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trunk/escript/src/DataLazy.cpp revision 2082 by caltinay, Fri Nov 21 01:46:05 2008 UTC trunk/escriptcore/src/DataLazy.cpp revision 4621 by jfenwick, Thu Jan 16 10:07:44 2014 UTC
# Line 1  Line 1 
1    
2  /*******************************************************  /*****************************************************************************
3  *  *
4  * Copyright (c) 2003-2008 by University of Queensland  * Copyright (c) 2003-2013 by 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 since 2012 by School of Earth Sciences
13    *
14    *****************************************************************************/
15    
16    
17  #include "DataLazy.h"  #include "DataLazy.h"
18  #ifdef USE_NETCDF  #include "esysUtils/Esys_MPI.h"
 #include <netcdfcpp.h>  
 #endif  
 #ifdef PASO_MPI  
 #include <mpi.h>  
 #endif  
19  #ifdef _OPENMP  #ifdef _OPENMP
20  #include <omp.h>  #include <omp.h>
21  #endif  #endif
# Line 28  Line 25 
25  #include "UnaryFuncs.h"     // for escript::fsign  #include "UnaryFuncs.h"     // for escript::fsign
26  #include "Utils.h"  #include "Utils.h"
27    
28    #include "EscriptParams.h"
29    
30    #ifdef USE_NETCDF
31    #include <netcdfcpp.h>
32    #endif
33    
34    #include <iomanip>      // for some fancy formatting in debug
35    
36    // #define LAZYDEBUG(X) if (privdebug){X;}
37    #define LAZYDEBUG(X)
38    namespace
39    {
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?
55  ~~~~~~~~~~~~~~~~~~~~~~~  ~~~~~~~~~~~~~~~~~~~~~~~
# 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 48  I will refer to individual DataLazy obje Line 70  I will refer to individual DataLazy obje
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    
# 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_UNARY_P,       // pointwise operations with one argument, requiring a parameter
142     G_NP1OUT,        // non-pointwise op with one output     G_NP1OUT,        // non-pointwise op with one output
143     G_TENSORPROD     // general tensor product     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    
# Line 108  string ES_opstrings[]={"UNKNOWN","IDENTI Line 155  string ES_opstrings[]={"UNKNOWN","IDENTI
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 140  resultFS(DataAbstract_ptr left, DataAbst Line 195  resultFS(DataAbstract_ptr left, DataAbst
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      }      }
# Line 164  resultShape(DataAbstract_ptr left, DataA Line 220  resultShape(DataAbstract_ptr left, DataA
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    
224        if (left->getRank()==0)   // we need to allow scalar * anything        if (left->getRank()==0)   // we need to allow scalar * anything
225        {        {
226          return right->getShape();          return right->getShape();
# Line 177  resultShape(DataAbstract_ptr left, DataA Line 234  resultShape(DataAbstract_ptr left, DataA
234      return left->getShape();      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
# Line 197  GTPShape(DataAbstract_ptr left, DataAbst Line 387  GTPShape(DataAbstract_ptr left, DataAbst
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
# Line 226  GTPShape(DataAbstract_ptr left, DataAbst Line 420  GTPShape(DataAbstract_ptr left, DataAbst
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    
# 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     {     {
# Line 296  DataLazy::DataLazy(DataAbstract_ptr p) Line 479  DataLazy::DataLazy(DataAbstract_ptr p)
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     }     }
# Line 334  DataLazy::DataLazy(DataAbstract_ptr left Line 510  DataLazy::DataLazy(DataAbstract_ptr 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    
# Line 346  DataLazy::DataLazy(DataAbstract_ptr left Line 524  DataLazy::DataLazy(DataAbstract_ptr left
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.");
# Line 362  DataLazy::DataLazy(DataAbstract_ptr left Line 541  DataLazy::DataLazy(DataAbstract_ptr left
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;
# Line 396  DataLazy::DataLazy(DataAbstract_ptr left Line 580  DataLazy::DataLazy(DataAbstract_ptr left
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)
