],
1588                 &(*right)[0],
1589                 chunksize,
1590                 onumsteps,
1591                 numsteps,
1592                 resultStep,
1593                 leftstep,
1594                 rightstep,
1595                 oleftstep,
1596                 orightstep,
1597                 lroffset,
1598                 rroffset,
1599                 escript::ESFunction::DIVIDESF);          
1600            break;
1601      case POW:      case POW:
1602         PROC_OP(double (double,double),::pow);         //PROC_OP(double (double,double),::pow);
1603      break;        DataMaths::binaryOpVectorLazyHelper<real_t, real_t, real_t>(resultp,
1604                 &(*left)[0],
1605                 &(*right)[0],
1606                 chunksize,
1607                 onumsteps,
1608                 numsteps,
1609                 resultStep,
1610                 leftstep,
1611                 rightstep,
1612                 oleftstep,
1613                 orightstep,
1614                 lroffset,
1615                 rroffset,
1616                 escript::ESFunction::POWF);          
1617            break;
1618      default:      default:
1619      throw DataException("Programmer error - resolveBinary can not resolve operator "+opToString(m_op)+".");          throw DataException("Programmer error - resolveBinary can not resolve operator "+opToString(m_op)+".");
1620    }    }
1621    roffset=offset;    LAZYDEBUG(cout << "Result res[" << roffset<< "]" << m_samples[roffset] << endl;)
1622    return &v;    return &m_samples;
1623  }  }
1624    
1625    
 /*  
   \brief Compute the value of the expression (tensor product) for the given sample.  
   \return Vector which stores the value of the subexpression for the given sample.  
   \param v A vector to store intermediate results.  
   \param offset Index in v to begin storing results.  
   \param sampleNo Sample number to evaluate.  
   \param roffset (output parameter) the offset in the return vector where the result begins.  
   
   The return value will be an existing vector so do not deallocate it.  
   If the result is stored in v it should be stored at the offset given.  
   Everything from offset to the end of v should be considered available for this method to use.  
 */  
1626  // This method assumes that any subexpressions which evaluate to Constant or Tagged Data  // This method assumes that any subexpressions which evaluate to Constant or Tagged Data
1627  // have already been collapsed to IDENTITY. So we must have at least one expanded child.  // have already been collapsed to IDENTITY. So we must have at least one expanded child.
1628  // unlike the other resolve helpers, we must treat these datapoints separately.  // unlike the other resolve helpers, we must treat these datapoints separately.
1629  DataTypes::ValueType*  const DataTypes::RealVectorType*
1630  DataLazy::resolveTProd(ValueType& v,  size_t offset, int sampleNo, size_t& roffset) const  DataLazy::resolveNodeTProd(int tid, int sampleNo, size_t& roffset) const
1631  {  {
1632  cout << "Resolve TensorProduct: " << toString() << endl;  LAZYDEBUG(cout << "Resolve TensorProduct: " << toString() << endl;)
1633    
1634    size_t lroffset=0, rroffset=0;    // offsets in the left and right result vectors    size_t lroffset=0, rroffset=0;        // offsets in the left and right result vectors
1635      // first work out which of the children are expanded          // first work out which of the children are expanded
1636    bool leftExp=(m_left->m_readytype=='E');    bool leftExp=(m_left->m_readytype=='E');
1637    bool rightExp=(m_right->m_readytype=='E');    bool rightExp=(m_right->m_readytype=='E');
1638    int steps=getNumDPPSample();    int steps=getNumDPPSample();
1639    int leftStep=((leftExp && !rightExp)? m_right->getNoValues() : 0);    int leftStep=(leftExp? m_left->getNoValues() : 0);            // do not have scalars as input to this method
1640    int rightStep=((rightExp && !leftExp)? m_left->getNoValues() : 0);    int rightStep=(rightExp?m_right->getNoValues() : 0);
1641    int resultStep=max(leftStep,rightStep);   // only one (at most) should be !=0  
1642      // Get the values of sub-expressions (leave a gap of one sample for the result).    int resultStep=getNoValues();
