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trunk/escript/src/DataLazy.cpp revision 2066 by jfenwick, Thu Nov 20 05:31:33 2008 UTC trunk/escriptcore/src/DataLazy.cpp revision 6042 by jfenwick, Wed Mar 9 04:30:36 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
 DataLazy::getBuffsRequired() const  
750  {  {
751      return m_buffsRequired;  
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    
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  /*  
995    \brief Compute the value of the expression (unary operation) for the given sample.  
996    \return Vector which stores the value of the subexpression for the given sample.  
997    \param v A vector to store intermediate results.  
998    \param offset Index in v to begin storing results.  
999    \param sampleNo Sample number to evaluate.  #define PROC_OP(TYPE,X)                               \
1000    \param roffset (output parameter) the offset in the return vector where the result begins.          for (int j=0;j<onumsteps;++j)\
1001            {\
1002    The return value will be an existing vector so do not deallocate it.            for (int i=0;i<numsteps;++i,resultp+=resultStep) \
1003    If the result is stored in v it should be stored at the offset given.            { \
1004    Everything from offset to the end of v should be considered available for this method to use.  LAZYDEBUG(cout << "[left,right]=[" << lroffset << "," << rroffset << "]" << endl;)\
1005  */  LAZYDEBUG(cout << "{left,right}={" << (*left)[lroffset] << "," << (*right)[rroffset] << "}\n";)\
1006  DataTypes::ValueType*               tensor_binary_operation< TYPE >(chunksize, &((*left)[lroffset]), &((*right)[rroffset]), resultp, X); \
1007  DataLazy::resolveUnary(ValueType& v, size_t offset, int sampleNo, size_t& roffset) const  LAZYDEBUG(cout << " result=      " << resultp[0] << endl;) \
1008                 lroffset+=leftstep; \
1009                 rroffset+=rightstep; \
1010              }\
1011              lroffset+=oleftstep;\
1012              rroffset+=orightstep;\
1013            }
1014    
1015    
1016    // The result will be stored in m_samples
1017    // The return value is a pointer to the DataVector, offset is the offset within the return value
1018    const DataTypes::RealVectorType*
1019    DataLazy::resolveNodeSample(int tid, int sampleNo, size_t& roffset) const
1020  {  {
1021      // we assume that any collapsing has been done before we get here  LAZYDEBUG(cout << "Resolve sample " << toString() << endl;)
1022      // since we only have one argument we don't need to think about only          // collapse so we have a 'E' node or an IDENTITY for some other type
1023      // processing single points.    if (m_readytype!='E' && m_op!=IDENTITY)
1024      {
1025            collapse();
1026      }
1027      if (m_op==IDENTITY)  
1028      {
1029        const RealVectorType& vec=m_id->getVectorRO();
1030        roffset=m_id->getPointOffset(sampleNo, 0);
1031    #ifdef LAZY_STACK_PROF
1032    int x;
1033    if (&x<stackend[omp_get_thread_num()])
1034    {
1035           stackend[omp_get_thread_num()]=&x;
1036    }
1037    #endif
1038        return &(vec);
1039      }
1040      if (m_readytype!='E')
1041      {
1042        throw DataException("Programmer Error - Collapse did not produce an expanded node.");
1043      }
1044      if (m_sampleids[tid]==sampleNo)
1045      {
1046            roffset=tid*m_samplesize;
1047            return &(m_samples);            // sample is already resolved
1048      }
1049      m_sampleids[tid]=sampleNo;
1050    
1051      switch (getOpgroup(m_op))
1052      {
1053      case G_UNARY:
1054      case G_UNARY_P: return resolveNodeUnary(tid, sampleNo, roffset);
1055      case G_BINARY: return resolveNodeBinary(tid, sampleNo, roffset);
1056      case G_NP1OUT: return resolveNodeNP1OUT(tid, sampleNo, roffset);
1057      case G_NP1OUT_P: return resolveNodeNP1OUT_P(tid, sampleNo, roffset);
1058      case G_TENSORPROD: return resolveNodeTProd(tid, sampleNo, roffset);
1059      case G_NP1OUT_2P: return resolveNodeNP1OUT_2P(tid, sampleNo, roffset);
1060      case G_REDUCTION: return resolveNodeReduction(tid, sampleNo, roffset);
1061      case G_CONDEVAL: return resolveNodeCondEval(tid, sampleNo, roffset);
1062      default:
1063        throw DataException("Programmer Error - resolveSample does not know how to process "+opToString(m_op)+".");
1064      }
1065    }
1066    
1067    const DataTypes::RealVectorType*
1068    DataLazy::resolveNodeUnary(int tid, int sampleNo, size_t& roffset) const
1069    {
1070            // we assume that any collapsing has been done before we get here
1071            // since we only have one argument we don't need to think about only
1072            // processing single points.
1073            // we will also know we won't get identity nodes
1074    if (m_readytype!='E')    if (m_readytype!='E')
1075    {    {
1076      throw DataException("Programmer error - resolveUnary should only be called on expanded Data.");      throw DataException("Programmer error - resolveUnary should only be called on expanded Data.");
1077    }    }
1078    const ValueType* vleft=m_left->resolveSample(v,offset,sampleNo,roffset);    if (m_op==IDENTITY)
1079    const double* left=&((*vleft)[roffset]);    {
1080    double* result=&(v[offset]);      throw DataException("Programmer error - resolveNodeUnary should not be called on identity nodes.");
1081    roffset=offset;    }
1082      const DataTypes::RealVectorType* leftres=m_left->resolveNodeSample(tid, sampleNo, roffset);
1083      const double* left=&((*leftres)[roffset]);
1084      roffset=m_samplesize*tid;
1085      double* result=&(m_samples[roffset]);
1086    switch (m_op)    switch (m_op)
1087    {    {
1088      case SIN:        case SIN:  
1089      tensor_unary_operation<double (*)(double)>(m_samplesize, left, result, ::sin);          tensor_unary_operation<double (*)(double)>(m_samplesize, left, result, ::sin);
1090      break;          break;
1091      case COS:      case COS:
1092      tensor_unary_operation<double (*)(double)>(m_samplesize, left, result, ::cos);          tensor_unary_operation<double (*)(double)>(m_samplesize, left, result, ::cos);
1093      break;          break;
1094      case TAN:      case TAN:
1095      tensor_unary_operation<double (*)(double)>(m_samplesize, left, result, ::tan);          tensor_unary_operation<double (*)(double)>(m_samplesize, left, result, ::tan);
1096      break;          break;
1097      case ASIN:      case ASIN:
1098      tensor_unary_operation<double (*)(double)>(m_samplesize, left, result, ::asin);          tensor_unary_operation<double (*)(double)>(m_samplesize, left, result, ::asin);
1099      break;          break;
1100      case ACOS:      case ACOS:
1101      tensor_unary_operation<double (*)(double)>(m_samplesize, left, result, ::acos);          tensor_unary_operation<double (*)(double)>(m_samplesize, left, result, ::acos);
1102      break;          break;
1103      case ATAN:      case ATAN:
1104      tensor_unary_operation<double (*)(double)>(m_samplesize, left, result, ::atan);          tensor_unary_operation<double (*)(double)>(m_samplesize, left, result, ::atan);
1105      break;          break;