# Line 427  DataLazy::DataLazy(DataAbstract_ptr left Line 613  DataLazy::DataLazy(DataAbstract_ptr left
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     {     {
# Line 460  DataLazy::DataLazy(DataAbstract_ptr left Line 646  DataLazy::DataLazy(DataAbstract_ptr left
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  int  DataLazy::DataLazy(DataAbstract_ptr left, ES_optype op, const int axis0, const int axis1)
719  DataLazy::getBuffsRequired() const      : 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      return m_buffsRequired;     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  size_t  namespace
 DataLazy::getMaxSampleSize() const  
750  {  {
751      return m_maxsamplesize;  
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    
813    DataLazy::~DataLazy()
814    {
815       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
# Line 602  DataLazy::collapseToReady() Line 934  DataLazy::collapseToReady()
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;
# Line 611  DataLazy::collapseToReady() Line 949  DataLazy::collapseToReady()
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    }    }
# 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    {    {
# Line 638  DataLazy::collapse() Line 991  DataLazy::collapse()
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::ValueType*
1019    DataLazy::resolveNodeSample(int tid, int sampleNo, size_t& roffset) const
1020    {
1021    LAZYDEBUG(cout << "Resolve sample " << toString() << endl;)
1022        // collapse so we have a 'E' node or an IDENTITY for some other type
1023      if (m_readytype!='E' && m_op!=IDENTITY)
1024      {
1025        collapse();
1026      }
1027      if (m_op==IDENTITY)  
1028      {
1029        const ValueType& 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::ValueType*
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      // 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      // since we only have one argument we don't need to think about only
1072      // processing single points.      // 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::ValueType* 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:  
# Line 762  DataLazy::resolveUnary(ValueType& v, siz Line 1181  DataLazy::resolveUnary(ValueType& v, siz
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::ValueType*
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::ValueType* 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::ValueType*
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 ValueType* 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        {
1277            DataMaths::symmetric(*leftres,m_left->getShape(),subroffset, m_samples, getShape(), offset);
1278            subroffset+=step;
1279            offset+=step;
1280        }
1281      break;      break;
1282      case NSYM:      case NSYM:
1283      DataMaths::nonsymmetric(*vleft,m_left->getShape(),subroffset, v, getShape(), offset);      for (loop=0;loop<numsteps;++loop)
1284        {
1285            DataMaths::nonsymmetric(*leftres,m_left->getShape(),subroffset, m_samples, getShape(), offset);
1286            subroffset+=step;
1287            offset+=step;
1288        }
1289      break;      break;
1290      default:      default:
1291      throw DataException("Programmer error - resolveNP1OUT can not resolve operator "+opToString(m_op)+".");      throw DataException("Programmer error - resolveNP1OUT can not resolve operator "+opToString(m_op)+".");
1292    }    }
1293    return &v;    return &m_samples;
1294  }  }
1295    
1296    const DataTypes::ValueType*
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 ValueType* 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:
1338        throw DataException("Programmer error - resolveNP1OUTP can not resolve operator "+opToString(m_op)+".");
1339      }
1340      return &m_samples;
1341    }
1342    
1343    
1344    const DataTypes::ValueType*
1345  #define PROC_OP(TYPE,X)                               \  DataLazy::resolveNodeNP1OUT_2P(int tid, int sampleNo, size_t& roffset) const
