/[escript]/branches/clazy/escriptcore/src/DataLazy.cpp
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revision 2147 by jfenwick, Wed Dec 10 04:41:26 2008 UTC revision 2881 by jfenwick, Thu Jan 28 02:03:15 2010 UTC
# Line 1  Line 1 
1    
2  /*******************************************************  /*******************************************************
3  *  *
4  * Copyright (c) 2003-2008 by University of Queensland  * Copyright (c) 2003-2010 by University of Queensland
5  * Earth Systems Science Computational Center (ESSCC)  * Earth Systems Science Computational Center (ESSCC)
6  * http://www.uq.edu.au/esscc  * http://www.uq.edu.au/esscc
7  *  *
# Line 28  Line 28 
28  #include "UnaryFuncs.h"     // for escript::fsign  #include "UnaryFuncs.h"     // for escript::fsign
29  #include "Utils.h"  #include "Utils.h"
30    
31  // #define LAZYDEBUG(X) X;  #include "EscriptParams.h"
32    
33    #include <iomanip>      // for some fancy formatting in debug
34    
35    // #define LAZYDEBUG(X) if (privdebug){X;}
36  #define LAZYDEBUG(X)  #define LAZYDEBUG(X)
37    namespace
38    {
39    bool privdebug=false;
40    
41    #define ENABLEDEBUG privdebug=true;
42    #define DISABLEDEBUG privdebug=false;
43    }
44    
45    // #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();}
46    
47    // #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();}
48    
49    
50    #define SIZELIMIT if (m_height>escript::escriptParams.getTOO_MANY_LEVELS())  {if (escript::escriptParams.getLAZY_VERBOSE()){cerr << "SIZE LIMIT EXCEEDED height=" << m_height << endl;}resolveToIdentity();}
51    
52  /*  /*
53  How does DataLazy work?  How does DataLazy work?
# Line 93  namespace escript Line 111  namespace escript
111  namespace  namespace
112  {  {
113    
114    
115    // enabling this will print out when ever the maximum stacksize used by resolve increases
116    // it assumes _OPENMP is also in use
117    //#define LAZY_STACK_PROF
118    
119    
120    
121    #ifndef _OPENMP
122      #ifdef LAZY_STACK_PROF
123      #undef LAZY_STACK_PROF
124      #endif
125    #endif
126    
127    
128    #ifdef LAZY_STACK_PROF
129    std::vector<void*> stackstart(getNumberOfThreads());
130    std::vector<void*> stackend(getNumberOfThreads());
131    size_t maxstackuse=0;
132    #endif
133    
134  enum ES_opgroup  enum ES_opgroup
135  {  {
136     G_UNKNOWN,     G_UNKNOWN,
# Line 102  enum ES_opgroup Line 140  enum ES_opgroup
140     G_UNARY_P,       // pointwise operations with one argument, requiring a parameter     G_UNARY_P,       // pointwise operations with one argument, requiring a parameter
141     G_NP1OUT,        // non-pointwise op with one output     G_NP1OUT,        // non-pointwise op with one output
142     G_NP1OUT_P,      // non-pointwise op with one output requiring a parameter     G_NP1OUT_P,      // non-pointwise op with one output requiring a parameter
143     G_TENSORPROD     // general tensor product     G_TENSORPROD,    // general tensor product
144       G_NP1OUT_2P,     // non-pointwise op with one output requiring two params
145       G_REDUCTION      // non-pointwise unary op with a scalar output
146  };  };
147    
148    
# Line 116  string ES_opstrings[]={"UNKNOWN","IDENTI Line 156  string ES_opstrings[]={"UNKNOWN","IDENTI
156              "1/","where>0","where<0","where>=0","where<=0", "where<>0","where=0",              "1/","where>0","where<0","where>=0","where<=0", "where<>0","where=0",
157              "symmetric","nonsymmetric",              "symmetric","nonsymmetric",
158              "prod",              "prod",
159              "transpose", "trace"};              "transpose", "trace",
160  int ES_opcount=40;              "swapaxes",
161                "minval", "maxval"};
162    int ES_opcount=43;
163  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,
164              G_UNARY,G_UNARY,G_UNARY, //10              G_UNARY,G_UNARY,G_UNARY, //10
165              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
# Line 126  ES_opgroup opgroups[]={G_UNKNOWN,G_IDENT Line 168  ES_opgroup opgroups[]={G_UNKNOWN,G_IDENT
168              G_UNARY,G_UNARY,G_UNARY,G_UNARY,G_UNARY, G_UNARY_P, G_UNARY_P,      // 35              G_UNARY,G_UNARY,G_UNARY,G_UNARY,G_UNARY, G_UNARY_P, G_UNARY_P,      // 35
169              G_NP1OUT,G_NP1OUT,              G_NP1OUT,G_NP1OUT,
170              G_TENSORPROD,              G_TENSORPROD,
171              G_NP1OUT_P, G_NP1OUT_P};              G_NP1OUT_P, G_NP1OUT_P,
172                G_NP1OUT_2P,
173                G_REDUCTION, G_REDUCTION};
174  inline  inline
175  ES_opgroup  ES_opgroup
176  getOpgroup(ES_optype op)  getOpgroup(ES_optype op)
# Line 171  resultShape(DataAbstract_ptr left, DataA Line 215  resultShape(DataAbstract_ptr left, DataA
215        {        {
216          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.");
217        }        }
218    
219        if (left->getRank()==0)   // we need to allow scalar * anything        if (left->getRank()==0)   // we need to allow scalar * anything
220        {        {
221          return right->getShape();          return right->getShape();
# Line 187  resultShape(DataAbstract_ptr left, DataA Line 232  resultShape(DataAbstract_ptr left, DataA
232  // return the shape for "op left"  // return the shape for "op left"
233    
234  DataTypes::ShapeType  DataTypes::ShapeType
235  resultShape(DataAbstract_ptr left, ES_optype op)  resultShape(DataAbstract_ptr left, ES_optype op, int axis_offset)
236  {  {
237      switch(op)      switch(op)
238      {      {
239          case TRANS:          case TRANS:
240          return left->getShape();         {            // for the scoping of variables
241            const DataTypes::ShapeType& s=left->getShape();
242            DataTypes::ShapeType sh;
243            int rank=left->getRank();
244            if (axis_offset<0 || axis_offset>rank)
245            {
246                stringstream e;
247                e << "Error - Data::transpose must have 0 <= axis_offset <= rank=" << rank;
248                throw DataException(e.str());
249            }
250            for (int i=0; i<rank; i++)
251            {
252               int index = (axis_offset+i)%rank;
253               sh.push_back(s[index]); // Append to new shape
254            }
255            return sh;
256           }
257      break;      break;
258      case TRACE:      case TRACE:
259          return DataTypes::scalarShape;         {
260            int rank=left->getRank();
261            if (rank<2)
262            {
263               throw DataException("Trace can only be computed for objects with rank 2 or greater.");
264            }
265            if ((axis_offset>rank-2) || (axis_offset<0))
266            {
267               throw DataException("Trace: axis offset must lie between 0 and rank-2 inclusive.");
268            }
269            if (rank==2)
270            {
271               return DataTypes::scalarShape;
272            }
273            else if (rank==3)
274            {
275               DataTypes::ShapeType sh;
276                   if (axis_offset==0)
277               {
278                    sh.push_back(left->getShape()[2]);
279                   }
280                   else     // offset==1
281               {
282                sh.push_back(left->getShape()[0]);
283                   }
284               return sh;
285            }
286            else if (rank==4)
287            {
288               DataTypes::ShapeType sh;
289               const DataTypes::ShapeType& s=left->getShape();
290                   if (axis_offset==0)
291               {
292                    sh.push_back(s[2]);
293                    sh.push_back(s[3]);
294                   }
295                   else if (axis_offset==1)
296               {
297                    sh.push_back(s[0]);
298                    sh.push_back(s[3]);
299                   }
300               else     // offset==2
301               {
302                sh.push_back(s[0]);
303                sh.push_back(s[1]);
304               }
305               return sh;
306            }
307            else        // unknown rank
308            {
309               throw DataException("Error - Data::trace can only be calculated for rank 2, 3 or 4 object.");
310            }
311           }
312      break;      break;
313          default:          default:
314      throw DataException("Programmer error - resultShape(left,op) can't compute shapes for operator "+opToString(op)+".");      throw DataException("Programmer error - resultShape(left,op) can't compute shapes for operator "+opToString(op)+".");
315      }      }
316  }  }
317    
318    DataTypes::ShapeType
319    SwapShape(DataAbstract_ptr left, const int axis0, const int axis1)
320    {
321         // This code taken from the Data.cpp swapaxes() method
322         // Some of the checks are probably redundant here
323         int axis0_tmp,axis1_tmp;
324         const DataTypes::ShapeType& s=left->getShape();
325         DataTypes::ShapeType out_shape;