1643    const ValueType* left=m_left->resolveSample(v,offset+m_samplesize,sampleNo,lroffset);    roffset=m_samplesize*tid;
1644    const ValueType* right=m_right->resolveSample(v,offset+2*m_samplesize,sampleNo,rroffset);    size_t offset=roffset;
1645    double* resultp=&(v[offset]);     // results are stored at the vector offset we recieved  
1646      const RealVectorType* left=m_left->resolveNodeSample(tid, sampleNo, lroffset);
1647    
1648      const RealVectorType* right=m_right->resolveNodeSample(tid, sampleNo, rroffset);
1649    
1650    LAZYDEBUG(cerr << "[Left shape]=" << DataTypes::shapeToString(m_left->getShape()) << "\n[Right shape]=" << DataTypes::shapeToString(m_right->getShape()) << " result=" <<DataTypes::shapeToString(getShape()) <<  endl;
1651    cout << getNoValues() << endl;)
1652    
1653    
1654    LAZYDEBUG(cerr << "Post sub calls: " << toString() << endl;)
1655    LAZYDEBUG(cout << "LeftExp=" << leftExp << " rightExp=" << rightExp << endl;)
1656    LAZYDEBUG(cout << "LeftR=" << m_left->getRank() << " rightExp=" << m_right->getRank() << endl;)
1657    LAZYDEBUG(cout << "LeftSize=" << m_left->getNoValues() << " RightSize=" << m_right->getNoValues() << endl;)
1658    LAZYDEBUG(cout << "m_samplesize=" << m_samplesize << endl;)
1659    LAZYDEBUG(cout << "outputshape=" << DataTypes::shapeToString(getShape()) << endl;)
1660    LAZYDEBUG(cout << "DPPS=" << m_right->getNumDPPSample() <<"."<<endl;)
1661    
1662      double* resultp=&(m_samples[offset]);         // results are stored at the vector offset we received
1663    switch(m_op)    switch(m_op)
1664    {    {
1665      case PROD:      case PROD:
1666      for (int i=0;i<steps;++i,resultp+=resultStep)          for (int i=0;i<steps;++i,resultp+=resultStep)
1667      {          {
1668            const double *ptr_0 = &((*left)[lroffset]);            const double *ptr_0 = &((*left)[lroffset]);
1669            const double *ptr_1 = &((*right)[rroffset]);            const double *ptr_1 = &((*right)[rroffset]);
1670            matrix_matrix_product(m_SL, m_SM, m_SR, ptr_0, ptr_1, resultp, m_transpose);  
1671        lroffset+=leftStep;  LAZYDEBUG(cout << DataTypes::pointToString(*left, m_left->getShape(),lroffset,"LEFT") << endl;)
1672        rroffset+=rightStep;  LAZYDEBUG(cout << DataTypes::pointToString(*right,m_right->getShape(),rroffset, "RIGHT") << endl;)
1673      }  
1674      break;            matrix_matrix_product(m_SL, m_SM, m_SR, ptr_0, ptr_1, resultp, m_transpose);
1675    
1676              lroffset+=leftStep;
1677              rroffset+=rightStep;
1678            }
1679            break;
1680      default:      default:
1681      throw DataException("Programmer error - resolveTProduct can not resolve operator "+opToString(m_op)+".");          throw DataException("Programmer error - resolveTProduct can not resolve operator "+opToString(m_op)+".");
1682    }    }
1683    roffset=offset;    roffset=offset;
1684    return &v;    return &m_samples;
1685  }  }
1686    
1687    
1688    const DataTypes::RealVectorType*
1689    DataLazy::resolveSample(int sampleNo, size_t& roffset) const
1690    {
1691    #ifdef _OPENMP
1692            int tid=omp_get_thread_num();
1693    #else
1694            int tid=0;
1695    #endif
1696    
1697  /*  #ifdef LAZY_STACK_PROF
1698    \brief Compute the value of the expression for the given sample.          stackstart[tid]=&tid;
1699    \return Vector which stores the value of the subexpression for the given sample.          stackend[tid]=&tid;
1700    \param v A vector to store intermediate results.          const DataTypes::RealVectorType* r=resolveNodeSample(tid, sampleNo, roffset);
1701    \param offset Index in v to begin storing results.          size_t d=(size_t)stackstart[tid]-(size_t)stackend[tid];
1702    \param sampleNo Sample number to evaluate.          #pragma omp critical
1703    \param roffset (output parameter) the offset in the return vector where the result begins.          if (d>maxstackuse)
1704            {
1705    cout << "Max resolve Stack use " << d << endl;
1706                    maxstackuse=d;
1707            }
1708            return r;
1709    #else
1710            return resolveNodeSample(tid, sampleNo, roffset);