1106      case SINH:      case SINH:
1107      tensor_unary_operation<double (*)(double)>(m_samplesize, left, result, ::sinh);          tensor_unary_operation<double (*)(double)>(m_samplesize, left, result, ::sinh);
1108      break;          break;
1109      case COSH:      case COSH:
1110      tensor_unary_operation<double (*)(double)>(m_samplesize, left, result, ::cosh);          tensor_unary_operation<double (*)(double)>(m_samplesize, left, result, ::cosh);
1111      break;          break;
1112      case TANH:      case TANH:
1113      tensor_unary_operation<double (*)(double)>(m_samplesize, left, result, ::tanh);          tensor_unary_operation<double (*)(double)>(m_samplesize, left, result, ::tanh);
1114      break;          break;
1115      case ERF:      case ERF:
1116  #if defined (_WIN32) && !defined(__INTEL_COMPILER)  #if defined (_WIN32) && !defined(__INTEL_COMPILER)
1117      throw DataException("Error - Data:: erf function is not supported on _WIN32 platforms.");          throw DataException("Error - Data:: erf function is not supported on _WIN32 platforms.");
1118  #else  #else
1119      tensor_unary_operation(m_samplesize, left, result, ::erf);          tensor_unary_operation(m_samplesize, left, result, ::erf);
1120      break;          break;
1121  #endif  #endif
1122     case ASINH:     case ASINH:
1123  #if defined (_WIN32) && !defined(__INTEL_COMPILER)  #if defined (_WIN32) && !defined(__INTEL_COMPILER)
1124      tensor_unary_operation(m_samplesize, left, result, escript::asinh_substitute);          tensor_unary_operation(m_samplesize, left, result, escript::asinh_substitute);
1125  #else  #else
1126      tensor_unary_operation(m_samplesize, left, result, ::asinh);          tensor_unary_operation(m_samplesize, left, result, ::asinh);
1127  #endif    #endif  
1128      break;          break;
1129     case ACOSH:     case ACOSH:
1130  #if defined (_WIN32) && !defined(__INTEL_COMPILER)  #if defined (_WIN32) && !defined(__INTEL_COMPILER)
1131      tensor_unary_operation(m_samplesize, left, result, escript::acosh_substitute);          tensor_unary_operation(m_samplesize, left, result, escript::acosh_substitute);
1132  #else  #else
1133      tensor_unary_operation(m_samplesize, left, result, ::acosh);          tensor_unary_operation(m_samplesize, left, result, ::acosh);
1134  #endif    #endif  
1135      break;          break;
1136     case ATANH:     case ATANH:
1137  #if defined (_WIN32) && !defined(__INTEL_COMPILER)  #if defined (_WIN32) && !defined(__INTEL_COMPILER)
1138      tensor_unary_operation(m_samplesize, left, result, escript::atanh_substitute);          tensor_unary_operation(m_samplesize, left, result, escript::atanh_substitute);
1139  #else  #else
1140      tensor_unary_operation(m_samplesize, left, result, ::atanh);          tensor_unary_operation(m_samplesize, left, result, ::atanh);
1141  #endif    #endif  
1142      break;          break;
1143      case LOG10:      case LOG10:
1144      tensor_unary_operation<double (*)(double)>(m_samplesize, left, result, ::log10);          tensor_unary_operation<double (*)(double)>(m_samplesize, left, result, ::log10);
1145      break;          break;
1146      case LOG:      case LOG:
1147      tensor_unary_operation<double (*)(double)>(m_samplesize, left, result, ::log);          tensor_unary_operation<double (*)(double)>(m_samplesize, left, result, ::log);
1148      break;          break;
1149      case SIGN:      case SIGN:
1150      tensor_unary_operation(m_samplesize, left, result, escript::fsign);          tensor_unary_operation(m_samplesize, left, result, escript::fsign);
1151      break;          break;
1152      case ABS:      case ABS:
1153      tensor_unary_operation<double (*)(double)>(m_samplesize, left, result, ::fabs);          tensor_unary_operation<double (*)(double)>(m_samplesize, left, result, ::fabs);
1154      break;          break;
1155      case NEG:      case NEG:
1156      tensor_unary_operation(m_samplesize, left, result, negate<double>());          tensor_unary_operation(m_samplesize, left, result, negate<double>());
1157      break;          break;
1158      case POS:      case POS:
1159      // it doesn't mean anything for delayed.          // it doesn't mean anything for delayed.
1160      // it will just trigger a deep copy of the lazy object          // it will just trigger a deep copy of the lazy object
1161      throw DataException("Programmer error - POS not supported for lazy data.");          throw DataException("Programmer error - POS not supported for lazy data.");
1162      break;          break;
1163      case EXP:      case EXP:
1164      tensor_unary_operation<double (*)(double)>(m_samplesize, left, result, ::exp);          tensor_unary_operation<double (*)(double)>(m_samplesize, left, result, ::exp);
1165      break;          break;
1166      case SQRT:      case SQRT:
1167      tensor_unary_operation<double (*)(double)>(m_samplesize, left, result, ::sqrt);          tensor_unary_operation<double (*)(double)>(m_samplesize, left, result, ::sqrt);
1168      break;          break;
1169      case RECIP:      case RECIP:
1170      tensor_unary_operation(m_samplesize, left, result, bind1st(divides<double>(),1.));          tensor_unary_operation(m_samplesize, left, result, bind1st(divides<double>(),1.));
1171      break;          break;
1172      case GZ:      case GZ:
1173      tensor_unary_operation(m_samplesize, left, result, bind2nd(greater<double>(),0.0));          tensor_unary_operation(m_samplesize, left, result, bind2nd(greater<double>(),0.0));
1174      break;          break;
1175      case LZ:      case LZ:
1176      tensor_unary_operation(m_samplesize, left, result, bind2nd(less<double>(),0.0));          tensor_unary_operation(m_samplesize, left, result, bind2nd(less<double>(),0.0));
1177      break;          break;
1178      case GEZ:      case GEZ:
1179      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));
1180      break;          break;
1181      case LEZ:      case LEZ:
1182      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));
1183      break;          break;
1184    // There are actually G_UNARY_P but I don't see a compelling reason to treat them differently
1185        case NEZ:
1186            tensor_unary_operation(m_samplesize, left, result, bind2nd(AbsGT(),m_tol));
1187            break;
1188        case EZ:
1189            tensor_unary_operation(m_samplesize, left, result, bind2nd(AbsLTE(),m_tol));
1190            break;
1191    
1192      default:      default:
1193      throw DataException("Programmer error - resolveUnary can not resolve operator "+opToString(m_op)+".");          throw DataException("Programmer error - resolveUnary can not resolve operator "+opToString(m_op)+".");
1194    }    }
1195    return &v;    return &(m_samples);
1196  }  }
1197    
1198    
1199  /*  const DataTypes::RealVectorType*
1200    \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  
1201  {  {
1202      // we assume that any collapsing has been done before we get here          // we assume that any collapsing has been done before we get here
1203      // 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
1204      // processing single points.          // processing single points.