326         // Here's the equivalent of python s_out=s[axis_offset:]+s[:axis_offset]
327         // which goes thru all shape vector elements starting with axis_offset (at index=rank wrap around to 0)
328         int rank=left->getRank();
329         if (rank<2) {
330            throw DataException("Error - Data::swapaxes argument must have at least rank 2.");
331         }
332         if (axis0<0 || axis0>rank-1) {
333            stringstream e;
334            e << "Error - Data::swapaxes: axis0 must be between 0 and rank-1=" << (rank-1);
335            throw DataException(e.str());
336         }
337         if (axis1<0 || axis1>rank-1) {
338            stringstream e;
339            e << "Error - Data::swapaxes: axis1 must be between 0 and rank-1=" << (rank-1);
340            throw DataException(e.str());
341         }
342         if (axis0 == axis1) {
343             throw DataException("Error - Data::swapaxes: axis indices must be different.");
344         }
345         if (axis0 > axis1) {
346             axis0_tmp=axis1;
347             axis1_tmp=axis0;
348         } else {
349             axis0_tmp=axis0;
350             axis1_tmp=axis1;
351         }
352         for (int i=0; i<rank; i++) {
353           if (i == axis0_tmp) {
354              out_shape.push_back(s[axis1_tmp]);
355           } else if (i == axis1_tmp) {
356              out_shape.push_back(s[axis0_tmp]);
357           } else {
358              out_shape.push_back(s[i]);
359           }
360         }
361        return out_shape;
362    }
363    
364    
365  // determine the output shape for the general tensor product operation  // determine the output shape for the general tensor product operation
366  // the additional parameters return information required later for the product  // the additional parameters return information required later for the product
367  // 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
# Line 266  GTPShape(DataAbstract_ptr left, DataAbst Line 426  GTPShape(DataAbstract_ptr left, DataAbst
426    return shape2;    return shape2;
427  }  }
428    
   
 // determine the number of points in the result of "left op right"  
 // 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:  
    case G_UNARY_P: return max(left->getBuffsRequired(),1);  
    case G_NP1OUT: return 1+max(left->getBuffsRequired(),1);  
    case G_NP1OUT_P: 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)+".");  
    }  
 }  
   
   
429  }   // end anonymous namespace  }   // end anonymous namespace
430    
431    
# Line 318  opToString(ES_optype op) Line 441  opToString(ES_optype op)
441    return ES_opstrings[op];    return ES_opstrings[op];
442  }  }
443    
444    void DataLazy::LazyNodeSetup()
445    {
446    #ifdef _OPENMP
447        int numthreads=omp_get_max_threads();
448        m_samples.resize(numthreads*m_samplesize);
449        m_sampleids=new int[numthreads];
450        for (int i=0;i<numthreads;++i)
451        {
452            m_sampleids[i]=-1;  
453        }
454    #else
455        m_samples.resize(m_samplesize);
456        m_sampleids=new int[1];
457        m_sampleids[0]=-1;
458    #endif  // _OPENMP
459    }
460    
461    
462    // Creates an identity node
463  DataLazy::DataLazy(DataAbstract_ptr p)  DataLazy::DataLazy(DataAbstract_ptr p)
464      : parent(p->getFunctionSpace(),p->getShape()),      : parent(p->getFunctionSpace(),p->getShape())
465      m_op(IDENTITY),      ,m_sampleids(0),
466      m_axis_offset(0),      m_samples(1)
     m_transpose(0),  
     m_SL(0), m_SM(0), m_SR(0)  
467  {  {
468     if (p->isLazy())     if (p->isLazy())
469     {     {
# Line 335  DataLazy::DataLazy(DataAbstract_ptr p) Line 474  DataLazy::DataLazy(DataAbstract_ptr p)
474     }     }
475     else     else
476     {     {
477      m_id=dynamic_pointer_cast<DataReady>(p);      p->makeLazyShared();
478      if(p->isConstant()) {m_readytype='C';}      DataReady_ptr dr=dynamic_pointer_cast<DataReady>(p);
479      else if(p->isExpanded()) {m_readytype='E';}      makeIdentity(dr);
480      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.");}  
481     }     }
    m_buffsRequired=1;  
    m_samplesize=getNumDPPSample()*getNoValues();  
    m_maxsamplesize=m_samplesize;  
482  LAZYDEBUG(cout << "(1)Lazy created with " << m_samplesize << endl;)  LAZYDEBUG(cout << "(1)Lazy created with " << m_samplesize << endl;)
483  }  }
484    
   
   
   
485  DataLazy::DataLazy(DataAbstract_ptr left, ES_optype op)  DataLazy::DataLazy(DataAbstract_ptr left, ES_optype op)
486      : parent(left->getFunctionSpace(),left->getShape()),      : parent(left->getFunctionSpace(),(getOpgroup(op)!=G_REDUCTION)?left->getShape():DataTypes::scalarShape),
487      m_op(op),      m_op(op),
488      m_axis_offset(0),      m_axis_offset(0),
489      m_transpose(0),      m_transpose(0),
490      m_SL(0), m_SM(0), m_SR(0)      m_SL(0), m_SM(0), m_SR(0)
491  {  {
492     if ((getOpgroup(op)!=G_UNARY) && (getOpgroup(op)!=G_NP1OUT))     if ((getOpgroup(op)!=G_UNARY) && (getOpgroup(op)!=G_NP1OUT) && (getOpgroup(op)!=G_REDUCTION))
493     {     {
494      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.");
495     }     }
# Line 373  DataLazy::DataLazy(DataAbstract_ptr left Line 505  DataLazy::DataLazy(DataAbstract_ptr left
505     }     }
506     m_readytype=lleft->m_readytype;     m_readytype=lleft->m_readytype;
507     m_left=lleft;     m_left=lleft;
    m_buffsRequired=calcBuffs(m_left, m_right,m_op); // yeah m_right will be null at this point  
508     m_samplesize=getNumDPPSample()*getNoValues();     m_samplesize=getNumDPPSample()*getNoValues();
509     m_maxsamplesize=max(m_samplesize,m_left->getMaxSampleSize());     m_children=m_left->m_children+1;
510       m_height=m_left->m_height+1;
511       LazyNodeSetup();
512       SIZELIMIT
513  }  }
514    
515    
# Line 385  DataLazy::DataLazy(DataAbstract_ptr left Line 519  DataLazy::DataLazy(DataAbstract_ptr left
519      m_op(op),      m_op(op),
520      m_SL(0), m_SM(0), m_SR(0)      m_SL(0), m_SM(0), m_SR(0)
521  {  {
522    LAZYDEBUG(cout << "Forming operator with " << left.get() << " " << right.get() << endl;)
523     if ((getOpgroup(op)!=G_BINARY))     if ((getOpgroup(op)!=G_BINARY))
524     {     {
525      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.");
# Line 401  DataLazy::DataLazy(DataAbstract_ptr left Line 536  DataLazy::DataLazy(DataAbstract_ptr left
536     {     {
537      Data tmp(Data(right),getFunctionSpace());      Data tmp(Data(right),getFunctionSpace());
538      right=tmp.borrowDataPtr();      right=tmp.borrowDataPtr();
539    LAZYDEBUG(cout << "Right interpolation required " << right.get() << endl;)
540     }     }
541     left->operandCheck(*right);     left->operandCheck(*right);
542    
543     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
544     {     {
545      m_left=dynamic_pointer_cast<DataLazy>(left);      m_left=dynamic_pointer_cast<DataLazy>(left);
546    LAZYDEBUG(cout << "Left is " << m_left->toString() << endl;)
547     }     }
548     else     else
549     {     {
550      m_left=DataLazy_ptr(new DataLazy(left));      m_left=DataLazy_ptr(new DataLazy(left));
551    LAZYDEBUG(cout << "Left " << left.get() << " wrapped " << m_left->m_id.get() << endl;)
552     }     }
553     if (right->isLazy())     if (right->isLazy())
554     {     {
555      m_right=dynamic_pointer_cast<DataLazy>(right);      m_right=dynamic_pointer_cast<DataLazy>(right);
556    LAZYDEBUG(cout << "Right is " << m_right->toString() << endl;)
557     }     }
558     else     else
559     {     {
560      m_right=DataLazy_ptr(new DataLazy(right));      m_right=DataLazy_ptr(new DataLazy(right));
561    LAZYDEBUG(cout << "Right " << right.get() << " wrapped " << m_right->m_id.get() << endl;)
562     }     }
563     char lt=m_left->m_readytype;     char lt=m_left->m_readytype;
564     char rt=m_right->m_readytype;     char rt=m_right->m_readytype;
# Line 435  DataLazy::DataLazy(DataAbstract_ptr left Line 575  DataLazy::DataLazy(DataAbstract_ptr left
575      m_readytype='C';      m_readytype='C';
576     }     }
577     m_samplesize=getNumDPPSample()*getNoValues();     m_samplesize=getNumDPPSample()*getNoValues();
578     m_maxsamplesize=max(max(m_samplesize,m_right->getMaxSampleSize()),m_left->getMaxSampleSize());       m_children=m_left->m_children+m_right->m_children+2;
579     m_buffsRequired=calcBuffs(m_left, m_right,m_op);     m_height=max(m_left->m_height,m_right->m_height)+1;
580       LazyNodeSetup();
581       SIZELIMIT
582  LAZYDEBUG(cout << "(3)Lazy created with " << m_samplesize << endl;)  LAZYDEBUG(cout << "(3)Lazy created with " << m_samplesize << endl;)
583  }  }
584    
# Line 466  DataLazy::DataLazy(DataAbstract_ptr left Line 608  DataLazy::DataLazy(DataAbstract_ptr left
608      Data tmp(Data(right),getFunctionSpace());      Data tmp(Data(right),getFunctionSpace());
609      right=tmp.borrowDataPtr();      right=tmp.borrowDataPtr();
610     }     }
611     left->operandCheck(*right);  //    left->operandCheck(*right);
612    
613     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