1346      for (int i=0;i<steps;++i,resultp+=resultStep) \  {
1347      { \    if (m_readytype!='E')
1348         tensor_binary_operation< TYPE >(chunksize, &((*left)[lroffset]), &((*right)[rroffset]), resultp, X); \    {
1349         lroffset+=leftStep; \      throw DataException("Programmer error - resolveNodeNP1OUT_2P should only be called on expanded Data.");
1350         rroffset+=rightStep; \    }
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 ValueType* 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::ValueType*
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      const ValueType* maskres=m_mask->resolveNodeSample(tid, sampleNo, subroffset);
1394      const ValueType* srcres=0;
1395      if ((*maskres)[subroffset]>0)
1396      {
1397        srcres=m_left->resolveNodeSample(tid, sampleNo, subroffset);
1398      }
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::ValueType*
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      size_t chunksize=1;           // how many doubles will be processed in one go
1446      int leftstep=0;       // how far should the left offset advance after each step
1447      int rightstep=0;
1448      int numsteps=0;       // total number of steps for the inner loop
1449      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      // Get the values of sub-expressions
1537    const ValueType* left=m_left->resolveSample(v,offset,sampleNo,lroffset);    const ValueType* left=m_left->resolveNodeSample(tid,sampleNo,lroffset);  
1538    const ValueType* right=m_right->resolveSample(v,offset+m_samplesize,sampleNo,rroffset); // Note    const ValueType* right=m_right->resolveNodeSample(tid,sampleNo,rroffset);
1539      // the right child starts further along.  LAZYDEBUG(cout << "Post sub calls in " << toString() << endl;)
1540    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;)
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:
# Line 888  cout << "Resolve binary: " << toString() Line 1570  cout << "Resolve binary: " << toString()
1570      default:      default:
1571      throw DataException("Programmer error - resolveBinary can not resolve operator "+opToString(m_op)+".");      throw DataException("Programmer error - resolveBinary can not resolve operator "+opToString(m_op)+".");
1572    }    }
1573    roffset=offset;    LAZYDEBUG(cout << "Result res[" << roffset<< "]" << m_samples[roffset] << endl;)
1574    return &v;    return &m_samples;
1575  }  }
1576    
1577    
 /*  
   \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.  
 */  
1578  // 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
1579  // 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.
1580  // unlike the other resolve helpers, we must treat these datapoints separately.  // unlike the other resolve helpers, we must treat these datapoints separately.
1581  DataTypes::ValueType*  const DataTypes::ValueType*
1582  DataLazy::resolveTProd(ValueType& v,  size_t offset, int sampleNo, size_t& roffset) const  DataLazy::resolveNodeTProd(int tid, int sampleNo, size_t& roffset) const
1583  {  {
1584  cout << "Resolve TensorProduct: " << toString() << endl;  LAZYDEBUG(cout << "Resolve TensorProduct: " << toString() << endl;)
1585    
1586    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
1587      // first work out which of the children are expanded      // first work out which of the children are expanded
1588    bool leftExp=(m_left->m_readytype=='E');    bool leftExp=(m_left->m_readytype=='E');
1589    bool rightExp=(m_right->m_readytype=='E');    bool rightExp=(m_right->m_readytype=='E');
1590    int steps=getNumDPPSample();    int steps=getNumDPPSample();
1591    int leftStep=((leftExp && !rightExp)? m_right->getNoValues() : 0);    int leftStep=(leftExp? m_left->getNoValues() : 0);        // do not have scalars as input to this method
1592    int rightStep=((rightExp && !leftExp)? m_left->getNoValues() : 0);    int rightStep=(rightExp?m_right->getNoValues() : 0);
1593    int resultStep=max(leftStep,rightStep);   // only one (at most) should be !=0  
1594      // Get the values of sub-expressions (leave a gap of one sample for the result).    int resultStep=getNoValues();