1205            // we will also know we won't get identity nodes
1206    if (m_readytype!='E')    if (m_readytype!='E')
1207    {    {
1208      throw DataException("Programmer error - resolveNP1OUT should only be called on expanded Data.");      throw DataException("Programmer error - resolveUnary should only be called on expanded Data.");
1209    }    }
1210      // since we can't write the result over the input, we need a result offset further along    if (m_op==IDENTITY)
1211    size_t subroffset=roffset+m_samplesize;    {
1212    const ValueType* vleft=m_left->resolveSample(v,offset,sampleNo,subroffset);      throw DataException("Programmer error - resolveNodeUnary should not be called on identity nodes.");
1213    roffset=offset;    }
1214      size_t loffset=0;
1215      const DataTypes::RealVectorType* leftres=m_left->resolveNodeSample(tid, sampleNo, loffset);
1216    
1217      roffset=m_samplesize*tid;
1218      unsigned int ndpps=getNumDPPSample();
1219      unsigned int psize=DataTypes::noValues(m_left->getShape());
1220      double* result=&(m_samples[roffset]);
1221      switch (m_op)
1222      {
1223        case MINVAL:
1224            {
1225              for (unsigned int z=0;z<ndpps;++z)
1226              {
1227                FMin op;
1228                *result=DataMaths::reductionOp(*leftres, m_left->getShape(), loffset, op, numeric_limits<double>::max());
1229                loffset+=psize;
1230                result++;
1231              }
1232            }
1233            break;
1234        case MAXVAL:
1235            {
1236              for (unsigned int z=0;z<ndpps;++z)
1237              {
1238              FMax op;
1239              *result=DataMaths::reductionOp(*leftres, m_left->getShape(), loffset, op, numeric_limits<double>::max()*-1);
1240              loffset+=psize;
1241              result++;
1242              }
1243            }
1244            break;
1245        default:
1246            throw DataException("Programmer error - resolveUnary can not resolve operator "+opToString(m_op)+".");
1247      }
1248      return &(m_samples);
1249    }
1250    
1251    const DataTypes::RealVectorType*
1252    DataLazy::resolveNodeNP1OUT(int tid, int sampleNo, size_t& roffset) const
1253    {
1254            // we assume that any collapsing has been done before we get here
1255            // since we only have one argument we don't need to think about only
1256            // processing single points.
1257      if (m_readytype!='E')
1258      {
1259        throw DataException("Programmer error - resolveNodeNP1OUT should only be called on expanded Data.");
1260      }
1261      if (m_op==IDENTITY)
1262      {
1263        throw DataException("Programmer error - resolveNodeNP1OUT should not be called on identity nodes.");
1264      }
1265      size_t subroffset;
1266      const RealVectorType* leftres=m_left->resolveNodeSample(tid, sampleNo, subroffset);
1267      roffset=m_samplesize*tid;
1268      size_t loop=0;
1269      size_t numsteps=(m_readytype=='E')?getNumDPPSample():1;
1270      size_t step=getNoValues();
1271      size_t offset=roffset;
1272    switch (m_op)    switch (m_op)
1273    {    {
1274      case SYM:      case SYM:
1275      DataMaths::symmetric(*vleft,m_left->getShape(),subroffset, v, getShape(), offset);          for (loop=0;loop<numsteps;++loop)
1276      break;          {
1277                DataMaths::symmetric(*leftres,m_left->getShape(),subroffset, m_samples, getShape(), offset);
1278                subroffset+=step;
1279                offset+=step;
1280            }
1281            break;
1282      case NSYM:      case NSYM:
1283      DataMaths::nonsymmetric(*vleft,m_left->getShape(),subroffset, v, getShape(), offset);          for (loop=0;loop<numsteps;++loop)
1284      break;          {
1285                DataMaths::nonsymmetric(*leftres,m_left->getShape(),subroffset, m_samples, getShape(), offset);
1286                subroffset+=step;
1287                offset+=step;
1288            }
1289            break;
1290        default:
1291            throw DataException("Programmer error - resolveNP1OUT can not resolve operator "+opToString(m_op)+".");
1292      }
1293      return &m_samples;
1294    }
1295    
1296    const DataTypes::RealVectorType*
1297    DataLazy::resolveNodeNP1OUT_P(int tid, int sampleNo, size_t& roffset) const
1298    {
1299            // we assume that any collapsing has been done before we get here
1300            // since we only have one argument we don't need to think about only
1301            // processing single points.
1302      if (m_readytype!='E')
1303      {
1304        throw DataException("Programmer error - resolveNodeNP1OUT_P should only be called on expanded Data.");
1305      }
1306      if (m_op==IDENTITY)
1307      {
1308        throw DataException("Programmer error - resolveNodeNP1OUT_P should not be called on identity nodes.");
1309      }
1310      size_t subroffset;
1311      size_t offset;
1312      const RealVectorType* leftres=m_left->resolveNodeSample(tid, sampleNo, subroffset);
1313      roffset=m_samplesize*tid;
1314      offset=roffset;
1315      size_t loop=0;
1316      size_t numsteps=(m_readytype=='E')?getNumDPPSample():1;
1317      size_t outstep=getNoValues();
1318      size_t instep=m_left->getNoValues();
1319      switch (m_op)
1320      {
1321        case TRACE:
1322            for (loop=0;loop<numsteps;++loop)
1323            {
1324                DataMaths::trace(*leftres,m_left->getShape(),subroffset, m_samples ,getShape(),offset,m_axis_offset);
1325                subroffset+=instep;
1326                offset+=outstep;
1327            }
1328            break;
1329        case TRANS:
1330            for (loop=0;loop<numsteps;++loop)
1331            {
1332                DataMaths::transpose(*leftres,m_left->getShape(),subroffset, m_samples, getShape(),offset,m_axis_offset);
1333                subroffset+=instep;
1334                offset+=outstep;
1335            }
1336            break;
1337      default:      default:
1338      throw DataException("Programmer error - resolveNP1OUT can not resolve operator "+opToString(m_op)+".");          throw DataException("Programmer error - resolveNP1OUTP can not resolve operator "+opToString(m_op)+".");
1339    }    }
1340    return &v;    return &m_samples;
1341  }  }
1342    
1343    
1344    const DataTypes::RealVectorType*
1345    DataLazy::resolveNodeNP1OUT_2P(int tid, int sampleNo, size_t& roffset) const
1346    {
1347      if (m_readytype!='E')
1348      {
1349        throw DataException("Programmer error - resolveNodeNP1OUT_2P should only be called on expanded Data.");
1350      }
1351      if (m_op==IDENTITY)
1352      {
1353        throw DataException("Programmer error - resolveNodeNP1OUT_2P should not be called on identity nodes.");
1354      }
1355      size_t subroffset;
1356      size_t offset;
1357      const RealVectorType* leftres=m_left->resolveNodeSample(tid, sampleNo, subroffset);
1358      roffset=m_samplesize*tid;
1359      offset=roffset;
1360      size_t loop=0;
1361      size_t numsteps=(m_readytype=='E')?getNumDPPSample():1;
1362      size_t outstep=getNoValues();
1363      size_t instep=m_left->getNoValues();
1364      switch (m_op)
1365      {
1366        case SWAP:
1367            for (loop=0;loop<numsteps;++loop)
1368            {
1369                DataMaths::swapaxes(*leftres,m_left->getShape(),subroffset, m_samples, getShape(),offset, m_axis_offset, m_transpose);
1370                subroffset+=instep;
1371                offset+=outstep;
1372            }
1373            break;
1374        default:
1375            throw DataException("Programmer error - resolveNodeNP1OUT2P can not resolve operator "+opToString(m_op)+".");
1376      }
1377      return &m_samples;
1378    }
1379    
1380    const DataTypes::RealVectorType*
1381    DataLazy::resolveNodeCondEval(int tid, int sampleNo, size_t& roffset) const
1382    {
1383      if (m_readytype!='E')
1384      {
1385        throw DataException("Programmer error - resolveNodeCondEval should only be called on expanded Data.");
1386      }
1387      if (m_op!=CONDEVAL)
1388      {
1389        throw DataException("Programmer error - resolveNodeCondEval should only be called on CONDEVAL nodes.");
1390      }
1391      size_t subroffset;
1392    
1393  #define PROC_OP(TYPE,X)                               \    const RealVectorType* maskres=m_mask->resolveNodeSample(tid, sampleNo, subroffset);
1394      for (int i=0;i<steps;++i,resultp+=resultStep) \    const RealVectorType* srcres=0;
1395      { \    if ((*maskres)[subroffset]>0)
1396         tensor_binary_operation< TYPE >(chunksize, &((*left)[lroffset]), &((*right)[rroffset]), resultp, X); \    {
1397         lroffset+=leftStep; \          srcres=m_left->resolveNodeSample(tid, sampleNo, subroffset);
1398         rroffset+=rightStep; \    }
1399      }    else
1400      {
1401            srcres=m_right->resolveNodeSample(tid, sampleNo, subroffset);
1402      }
1403    
1404      // Now we need to copy the result
1405    
1406      roffset=m_samplesize*tid;
1407      for (int i=0;i<m_samplesize;++i)
1408      {
1409            m_samples[roffset+i]=(*srcres)[subroffset+i];  
1410      }
1411    
1412      return &m_samples;
1413    }
1414    
 /*  
   \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.  