614     {     {
# Line 499  DataLazy::DataLazy(DataAbstract_ptr left Line 641  DataLazy::DataLazy(DataAbstract_ptr left
641      m_readytype='C';      m_readytype='C';
642     }     }
643     m_samplesize=getNumDPPSample()*getNoValues();     m_samplesize=getNumDPPSample()*getNoValues();
644     m_maxsamplesize=max(max(m_samplesize,m_right->getMaxSampleSize()),m_left->getMaxSampleSize());       m_children=m_left->m_children+m_right->m_children+2;
645     m_buffsRequired=calcBuffs(m_left, m_right,m_op);     m_height=max(m_left->m_height,m_right->m_height)+1;
646       LazyNodeSetup();
647       SIZELIMIT
648  LAZYDEBUG(cout << "(4)Lazy created with " << m_samplesize << endl;)  LAZYDEBUG(cout << "(4)Lazy created with " << m_samplesize << endl;)
649  }  }
650    
651    
652  DataLazy::DataLazy(DataAbstract_ptr left, ES_optype op, int axis_offset)  DataLazy::DataLazy(DataAbstract_ptr left, ES_optype op, int axis_offset)
653      : parent(left->getFunctionSpace(), resultShape(left,op)),      : parent(left->getFunctionSpace(), resultShape(left,op, axis_offset)),
654      m_op(op),      m_op(op),
655      m_axis_offset(axis_offset),      m_axis_offset(axis_offset),
656      m_transpose(0),      m_transpose(0),
# Line 527  DataLazy::DataLazy(DataAbstract_ptr left Line 671  DataLazy::DataLazy(DataAbstract_ptr left
671     }     }
672     m_readytype=lleft->m_readytype;     m_readytype=lleft->m_readytype;
673     m_left=lleft;     m_left=lleft;
    m_buffsRequired=calcBuffs(m_left, m_right,m_op); // yeah m_right will be null at this point  
674     m_samplesize=getNumDPPSample()*getNoValues();     m_samplesize=getNumDPPSample()*getNoValues();
675     m_maxsamplesize=max(m_samplesize,m_left->getMaxSampleSize());     m_children=m_left->m_children+1;
676       m_height=m_left->m_height+1;
677       LazyNodeSetup();
678       SIZELIMIT
679  LAZYDEBUG(cout << "(5)Lazy created with " << m_samplesize << endl;)  LAZYDEBUG(cout << "(5)Lazy created with " << m_samplesize << endl;)
680  }  }
681    
# Line 555  DataLazy::DataLazy(DataAbstract_ptr left Line 701  DataLazy::DataLazy(DataAbstract_ptr left
701     }     }
702     m_readytype=lleft->m_readytype;     m_readytype=lleft->m_readytype;
703     m_left=lleft;     m_left=lleft;
    m_buffsRequired=calcBuffs(m_left, m_right,m_op); // yeah m_right will be null at this point  
704     m_samplesize=getNumDPPSample()*getNoValues();     m_samplesize=getNumDPPSample()*getNoValues();
705     m_maxsamplesize=max(m_samplesize,m_left->getMaxSampleSize());     m_children=m_left->m_children+1;
706       m_height=m_left->m_height+1;
707       LazyNodeSetup();
708       SIZELIMIT
709  LAZYDEBUG(cout << "(6)Lazy created with " << m_samplesize << endl;)  LAZYDEBUG(cout << "(6)Lazy created with " << m_samplesize << endl;)
710  }  }
711    
 DataLazy::~DataLazy()  
 {  
 }  
712    
713    DataLazy::DataLazy(DataAbstract_ptr left, ES_optype op, const int axis0, const int axis1)
714  int      : parent(left->getFunctionSpace(), SwapShape(left,axis0,axis1)),
715  DataLazy::getBuffsRequired() const      m_op(op),
716        m_axis_offset(axis0),
717        m_transpose(axis1),
718        m_tol(0)
719  {  {
720      return m_buffsRequired;     if ((getOpgroup(op)!=G_NP1OUT_2P))
721       {
722        throw DataException("Programmer error - constructor DataLazy(left, op, tol) will only process UNARY operations which require two integer parameters.");
723       }
724       DataLazy_ptr lleft;
725       if (!left->isLazy())
726       {
727        lleft=DataLazy_ptr(new DataLazy(left));
728       }
729       else
730       {
731        lleft=dynamic_pointer_cast<DataLazy>(left);
732       }
733       m_readytype=lleft->m_readytype;
734       m_left=lleft;
735       m_samplesize=getNumDPPSample()*getNoValues();
736       m_children=m_left->m_children+1;
737       m_height=m_left->m_height+1;
738       LazyNodeSetup();
739       SIZELIMIT
740    LAZYDEBUG(cout << "(7)Lazy created with " << m_samplesize << endl;)
741  }  }
742    
743    DataLazy::~DataLazy()
 size_t  
 DataLazy::getMaxSampleSize() const  
744  {  {
745      return m_maxsamplesize;     delete[] m_sampleids;
746  }  }
747    
748    
749  /*  /*
750    \brief Evaluates the expression using methods on Data.    \brief Evaluates the expression using methods on Data.
751    This does the work for the collapse method.    This does the work for the collapse method.
# Line 718  DataLazy::collapseToReady() Line 885  DataLazy::collapseToReady()
885      case TRACE:      case TRACE:
886      result=left.trace(m_axis_offset);      result=left.trace(m_axis_offset);
887      break;      break;
888        case SWAP:
889        result=left.swapaxes(m_axis_offset, m_transpose);
890        break;
891        case MINVAL:
892        result=left.minval();
893        break;
894        case MAXVAL:
895        result=left.minval();
896        break;
897      default:      default:
898      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)+".");
899    }    }
# Line 745  DataLazy::collapse() Line 921  DataLazy::collapse()
921    m_op=IDENTITY;    m_op=IDENTITY;
922  }  }
923    
924  /*  
925    \brief Compute the value of the expression (unary operation) for the given sample.  
926    \return Vector which stores the value of the subexpression for the given sample.  
927    \param v A vector to store intermediate results.  
928    \param offset Index in v to begin storing results.  
929    \param sampleNo Sample number to evaluate.  #define PROC_OP(TYPE,X)                               \
930    \param roffset (output parameter) the offset in the return vector where the result begins.      for (int j=0;j<onumsteps;++j)\
931        {\
932    The return value will be an existing vector so do not deallocate it.        for (int i=0;i<numsteps;++i,resultp+=resultStep) \
933    If the result is stored in v it should be stored at the offset given.        { \
934    Everything from offset to the end of v should be considered available for this method to use.  LAZYDEBUG(cout << "[left,right]=[" << lroffset << "," << rroffset << "]" << endl;)\
935  */  LAZYDEBUG(cout << "{left,right}={" << (*left)[lroffset] << "," << (*right)[rroffset] << "}\n";)\
936  DataTypes::ValueType*           tensor_binary_operation< TYPE >(chunksize, &((*left)[lroffset]), &((*right)[rroffset]), resultp, X); \
937  DataLazy::resolveUnary(ValueType& v, size_t offset, int sampleNo, size_t& roffset) const  LAZYDEBUG(cout << " result=      " << resultp[0] << endl;) \
938             lroffset+=leftstep; \
939             rroffset+=rightstep; \
940          }\
941          lroffset+=oleftstep;\
942          rroffset+=orightstep;\
943        }
944    
945    
946    // The result will be stored in m_samples
947    // The return value is a pointer to the DataVector, offset is the offset within the return value
948    const DataTypes::ValueType*
949    DataLazy::resolveNodeSample(int tid, int sampleNo, size_t& roffset)
950    {
951    LAZYDEBUG(cout << "Resolve sample " << toString() << endl;)
952        // collapse so we have a 'E' node or an IDENTITY for some other type
953      if (m_readytype!='E' && m_op!=IDENTITY)
954      {
955        collapse();
956      }
957      if (m_op==IDENTITY)  
958      {
959        const ValueType& vec=m_id->getVectorRO();
960        roffset=m_id->getPointOffset(sampleNo, 0);
961    #ifdef LAZY_STACK_PROF
962    int x;
963    if (&x<stackend[omp_get_thread_num()])
964    {
965           stackend[omp_get_thread_num()]=&x;
966    }
967    #endif
968        return &(vec);
969      }
970      if (m_readytype!='E')
971      {
972        throw DataException("Programmer Error - Collapse did not produce an expanded node.");
973      }
974      if (m_sampleids[tid]==sampleNo)
975      {
976        roffset=tid*m_samplesize;
977        return &(m_samples);        // sample is already resolved
978      }
979      m_sampleids[tid]=sampleNo;
980      switch (getOpgroup(m_op))
981      {
982      case G_UNARY:
983      case G_UNARY_P: return resolveNodeUnary(tid, sampleNo, roffset);
984      case G_BINARY: return resolveNodeBinary(tid, sampleNo, roffset);
985      case G_NP1OUT: return resolveNodeNP1OUT(tid, sampleNo, roffset);
986      case G_NP1OUT_P: return resolveNodeNP1OUT_P(tid, sampleNo, roffset);
987      case G_TENSORPROD: return resolveNodeTProd(tid, sampleNo, roffset);
988      case G_NP1OUT_2P: return resolveNodeNP1OUT_2P(tid, sampleNo, roffset);
989      case G_REDUCTION: return resolveNodeReduction(tid, sampleNo, roffset);
990      default:
991        throw DataException("Programmer Error - resolveSample does not know how to process "+opToString(m_op)+".");
992      }
993    }
994    
995    const DataTypes::ValueType*
996    DataLazy::resolveNodeUnary(int tid, int sampleNo, size_t& roffset)
997  {  {
998      // we assume that any collapsing has been done before we get here      // we assume that any collapsing has been done before we get here
999      // 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
1000      // processing single points.      // processing single points.