1711    #endif
1712    }
1713    
1714    The return value will be an existing vector so do not deallocate it.  
1715  */  // This needs to do the work of the identity constructor
1716  // the vector and the offset are a place where the method could write its data if it wishes  void
1717  // it is not obligated to do so. For example, if it has its own storage already, it can use that.  DataLazy::resolveToIdentity()
 // Hence the return value to indicate where the data is actually stored.  
 // Regardless, the storage should be assumed to be used, even if it isn't.  
   
 // the roffset is the offset within the returned vector where the data begins  
 const DataTypes::ValueType*  
 DataLazy::resolveSample(ValueType& v, size_t offset, int sampleNo, size_t& roffset)  
1718  {  {
1719  cout << "Resolve sample " << toString() << endl;     if (m_op==IDENTITY)
1720      // collapse so we have a 'E' node or an IDENTITY for some other type          return;
1721    if (m_readytype!='E' && m_op!=IDENTITY)     DataReady_ptr p=resolveNodeWorker();
1722    {     makeIdentity(p);
1723      collapse();  }
1724    }  
1725    if (m_op==IDENTITY)    void DataLazy::makeIdentity(const DataReady_ptr& p)
1726    {
1727       m_op=IDENTITY;
1728       m_axis_offset=0;
1729       m_transpose=0;
1730       m_SL=m_SM=m_SR=0;
1731       m_children=m_height=0;
1732       m_id=p;
1733       if(p->isConstant()) {m_readytype='C';}
1734       else if(p->isExpanded()) {m_readytype='E';}
1735       else if (p->isTagged()) {m_readytype='T';}
1736       else {throw DataException("Unknown DataReady instance in convertToIdentity constructor.");}
1737       m_samplesize=p->getNumDPPSample()*p->getNoValues();
1738       m_left.reset();
1739       m_right.reset();
1740    }
1741    
1742    
1743    DataReady_ptr
1744    DataLazy::resolve()
1745    {
1746        resolveToIdentity();
1747        return m_id;
1748    }
1749    
1750    
1751    /* This is really a static method but I think that caused problems in windows */
1752    void
1753    DataLazy::resolveGroupWorker(std::vector<DataLazy*>& dats)
1754    {
1755      if (dats.empty())
1756    {    {
1757      const ValueType& vec=m_id->getVector();          return;
     if (m_readytype=='C')  
     {  
     roffset=0;  
     return &(vec);  
     }  
     roffset=m_id->getPointOffset(sampleNo, 0);  
     return &(vec);  
1758    }    }
1759    if (m_readytype!='E')    vector<DataLazy*> work;
1760    {    FunctionSpace fs=dats[0]->getFunctionSpace();
1761      throw DataException("Programmer Error - Collapse did not produce an expanded node.");    bool match=true;
1762      for (int i=dats.size()-1;i>=0;--i)
1763      {
1764            if (dats[i]->m_readytype!='E')
1765            {
1766                    dats[i]->collapse();
1767            }
1768            if (dats[i]->m_op!=IDENTITY)
1769            {
1770                    work.push_back(dats[i]);
1771                    if (fs!=dats[i]->getFunctionSpace())
1772                    {
1773                            match=false;
1774                    }
1775            }
1776      }
1777      if (work.empty())
1778      {
1779            return;         // no work to do
1780      }
1781      if (match)    // all functionspaces match.  Yes I realise this is overly strict
1782      {             // it is possible that dats[0] is one of the objects which we discarded and
1783                    // all the other functionspaces match.