1595    const ValueType* left=m_left->resolveSample(v,offset+m_samplesize,sampleNo,lroffset);    roffset=m_samplesize*tid;
1596    const ValueType* right=m_right->resolveSample(v,offset+2*m_samplesize,sampleNo,rroffset);    size_t offset=roffset;
1597    double* resultp=&(v[offset]);     // results are stored at the vector offset we recieved  
1598      const ValueType* left=m_left->resolveNodeSample(tid, sampleNo, lroffset);
1599    
1600      const ValueType* right=m_right->resolveNodeSample(tid, sampleNo, rroffset);
1601    
1602    LAZYDEBUG(cerr << "[Left shape]=" << DataTypes::shapeToString(m_left->getShape()) << "\n[Right shape]=" << DataTypes::shapeToString(m_right->getShape()) << " result=" <<DataTypes::shapeToString(getShape()) <<  endl;
1603    cout << getNoValues() << endl;)
1604    
1605    
1606    LAZYDEBUG(cerr << "Post sub calls: " << toString() << endl;)
1607    LAZYDEBUG(cout << "LeftExp=" << leftExp << " rightExp=" << rightExp << endl;)
1608    LAZYDEBUG(cout << "LeftR=" << m_left->getRank() << " rightExp=" << m_right->getRank() << endl;)
1609    LAZYDEBUG(cout << "LeftSize=" << m_left->getNoValues() << " RightSize=" << m_right->getNoValues() << endl;)
1610    LAZYDEBUG(cout << "m_samplesize=" << m_samplesize << endl;)
1611    LAZYDEBUG(cout << "outputshape=" << DataTypes::shapeToString(getShape()) << endl;)
1612    LAZYDEBUG(cout << "DPPS=" << m_right->getNumDPPSample() <<"."<<endl;)
1613    
1614      double* resultp=&(m_samples[offset]);     // results are stored at the vector offset we received
1615    switch(m_op)    switch(m_op)
1616    {    {
1617      case PROD:      case PROD:
# Line 932  cout << "Resolve TensorProduct: " << toS Line 1619  cout << "Resolve TensorProduct: " << toS
1619      {      {
1620            const double *ptr_0 = &((*left)[lroffset]);            const double *ptr_0 = &((*left)[lroffset]);
1621            const double *ptr_1 = &((*right)[rroffset]);            const double *ptr_1 = &((*right)[rroffset]);
1622    
1623    LAZYDEBUG(cout << DataTypes::pointToString(*left, m_left->getShape(),lroffset,"LEFT") << endl;)
1624    LAZYDEBUG(cout << DataTypes::pointToString(*right,m_right->getShape(),rroffset, "RIGHT") << endl;)
1625    
1626            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);
1627    
1628        lroffset+=leftStep;        lroffset+=leftStep;
1629        rroffset+=rightStep;        rroffset+=rightStep;
1630      }      }
# Line 941  cout << "Resolve TensorProduct: " << toS Line 1633  cout << "Resolve TensorProduct: " << toS
1633      throw DataException("Programmer error - resolveTProduct can not resolve operator "+opToString(m_op)+".");      throw DataException("Programmer error - resolveTProduct can not resolve operator "+opToString(m_op)+".");
1634    }    }
1635    roffset=offset;    roffset=offset;
1636    return &v;    return &m_samples;
1637  }  }
1638    
1639    
1640    const DataTypes::ValueType*
1641    DataLazy::resolveSample(int sampleNo, size_t& roffset) const
1642    {
1643    #ifdef _OPENMP
1644        int tid=omp_get_thread_num();
1645    #else
1646        int tid=0;
1647    #endif
1648    
1649  /*  #ifdef LAZY_STACK_PROF
1650    \brief Compute the value of the expression for the given sample.      stackstart[tid]=&tid;
1651    \return Vector which stores the value of the subexpression for the given sample.      stackend[tid]=&tid;
1652    \param v A vector to store intermediate results.      const DataTypes::ValueType* r=resolveNodeSample(tid, sampleNo, roffset);
1653    \param offset Index in v to begin storing results.      size_t d=(size_t)stackstart[tid]-(size_t)stackend[tid];
1654    \param sampleNo Sample number to evaluate.      #pragma omp critical
1655    \param roffset (output parameter) the offset in the return vector where the result begins.      if (d>maxstackuse)
1656        {
1657    cout << "Max resolve Stack use " << d << endl;
1658            maxstackuse=d;
1659        }
1660        return r;
1661    #else
1662        return resolveNodeSample(tid, sampleNo, roffset);
1663    #endif
1664    }
1665    
   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.  