1001        // we will also know we won't get identity nodes
1002    if (m_readytype!='E')    if (m_readytype!='E')
1003    {    {
1004      throw DataException("Programmer error - resolveUnary should only be called on expanded Data.");      throw DataException("Programmer error - resolveUnary should only be called on expanded Data.");
1005    }    }
1006    const ValueType* vleft=m_left->resolveSample(v,offset,sampleNo,roffset);    if (m_op==IDENTITY)
1007    const double* left=&((*vleft)[roffset]);    {
1008    double* result=&(v[offset]);      throw DataException("Programmer error - resolveNodeUnary should not be called on identity nodes.");
1009    roffset=offset;    }
1010      const DataTypes::ValueType* leftres=m_left->resolveNodeSample(tid, sampleNo, roffset);
1011      const double* left=&((*leftres)[roffset]);
1012      roffset=m_samplesize*tid;
1013      double* result=&(m_samples[roffset]);
1014    switch (m_op)    switch (m_op)
1015    {    {
1016      case SIN:        case SIN:  
# Line 880  DataLazy::resolveUnary(ValueType& v, siz Line 1120  DataLazy::resolveUnary(ValueType& v, siz
1120      default:      default:
1121      throw DataException("Programmer error - resolveUnary can not resolve operator "+opToString(m_op)+".");      throw DataException("Programmer error - resolveUnary can not resolve operator "+opToString(m_op)+".");
1122    }    }
1123    return &v;    return &(m_samples);
1124  }  }
1125    
1126    
1127    const DataTypes::ValueType*
1128    DataLazy::resolveNodeReduction(int tid, int sampleNo, size_t& roffset)
1129    {
1130        // we assume that any collapsing has been done before we get here
1131        // since we only have one argument we don't need to think about only
1132        // processing single points.
1133        // we will also know we won't get identity nodes
1134      if (m_readytype!='E')
1135      {
1136        throw DataException("Programmer error - resolveUnary should only be called on expanded Data.");
1137      }
1138      if (m_op==IDENTITY)
1139      {
1140        throw DataException("Programmer error - resolveNodeUnary should not be called on identity nodes.");
1141      }
1142      size_t loffset=0;
1143      const DataTypes::ValueType* leftres=m_left->resolveNodeSample(tid, sampleNo, loffset);
1144    
1145      roffset=m_samplesize*tid;
1146      unsigned int ndpps=getNumDPPSample();
1147      unsigned int psize=DataTypes::noValues(getShape());
1148      double* result=&(m_samples[roffset]);
1149      switch (m_op)
1150      {
1151        case MINVAL:
1152        {
1153          for (unsigned int z=0;z<ndpps;++z)
1154          {
1155            FMin op;
1156            *result=DataMaths::reductionOp(*leftres, m_left->getShape(), loffset, op, numeric_limits<double>::max());
1157            loffset+=psize;
1158            result++;
1159          }
1160        }
1161        break;
1162        case MAXVAL:
1163        {
1164          for (unsigned int z=0;z<ndpps;++z)
1165          {
1166          FMax op;
1167          *result=DataMaths::reductionOp(*leftres, m_left->getShape(), loffset, op, numeric_limits<double>::max()*-1);
1168          loffset+=psize;
1169          result++;
1170          }
1171        }
1172        break;
1173        default:
1174        throw DataException("Programmer error - resolveUnary can not resolve operator "+opToString(m_op)+".");
1175      }
1176      return &(m_samples);
1177    }
1178    
1179    const DataTypes::ValueType*
1180    DataLazy::resolveNodeNP1OUT(int tid, int sampleNo, size_t& roffset)
 /*  
   \brief Compute the value of the expression (unary 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.  
 */  
 DataTypes::ValueType*  
 DataLazy::resolveNP1OUT(ValueType& v, size_t offset, int sampleNo, size_t& roffset) const  
1181  {  {
1182      // we assume that any collapsing has been done before we get here      // we assume that any collapsing has been done before we get here
1183      // 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
1184      // processing single points.      // processing single points.
1185    if (m_readytype!='E')    if (m_readytype!='E')
1186    {    {
1187      throw DataException("Programmer error - resolveNP1OUT should only be called on expanded Data.");      throw DataException("Programmer error - resolveNodeNP1OUT should only be called on expanded Data.");
1188    }    }
1189      // since we can't write the result over the input, we need a result offset further along    if (m_op==IDENTITY)
1190    size_t subroffset=roffset+m_samplesize;    {
1191    const ValueType* vleft=m_left->resolveSample(v,offset,sampleNo,subroffset);      throw DataException("Programmer error - resolveNodeNP1OUT should not be called on identity nodes.");
1192    roffset=offset;    }
1193      size_t subroffset;
1194      const ValueType* leftres=m_left->resolveNodeSample(tid, sampleNo, subroffset);
1195      roffset=m_samplesize*tid;
1196      size_t loop=0;
1197      size_t numsteps=(m_readytype=='E')?getNumDPPSample():1;
1198      size_t step=getNoValues();
1199      size_t offset=roffset;
1200    switch (m_op)    switch (m_op)
1201    {    {
1202      case SYM:      case SYM:
1203      DataMaths::symmetric(*vleft,m_left->getShape(),subroffset, v, getShape(), offset);      for (loop=0;loop<numsteps;++loop)
1204        {
1205            DataMaths::symmetric(*leftres,m_left->getShape(),subroffset, m_samples, getShape(), offset);
1206            subroffset+=step;
1207            offset+=step;
1208        }
1209      break;      break;
1210      case NSYM:      case NSYM:
1211      DataMaths::nonsymmetric(*vleft,m_left->getShape(),subroffset, v, getShape(), offset);      for (loop=0;loop<numsteps;++loop)
1212        {
1213            DataMaths::nonsymmetric(*leftres,m_left->getShape(),subroffset, m_samples, getShape(), offset);
1214            subroffset+=step;
1215            offset+=step;
1216        }
1217      break;      break;
1218      default:      default:
1219      throw DataException("Programmer error - resolveNP1OUT can not resolve operator "+opToString(m_op)+".");      throw DataException("Programmer error - resolveNP1OUT can not resolve operator "+opToString(m_op)+".");
1220    }    }
1221    return &v;    return &m_samples;
1222  }  }
1223    
1224  /*  const DataTypes::ValueType*
1225    \brief Compute the value of the expression (unary operation) for the given sample.  DataLazy::resolveNodeNP1OUT_P(int tid, int sampleNo, size_t& roffset)
   \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_P(ValueType& v, size_t offset, int sampleNo, size_t& roffset) const  
1226  {  {
1227      // we assume that any collapsing has been done before we get here      // we assume that any collapsing has been done before we get here
1228      // 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
1229      // processing single points.      // processing single points.
