/[escript]/branches/clazy/escriptcore/src/DataLazy.cpp
ViewVC logotype

Diff of /branches/clazy/escriptcore/src/DataLazy.cpp

Parent Directory Parent Directory | Revision Log Revision Log | View Patch Patch

trunk/escript/src/DataLazy.cpp revision 2147 by jfenwick, Wed Dec 10 04:41:26 2008 UTC branches/clazy/escriptcore/src/DataLazy.cpp revision 6512 by jfenwick, Fri Mar 3 02:01:57 2017 UTC
# Line 1  Line 1 
1    
2  /*******************************************************  /*****************************************************************************
3  *  *
4  * Copyright (c) 2003-2008 by University of Queensland  * Copyright (c) 2003-2016 by The University of Queensland
5  * Earth Systems Science Computational Center (ESSCC)  * http://www.uq.edu.au
 * http://www.uq.edu.au/esscc  
6  *  *
7  * Primary Business: Queensland, Australia  * Primary Business: Queensland, Australia
8  * Licensed under the Open Software License version 3.0  * Licensed under the Apache License, version 2.0
9  * http://www.opensource.org/licenses/osl-3.0.php  * http://www.apache.org/licenses/LICENSE-2.0
10  *  *
11  *******************************************************/  * Development until 2012 by Earth Systems Science Computational Center (ESSCC)
12    * Development 2012-2013 by School of Earth Sciences
13    * Development from 2014 by Centre for Geoscience Computing (GeoComp)
14    *
15    *****************************************************************************/
16    
17  #include "DataLazy.h"  #include "DataLazy.h"
 #ifdef USE_NETCDF  
 #include <netcdfcpp.h>  
 #endif  
 #ifdef PASO_MPI  
 #include <mpi.h>  
 #endif  
 #ifdef _OPENMP  
 #include <omp.h>  
 #endif  
 #include "FunctionSpace.h"  
 #include "DataTypes.h"  
18  #include "Data.h"  #include "Data.h"
19  #include "UnaryFuncs.h"     // for escript::fsign  #include "DataTypes.h"
20    #include "EscriptParams.h"
21    #include "FunctionSpace.h"
22  #include "Utils.h"  #include "Utils.h"
23    #include "DataVectorOps.h"
24    
25    #include <iomanip> // for some fancy formatting in debug
26    
27    using namespace escript::DataTypes;
28    
29    #define NO_ARG
30    
31  // #define LAZYDEBUG(X) X;  // #define LAZYDEBUG(X) if (privdebug){X;}
32  #define LAZYDEBUG(X)  #define LAZYDEBUG(X)
33    namespace
34    {
35    bool privdebug=false;
36    
37    #define ENABLEDEBUG privdebug=true;
38    #define DISABLEDEBUG privdebug=false;
39    }
40    
41    //#define SIZELIMIT if ((m_height>escript::escriptParams.getInt("TOO_MANY_LEVELS")) || (m_children>escript::escriptParams.getInt("TOO_MANY_NODES"))) {cerr << "\n!!!!!!! SIZE LIMIT EXCEEDED " << m_children << ";" << m_height << endl << toString() << endl;resolveToIdentity();}
42    
43    //#define SIZELIMIT if ((m_height>escript::escriptParams.getInt("TOO_MANY_LEVELS")) || (m_children>escript::escriptParams.getInt("TOO_MANY_NODES"))) {cerr << "SIZE LIMIT EXCEEDED " << m_height << endl;resolveToIdentity();}
44    
45    
46    #define SIZELIMIT \
47        if (m_height > escript::escriptParams.getTooManyLevels()) {\
48            if (escript::escriptParams.getLazyVerbose()) {\
49                cerr << "SIZE LIMIT EXCEEDED height=" << m_height << endl;\
50            }\
51            resolveToIdentity();\
52        }
53    
54  /*  /*
55  How does DataLazy work?  How does DataLazy work?
# Line 42  A special operation, IDENTITY, stores an Line 62  A special operation, IDENTITY, stores an
62  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.
63    
64  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, ...
65  Note that IDENITY is not considered a unary operation.  Note that IDENTITY is not considered a unary operation.
66    
67  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).
68  It must however form a DAG (directed acyclic graph).  It must however form a DAG (directed acyclic graph).
# Line 50  I will refer to individual DataLazy obje Line 70  I will refer to individual DataLazy obje
70    
71  Each node also stores:  Each node also stores:
72  - 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
73      evaluated.          evaluated.
74  - 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
75      evaluate the expression.          evaluate the expression.
76  - m_samplesize ~ the number of doubles stored in a sample.  - m_samplesize ~ the number of doubles stored in a sample.
77    
78  When a new node is created, the above values are computed based on the values in the child nodes.  When a new node is created, the above values are computed based on the values in the child nodes.
# Line 93  namespace escript Line 113  namespace escript
113  namespace  namespace
114  {  {
115    
 enum ES_opgroup  
 {  
    G_UNKNOWN,  
    G_IDENTITY,  
    G_BINARY,        // pointwise operations with two arguments  
    G_UNARY,     // pointwise operations with one argument  
    G_UNARY_P,       // pointwise operations with one argument, requiring a parameter  
    G_NP1OUT,        // non-pointwise op with one output  
    G_NP1OUT_P,      // non-pointwise op with one output requiring a parameter  