1666    
1667  // the roffset is the offset within the returned vector where the data begins  // This needs to do the work of the identity constructor
1668  const DataTypes::ValueType*  void
1669  DataLazy::resolveSample(ValueType& v, size_t offset, int sampleNo, size_t& roffset)  DataLazy::resolveToIdentity()
1670  {  {
1671  cout << "Resolve sample " << toString() << endl;     if (m_op==IDENTITY)
1672      // collapse so we have a 'E' node or an IDENTITY for some other type      return;
1673    if (m_readytype!='E' && m_op!=IDENTITY)     DataReady_ptr p=resolveNodeWorker();
1674       makeIdentity(p);
1675    }
1676    
1677    void DataLazy::makeIdentity(const DataReady_ptr& p)
1678    {
1679       m_op=IDENTITY;
1680       m_axis_offset=0;
1681       m_transpose=0;
1682       m_SL=m_SM=m_SR=0;
1683       m_children=m_height=0;
1684       m_id=p;
1685       if(p->isConstant()) {m_readytype='C';}
1686       else if(p->isExpanded()) {m_readytype='E';}
1687       else if (p->isTagged()) {m_readytype='T';}
1688       else {throw DataException("Unknown DataReady instance in convertToIdentity constructor.");}
1689       m_samplesize=p->getNumDPPSample()*p->getNoValues();
1690       m_left.reset();
1691       m_right.reset();
1692    }
1693    
1694    
1695    DataReady_ptr
1696    DataLazy::resolve()
1697    {
1698        resolveToIdentity();
1699        return m_id;
1700    }
1701    
1702    
1703    /* This is really a static method but I think that caused problems in windows */
1704    void
1705    DataLazy::resolveGroupWorker(std::vector<DataLazy*>& dats)
1706    {
1707      if (dats.empty())
1708    {    {
1709      collapse();      return;
1710    }    }
1711    if (m_op==IDENTITY)      vector<DataLazy*> work;
1712      FunctionSpace fs=dats[0]->getFunctionSpace();
1713      bool match=true;
1714      for (int i=dats.size()-1;i>=0;--i)
1715    {    {
1716      const ValueType& vec=m_id->getVector();      if (dats[i]->m_readytype!='E')
1717      if (m_readytype=='C')      {
1718      {          dats[i]->collapse();
1719      roffset=0;      }
1720      return &(vec);      if (dats[i]->m_op!=IDENTITY)
1721      }      {
1722      roffset=m_id->getPointOffset(sampleNo, 0);          work.push_back(dats[i]);
1723      return &(vec);          if (fs!=dats[i]->getFunctionSpace())
1724            {
1725                match=false;
1726            }
1727        }
1728    }    }
1729    if (m_readytype!='E')    if (work.empty())
1730    {    {
1731      throw DataException("Programmer Error - Collapse did not produce an expanded node.");      return;     // no work to do
1732    }    }
1733    switch (getOpgroup(m_op))    if (match)    // all functionspaces match.  Yes I realise this is overly strict
1734      {     // it is possible that dats[0] is one of the objects which we discarded and
1735            // all the other functionspaces match.
1736        vector<DataExpanded*> dep;
1737        vector<ValueType*> vecs;
1738        for (int i=0;i<work.size();++i)
1739        {
1740            dep.push_back(new DataExpanded(fs,work[i]->getShape(), ValueType(work[i]->getNoValues())));
1741            vecs.push_back(&(dep[i]->getVectorRW()));
1742        }
1743        int totalsamples=work[0]->getNumSamples();
1744        const ValueType* res=0; // Storage for answer
1745        int sample;
1746        #pragma omp parallel private(sample, res)
1747        {
1748            size_t roffset=0;
1749            #pragma omp for schedule(static)
1750            for (sample=0;sample<totalsamples;++sample)
1751            {
1752            roffset=0;
1753            int j;
1754            for (j=work.size()-1;j>=0;--j)
1755            {
1756    #ifdef _OPENMP
1757                    res=work[j]->resolveNodeSample(omp_get_thread_num(),sample,roffset);
1758    #else
1759                    res=work[j]->resolveNodeSample(0,sample,roffset);
1760    #endif
1761                    DataVector::size_type outoffset=dep[j]->getPointOffset(sample,0);
1762                    memcpy(&((*vecs[j])[outoffset]),&((*res)[roffset]),work[j]->m_samplesize*sizeof(DataVector::ElementType));
1763            }
1764            }
1765        }
1766        // Now we need to load the new results as identity ops into the lazy nodes
1767        for (int i=work.size()-1;i>=0;--i)
1768        {
1769            work[i]->makeIdentity(boost::dynamic_pointer_cast<DataReady>(dep[i]->getPtr()));
1770        }
1771      }
1772      else  // functionspaces do not match
1773    {    {
1774    case G_UNARY: return resolveUnary(v, offset,sampleNo,roffset);      for (int i=0;i<work.size();++i)
1775    case G_BINARY: return resolveBinary(v, offset,sampleNo,roffset);      {
1776    case G_NP1OUT: return resolveNP1OUT(v, offset, sampleNo,roffset);          work[i]->resolveToIdentity();
1777    case G_TENSORPROD: return resolveTProd(v,offset, sampleNo,roffset);      }
   default:  
     throw DataException("Programmer Error - resolveSample does not know how to process "+opToString(m_op)+".");  
1778    }    }
1779  }  }
1780    
1781    
1782  // To simplify the memory management, all threads operate on one large vector, rather than one each.  