1230    if (m_readytype!='E')    if (m_readytype!='E')
1231    {    {
1232      throw DataException("Programmer error - resolveNP1OUT_P should only be called on expanded Data.");      throw DataException("Programmer error - resolveNodeNP1OUT_P should only be called on expanded Data.");
1233    }    }
1234      // since we can't write the result over the input, we need a result offset further along    if (m_op==IDENTITY)
1235    size_t subroffset=roffset+m_samplesize;    {
1236    const ValueType* vleft=m_left->resolveSample(v,offset,sampleNo,subroffset);      throw DataException("Programmer error - resolveNodeNP1OUT_P should not be called on identity nodes.");
1237    roffset=offset;    }
1238      size_t subroffset;
1239      size_t offset;
1240      const ValueType* leftres=m_left->resolveNodeSample(tid, sampleNo, subroffset);
1241      roffset=m_samplesize*tid;
1242      offset=roffset;
1243      size_t loop=0;
1244      size_t numsteps=(m_readytype=='E')?getNumDPPSample():1;
1245      size_t outstep=getNoValues();
1246      size_t instep=m_left->getNoValues();
1247    switch (m_op)    switch (m_op)
1248    {    {
1249      case TRACE:      case TRACE:
1250           DataMaths::trace(*vleft,m_left->getShape(),subroffset, v,getShape(),offset,m_axis_offset);      for (loop=0;loop<numsteps;++loop)
1251        {
1252                DataMaths::trace(*leftres,m_left->getShape(),subroffset, m_samples ,getShape(),offset,m_axis_offset);
1253            subroffset+=instep;
1254            offset+=outstep;
1255        }
1256      break;      break;
1257      case TRANS:      case TRANS:
1258           DataMaths::transpose(*vleft,m_left->getShape(),subroffset, v,getShape(),offset,m_axis_offset);      for (loop=0;loop<numsteps;++loop)
1259        {
1260                DataMaths::transpose(*leftres,m_left->getShape(),subroffset, m_samples, getShape(),offset,m_axis_offset);
1261            subroffset+=instep;
1262            offset+=outstep;
1263        }
1264      break;      break;
1265      default:      default:
1266      throw DataException("Programmer error - resolveNP1OUTP can not resolve operator "+opToString(m_op)+".");      throw DataException("Programmer error - resolveNP1OUTP can not resolve operator "+opToString(m_op)+".");
1267    }    }
1268    return &v;    return &m_samples;
1269  }  }
1270    
1271    
1272  #define PROC_OP(TYPE,X)                               \  const DataTypes::ValueType*
1273      for (int i=0;i<steps;++i,resultp+=resultStep) \  DataLazy::resolveNodeNP1OUT_2P(int tid, int sampleNo, size_t& roffset)
1274      { \  {
1275         tensor_binary_operation< TYPE >(chunksize, &((*left)[lroffset]), &((*right)[rroffset]), resultp, X); \    if (m_readytype!='E')
1276         lroffset+=leftStep; \    {
1277         rroffset+=rightStep; \      throw DataException("Programmer error - resolveNodeNP1OUT_2P should only be called on expanded Data.");
1278      }
1279      if (m_op==IDENTITY)
1280      {
1281        throw DataException("Programmer error - resolveNodeNP1OUT_2P should not be called on identity nodes.");
1282      }
1283      size_t subroffset;
1284      size_t offset;
1285      const ValueType* leftres=m_left->resolveNodeSample(tid, sampleNo, subroffset);
1286      roffset=m_samplesize*tid;
1287      offset=roffset;
1288      size_t loop=0;
1289      size_t numsteps=(m_readytype=='E')?getNumDPPSample():1;
1290      size_t outstep=getNoValues();
1291      size_t instep=m_left->getNoValues();
1292      switch (m_op)
1293      {
1294        case SWAP:
1295        for (loop=0;loop<numsteps;++loop)
1296        {
1297                DataMaths::swapaxes(*leftres,m_left->getShape(),subroffset, m_samples, getShape(),offset, m_axis_offset, m_transpose);
1298            subroffset+=instep;
1299            offset+=outstep;
1300      }      }
1301        break;
1302        default:
1303        throw DataException("Programmer error - resolveNodeNP1OUT2P can not resolve operator "+opToString(m_op)+".");
1304      }
1305      return &m_samples;
1306    }
1307    
1308    
1309    
 /*  
   \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.  
 */  
1310  // 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
1311  // 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.
1312  // 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 998  DataLazy::resolveNP1OUT_P(ValueType& v, Line 1316  DataLazy::resolveNP1OUT_P(ValueType& v,
1316  // There is an additional complication when scalar operations are considered.  // There is an additional complication when scalar operations are considered.
1317  // For example, 2+Vector.  // For example, 2+Vector.
1318  // In this case each double within the point is treated individually  // In this case each double within the point is treated individually
1319  DataTypes::ValueType*  const DataTypes::ValueType*
1320  DataLazy::resolveBinary(ValueType& v,  size_t offset, int sampleNo, size_t& roffset) const  DataLazy::resolveNodeBinary(int tid, int sampleNo, size_t& roffset)
1321  {  {
1322  LAZYDEBUG(cout << "Resolve binary: " << toString() << endl;)  LAZYDEBUG(cout << "Resolve binary: " << toString() << endl;)
1323    
# Line 1013  LAZYDEBUG(cout << "Resolve binary: " << Line 1331  LAZYDEBUG(cout << "Resolve binary: " <<
1331    }    }
1332    bool leftScalar=(m_left->getRank()==0);    bool leftScalar=(m_left->getRank()==0);
1333    bool rightScalar=(m_right->getRank()==0);    bool rightScalar=(m_right->getRank()==0);
1334    bool bigloops=((leftExp && rightExp) || (!leftExp && !rightExp)); // is processing in single step?    if ((m_left->getRank()!=m_right->getRank()) && (!leftScalar && !rightScalar))
1335    int steps=(bigloops?1:getNumDPPSample());    {
1336    size_t chunksize=(bigloops? m_samplesize : getNoValues());    // if bigloops, pretend the whole sample is a datapoint      throw DataException("resolveBinary - ranks of arguments must match unless one of them is scalar.");
1337    if (m_left->getRank()!=m_right->getRank())    // need to deal with scalar * ? ops    }
1338    {    size_t leftsize=m_left->getNoValues();
1339      if (!leftScalar && !rightScalar)    size_t rightsize=m_right->getNoValues();
1340      {    size_t chunksize=1;           // how many doubles will be processed in one go
1341         throw DataException("resolveBinary - ranks of arguments must match unless one of them is scalar.");    int leftstep=0;       // how far should the left offset advance after each step
1342      }    int rightstep=0;
1343      steps=getNumDPPSample()*max(m_left->getNoValues(),m_right->getNoValues());    int numsteps=0;       // total number of steps for the inner loop
1344      chunksize=1;    // for scalar    int oleftstep=0;  // the o variables refer to the outer loop
1345    }        int orightstep=0; // The outer loop is only required in cases where there is an extended scalar
1346    int leftStep=((leftExp && (!rightExp || rightScalar))? m_right->getNoValues() : 0);    int onumsteps=1;
1347    int rightStep=((rightExp && (!leftExp || leftScalar))? m_left->getNoValues() : 0);    
1348    int resultStep=max(leftStep,rightStep);   // only one (at most) should be !=0    bool LES=(leftExp && leftScalar); // Left is an expanded scalar
1349      bool RES=(rightExp && rightScalar);
1350      bool LS=(!leftExp && leftScalar); // left is a single scalar
1351      bool RS=(!rightExp && rightScalar);
1352      bool LN=(!leftExp && !leftScalar);    // left is a single non-scalar
1353      bool RN=(!rightExp && !rightScalar);
1354      bool LEN=(leftExp && !leftScalar);    // left is an expanded non-scalar
1355      bool REN=(rightExp && !rightScalar);
1356    
1357      if ((LES && RES) || (LEN && REN)) // both are Expanded scalars or both are expanded non-scalars
1358      {
1359        chunksize=m_left->getNumDPPSample()*leftsize;
1360        leftstep=0;
1361        rightstep=0;
1362        numsteps=1;
1363      }
1364      else if (LES || RES)
1365      {
1366        chunksize=1;
1367        if (LES)        // left is an expanded scalar
1368        {
1369            if (RS)
1370            {
1371               leftstep=1;
1372               rightstep=0;
1373               numsteps=m_left->getNumDPPSample();
1374            }
1375            else        // RN or REN
1376            {
1377               leftstep=0;
1378               oleftstep=1;
1379               rightstep=1;
1380               orightstep=(RN ? -(int)rightsize : 0);
1381               numsteps=rightsize;
1382               onumsteps=m_left->getNumDPPSample();
1383            }
1384        }
1385        else        // right is an expanded scalar
1386        {
1387            if (LS)
1388            {
1389               rightstep=1;
1390               leftstep=0;
1391               numsteps=m_right->getNumDPPSample();
1392            }
1393            else
1394            {
1395               rightstep=0;
1396               orightstep=1;
1397               leftstep=1;
1398               oleftstep=(LN ? -(int)leftsize : 0);
1399               numsteps=leftsize;
1400               onumsteps=m_right->getNumDPPSample();