 */  
1415  // 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
1416  // 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.
1417  // 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 1421  DataLazy::resolveNP1OUT(ValueType& v, si
1421  // There is an additional complication when scalar operations are considered.  // There is an additional complication when scalar operations are considered.
1422  // For example, 2+Vector.  // For example, 2+Vector.
1423  // In this case each double within the point is treated individually  // In this case each double within the point is treated individually
1424  DataTypes::ValueType*  const DataTypes::RealVectorType*
1425  DataLazy::resolveBinary(ValueType& v,  size_t offset, int sampleNo, size_t& roffset) const  DataLazy::resolveNodeBinary(int tid, int sampleNo, size_t& roffset) const
1426  {  {
1427  cout << "Resolve binary: " << toString() << endl;  LAZYDEBUG(cout << "Resolve binary: " << toString() << endl;)
1428    
1429    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
1430      // first work out which of the children are expanded          // first work out which of the children are expanded
1431    bool leftExp=(m_left->m_readytype=='E');    bool leftExp=(m_left->m_readytype=='E');
1432    bool rightExp=(m_right->m_readytype=='E');    bool rightExp=(m_right->m_readytype=='E');
1433    bool bigloops=((leftExp && rightExp) || (!leftExp && !rightExp)); // is processing in single step?    if (!leftExp && !rightExp)
1434    int steps=(bigloops?1:getNumDPPSample());    {
1435    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'.");
1436    if (m_left->getRank()!=m_right->getRank())    // need to deal with scalar * ? ops    }
1437    {    bool leftScalar=(m_left->getRank()==0);
1438      EsysAssert((m_left->getRank()==0) || (m_right->getRank()==0), "Error - Ranks must match unless one is 0.");    bool rightScalar=(m_right->getRank()==0);
1439      steps=getNumDPPSample()*max(m_left->getNoValues(),m_right->getNoValues());    if ((m_left->getRank()!=m_right->getRank()) && (!leftScalar && !rightScalar))
1440      chunksize=1;    // for scalar    {
1441    }              throw DataException("resolveBinary - ranks of arguments must match unless one of them is scalar.");
1442    int leftStep=((leftExp && !rightExp)? m_right->getNoValues() : 0);    }
1443    int rightStep=((rightExp && !leftExp)? m_left->getNoValues() : 0);    size_t leftsize=m_left->getNoValues();
1444    int resultStep=max(leftStep,rightStep);   // only one (at most) should be !=0    size_t rightsize=m_right->getNoValues();
1445      // Get the values of sub-expressions    size_t chunksize=1;                   // how many doubles will be processed in one go
1446    const ValueType* left=m_left->resolveSample(v,offset,sampleNo,lroffset);    int leftstep=0;               // how far should the left offset advance after each step
1447    const ValueType* right=m_right->resolveSample(v,offset+m_samplesize,sampleNo,rroffset); // Note    int rightstep=0;
1448      // the right child starts further along.    int numsteps=0;               // total number of steps for the inner loop
1449    double* resultp=&(v[offset]);     // results are stored at the vector offset we recieved    int oleftstep=0;      // the o variables refer to the outer loop
1450      int orightstep=0;     // The outer loop is only required in cases where there is an extended scalar
1451      int onumsteps=1;
1452      
1453      bool LES=(leftExp && leftScalar);     // Left is an expanded scalar
1454      bool RES=(rightExp && rightScalar);
1455      bool LS=(!leftExp && leftScalar);     // left is a single scalar
1456      bool RS=(!rightExp && rightScalar);
1457      bool LN=(!leftExp && !leftScalar);    // left is a single non-scalar
1458      bool RN=(!rightExp && !rightScalar);
1459      bool LEN=(leftExp && !leftScalar);    // left is an expanded non-scalar
1460      bool REN=(rightExp && !rightScalar);
1461    
1462      if ((LES && RES) || (LEN && REN))     // both are Expanded scalars or both are expanded non-scalars
1463      {
1464            chunksize=m_left->getNumDPPSample()*leftsize;
1465            leftstep=0;
1466            rightstep=0;
1467            numsteps=1;
1468      }
1469      else if (LES || RES)
1470      {
1471            chunksize=1;
1472            if (LES)                // left is an expanded scalar
1473            {
1474                    if (RS)
1475                    {
1476                       leftstep=1;
1477                       rightstep=0;
1478                       numsteps=m_left->getNumDPPSample();
1479                    }
1480                    else            // RN or REN
1481                    {
1482                       leftstep=0;
1483                       oleftstep=1;
1484                       rightstep=1;
1485                       orightstep=(RN ? -(int)rightsize : 0);
1486                       numsteps=rightsize;
1487                       onumsteps=m_left->getNumDPPSample();
1488                    }
1489            }
1490            else            // right is an expanded scalar
1491            {
1492                    if (LS)
1493                    {
1494                       rightstep=1;
1495                       leftstep=0;
1496                       numsteps=m_right->getNumDPPSample();
1497                    }
1498                    else
1499                    {
1500                       rightstep=0;
1501                       orightstep=1;
1502                       leftstep=1;
1503                       oleftstep=(LN ? -(int)leftsize : 0);
1504                       numsteps=leftsize;
1505                       onumsteps=m_right->getNumDPPSample();
1506                    }
1507            }
1508      }
1509      else  // this leaves (LEN, RS), (LEN, RN) and their transposes
1510      {
1511            if (LEN)        // and Right will be a single value
1512            {
1513                    chunksize=rightsize;
1514                    leftstep=rightsize;
1515                    rightstep=0;
1516                    numsteps=m_left->getNumDPPSample();
1517                    if (RS)
1518                    {
1519                       numsteps*=leftsize;
1520                    }
1521            }
1522            else    // REN
1523            {
1524                    chunksize=leftsize;
1525                    rightstep=leftsize;
1526                    leftstep=0;
1527                    numsteps=m_right->getNumDPPSample();
1528                    if (LS)
1529                    {
1530                       numsteps*=rightsize;
1531                    }
1532            }
1533      }
1534    
1535      int resultStep=max(leftstep,rightstep);       // only one (at most) should be !=0
1536            // Get the values of sub-expressions
1537      const RealVectorType* left=m_left->resolveNodeSample(tid,sampleNo,lroffset);      
1538      const RealVectorType* right=m_right->resolveNodeSample(tid,sampleNo,rroffset);
1539    LAZYDEBUG(cout << "Post sub calls in " << toString() << endl;)
1540    LAZYDEBUG(cout << "shapes=" << DataTypes::shapeToString(m_left->getShape()) << "," << DataTypes::shapeToString(m_right->getShape()) << endl;)
1541    LAZYDEBUG(cout << "chunksize=" << chunksize << endl << "leftstep=" << leftstep << " rightstep=" << rightstep;)
1542    LAZYDEBUG(cout << " numsteps=" << numsteps << endl << "oleftstep=" << oleftstep << " orightstep=" << orightstep;)
1543    LAZYDEBUG(cout << "onumsteps=" << onumsteps << endl;)
1544    LAZYDEBUG(cout << " DPPS=" << m_left->getNumDPPSample() << "," <<m_right->getNumDPPSample() << endl;)
1545    LAZYDEBUG(cout << "" << LS << RS << LN << RN << LES << RES <<LEN << REN <<   endl;)
1546    
1547    LAZYDEBUG(cout << "Left res["<< lroffset<< "]=" << (*left)[lroffset] << endl;)
1548    LAZYDEBUG(cout << "Right res["<< rroffset<< "]=" << (*right)[rroffset] << endl;)