1783  // Each sample is evaluated independently and copied into the result DataExpanded.  // This version of resolve uses storage in each node to hold results
1784  DataReady_ptr  DataReady_ptr
1785  DataLazy::resolve()  DataLazy::resolveNodeWorker()
1786  {  {
   
 cout << "Sample size=" << m_samplesize << endl;  
 cout << "Buffers=" << m_buffsRequired << endl;  
   
1787    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
1788    {    {
1789      collapse();      collapse();
# Line 1016  cout << "Buffers=" << m_buffsRequired << Line 1793  cout << "Buffers=" << m_buffsRequired <<
1793      return m_id;      return m_id;
1794    }    }
1795      // 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);  
 cout << "Buffer created with size=" << v.size() << endl;  
1796    DataExpanded* result=new DataExpanded(getFunctionSpace(),getShape(),  ValueType(getNoValues()));    DataExpanded* result=new DataExpanded(getFunctionSpace(),getShape(),  ValueType(getNoValues()));
1797    ValueType& resvec=result->getVector();    ValueType& resvec=result->getVectorRW();
1798    DataReady_ptr resptr=DataReady_ptr(result);    DataReady_ptr resptr=DataReady_ptr(result);
1799    
1800    int sample;    int sample;
   size_t outoffset;     // offset in the output data  
1801    int totalsamples=getNumSamples();    int totalsamples=getNumSamples();
1802    const ValueType* res=0;   // Vector storing the answer    const ValueType* res=0;   // Storage for answer
1803    size_t resoffset=0;       // where in the vector to find the answer  LAZYDEBUG(cout << "Total number of samples=" <<totalsamples << endl;)
1804    #pragma omp parallel for private(sample,resoffset,outoffset,res) schedule(static)    #pragma omp parallel private(sample,res)
1805    for (sample=0;sample<totalsamples;++sample)    {
1806    {      size_t roffset=0;
1807  cout << "################################# " << sample << endl;  #ifdef LAZY_STACK_PROF
1808        stackstart[omp_get_thread_num()]=&roffset;
1809        stackend[omp_get_thread_num()]=&roffset;
1810    #endif
1811        #pragma omp for schedule(static)
1812        for (sample=0;sample<totalsamples;++sample)
1813        {
1814            roffset=0;
1815  #ifdef _OPENMP  #ifdef _OPENMP
1816      res=resolveSample(v,threadbuffersize*omp_get_thread_num(),sample,resoffset);              res=resolveNodeSample(omp_get_thread_num(),sample,roffset);
1817  #else  #else
1818      res=resolveSample(v,0,sample,resoffset);   // res would normally be v, but not if its a single IDENTITY op.              res=resolveNodeSample(0,sample,roffset);
1819  #endif  #endif
1820  cerr << "-------------------------------- " << endl;  LAZYDEBUG(cout << "Sample #" << sample << endl;)
1821      outoffset=result->getPointOffset(sample,0);  LAZYDEBUG(cout << "Final res[" << roffset<< "]=" << (*res)[roffset] << (*res)[roffset]<< endl; )
1822  cerr << "offset=" << outoffset << endl;              DataVector::size_type outoffset=result->getPointOffset(sample,0);
1823      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));
1824      {      }
1825      resvec[outoffset]=(*res)[resoffset];    }
1826      }  #ifdef LAZY_STACK_PROF
1827  cerr << "*********************************" << endl;    for (int i=0;i<getNumberOfThreads();++i)
1828      {
1829        size_t r=((size_t)stackstart[i] - (size_t)stackend[i]);
1830    //  cout << i << " " << stackstart[i] << " .. " << stackend[i] << " = " <<  r << endl;
1831        if (r>maxstackuse)
1832        {
1833            maxstackuse=r;
1834        }
1835    }    }