1784            vector<DataExpanded*> dep;
1785            vector<RealVectorType*> vecs;
1786            for (int i=0;i<work.size();++i)
1787            {
1788                    dep.push_back(new DataExpanded(fs,work[i]->getShape(), RealVectorType(work[i]->getNoValues())));
1789                    vecs.push_back(&(dep[i]->getVectorRW()));
1790            }
1791            int totalsamples=work[0]->getNumSamples();
1792            const RealVectorType* res=0; // Storage for answer
1793            int sample;
1794            #pragma omp parallel private(sample, res)
1795            {
1796                size_t roffset=0;
1797                #pragma omp for schedule(static)
1798                for (sample=0;sample<totalsamples;++sample)
1799                {
1800                    roffset=0;
1801                    int j;
1802                    for (j=work.size()-1;j>=0;--j)
1803                    {
1804    #ifdef _OPENMP
1805                        res=work[j]->resolveNodeSample(omp_get_thread_num(),sample,roffset);
1806    #else
1807                        res=work[j]->resolveNodeSample(0,sample,roffset);
1808    #endif
1809                        RealVectorType::size_type outoffset=dep[j]->getPointOffset(sample,0);
1810                        memcpy(&((*vecs[j])[outoffset]),&((*res)[roffset]),work[j]->m_samplesize*sizeof(RealVectorType::ElementType));
1811                    }
1812                }
1813            }
1814            // Now we need to load the new results as identity ops into the lazy nodes
1815            for (int i=work.size()-1;i>=0;--i)
1816            {
1817                work[i]->makeIdentity(REFCOUNTNS::dynamic_pointer_cast<DataReady>(dep[i]->getPtr()));
1818            }
1819    }    }
1820    switch (getOpgroup(m_op))    else  // functionspaces do not match
1821    {    {
1822    case G_UNARY: return resolveUnary(v, offset,sampleNo,roffset);          for (int i=0;i<work.size();++i)
1823    case G_BINARY: return resolveBinary(v, offset,sampleNo,roffset);          {
1824    case G_NP1OUT: return resolveNP1OUT(v, offset, sampleNo,roffset);                  work[i]->resolveToIdentity();
1825    case G_TENSORPROD: return resolveTProd(v,offset, sampleNo,roffset);          }
   default:  
     throw DataException("Programmer Error - resolveSample does not know how to process "+opToString(m_op)+".");  
1826    }    }
1827  }  }
1828    
1829    
1830  // To simplify the memory management, all threads operate on one large vector, rather than one each.  
1831  // Each sample is evaluated independently and copied into the result DataExpanded.  // This version of resolve uses storage in each node to hold results
1832  DataReady_ptr  DataReady_ptr
1833  DataLazy::resolve()  DataLazy::resolveNodeWorker()
1834  {  {
1835      if (m_readytype!='E')         // if the whole sub-expression is Constant or Tagged, then evaluate it normally
 cout << "Sample size=" << m_samplesize << endl;  
 cout << "Buffers=" << m_buffsRequired << endl;  
   
   if (m_readytype!='E')     // if the whole sub-expression is Constant or Tagged, then evaluate it normally  
1836    {    {
1837      collapse();      collapse();
1838    }    }
1839    if (m_op==IDENTITY)       // So a lazy expression of Constant or Tagged data will be returned here.    if (m_op==IDENTITY)           // So a lazy expression of Constant or Tagged data will be returned here.
1840    {    {
1841      return m_id;      return m_id;
1842    }    }
1843      // from this point on we must have m_op!=IDENTITY and m_readytype=='E'          // from this point on we must have m_op!=IDENTITY and m_readytype=='E'
1844    size_t threadbuffersize=m_maxsamplesize*(max(1,m_buffsRequired)); // Each thread needs to have enough    DataExpanded* result=new DataExpanded(getFunctionSpace(),getShape(),  RealVectorType(getNoValues()));
1845      // storage to evaluate its expression    RealVectorType& resvec=result->getVectorRW();
   int numthreads=1;  
 #ifdef _OPENMP  
   numthreads=getNumberOfThreads();  
 #endif  
   ValueType v(numthreads*threadbuffersize);  
 cout << "Buffer created with size=" << v.size() << endl;  