1549    
1550    
1551      roffset=m_samplesize*tid;
1552      double* resultp=&(m_samples[roffset]);                // results are stored at the vector offset we received
1553    switch(m_op)    switch(m_op)
1554    {    {
1555      case ADD:      case ADD:
1556          PROC_OP(NO_ARG,plus<double>());          //PROC_OP(NO_ARG,plus<double>());
1557      break;        DataMaths::binaryOpVectorLazyHelper<real_t, real_t, real_t>(resultp,
1558                 &(*left)[0],
1559                 &(*right)[0],
1560                 chunksize,
1561                 onumsteps,
1562                 numsteps,
1563                 resultStep,
1564                 leftstep,
1565                 rightstep,
1566                 oleftstep,
1567                 orightstep,
1568                 escript::ESFunction::PLUSF);  
1569            break;
1570      case SUB:      case SUB:
1571      PROC_OP(NO_ARG,minus<double>());          PROC_OP(NO_ARG,minus<double>());
1572      break;          break;
1573      case MUL:      case MUL:
1574      PROC_OP(NO_ARG,multiplies<double>());          PROC_OP(NO_ARG,multiplies<double>());
1575      break;          break;
1576      case DIV:      case DIV:
1577      PROC_OP(NO_ARG,divides<double>());          PROC_OP(NO_ARG,divides<double>());
1578      break;          break;
1579      case POW:      case POW:
1580         PROC_OP(double (double,double),::pow);         PROC_OP(double (double,double),::pow);
1581      break;          break;
1582      default:      default:
1583      throw DataException("Programmer error - resolveBinary can not resolve operator "+opToString(m_op)+".");          throw DataException("Programmer error - resolveBinary can not resolve operator "+opToString(m_op)+".");
1584    }    }
1585    roffset=offset;    LAZYDEBUG(cout << "Result res[" << roffset<< "]" << m_samples[roffset] << endl;)
1586    return &v;    return &m_samples;
1587  }  }
1588    
1589    
 /*  
   \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.  
 */  
1590  // 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
1591  // 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.
1592  // unlike the other resolve helpers, we must treat these datapoints separately.  // unlike the other resolve helpers, we must treat these datapoints separately.
1593  DataTypes::ValueType*  const DataTypes::RealVectorType*
1594  DataLazy::resolveTProd(ValueType& v,  size_t offset, int sampleNo, size_t& roffset) const  DataLazy::resolveNodeTProd(int tid, int sampleNo, size_t& roffset) const
1595  {  {
1596  cout << "Resolve TensorProduct: " << toString() << endl;  LAZYDEBUG(cout << "Resolve TensorProduct: " << toString() << endl;)
1597    
1598    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
1599      // first work out which of the children are expanded          // first work out which of the children are expanded
1600    bool leftExp=(m_left->m_readytype=='E');    bool leftExp=(m_left->m_readytype=='E');
1601    bool rightExp=(m_right->m_readytype=='E');    bool rightExp=(m_right->m_readytype=='E');
1602    int steps=getNumDPPSample();    int steps=getNumDPPSample();
1603    int leftStep=((leftExp && !rightExp)? m_right->getNoValues() : 0);    int leftStep=(leftExp? m_left->getNoValues() : 0);            // do not have scalars as input to this method
1604    int rightStep=((rightExp && !leftExp)? m_left->getNoValues() : 0);    int rightStep=(rightExp?m_right->getNoValues() : 0);
1605    int resultStep=max(leftStep,rightStep);   // only one (at most) should be !=0  
1606      // Get the values of sub-expressions (leave a gap of one sample for the result).    int resultStep=getNoValues();
1607    const ValueType* left=m_left->resolveSample(v,offset+m_samplesize,sampleNo,lroffset);    roffset=m_samplesize*tid;
1608    const ValueType* right=m_right->resolveSample(v,offset+2*m_samplesize,sampleNo,rroffset);    size_t offset=roffset;
1609    double* resultp=&(v[offset]);     // results are stored at the vector offset we recieved  
1610      const RealVectorType* left=m_left->resolveNodeSample(tid, sampleNo, lroffset);
1611    
1612      const RealVectorType* right=m_right->resolveNodeSample(tid, sampleNo, rroffset);
1613    
1614    LAZYDEBUG(cerr << "[Left shape]=" << DataTypes::shapeToString(m_left->getShape()) << "\n[Right shape]=" << DataTypes::shapeToString(m_right->getShape()) << " result=" <<DataTypes::shapeToString(getShape()) <<  endl;
1615    cout << getNoValues() << endl;)
1616    
1617    
1618    LAZYDEBUG(cerr << "Post sub calls: " << toString() << endl;)
1619    LAZYDEBUG(cout << "LeftExp=" << leftExp << " rightExp=" << rightExp << endl;)
1620    LAZYDEBUG(cout << "LeftR=" << m_left->getRank() << " rightExp=" << m_right->getRank() << endl;)
1621    LAZYDEBUG(cout << "LeftSize=" << m_left->getNoValues() << " RightSize=" << m_right->getNoValues() << endl;)
1622    LAZYDEBUG(cout << "m_samplesize=" << m_samplesize << endl;)
1623    LAZYDEBUG(cout << "outputshape=" << DataTypes::shapeToString(getShape()) << endl;)
1624    LAZYDEBUG(cout << "DPPS=" << m_right->getNumDPPSample() <<"."<<endl;)
1625    
1626      double* resultp=&(m_samples[offset]);         // results are stored at the vector offset we received
1627    switch(m_op)    switch(m_op)
1628    {    {
1629      case PROD:      case PROD:
1630      for (int i=0;i<steps;++i,resultp+=resultStep)          for (int i=0;i<steps;++i,resultp+=resultStep)
1631      {          {
1632            const double *ptr_0 = &((*left)[lroffset]);            const double *ptr_0 = &((*left)[lroffset]);
1633            const double *ptr_1 = &((*right)[rroffset]);            const double *ptr_1 = &((*right)[rroffset]);
1634            matrix_matrix_product(m_SL, m_SM, m_SR, ptr_0, ptr_1, resultp, m_transpose);  
1635        lroffset+=leftStep;  LAZYDEBUG(cout << DataTypes::pointToString(*left, m_left->getShape(),lroffset,"LEFT") << endl;)
1636        rroffset+=rightStep;  LAZYDEBUG(cout << DataTypes::pointToString(*right,m_right->getShape(),rroffset, "RIGHT") << endl;)
1637      }  
1638      break;            matrix_matrix_product(m_SL, m_SM, m_SR, ptr_0, ptr_1, resultp, m_transpose);
1639    
1640              lroffset+=leftStep;
1641              rroffset+=rightStep;
1642            }
1643            break;
1644      default:      default:
1645      throw DataException("Programmer error - resolveTProduct can not resolve operator "+opToString(m_op)+".");          throw DataException("Programmer error - resolveTProduct can not resolve operator "+opToString(m_op)+".");
1646    }    }
1647    roffset=offset;    roffset=offset;
1648    return &v;    return &m_samples;
1649  }  }
1650    
1651    
1652    const DataTypes::RealVectorType*
1653    DataLazy::resolveSample(int sampleNo, size_t& roffset) const
1654    {
1655    #ifdef _OPENMP
1656            int tid=omp_get_thread_num();
1657    #else
1658            int tid=0;
1659    #endif
1660    
1661  /*  #ifdef LAZY_STACK_PROF
1662    \brief Compute the value of the expression for the given sample.          stackstart[tid]=&tid;
1663    \return Vector which stores the value of the subexpression for the given sample.          stackend[tid]=&tid;
1664    \param v A vector to store intermediate results.          const DataTypes::RealVectorType* r=resolveNodeSample(tid, sampleNo, roffset);
1665    \param offset Index in v to begin storing results.          size_t d=(size_t)stackstart[tid]-(size_t)stackend[tid];
1666    \param sampleNo Sample number to evaluate.          #pragma omp critical
1667    \param roffset (output parameter) the offset in the return vector where the result begins.          if (d>maxstackuse)
1668            {
1669    cout << "Max resolve Stack use " << d << endl;
1670                    maxstackuse=d;
1671            }
1672            return r;
1673    #else
1674            return resolveNodeSample(tid, sampleNo, roffset);