1836      cout << "Max resolve Stack use=" << maxstackuse << endl;
1837    #endif
1838    return resptr;    return resptr;
1839  }  }
1840    
# Line 1057  std::string Line 1842  std::string
1842  DataLazy::toString() const  DataLazy::toString() const
1843  {  {
1844    ostringstream oss;    ostringstream oss;
1845    oss << "Lazy Data:";    oss << "Lazy Data: [depth=" << m_height<< "] ";
1846    intoString(oss);    switch (escriptParams.getLAZY_STR_FMT())
1847      {
1848      case 1:   // tree format
1849        oss << endl;
1850        intoTreeString(oss,"");
1851        break;
1852      case 2:   // just the depth
1853        break;
1854      default:
1855        intoString(oss);
1856        break;
1857      }
1858    return oss.str();    return oss.str();
1859  }  }
1860    
# Line 1066  DataLazy::toString() const Line 1862  DataLazy::toString() const
1862  void  void
1863  DataLazy::intoString(ostringstream& oss) const  DataLazy::intoString(ostringstream& oss) const
1864  {  {
1865    //    oss << "[" << m_children <<";"<<m_height <<"]";
1866    switch (getOpgroup(m_op))    switch (getOpgroup(m_op))
1867    {    {
1868    case G_IDENTITY:    case G_IDENTITY:
# Line 1095  DataLazy::intoString(ostringstream& oss) Line 1892  DataLazy::intoString(ostringstream& oss)
1892      oss << ')';      oss << ')';
1893      break;      break;
1894    case G_UNARY:    case G_UNARY:
1895      case G_UNARY_P:
1896    case G_NP1OUT:    case G_NP1OUT:
1897      case G_NP1OUT_P:
1898      case G_REDUCTION:
1899      oss << opToString(m_op) << '(';      oss << opToString(m_op) << '(';
1900      m_left->intoString(oss);      m_left->intoString(oss);
1901      oss << ')';      oss << ')';
# Line 1107  DataLazy::intoString(ostringstream& oss) Line 1907  DataLazy::intoString(ostringstream& oss)
1907      m_right->intoString(oss);      m_right->intoString(oss);
1908      oss << ')';      oss << ')';
1909      break;      break;
1910      case G_NP1OUT_2P:
1911        oss << opToString(m_op) << '(';
1912        m_left->intoString(oss);
1913        oss << ", " << m_axis_offset << ", " << m_transpose;
1914        oss << ')';
1915        break;
1916      case G_CONDEVAL:
1917        oss << opToString(m_op)<< '(' ;
1918        m_mask->intoString(oss);
1919        oss << " ? ";
1920        m_left->intoString(oss);
1921        oss << " : ";
1922        m_right->intoString(oss);
1923        oss << ')';
1924        break;
1925      default:
1926        oss << "UNKNOWN";
1927      }
1928    }
1929    
1930    
1931    void
1932    DataLazy::intoTreeString(ostringstream& oss, string indent) const
1933    {
1934      oss << '[' << m_rank << ':' << setw(3) << m_samplesize << "] " << indent;
1935      switch (getOpgroup(m_op))
1936      {
1937      case G_IDENTITY:
1938        if (m_id->isExpanded())
1939        {
1940           oss << "E";
1941        }
1942        else if (m_id->isTagged())
1943        {
1944          oss << "T";
1945        }
1946        else if (m_id->isConstant())
1947        {
1948          oss << "C";
1949        }
1950        else
1951        {
1952          oss << "?";
1953        }
1954        oss << '@' << m_id.get() << endl;
1955        break;
1956      case G_BINARY:
1957        oss << opToString(m_op) << endl;
1958        indent+='.';
1959        m_left->intoTreeString(oss, indent);
1960        m_right->intoTreeString(oss, indent);
1961        break;
1962      case G_UNARY:
1963      case G_UNARY_P:
1964      case G_NP1OUT:
1965      case G_NP1OUT_P:
1966      case G_REDUCTION:
1967        oss << opToString(m_op) << endl;