   DataExpanded* result=new DataExpanded(getFunctionSpace(),getShape(),  ValueType(getNoValues()));  
   ValueType& resvec=result->getVector();  
1846    DataReady_ptr resptr=DataReady_ptr(result);    DataReady_ptr resptr=DataReady_ptr(result);
1847    
1848    int sample;    int sample;
   size_t outoffset;     // offset in the output data  
1849    int totalsamples=getNumSamples();    int totalsamples=getNumSamples();
1850    const ValueType* res=0;   // Vector storing the answer    const RealVectorType* res=0;       // Storage for answer
1851    size_t resoffset=0;       // where in the vector to find the answer  LAZYDEBUG(cout << "Total number of samples=" <<totalsamples << endl;)
1852    #pragma omp parallel for private(sample,resoffset,outoffset,res) schedule(static)    #pragma omp parallel private(sample,res)
1853    for (sample=0;sample<totalsamples;++sample)    {
1854    {          size_t roffset=0;
1855  cout << "################################# " << sample << endl;  #ifdef LAZY_STACK_PROF
1856            stackstart[omp_get_thread_num()]=&roffset;
1857            stackend[omp_get_thread_num()]=&roffset;
1858    #endif
1859            #pragma omp for schedule(static)
1860            for (sample=0;sample<totalsamples;++sample)
1861            {
1862                    roffset=0;
1863  #ifdef _OPENMP  #ifdef _OPENMP
1864      res=resolveSample(v,threadbuffersize*omp_get_thread_num(),sample,resoffset);                  res=resolveNodeSample(omp_get_thread_num(),sample,roffset);
1865  #else  #else
1866      res=resolveSample(v,0,sample,resoffset);   // res would normally be v, but not if its a single IDENTITY op.                  res=resolveNodeSample(0,sample,roffset);
1867  #endif  #endif
1868  cerr << "-------------------------------- " << endl;  LAZYDEBUG(cout << "Sample #" << sample << endl;)
1869      outoffset=result->getPointOffset(sample,0);  LAZYDEBUG(cout << "Final res[" << roffset<< "]=" << (*res)[roffset] << (*res)[roffset]<< endl; )
1870  cerr << "offset=" << outoffset << endl;                  RealVectorType::size_type outoffset=result->getPointOffset(sample,0);
1871      for (unsigned int i=0;i<m_samplesize;++i,++outoffset,++resoffset)   // copy values into the output vector                  memcpy(&(resvec[outoffset]),&((*res)[roffset]),m_samplesize*sizeof(RealVectorType::ElementType));
1872      {          }
1873      resvec[outoffset]=(*res)[resoffset];    }
1874      }  #ifdef LAZY_STACK_PROF
1875  cerr << "*********************************" << endl;    for (int i=0;i<getNumberOfThreads();++i)
1876      {
1877            size_t r=((size_t)stackstart[i] - (size_t)stackend[i]);
1878    //      cout << i << " " << stackstart[i] << " .. " << stackend[i] << " = " <<  r << endl;
1879            if (r>maxstackuse)
1880            {
1881                    maxstackuse=r;
1882            }
1883    }    }
1884      cout << "Max resolve Stack use=" << maxstackuse << endl;
1885    #endif
1886    return resptr;    return resptr;
1887  }  }
1888    
# Line 1057  std::string Line 1890  std::string
1890  DataLazy::toString() const  DataLazy::toString() const
1891  {  {
1892    ostringstream oss;    ostringstream oss;
1893    oss << "Lazy Data:";    oss << "Lazy Data: [depth=" << m_height<< "] ";
1894    intoString(oss);    switch (escriptParams.getLAZY_STR_FMT())
1895      {
1896      case 1:       // tree format
1897            oss << endl;
1898            intoTreeString(oss,"");
1899            break;
1900      case 2:       // just the depth
1901            break;
1902      default:
1903            intoString(oss);
1904            break;
1905      }
1906    return oss.str();    return oss.str();
1907  }  }
1908    
# Line 1066  DataLazy::toString() const Line 1910  DataLazy::toString() const
1910  void  void
1911  DataLazy::intoString(ostringstream& oss) const  DataLazy::intoString(ostringstream& oss) const
1912  {  {
1913    //    oss << "[" << m_children <<";"<<m_height <<"]";
1914    switch (getOpgroup(m_op))    switch (getOpgroup(m_op))
1915    {    {
1916    case G_IDENTITY:    case G_IDENTITY:
1917      if (m_id->isExpanded())          if (m_id->isExpanded())
1918      {          {