1401            }
1402        }
1403      }
1404      else  // this leaves (LEN, RS), (LEN, RN) and their transposes
1405      {
1406        if (LEN)    // and Right will be a single value
1407        {
1408            chunksize=rightsize;
1409            leftstep=rightsize;
1410            rightstep=0;
1411            numsteps=m_left->getNumDPPSample();
1412            if (RS)
1413            {
1414               numsteps*=leftsize;
1415            }
1416        }
1417        else    // REN
1418        {
1419            chunksize=leftsize;
1420            rightstep=leftsize;
1421            leftstep=0;
1422            numsteps=m_right->getNumDPPSample();
1423            if (LS)
1424            {
1425               numsteps*=rightsize;
1426            }
1427        }
1428      }
1429    
1430      int resultStep=max(leftstep,rightstep);   // only one (at most) should be !=0
1431      // Get the values of sub-expressions      // Get the values of sub-expressions
1432    const ValueType* left=m_left->resolveSample(v,offset,sampleNo,lroffset);    const ValueType* left=m_left->resolveNodeSample(tid,sampleNo,lroffset);  
1433    const ValueType* right=m_right->resolveSample(v,offset+m_samplesize,sampleNo,rroffset); // Note    const ValueType* right=m_right->resolveNodeSample(tid,sampleNo,rroffset);
1434      // the right child starts further along.  LAZYDEBUG(cout << "Post sub calls in " << toString() << endl;)
1435    double* resultp=&(v[offset]);     // results are stored at the vector offset we recieved  LAZYDEBUG(cout << "shapes=" << DataTypes::shapeToString(m_left->getShape()) << "," << DataTypes::shapeToString(m_right->getShape()) << endl;)
1436    LAZYDEBUG(cout << "chunksize=" << chunksize << endl << "leftstep=" << leftstep << " rightstep=" << rightstep;)
1437    LAZYDEBUG(cout << " numsteps=" << numsteps << endl << "oleftstep=" << oleftstep << " orightstep=" << orightstep;)
1438    LAZYDEBUG(cout << "onumsteps=" << onumsteps << endl;)
1439    LAZYDEBUG(cout << " DPPS=" << m_left->getNumDPPSample() << "," <<m_right->getNumDPPSample() << endl;)
1440    LAZYDEBUG(cout << "" << LS << RS << LN << RN << LES << RES <<LEN << REN <<   endl;)
1441    
1442    LAZYDEBUG(cout << "Left res["<< lroffset<< "]=" << (*left)[lroffset] << endl;)
1443    LAZYDEBUG(cout << "Right res["<< rroffset<< "]=" << (*right)[rroffset] << endl;)
1444    
1445    
1446      roffset=m_samplesize*tid;
1447      double* resultp=&(m_samples[roffset]);        // results are stored at the vector offset we recieved
1448    switch(m_op)    switch(m_op)
1449    {    {
1450      case ADD:      case ADD:
# Line 1053  LAZYDEBUG(cout << "Resolve binary: " << Line 1465  LAZYDEBUG(cout << "Resolve binary: " <<
1465      default:      default:
1466      throw DataException("Programmer error - resolveBinary can not resolve operator "+opToString(m_op)+".");      throw DataException("Programmer error - resolveBinary can not resolve operator "+opToString(m_op)+".");
1467    }    }
1468    roffset=offset;    LAZYDEBUG(cout << "Result res[" << roffset<< "]" << m_samples[roffset] << endl;)
1469    return &v;    return &m_samples;
1470  }  }
1471    
1472    
 /*  
   \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.  
 */  
1473  // 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
1474  // 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.
1475  // unlike the other resolve helpers, we must treat these datapoints separately.  // unlike the other resolve helpers, we must treat these datapoints separately.
1476  DataTypes::ValueType*  const DataTypes::ValueType*
1477  DataLazy::resolveTProd(ValueType& v,  size_t offset, int sampleNo, size_t& roffset) const  DataLazy::resolveNodeTProd(int tid, int sampleNo, size_t& roffset)
1478  {  {
1479  LAZYDEBUG(cout << "Resolve TensorProduct: " << toString() << endl;)  LAZYDEBUG(cout << "Resolve TensorProduct: " << toString() << endl;)
1480    
# Line 1083  LAZYDEBUG(cout << "Resolve TensorProduct Line 1483  LAZYDEBUG(cout << "Resolve TensorProduct
1483    bool leftExp=(m_left->m_readytype=='E');    bool leftExp=(m_left->m_readytype=='E');
1484    bool rightExp=(m_right->m_readytype=='E');    bool rightExp=(m_right->m_readytype=='E');
1485    int steps=getNumDPPSample();    int steps=getNumDPPSample();
1486    int leftStep=((leftExp && !rightExp)? m_right->getNoValues() : 0);    int leftStep=(leftExp? m_left->getNoValues() : 0);        // do not have scalars as input to this method
1487    int rightStep=((rightExp && !leftExp)? m_left->getNoValues() : 0);    int rightStep=(rightExp?m_right->getNoValues() : 0);
1488    int resultStep=max(leftStep,rightStep);   // only one (at most) should be !=0  
1489      // Get the values of sub-expressions (leave a gap of one sample for the result).    int resultStep=getNoValues();
1490    const ValueType* left=m_left->resolveSample(v,offset+m_samplesize,sampleNo,lroffset);    roffset=m_samplesize*tid;
1491    const ValueType* right=m_right->resolveSample(v,offset+2*m_samplesize,sampleNo,rroffset);    size_t offset=roffset;
1492    double* resultp=&(v[offset]);     // results are stored at the vector offset we recieved  
1493      const ValueType* left=m_left->resolveNodeSample(tid, sampleNo, lroffset);
1494    
1495      const ValueType* right=m_right->resolveNodeSample(tid, sampleNo, rroffset);
1496    
1497    LAZYDEBUG(cerr << "[Left shape]=" << DataTypes::shapeToString(m_left->getShape()) << "\n[Right shape]=" << DataTypes::shapeToString(m_right->getShape()) << " result=" <<DataTypes::shapeToString(getShape()) <<  endl;
1498    cout << getNoValues() << endl;)
1499    
1500    
1501    LAZYDEBUG(cerr << "Post sub calls: " << toString() << endl;)
1502    LAZYDEBUG(cout << "LeftExp=" << leftExp << " rightExp=" << rightExp << endl;)
1503    LAZYDEBUG(cout << "LeftR=" << m_left->getRank() << " rightExp=" << m_right->getRank() << endl;)
1504    LAZYDEBUG(cout << "LeftSize=" << m_left->getNoValues() << " RightSize=" << m_right->getNoValues() << endl;)
1505    LAZYDEBUG(cout << "m_samplesize=" << m_samplesize << endl;)
1506    LAZYDEBUG(cout << "outputshape=" << DataTypes::shapeToString(getShape()) << endl;)
1507    LAZYDEBUG(cout << "DPPS=" << m_right->getNumDPPSample() <<"."<<endl;)
1508    
1509      double* resultp=&(m_samples[offset]);     // results are stored at the vector offset we recieved
1510    switch(m_op)    switch(m_op)
1511    {    {
1512      case PROD:      case PROD:
# Line 1097  LAZYDEBUG(cout << "Resolve TensorProduct Line 1514  LAZYDEBUG(cout << "Resolve TensorProduct
1514      {      {
1515            const double *ptr_0 = &((*left)[lroffset]);            const double *ptr_0 = &((*left)[lroffset]);
1516            const double *ptr_1 = &((*right)[rroffset]);            const double *ptr_1 = &((*right)[rroffset]);
1517    
1518    LAZYDEBUG(cout << DataTypes::pointToString(*left, m_left->getShape(),lroffset,"LEFT") << endl;)
1519    LAZYDEBUG(cout << DataTypes::pointToString(*right,m_right->getShape(),rroffset, "RIGHT") << endl;)
1520    
1521            matrix_matrix_product(m_SL, m_SM, m_SR, ptr_0, ptr_1, resultp, m_transpose);            matrix_matrix_product(m_SL, m_SM, m_SR, ptr_0, ptr_1, resultp, m_transpose);
1522    
1523        lroffset+=leftStep;        lroffset+=leftStep;
1524        rroffset+=rightStep;        rroffset+=rightStep;
1525      }      }
# Line 1106  LAZYDEBUG(cout << "Resolve TensorProduct Line 1528  LAZYDEBUG(cout << "Resolve TensorProduct
1528      throw DataException("Programmer error - resolveTProduct can not resolve operator "+opToString(m_op)+".");      throw DataException("Programmer error - resolveTProduct can not resolve operator "+opToString(m_op)+".");
1529    }    }
1530    roffset=offset;    roffset=offset;
1531    return &v;    return &m_samples;
1532  }  }
1533    
1534    
1535    const DataTypes::ValueType*
1536    DataLazy::resolveSample(int sampleNo, size_t& roffset)
1537    {
1538    #ifdef _OPENMP
1539        int tid=omp_get_thread_num();
1540    #else
1541        int tid=0;
1542    #endif
1543    
1544  /*  #ifdef LAZY_STACK_PROF
1545    \brief Compute the value of the expression for the given sample.      stackstart[tid]=&tid;
1546    \return Vector which stores the value of the subexpression for the given sample.      stackend[tid]=&tid;
1547    \param v A vector to store intermediate results.      const DataTypes::ValueType* r=resolveNodeSample(tid, sampleNo, roffset);
1548    \param offset Index in v to begin storing results.      size_t d=(size_t)stackstart[tid]-(size_t)stackend[tid];
1549    \param sampleNo Sample number to evaluate.      #pragma omp critical
1550    \param roffset (output parameter) the offset in the return vector where the result begins.      if (d>maxstackuse)
1551        {
1552    cout << "Max resolve Stack use " << d << endl;
1553            maxstackuse=d;
1554        }
1555        return r;
1556    #else
1557        return resolveNodeSample(tid, sampleNo, roffset);
1558    #endif
1559    }
1560    
   The return value will be an existing vector so do not deallocate it.  