1968        indent+='.';
1969        m_left->intoTreeString(oss, indent);
1970        break;
1971      case G_TENSORPROD:
1972        oss << opToString(m_op) << endl;
1973        indent+='.';
1974        m_left->intoTreeString(oss, indent);
1975        m_right->intoTreeString(oss, indent);
1976        break;
1977      case G_NP1OUT_2P:
1978        oss << opToString(m_op) << ", " << m_axis_offset << ", " << m_transpose<< endl;
1979        indent+='.';
1980        m_left->intoTreeString(oss, indent);
1981        break;
1982    default:    default:
1983      oss << "UNKNOWN";      oss << "UNKNOWN";
1984    }    }
1985  }  }
1986    
1987    
1988  DataAbstract*  DataAbstract*
1989  DataLazy::deepCopy()  DataLazy::deepCopy()
1990  {  {
1991    switch (getOpgroup(m_op))    switch (getOpgroup(m_op))
1992    {    {
1993    case G_IDENTITY:  return new DataLazy(m_id->deepCopy()->getPtr());    case G_IDENTITY:  return new DataLazy(m_id->deepCopy()->getPtr());
1994    case G_UNARY: return new DataLazy(m_left->deepCopy()->getPtr(),m_op);    case G_UNARY:
1995      case G_REDUCTION:      return new DataLazy(m_left->deepCopy()->getPtr(),m_op);
1996      case G_UNARY_P:   return new DataLazy(m_left->deepCopy()->getPtr(), m_op, m_tol);
1997    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);
1998    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);
1999    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);
2000      case G_NP1OUT_P:   return new DataLazy(m_left->deepCopy()->getPtr(),m_op,  m_axis_offset);
2001      case G_NP1OUT_2P:  return new DataLazy(m_left->deepCopy()->getPtr(), m_op, m_axis_offset, m_transpose);
2002    default:    default:
2003      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)+".");
2004    }    }
2005  }  }
2006    
2007    
2008    
2009  // There is no single, natural interpretation of getLength on DataLazy.  // There is no single, natural interpretation of getLength on DataLazy.
2010  // Instances of DataReady can look at the size of their vectors.  // Instances of DataReady can look at the size of their vectors.
2011  // 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 1203  DataLazy::getPointOffset(int sampleNo, Line 2081  DataLazy::getPointOffset(int sampleNo,
2081    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).");
2082  }  }
2083    
2084  // It would seem that DataTagged will need to be treated differently since even after setting all tags  
2085  // 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.  
2086  void  void
2087  DataLazy::setToZero()  DataLazy::setToZero()
2088  {  {
2089    DataTypes::ValueType v(getNoValues(),0);  //   DataTypes::ValueType v(getNoValues(),0);
2090    m_id=DataReady_ptr(new DataConstant(getFunctionSpace(),getShape(),v));  //   m_id=DataReady_ptr(new DataConstant(getFunctionSpace(),getShape(),v));
2091    m_op=IDENTITY;  //   m_op=IDENTITY;
2092    m_right.reset();    //   m_right.reset();  
2093    m_left.reset();  //   m_left.reset();
2094    m_readytype='C';  //   m_readytype='C';
2095    m_buffsRequired=1;  //   m_buffsRequired=1;
2096    
2097      privdebug=privdebug;  // to stop the compiler complaining about unused privdebug
2098      throw DataException("Programmer error - setToZero not supported for DataLazy (DataLazy objects should be read only).");
2099    }
2100    
2101    bool
2102    DataLazy::actsExpanded() const
2103    {
2104        return (m_readytype=='E');
2105  }  }
2106    
2107  }   // end namespace  }   // end namespace

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