1919         oss << "E";             oss << "E";
1920      }          }
1921      else if (m_id->isTagged())          else if (m_id->isTagged())
1922      {          {
1923        oss << "T";            oss << "T";
1924      }          }
1925      else if (m_id->isConstant())          else if (m_id->isConstant())
1926      {          {
1927        oss << "C";            oss << "C";
1928      }          }
1929      else          else
1930      {          {
1931        oss << "?";            oss << "?";
1932      }          }
1933      oss << '@' << m_id.get();          oss << '@' << m_id.get();
1934      break;          break;
1935    case G_BINARY:    case G_BINARY:
1936      oss << '(';          oss << '(';
1937      m_left->intoString(oss);          m_left->intoString(oss);
1938      oss << ' ' << opToString(m_op) << ' ';          oss << ' ' << opToString(m_op) << ' ';
1939      m_right->intoString(oss);          m_right->intoString(oss);
1940      oss << ')';          oss << ')';
1941      break;          break;
1942    case G_UNARY:    case G_UNARY:
1943      case G_UNARY_P:
1944    case G_NP1OUT:    case G_NP1OUT:
1945      oss << opToString(m_op) << '(';    case G_NP1OUT_P:
1946      m_left->intoString(oss);    case G_REDUCTION:
1947      oss << ')';          oss << opToString(m_op) << '(';
1948      break;          m_left->intoString(oss);
1949            oss << ')';
1950            break;
1951    case G_TENSORPROD:    case G_TENSORPROD:
1952      oss << opToString(m_op) << '(';          oss << opToString(m_op) << '(';
1953      m_left->intoString(oss);          m_left->intoString(oss);
1954      oss << ", ";          oss << ", ";
1955      m_right->intoString(oss);          m_right->intoString(oss);
1956      oss << ')';          oss << ')';
1957      break;          break;
1958      case G_NP1OUT_2P:
1959            oss << opToString(m_op) << '(';
1960            m_left->intoString(oss);
1961            oss << ", " << m_axis_offset << ", " << m_transpose;
1962            oss << ')';
1963            break;
1964      case G_CONDEVAL:
1965            oss << opToString(m_op)<< '(' ;
1966            m_mask->intoString(oss);
1967            oss << " ? ";
1968            m_left->intoString(oss);
1969            oss << " : ";
1970            m_right->intoString(oss);
1971            oss << ')';
1972            break;
1973    default:    default:
1974      oss << "UNKNOWN";          oss << "UNKNOWN";
1975    }    }
1976  }  }
1977    
1978    
1979    void
1980    DataLazy::intoTreeString(ostringstream& oss, string indent) const
1981    {
1982      oss << '[' << m_rank << ':' << setw(3) << m_samplesize << "] " << indent;
1983      switch (getOpgroup(m_op))
1984      {
1985      case G_IDENTITY:
1986            if (m_id->isExpanded())
1987            {
1988               oss << "E";
1989            }
1990            else if (m_id->isTagged())
1991            {
1992              oss << "T";
1993            }
1994            else if (m_id->isConstant())
1995            {
1996              oss << "C";
1997            }
1998            else
1999            {
2000              oss << "?";
2001            }
2002            oss << '@' << m_id.get() << endl;
2003            break;
2004      case G_BINARY:
2005            oss << opToString(m_op) << endl;
2006            indent+='.';
2007            m_left->intoTreeString(oss, indent);
2008            m_right->intoTreeString(oss, indent);
2009            break;
2010      case G_UNARY:
2011      case G_UNARY_P:
2012      case G_NP1OUT:
2013      case G_NP1OUT_P:
2014      case G_REDUCTION:
2015            oss << opToString(m_op) << endl;
2016            indent+='.';
2017            m_left->intoTreeString(oss, indent);
2018            break;
2019      case G_TENSORPROD:
2020            oss << opToString(m_op) << endl;
2021            indent+='.';
2022            m_left->intoTreeString(oss, indent);
2023            m_right->intoTreeString(oss, indent);
2024            break;
2025      case G_NP1OUT_2P:
2026            oss << opToString(m_op) << ", " << m_axis_offset << ", " << m_transpose<< endl;
2027            indent+='.';