 */  
 // the vector and the offset are a place where the method could write its data if it wishes  
 // it is not obligated to do so. For example, if it has its own storage already, it can use that.  
 // 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.  
1561    
1562  // the roffset is the offset within the returned vector where the data begins  // This needs to do the work of the identity constructor
1563  const DataTypes::ValueType*  void
1564  DataLazy::resolveSample(ValueType& v, size_t offset, int sampleNo, size_t& roffset)  DataLazy::resolveToIdentity()
1565  {  {
1566  LAZYDEBUG(cout << "Resolve sample " << toString() << endl;)     if (m_op==IDENTITY)
1567      // collapse so we have a 'E' node or an IDENTITY for some other type      return;
1568    if (m_readytype!='E' && m_op!=IDENTITY)     DataReady_ptr p=resolveNodeWorker();
1569       makeIdentity(p);
1570    }
1571    
1572    void DataLazy::makeIdentity(const DataReady_ptr& p)
1573    {
1574       m_op=IDENTITY;
1575       m_axis_offset=0;
1576       m_transpose=0;
1577       m_SL=m_SM=m_SR=0;
1578       m_children=m_height=0;
1579       m_id=p;
1580       if(p->isConstant()) {m_readytype='C';}
1581       else if(p->isExpanded()) {m_readytype='E';}
1582       else if (p->isTagged()) {m_readytype='T';}
1583       else {throw DataException("Unknown DataReady instance in convertToIdentity constructor.");}
1584       m_samplesize=p->getNumDPPSample()*p->getNoValues();
1585       m_left.reset();
1586       m_right.reset();
1587    }
1588    
1589    
1590    DataReady_ptr
1591    DataLazy::resolve()
1592    {
1593        resolveToIdentity();
1594        return m_id;
1595    }
1596    
1597    
1598    /* This is really a static method but I think that caused problems in windows */
1599    void
1600    DataLazy::resolveGroupWorker(std::vector<DataLazy*>& dats)
1601    {
1602      if (dats.empty())
1603    {    {
1604      collapse();      return;
1605    }    }
1606    if (m_op==IDENTITY)      vector<DataLazy*> work;
1607      FunctionSpace fs=dats[0]->getFunctionSpace();
1608      bool match=true;
1609      for (int i=dats.size()-1;i>=0;--i)
1610    {    {
1611      const ValueType& vec=m_id->getVector();      if (dats[i]->m_readytype!='E')
1612      if (m_readytype=='C')      {
1613      {          dats[i]->collapse();
1614      roffset=0;      }
1615      return &(vec);      if (dats[i]->m_op!=IDENTITY)
1616      }      {
1617      roffset=m_id->getPointOffset(sampleNo, 0);          work.push_back(dats[i]);
1618      return &(vec);          if (fs!=dats[i]->getFunctionSpace())
1619            {
1620                match=false;
1621            }
1622        }
1623    }    }
1624    if (m_readytype!='E')    if (work.empty())
1625    {    {
1626      throw DataException("Programmer Error - Collapse did not produce an expanded node.");      return;     // no work to do
1627    }    }
1628    switch (getOpgroup(m_op))    if (match)    // all functionspaces match.  Yes I realise this is overly strict
1629      {     // it is possible that dats[0] is one of the objects which we discarded and
1630            // all the other functionspaces match.
1631        vector<DataExpanded*> dep;
1632        vector<ValueType*> vecs;
1633        for (int i=0;i<work.size();++i)
1634        {
1635            dep.push_back(new DataExpanded(fs,work[i]->getShape(), ValueType(work[i]->getNoValues())));
1636            vecs.push_back(&(dep[i]->getVectorRW()));
1637        }
1638        int totalsamples=work[0]->getNumSamples();
1639        const ValueType* res=0; // Storage for answer
1640        int sample;
1641        #pragma omp parallel private(sample, res)
1642        {
1643            size_t roffset=0;
1644            #pragma omp for schedule(static)
1645            for (sample=0;sample<totalsamples;++sample)
1646            {
1647            roffset=0;
1648            int j;
1649            for (j=work.size()-1;j>=0;--j)
1650            {
1651    #ifdef _OPENMP
1652                    res=work[j]->resolveNodeSample(omp_get_thread_num(),sample,roffset);
1653    #else
1654                    res=work[j]->resolveNodeSample(0,sample,roffset);
1655    #endif
1656                    DataVector::size_type outoffset=dep[j]->getPointOffset(sample,0);
1657                    memcpy(&((*vecs[j])[outoffset]),&((*res)[roffset]),work[j]->m_samplesize*sizeof(DataVector::ElementType));
1658            }
1659            }
1660        }
1661        // Now we need to load the new results as identity ops into the lazy nodes
1662        for (int i=work.size()-1;i>=0;--i)
1663        {
1664            work[i]->makeIdentity(boost::dynamic_pointer_cast<DataReady>(dep[i]->getPtr()));
1665        }
1666      }
1667      else  // functionspaces do not match
1668    {    {
1669    case G_UNARY:      for (int i=0;i<work.size();++i)
1670    case G_UNARY_P: return resolveUnary(v, offset,sampleNo,roffset);      {
1671    case G_BINARY: return resolveBinary(v, offset,sampleNo,roffset);          work[i]->resolveToIdentity();
1672    case G_NP1OUT: return resolveNP1OUT(v, offset, sampleNo,roffset);      }
   case G_NP1OUT_P: return resolveNP1OUT_P(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)+".");  
1673    }    }
1674  }  }
1675    
1676    
1677  // To simplify the memory management, all threads operate on one large vector, rather than one each.  
1678  // Each sample is evaluated independently and copied into the result DataExpanded.  // This version of resolve uses storage in each node to hold results
1679  DataReady_ptr  DataReady_ptr
1680  DataLazy::resolve()  DataLazy::resolveNodeWorker()
1681  {  {
   
 LAZYDEBUG(cout << "Sample size=" << m_samplesize << endl;)  
 LAZYDEBUG(cout << "Buffers=" << m_buffsRequired << endl;)  
   
1682    if (m_readytype!='E')     // if the whole sub-expression is Constant or Tagged, then evaluate it normally    if (m_readytype!='E')     // if the whole sub-expression is Constant or Tagged, then evaluate it normally
1683    {    {
1684      collapse();      collapse();
# Line 1183  LAZYDEBUG(cout << "Buffers=" << m_buffsR Line 1688  LAZYDEBUG(cout << "Buffers=" << m_buffsR
1688      return m_id;      return m_id;
1689    }    }
1690      // 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'
   size_t threadbuffersize=m_maxsamplesize*(max(1,m_buffsRequired)); // Each thread needs to have enough  
     // storage to evaluate its expression  
   int numthreads=1;  
 #ifdef _OPENMP  
   numthreads=getNumberOfThreads();  
 #endif  
   ValueType v(numthreads*threadbuffersize);  
 LAZYDEBUG(cout << "Buffer created with size=" << v.size() << endl;)  
1691    DataExpanded* result=new DataExpanded(getFunctionSpace(),getShape(),  ValueType(getNoValues()));    DataExpanded* result=new DataExpanded(getFunctionSpace(),getShape(),  ValueType(getNoValues()));
1692    ValueType& resvec=result->getVector();    ValueType& resvec=result->getVectorRW();
1693    DataReady_ptr resptr=DataReady_ptr(result);    DataReady_ptr resptr=DataReady_ptr(result);
1694    
1695    int sample;    int sample;
   size_t outoffset;     // offset in the output data  
1696    int totalsamples=getNumSamples();    int totalsamples=getNumSamples();
1697    const ValueType* res=0;   // Vector storing the answer    const ValueType* res=0;   // Storage for answer
1698    size_t resoffset=0;       // where in the vector to find the answer  LAZYDEBUG(cout << "Total number of samples=" <<totalsamples << endl;)
1699    #pragma omp parallel for private(sample,resoffset,outoffset,res) schedule(static)    #pragma omp parallel private(sample,res)
1700    for (sample=0;sample<totalsamples;++sample)    {
1701    {      size_t roffset=0;
1702  LAZYDEBUG(cout << "################################# " << sample << endl;)  #ifdef LAZY_STACK_PROF
1703        stackstart[omp_get_thread_num()]=&roffset;
1704        stackend[omp_get_thread_num()]=&roffset;
1705    #endif
1706        #pragma omp for schedule(static)
1707        for (sample=0;sample<totalsamples;++sample)
1708        {
1709            roffset=0;
1710  #ifdef _OPENMP  #ifdef _OPENMP
1711      res=resolveSample(v,threadbuffersize*omp_get_thread_num(),sample,resoffset);              res=resolveNodeSample(omp_get_thread_num(),sample,roffset);