1675    #endif
1676    }
1677    
1678    The return value will be an existing vector so do not deallocate it.  
1679  */  // This needs to do the work of the identity constructor
1680  // the vector and the offset are a place where the method could write its data if it wishes  void
1681  // 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)  
1682  {  {
1683  cout << "Resolve sample " << toString() << endl;     if (m_op==IDENTITY)
1684      // collapse so we have a 'E' node or an IDENTITY for some other type          return;
1685    if (m_readytype!='E' && m_op!=IDENTITY)     DataReady_ptr p=resolveNodeWorker();
1686       makeIdentity(p);
1687    }
1688    
1689    void DataLazy::makeIdentity(const DataReady_ptr& p)
1690    {
1691       m_op=IDENTITY;
1692       m_axis_offset=0;
1693       m_transpose=0;
1694       m_SL=m_SM=m_SR=0;
1695       m_children=m_height=0;
1696       m_id=p;
1697       if(p->isConstant()) {m_readytype='C';}
1698       else if(p->isExpanded()) {m_readytype='E';}
1699       else if (p->isTagged()) {m_readytype='T';}
1700       else {throw DataException("Unknown DataReady instance in convertToIdentity constructor.");}
1701       m_samplesize=p->getNumDPPSample()*p->getNoValues();
1702       m_left.reset();
1703       m_right.reset();
1704    }
1705    
1706    
1707    DataReady_ptr
1708    DataLazy::resolve()
1709    {
1710        resolveToIdentity();
1711        return m_id;
1712    }
1713    
1714    
1715    /* This is really a static method but I think that caused problems in windows */
1716    void
1717    DataLazy::resolveGroupWorker(std::vector<DataLazy*>& dats)
1718    {
1719      if (dats.empty())
1720    {    {
1721      collapse();          return;
1722    }    }
1723    if (m_op==IDENTITY)      vector<DataLazy*> work;
1724    {    FunctionSpace fs=dats[0]->getFunctionSpace();
1725      const ValueType& vec=m_id->getVector();    bool match=true;
1726      if (m_readytype=='C')    for (int i=dats.size()-1;i>=0;--i)
1727      {    {
1728      roffset=0;          if (dats[i]->m_readytype!='E')
1729      return &(vec);          {
1730      }                  dats[i]->collapse();
1731      roffset=m_id->getPointOffset(sampleNo, 0);          }
1732      return &(vec);          if (dats[i]->m_op!=IDENTITY)
1733            {
1734                    work.push_back(dats[i]);
1735                    if (fs!=dats[i]->getFunctionSpace())
1736                    {
1737                            match=false;
1738                    }
1739            }
1740      }
1741      if (work.empty())
1742      {
1743            return;         // no work to do
1744      }
1745      if (match)    // all functionspaces match.  Yes I realise this is overly strict
1746      {             // it is possible that dats[0] is one of the objects which we discarded and
1747                    // all the other functionspaces match.
1748            vector<DataExpanded*> dep;
1749            vector<RealVectorType*> vecs;
1750            for (int i=0;i<work.size();++i)
1751            {
1752                    dep.push_back(new DataExpanded(fs,work[i]->getShape(), RealVectorType(work[i]->getNoValues())));
1753                    vecs.push_back(&(dep[i]->getVectorRW()));
1754            }
1755            int totalsamples=work[0]->getNumSamples();
1756            const RealVectorType* res=0; // Storage for answer
1757            int sample;
1758            #pragma omp parallel private(sample, res)
1759            {
1760                size_t roffset=0;
1761                #pragma omp for schedule(static)
1762                for (sample=0;sample<totalsamples;++sample)
1763                {
1764                    roffset=0;
1765                    int j;
1766                    for (j=work.size()-1;j>=0;--j)
1767                    {
1768    #ifdef _OPENMP
1769                        res=work[j]->resolveNodeSample(omp_get_thread_num(),sample,roffset);
1770    #else
1771                        res=work[j]->resolveNodeSample(0,sample,roffset);
1772    #endif
1773                        RealVectorType::size_type outoffset=dep[j]->getPointOffset(sample,0);
1774                        memcpy(&((*vecs[j])[outoffset]),&((*res)[roffset]),work[j]->m_samplesize*sizeof(RealVectorType::ElementType));
1775                    }
1776                }
1777            }
1778            // Now we need to load the new results as identity ops into the lazy nodes
1779            for (int i=work.size()-1;i>=0;--i)
1780            {
1781                work[i]->makeIdentity(REFCOUNTNS::dynamic_pointer_cast<DataReady>(dep[i]->getPtr()));
1782            }
1783    }    }
1784    if (m_readytype!='E')    else  // functionspaces do not match
1785    {    {
1786      throw DataException("Programmer Error - Collapse did not produce an expanded node.");          for (int i=0;i<work.size();++i)
1787    }          {
1788    switch (getOpgroup(m_op))                  work[i]->resolveToIdentity();
1789    {          }
   case G_UNARY: return resolveUnary(v, offset,sampleNo,roffset);  
   case G_BINARY: return resolveBinary(v, offset,sampleNo,roffset);  
   case G_NP1OUT: return resolveNP1OUT(v, offset, sampleNo,roffset);  
   case G_TENSORPROD: return resolveTProd(v,offset, sampleNo,roffset);  
   default:  
     throw DataException("Programmer Error - resolveSample does not know how to process "+opToString(m_op)+".");  
1790    }    }
1791  }  }
1792    
1793    
1794  // To simplify the memory management, all threads operate on one large vector, rather than one each.  
1795  // Each sample is evaluated independently and copied into the result DataExpanded.  // This version of resolve uses storage in each node to hold results
1796  DataReady_ptr  DataReady_ptr
1797  DataLazy::resolve()  DataLazy::resolveNodeWorker()
1798  {  {
1799      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  
1800    {    {
1801      collapse();      collapse();
1802    }    }
1803    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.