2028            m_left->intoTreeString(oss, indent);
2029            break;
2030      default:
2031            oss << "UNKNOWN";
2032      }
2033    }
2034    
2035    
2036  DataAbstract*  DataAbstract*
2037  DataLazy::deepCopy()  DataLazy::deepCopy() const
2038  {  {
2039    switch (getOpgroup(m_op))    switch (getOpgroup(m_op))
2040    {    {
2041    case G_IDENTITY:  return new DataLazy(m_id->deepCopy()->getPtr());    case G_IDENTITY:  return new DataLazy(m_id->deepCopy()->getPtr());
2042    case G_UNARY: return new DataLazy(m_left->deepCopy()->getPtr(),m_op);    case G_UNARY:
2043    case G_BINARY:    return new DataLazy(m_left->deepCopy()->getPtr(),m_right->deepCopy()->getPtr(),m_op);    case G_REDUCTION:      return new DataLazy(m_left->deepCopy()->getPtr(),m_op);
2044      case G_UNARY_P:       return new DataLazy(m_left->deepCopy()->getPtr(), m_op, m_tol);
2045      case G_BINARY:        return new DataLazy(m_left->deepCopy()->getPtr(),m_right->deepCopy()->getPtr(),m_op);
2046    case G_NP1OUT: return new DataLazy(m_left->deepCopy()->getPtr(), m_right->deepCopy()->getPtr(),m_op);    case G_NP1OUT: return new DataLazy(m_left->deepCopy()->getPtr(), m_right->deepCopy()->getPtr(),m_op);
2047    case G_TENSORPROD: return new DataLazy(m_left->deepCopy()->getPtr(), m_right->deepCopy()->getPtr(), m_op, m_axis_offset, m_transpose);    case G_TENSORPROD: return new DataLazy(m_left->deepCopy()->getPtr(), m_right->deepCopy()->getPtr(), m_op, m_axis_offset, m_transpose);
2048      case G_NP1OUT_P:   return new DataLazy(m_left->deepCopy()->getPtr(),m_op,  m_axis_offset);
2049      case G_NP1OUT_2P:  return new DataLazy(m_left->deepCopy()->getPtr(), m_op, m_axis_offset, m_transpose);
2050    default:    default:
2051      throw DataException("Programmer error - do not know how to deepcopy operator "+opToString(m_op)+".");          throw DataException("Programmer error - do not know how to deepcopy operator "+opToString(m_op)+".");
2052    }    }
2053  }  }
2054    
2055    
2056    
2057  // There is no single, natural interpretation of getLength on DataLazy.  // There is no single, natural interpretation of getLength on DataLazy.
2058  // Instances of DataReady can look at the size of their vectors.  // Instances of DataReady can look at the size of their vectors.
2059  // For lazy though, it could be the size the data would be if it were resolved;  // For lazy though, it could be the size the data would be if it were resolved;
2060  // or it could be some function of the lengths of the DataReady instances which  // or it could be some function of the lengths of the DataReady instances which
2061  // form part of the expression.  // form part of the expression.
2062  // Rather than have people making assumptions, I have disabled the method.  // Rather than have people making assumptions, I have disabled the method.
2063  DataTypes::ValueType::size_type  DataTypes::RealVectorType::size_type
2064  DataLazy::getLength() const  DataLazy::getLength() const
2065  {  {
2066    throw DataException("getLength() does not make sense for lazy data.");    throw DataException("getLength() does not make sense for lazy data.");
# Line 1149  DataLazy::getSlice(const DataTypes::Regi Line 2075  DataLazy::getSlice(const DataTypes::Regi
2075    
2076    
2077  // To do this we need to rely on our child nodes  // To do this we need to rely on our child nodes
2078  DataTypes::ValueType::size_type  DataTypes::RealVectorType::size_type
2079  DataLazy::getPointOffset(int sampleNo,  DataLazy::getPointOffset(int sampleNo,
2080                   int dataPointNo)                   int dataPointNo)
2081  {  {
2082    if (m_op==IDENTITY)    if (m_op==IDENTITY)
2083    {    {
2084      return m_id->getPointOffset(sampleNo,dataPointNo);          return m_id->getPointOffset(sampleNo,dataPointNo);
2085    }    }
2086    if (m_readytype!='E')    if (m_readytype!='E')
2087    {    {
2088      collapse();          collapse();
2089      return m_id->getPointOffset(sampleNo,dataPointNo);          return m_id->getPointOffset(sampleNo,dataPointNo);
2090    }    }