1712  #else  #else
1713      res=resolveSample(v,0,sample,resoffset);   // res would normally be v, but not if its a single IDENTITY op.              res=resolveNodeSample(0,sample,roffset);
1714  #endif  #endif
1715  LAZYDEBUG(cerr << "-------------------------------- " << endl;)  LAZYDEBUG(cout << "Sample #" << sample << endl;)
1716      outoffset=result->getPointOffset(sample,0);  LAZYDEBUG(cout << "Final res[" << roffset<< "]=" << (*res)[roffset] << (*res)[roffset]<< endl; )
1717  LAZYDEBUG(cerr << "offset=" << outoffset << endl;)              DataVector::size_type outoffset=result->getPointOffset(sample,0);
1718      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(DataVector::ElementType));
1719      {      }
1720      resvec[outoffset]=(*res)[resoffset];    }
1721      }  #ifdef LAZY_STACK_PROF
1722  LAZYDEBUG(cerr << "*********************************" << endl;)    for (int i=0;i<getNumberOfThreads();++i)
1723      {
1724        size_t r=((size_t)stackstart[i] - (size_t)stackend[i]);
1725    //  cout << i << " " << stackstart[i] << " .. " << stackend[i] << " = " <<  r << endl;
1726        if (r>maxstackuse)
1727        {
1728            maxstackuse=r;
1729        }
1730    }    }
1731      cout << "Max resolve Stack use=" << maxstackuse << endl;
1732    #endif
1733    return resptr;    return resptr;
1734  }  }
1735    
# Line 1224  std::string Line 1737  std::string
1737  DataLazy::toString() const  DataLazy::toString() const
1738  {  {
1739    ostringstream oss;    ostringstream oss;
1740    oss << "Lazy Data:";    oss << "Lazy Data: [depth=" << m_height<< "] ";
1741    intoString(oss);    switch (escriptParams.getLAZY_STR_FMT())
1742      {
1743      case 1:   // tree format
1744        oss << endl;
1745        intoTreeString(oss,"");
1746        break;
1747      case 2:   // just the depth
1748        break;
1749      default:
1750        intoString(oss);
1751        break;
1752      }
1753    return oss.str();    return oss.str();
1754  }  }
1755    
# Line 1233  DataLazy::toString() const Line 1757  DataLazy::toString() const
1757  void  void
1758  DataLazy::intoString(ostringstream& oss) const  DataLazy::intoString(ostringstream& oss) const
1759  {  {
1760    //    oss << "[" << m_children <<";"<<m_height <<"]";
1761    switch (getOpgroup(m_op))    switch (getOpgroup(m_op))
1762    {    {
1763    case G_IDENTITY:    case G_IDENTITY:
# Line 1265  DataLazy::intoString(ostringstream& oss) Line 1790  DataLazy::intoString(ostringstream& oss)
1790    case G_UNARY_P:    case G_UNARY_P:
1791    case G_NP1OUT:    case G_NP1OUT:
1792    case G_NP1OUT_P:    case G_NP1OUT_P:
1793      case G_REDUCTION:
1794      oss << opToString(m_op) << '(';      oss << opToString(m_op) << '(';
1795      m_left->intoString(oss);      m_left->intoString(oss);
1796      oss << ')';      oss << ')';
# Line 1276  DataLazy::intoString(ostringstream& oss) Line 1802  DataLazy::intoString(ostringstream& oss)
1802      m_right->intoString(oss);      m_right->intoString(oss);
1803      oss << ')';      oss << ')';
1804      break;      break;
1805      case G_NP1OUT_2P:
1806        oss << opToString(m_op) << '(';
1807        m_left->intoString(oss);
1808        oss << ", " << m_axis_offset << ", " << m_transpose;
1809        oss << ')';
1810        break;
1811      default:
1812        oss << "UNKNOWN";
1813      }
1814    }
1815    
1816    
1817    void
1818    DataLazy::intoTreeString(ostringstream& oss, string indent) const
1819    {
1820      oss << '[' << m_rank << ':' << setw(3) << m_samplesize << "] " << indent;
1821      switch (getOpgroup(m_op))
1822      {
1823      case G_IDENTITY:
1824        if (m_id->isExpanded())
1825        {
1826           oss << "E";
1827        }
1828        else if (m_id->isTagged())
1829        {
1830          oss << "T";
1831        }
1832        else if (m_id->isConstant())
1833        {
1834          oss << "C";
1835        }
1836        else
1837        {
1838          oss << "?";
1839        }
1840        oss << '@' << m_id.get() << endl;
1841        break;
1842      case G_BINARY:
1843        oss << opToString(m_op) << endl;
1844        indent+='.';
1845        m_left->intoTreeString(oss, indent);
1846        m_right->intoTreeString(oss, indent);
1847        break;
1848      case G_UNARY:
1849      case G_UNARY_P:
1850      case G_NP1OUT:
1851      case G_NP1OUT_P:
1852      case G_REDUCTION:
1853        oss << opToString(m_op) << endl;
1854        indent+='.';
1855        m_left->intoTreeString(oss, indent);
1856        break;
1857      case G_TENSORPROD:
1858        oss << opToString(m_op) << endl;
1859        indent+='.';
1860        m_left->intoTreeString(oss, indent);
1861        m_right->intoTreeString(oss, indent);
1862        break;
1863      case G_NP1OUT_2P:
1864        oss << opToString(m_op) << ", " << m_axis_offset << ", " << m_transpose<< endl;
1865        indent+='.';
1866        m_left->intoTreeString(oss, indent);
1867        break;
1868    default:    default:
1869      oss << "UNKNOWN";      oss << "UNKNOWN";
1870    }    }
1871  }  }
1872    
1873    
1874  DataAbstract*  DataAbstract*
1875  DataLazy::deepCopy()  DataLazy::deepCopy()
1876  {  {
1877    switch (getOpgroup(m_op))    switch (getOpgroup(m_op))
1878    {    {
1879    case G_IDENTITY:  return new DataLazy(m_id->deepCopy()->getPtr());    case G_IDENTITY:  return new DataLazy(m_id->deepCopy()->getPtr());
1880    case G_UNARY: return new DataLazy(m_left->deepCopy()->getPtr(),m_op);    case G_UNARY:
1881      case G_REDUCTION:      return new DataLazy(m_left->deepCopy()->getPtr(),m_op);
1882      case G_UNARY_P:   return new DataLazy(m_left->deepCopy()->getPtr(), m_op, m_tol);
1883    case G_BINARY:    return new DataLazy(m_left->deepCopy()->getPtr(),m_right->deepCopy()->getPtr(),m_op);    case G_BINARY:    return new DataLazy(m_left->deepCopy()->getPtr(),m_right->deepCopy()->getPtr(),m_op);
1884    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);
1885    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);
1886      case G_NP1OUT_P:   return new DataLazy(m_left->deepCopy()->getPtr(),m_op,  m_axis_offset);
1887      case G_NP1OUT_2P:  return new DataLazy(m_left->deepCopy()->getPtr(), m_op, m_axis_offset, m_transpose);
1888    default:    default:
1889      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)+".");
1890    }    }
1891  }  }
1892    
1893    
1894    
1895  // There is no single, natural interpretation of getLength on DataLazy.  // There is no single, natural interpretation of getLength on DataLazy.
1896  // Instances of DataReady can look at the size of their vectors.  // Instances of DataReady can look at the size of their vectors.
1897  // 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;
# Line 1372  DataLazy::getPointOffset(int sampleNo, Line 1967  DataLazy::getPointOffset(int sampleNo,
1967    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).");
1968  }  }
1969    
1970  // It would seem that DataTagged will need to be treated differently since even after setting all tags  
1971  // 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.  
1972  void  void
1973  DataLazy::setToZero()  DataLazy::setToZero()
1974  {  {
1975    DataTypes::ValueType v(getNoValues(),0);  //   DataTypes::ValueType v(getNoValues(),0);
1976    m_id=DataReady_ptr(new DataConstant(getFunctionSpace(),getShape(),v));  //   m_id=DataReady_ptr(new DataConstant(getFunctionSpace(),getShape(),v));
1977    m_op=IDENTITY;  //   m_op=IDENTITY;
1978    m_right.reset();    //   m_right.reset();  
1979    m_left.reset();  //   m_left.reset();
1980    m_readytype='C';  //   m_readytype='C';
1981    m_buffsRequired=1;  //   m_buffsRequired=1;
1982    
1983      privdebug=privdebug;  // to stop the compiler complaining about unused privdebug
1984      throw DataException("Programmer error - setToZero not supported for DataLazy (DataLazy objects should be read only).");
1985    }
1986    
1987    bool
1988    DataLazy::actsExpanded() const
1989    {
1990        return (m_readytype=='E');
1991  }  }
1992    
1993  }   // end namespace  }   // end namespace

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