1804    {    {
1805      return m_id;      return m_id;
1806    }    }
1807      // 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'
1808    size_t threadbuffersize=m_maxsamplesize*(max(1,m_buffsRequired)); // Each thread needs to have enough    DataExpanded* result=new DataExpanded(getFunctionSpace(),getShape(),  RealVectorType(getNoValues()));
1809      // storage to evaluate its expression    RealVectorType& resvec=result->getVectorRW();
   int numthreads=1;  
 #ifdef _OPENMP  
   numthreads=getNumberOfThreads();  
   int threadnum=0;  
 #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();  
1810    DataReady_ptr resptr=DataReady_ptr(result);    DataReady_ptr resptr=DataReady_ptr(result);
1811    
1812    int sample;    int sample;
   size_t outoffset;     // offset in the output data  
1813    int totalsamples=getNumSamples();    int totalsamples=getNumSamples();
1814    const ValueType* res=0;   // Vector storing the answer    const RealVectorType* res=0;       // Storage for answer
1815    size_t resoffset=0;       // where in the vector to find the answer  LAZYDEBUG(cout << "Total number of samples=" <<totalsamples << endl;)
1816    #pragma omp parallel for private(sample,resoffset,outoffset,threadnum,res) schedule(static)    #pragma omp parallel private(sample,res)
1817    for (sample=0;sample<totalsamples;++sample)    {
1818    {          size_t roffset=0;
1819  cout << "################################# " << sample << endl;  #ifdef LAZY_STACK_PROF
1820            stackstart[omp_get_thread_num()]=&roffset;
1821            stackend[omp_get_thread_num()]=&roffset;
1822    #endif
1823            #pragma omp for schedule(static)
1824            for (sample=0;sample<totalsamples;++sample)
1825            {
1826                    roffset=0;
1827  #ifdef _OPENMP  #ifdef _OPENMP
1828      res=resolveSample(v,threadbuffersize*omp_get_thread_num(),sample,resoffset);                  res=resolveNodeSample(omp_get_thread_num(),sample,roffset);
1829  #else  #else
1830      res=resolveSample(v,0,sample,resoffset);   // res would normally be v, but not if its a single IDENTITY op.                  res=resolveNodeSample(0,sample,roffset);
1831  #endif  #endif
1832  cerr << "-------------------------------- " << endl;  LAZYDEBUG(cout << "Sample #" << sample << endl;)
1833      outoffset=result->getPointOffset(sample,0);  LAZYDEBUG(cout << "Final res[" << roffset<< "]=" << (*res)[roffset] << (*res)[roffset]<< endl; )
1834  cerr << "offset=" << outoffset << endl;                  RealVectorType::size_type outoffset=result->getPointOffset(sample,0);
1835      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));
1836      {          }
1837      resvec[outoffset]=(*res)[resoffset];    }
1838      }  #ifdef LAZY_STACK_PROF
1839  cerr << "*********************************" << endl;    for (int i=0;i<getNumberOfThreads();++i)
1840      {
1841            size_t r=((size_t)stackstart[i] - (size_t)stackend[i]);
1842    //      cout << i << " " << stackstart[i] << " .. " << stackend[i] << " = " <<  r << endl;
1843            if (r>maxstackuse)
1844            {
1845                    maxstackuse=r;
1846            }
1847    }    }
1848      cout << "Max resolve Stack use=" << maxstackuse << endl;
1849    #endif
1850    return resptr;    return resptr;
1851  }  }
1852    
# Line 1058  std::string Line 1854  std::string
1854  DataLazy::toString() const  DataLazy::toString() const
1855  {  {
1856    ostringstream oss;    ostringstream oss;
1857    oss << "Lazy Data:";    oss << "Lazy Data: [depth=" << m_height<< "] ";
1858    intoString(oss);    switch (escriptParams.getLAZY_STR_FMT())
1859      {
1860      case 1:       // tree format
1861            oss << endl;
1862            intoTreeString(oss,"");
1863            break;
1864      case 2:       // just the depth
1865            break;
1866      default:
1867            intoString(oss);
1868            break;
1869      }
1870    return oss.str();    return oss.str();
1871  }  }
1872    
# Line 1067  DataLazy::toString() const Line 1874  DataLazy::toString() const
1874  void  void
1875  DataLazy::intoString(ostringstream& oss) const  DataLazy::intoString(ostringstream& oss) const
1876  {  {
1877    //    oss << "[" << m_children <<";"<<m_height <<"]";
1878    switch (getOpgroup(m_op))    switch (getOpgroup(m_op))
1879    {    {
1880    case G_IDENTITY:    case G_IDENTITY:
1881      if (m_id->isExpanded())          if (m_id->isExpanded())
1882      {          {
1883         oss << "E";             oss << "E";
1884      }          }
1885      else if (m_id->isTagged())          else if (m_id->isTagged())
1886      {          {
1887        oss << "T";            oss << "T";
1888      }          }
1889      else if (m_id->isConstant())          else if (m_id->isConstant())
1890      {          {
1891        oss << "C";            oss << "C";
1892      }          }
1893      else          else
1894      {          {
1895        oss << "?";            oss << "?";
1896      }          }
1897      oss << '@' << m_id.get();          oss << '@' << m_id.get();
1898      break;          break;
1899    case G_BINARY:    case G_BINARY:
1900      oss << '(';          oss << '(';
1901      m_left->intoString(oss);          m_left->intoString(oss);
1902      oss << ' ' << opToString(m_op) << ' ';          oss << ' ' << opToString(m_op) << ' ';
1903      m_right->intoString(oss);          m_right->intoString(oss);
1904      oss << ')';          oss << ')';
1905      break;          break;
1906    case G_UNARY:    case G_UNARY:
1907      case G_UNARY_P:
1908    case G_NP1OUT:    case G_NP1OUT:
1909      oss << opToString(m_op) << '(';    case G_NP1OUT_P:
1910      m_left->intoString(oss);    case G_REDUCTION:
1911      oss << ')';          oss << opToString(m_op) << '(';
1912      break;          m_left->intoString(oss);
1913            oss << ')';
1914            break;
1915    case G_TENSORPROD:    case G_TENSORPROD:
1916      oss << opToString(m_op) << '(';          oss << opToString(m_op) << '(';
1917      m_left->intoString(oss);          m_left->intoString(oss);
1918      oss << ", ";          oss << ", ";
1919      m_right->intoString(oss);          m_right->intoString(oss);
1920      oss << ')';          oss << ')';
1921      break;          break;
1922      case G_NP1OUT_2P:
1923            oss << opToString(m_op) << '(';
1924            m_left->intoString(oss);
1925            oss << ", " << m_axis_offset << ", " << m_transpose;
1926            oss << ')';
1927            break;
1928      case G_CONDEVAL:
1929            oss << opToString(m_op)<< '(' ;
1930            m_mask->intoString(oss);
1931            oss << " ? ";
1932            m_left->intoString(oss);
1933            oss << " : ";
1934            m_right->intoString(oss);
1935            oss << ')';
1936            break;
1937    default:    default:
1938      oss << "UNKNOWN";          oss << "UNKNOWN";
1939    }    }
1940  }  }
1941    
1942    
1943    void
1944    DataLazy::intoTreeString(ostringstream& oss, string indent) const
1945    {
1946      oss << '[' << m_rank << ':' << setw(3) << m_samplesize << "] " << indent;
1947      switch (getOpgroup(m_op))
1948      {
1949      case G_IDENTITY:
1950            if (m_id->isExpanded())
1951            {
1952               oss << "E";
1953            }
1954            else if (m_id->isTagged())
1955            {
1956              oss << "T";
1957            }
1958            else if (m_id->isConstant())
1959            {
1960              oss << "C";
1961            }
1962            else
1963            {
1964              oss << "?";
1965            }