2091    // at this point we do not have an identity node and the expression will be Expanded    // at this point we do not have an identity node and the expression will be Expanded
2092    // so we only need to know which child to ask    // so we only need to know which child to ask
2093    if (m_left->m_readytype=='E')    if (m_left->m_readytype=='E')
2094    {    {
2095      return m_left->getPointOffset(sampleNo,dataPointNo);          return m_left->getPointOffset(sampleNo,dataPointNo);
2096    }    }
2097    else    else
2098    {    {
2099      return m_right->getPointOffset(sampleNo,dataPointNo);          return m_right->getPointOffset(sampleNo,dataPointNo);
2100    }    }
2101  }  }
2102    
2103  // To do this we need to rely on our child nodes  // To do this we need to rely on our child nodes
2104  DataTypes::ValueType::size_type  DataTypes::RealVectorType::size_type
2105  DataLazy::getPointOffset(int sampleNo,  DataLazy::getPointOffset(int sampleNo,
2106                   int dataPointNo) const                   int dataPointNo) const
2107  {  {
2108    if (m_op==IDENTITY)    if (m_op==IDENTITY)
2109    {    {
2110      return m_id->getPointOffset(sampleNo,dataPointNo);          return m_id->getPointOffset(sampleNo,dataPointNo);
2111    }    }
2112    if (m_readytype=='E')    if (m_readytype=='E')
2113    {    {
# Line 1189  DataLazy::getPointOffset(int sampleNo, Line 2115  DataLazy::getPointOffset(int sampleNo,
2115      // so we only need to know which child to ask      // so we only need to know which child to ask
2116      if (m_left->m_readytype=='E')      if (m_left->m_readytype=='E')
2117      {      {
2118      return m_left->getPointOffset(sampleNo,dataPointNo);          return m_left->getPointOffset(sampleNo,dataPointNo);
2119      }      }
2120      else      else
2121      {      {
2122      return m_right->getPointOffset(sampleNo,dataPointNo);          return m_right->getPointOffset(sampleNo,dataPointNo);
2123      }      }
2124    }    }
2125    if (m_readytype=='C')    if (m_readytype=='C')
2126    {    {
2127      return m_left->getPointOffset(sampleNo,dataPointNo); // which child doesn't matter          return m_left->getPointOffset(sampleNo,dataPointNo); // which child doesn't matter
2128    }    }
2129    throw DataException("Programmer error - getPointOffset on lazy data may require collapsing (but this object is marked const).");    throw DataException("Programmer error - getPointOffset on lazy data may require collapsing (but this object is marked const).");
2130  }  }
2131    
2132  // It would seem that DataTagged will need to be treated differently since even after setting all tags  
2133  // to zero, all the tags from all the DataTags would be in the result.  // I have decided to let Data:: handle this issue.
 // However since they all have the same value (0) whether they are there or not should not matter.  
 // So I have decided that for all types this method will create a constant 0.  
 // It can be promoted up as required.  
 // A possible efficiency concern might be expanded->constant->expanded which has an extra memory management  
 // but we can deal with that if it arrises.  
2134  void  void
2135  DataLazy::setToZero()  DataLazy::setToZero()
2136  {  {
2137    DataTypes::ValueType v(getNoValues(),0);  //   DataTypes::RealVectorType v(getNoValues(),0);
2138    m_id=DataReady_ptr(new DataConstant(getFunctionSpace(),getShape(),v));  //   m_id=DataReady_ptr(new DataConstant(getFunctionSpace(),getShape(),v));
2139    m_op=IDENTITY;  //   m_op=IDENTITY;
2140    m_right.reset();    //   m_right.reset();  
2141    m_left.reset();  //   m_left.reset();
2142    m_readytype='C';  //   m_readytype='C';
2143    m_buffsRequired=1;  //   m_buffsRequired=1;
2144    
2145      (void)privdebug;  // to stop the compiler complaining about unused privdebug
2146      throw DataException("Programmer error - setToZero not supported for DataLazy (DataLazy objects should be read only).");
2147  }  }
2148    
2149  }   // end namespace  bool
2150    DataLazy::actsExpanded() const
2151    {
2152            return (m_readytype=='E');
2153    }
2154    
2155    } // end namespace
2156    

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