1966            oss << '@' << m_id.get() << endl;
1967            break;
1968      case G_BINARY:
1969            oss << opToString(m_op) << endl;
1970            indent+='.';
1971            m_left->intoTreeString(oss, indent);
1972            m_right->intoTreeString(oss, indent);
1973            break;
1974      case G_UNARY:
1975      case G_UNARY_P:
1976      case G_NP1OUT:
1977      case G_NP1OUT_P:
1978      case G_REDUCTION:
1979            oss << opToString(m_op) << endl;
1980            indent+='.';
1981            m_left->intoTreeString(oss, indent);
1982            break;
1983      case G_TENSORPROD:
1984            oss << opToString(m_op) << endl;
1985            indent+='.';
1986            m_left->intoTreeString(oss, indent);
1987            m_right->intoTreeString(oss, indent);
1988            break;
1989      case G_NP1OUT_2P:
1990            oss << opToString(m_op) << ", " << m_axis_offset << ", " << m_transpose<< endl;
1991            indent+='.';
1992            m_left->intoTreeString(oss, indent);
1993            break;
1994      default:
1995            oss << "UNKNOWN";
1996      }
1997    }
1998    
1999    
2000  DataAbstract*  DataAbstract*
2001  DataLazy::deepCopy()  DataLazy::deepCopy() const
2002  {  {
2003    switch (getOpgroup(m_op))    switch (getOpgroup(m_op))
2004    {    {
2005    case G_IDENTITY:  return new DataLazy(m_id->deepCopy()->getPtr());    case G_IDENTITY:  return new DataLazy(m_id->deepCopy()->getPtr());
2006    case G_UNARY: return new DataLazy(m_left->deepCopy()->getPtr(),m_op);    case G_UNARY:
2007    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);
2008      case G_UNARY_P:       return new DataLazy(m_left->deepCopy()->getPtr(), m_op, m_tol);
2009      case G_BINARY:        return new DataLazy(m_left->deepCopy()->getPtr(),m_right->deepCopy()->getPtr(),m_op);
2010    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);
2011    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);
2012      case G_NP1OUT_P:   return new DataLazy(m_left->deepCopy()->getPtr(),m_op,  m_axis_offset);
2013      case G_NP1OUT_2P:  return new DataLazy(m_left->deepCopy()->getPtr(), m_op, m_axis_offset, m_transpose);
2014    default:    default:
2015      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)+".");
2016    }    }
2017  }  }
2018    
2019    
2020    
2021  // There is no single, natural interpretation of getLength on DataLazy.  // There is no single, natural interpretation of getLength on DataLazy.
2022  // Instances of DataReady can look at the size of their vectors.  // Instances of DataReady can look at the size of their vectors.
2023  // 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;
2024  // 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
2025  // form part of the expression.  // form part of the expression.
2026  // Rather than have people making assumptions, I have disabled the method.  // Rather than have people making assumptions, I have disabled the method.
2027  DataTypes::ValueType::size_type  DataTypes::RealVectorType::size_type
2028  DataLazy::getLength() const  DataLazy::getLength() const
2029  {  {
2030    throw DataException("getLength() does not make sense for lazy data.");    throw DataException("getLength() does not make sense for lazy data.");
# Line 1150  DataLazy::getSlice(const DataTypes::Regi Line 2039  DataLazy::getSlice(const DataTypes::Regi
2039    
2040    
2041  // To do this we need to rely on our child nodes  // To do this we need to rely on our child nodes
2042  DataTypes::ValueType::size_type  DataTypes::RealVectorType::size_type
2043  DataLazy::getPointOffset(int sampleNo,  DataLazy::getPointOffset(int sampleNo,
2044                   int dataPointNo)                   int dataPointNo)
2045  {  {
2046    if (m_op==IDENTITY)    if (m_op==IDENTITY)
2047    {    {
2048      return m_id->getPointOffset(sampleNo,dataPointNo);          return m_id->getPointOffset(sampleNo,dataPointNo);
2049    }    }
2050    if (m_readytype!='E')    if (m_readytype!='E')
2051    {    {
2052      collapse();          collapse();
2053      return m_id->getPointOffset(sampleNo,dataPointNo);          return m_id->getPointOffset(sampleNo,dataPointNo);
2054    }    }
2055    // 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
2056    // so we only need to know which child to ask    // so we only need to know which child to ask
2057    if (m_left->m_readytype=='E')    if (m_left->m_readytype=='E')
2058    {    {
2059      return m_left->getPointOffset(sampleNo,dataPointNo);          return m_left->getPointOffset(sampleNo,dataPointNo);
2060    }    }
2061    else    else
2062    {    {
2063      return m_right->getPointOffset(sampleNo,dataPointNo);          return m_right->getPointOffset(sampleNo,dataPointNo);
2064    }    }
2065  }  }
2066    
2067  // To do this we need to rely on our child nodes  // To do this we need to rely on our child nodes
2068  DataTypes::ValueType::size_type  DataTypes::RealVectorType::size_type
2069  DataLazy::getPointOffset(int sampleNo,  DataLazy::getPointOffset(int sampleNo,
2070                   int dataPointNo) const                   int dataPointNo) const
2071  {  {
2072    if (m_op==IDENTITY)    if (m_op==IDENTITY)
2073    {    {
2074      return m_id->getPointOffset(sampleNo,dataPointNo);          return m_id->getPointOffset(sampleNo,dataPointNo);
2075    }    }
2076    if (m_readytype=='E')    if (m_readytype=='E')
2077    {    {
# Line 1190  DataLazy::getPointOffset(int sampleNo, Line 2079  DataLazy::getPointOffset(int sampleNo,
2079      // so we only need to know which child to ask      // so we only need to know which child to ask
2080      if (m_left->m_readytype=='E')      if (m_left->m_readytype=='E')
2081      {      {
2082      return m_left->getPointOffset(sampleNo,dataPointNo);          return m_left->getPointOffset(sampleNo,dataPointNo);
2083      }      }
2084      else      else
2085      {      {
2086      return m_right->getPointOffset(sampleNo,dataPointNo);          return m_right->getPointOffset(sampleNo,dataPointNo);
2087      }      }
2088    }    }
2089    if (m_readytype=='C')    if (m_readytype=='C')
2090    {    {
2091      return m_left->getPointOffset(sampleNo,dataPointNo); // which child doesn't matter          return m_left->getPointOffset(sampleNo,dataPointNo); // which child doesn't matter
2092    }    }
2093    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).");
2094  }  }
2095    
2096  // It would seem that DataTagged will need to be treated differently since even after setting all tags  
2097  // 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.  
2098  void  void
2099  DataLazy::setToZero()  DataLazy::setToZero()
2100  {  {
2101    DataTypes::ValueType v(getNoValues(),0);  //   DataTypes::RealVectorType v(getNoValues(),0);
2102    m_id=DataReady_ptr(new DataConstant(getFunctionSpace(),getShape(),v));  //   m_id=DataReady_ptr(new DataConstant(getFunctionSpace(),getShape(),v));
2103    m_op=IDENTITY;  //   m_op=IDENTITY;
2104    m_right.reset();    //   m_right.reset();  
2105    m_left.reset();  //   m_left.reset();
2106    m_readytype='C';  //   m_readytype='C';
2107    m_buffsRequired=1;  //   m_buffsRequired=1;
2108    
2109      (void)privdebug;  // to stop the compiler complaining about unused privdebug
2110      throw DataException("Programmer error - setToZero not supported for DataLazy (DataLazy objects should be read only).");
2111  }  }
2112    
2113  }   // end namespace  bool
2114    DataLazy::actsExpanded() const
2115    {
2116            return (m_readytype=='E');
2117    }
2118    
2119    } // end namespace
2120    

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