    G_TENSORPROD     // general tensor product  
 };  
116    
117    // enabling this will print out when ever the maximum stacksize used by resolve increases
118    // it assumes _OPENMP is also in use
119    //#define LAZY_STACK_PROF
120    
121    
122    
123  string ES_opstrings[]={"UNKNOWN","IDENTITY","+","-","*","/","^",  #ifndef _OPENMP
124              "sin","cos","tan",    #ifdef LAZY_STACK_PROF
125              "asin","acos","atan","sinh","cosh","tanh","erf",    #undef LAZY_STACK_PROF
126              "asinh","acosh","atanh",    #endif
127              "log10","log","sign","abs","neg","pos","exp","sqrt",  #endif
128              "1/","where>0","where<0","where>=0","where<=0", "where<>0","where=0",  
129              "symmetric","nonsymmetric",  
130              "prod",  #ifdef LAZY_STACK_PROF
131              "transpose", "trace"};  std::vector<void*> stackstart(getNumberOfThreads());
132  int ES_opcount=40;  std::vector<void*> stackend(getNumberOfThreads());
133  ES_opgroup opgroups[]={G_UNKNOWN,G_IDENTITY,G_BINARY,G_BINARY,G_BINARY,G_BINARY, G_BINARY,  size_t maxstackuse=0;
134              G_UNARY,G_UNARY,G_UNARY, //10  #endif
135              G_UNARY,G_UNARY,G_UNARY,G_UNARY,G_UNARY,G_UNARY,G_UNARY,    // 17  
             G_UNARY,G_UNARY,G_UNARY,                    // 20  
             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_P, G_UNARY_P,      // 35  
             G_NP1OUT,G_NP1OUT,  
             G_TENSORPROD,  
             G_NP1OUT_P, G_NP1OUT_P};  
 inline  
 ES_opgroup  
 getOpgroup(ES_optype op)  
 {  
   return opgroups[op];  
 }  
136    
137  // return the FunctionSpace of the result of "left op right"  // return the FunctionSpace of the result of "left op right"
138  FunctionSpace  FunctionSpace
139  resultFS(DataAbstract_ptr left, DataAbstract_ptr right, ES_optype op)  resultFS(DataAbstract_ptr left, DataAbstract_ptr right, ES_optype op)
140  {  {
141      // perhaps this should call interpolate and throw or something?          // perhaps this should call interpolate and throw or something?
142      // maybe we need an interpolate node -          // maybe we need an interpolate node -
143      // that way, if interpolate is required in any other op we can just throw a          // that way, if interpolate is required in any other op we can just throw a
144      // programming error exception.          // programming error exception.
145    
146    FunctionSpace l=left->getFunctionSpace();    FunctionSpace l=left->getFunctionSpace();
147    FunctionSpace r=right->getFunctionSpace();    FunctionSpace r=right->getFunctionSpace();
148    if (l!=r)    if (l!=r)
149    {    {
150      if (r.probeInterpolation(l))      signed char res=r.getDomain()->preferredInterpolationOnDomain(r.getTypeCode(), l.getTypeCode());
151        if (res==1)
152      {      {
153      return l;          return l;
154      }      }
155      if (l.probeInterpolation(r))      if (res==-1)
156      {      {
157      return r;          return r;
158      }      }
159      throw DataException("Cannot interpolate between the FunctionSpaces given for operation "+opToString(op)+".");      throw DataException("Cannot interpolate between the FunctionSpaces given for operation "+opToString(op)+".");
160    }    }
# Line 165  resultFS(DataAbstract_ptr left, DataAbst Line 166  resultFS(DataAbstract_ptr left, DataAbst
166  DataTypes::ShapeType  DataTypes::ShapeType
167  resultShape(DataAbstract_ptr left, DataAbstract_ptr right, ES_optype op)  resultShape(DataAbstract_ptr left, DataAbstract_ptr right, ES_optype op)
168  {  {
169      if (left->getShape()!=right->getShape())          if (left->getShape()!=right->getShape())
170      {          {
171        if ((getOpgroup(op)!=G_BINARY) && (getOpgroup(op)!=G_NP1OUT))            if ((getOpgroup(op)!=G_BINARY) && (getOpgroup(op)!=G_NP1OUT))
172        {            {
173          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.");
174        }            }
175        if (left->getRank()==0)   // we need to allow scalar * anything  
176        {            if (left->getRank()==0)       // we need to allow scalar * anything
177          return right->getShape();            {
178        }                  return right->getShape();
179        if (right->getRank()==0)            }
180        {            if (right->getRank()==0)
181          return left->getShape();            {
182        }                  return left->getShape();
183        throw DataException("Shapes not the same - arguments must have matching shapes (or be scalars) for (point)binary operations on lazy data.");            }
184      }            throw DataException("Shapes not the same - arguments must have matching shapes (or be scalars) for (point)binary operations on lazy data.");
185      return left->getShape();          }
186            return left->getShape();
187  }  }
188    
189  // return the shape for "op left"  // return the shape for "op left"
190    
191  DataTypes::ShapeType  DataTypes::ShapeType
192  resultShape(DataAbstract_ptr left, ES_optype op)  resultShape(DataAbstract_ptr left, ES_optype op, int axis_offset)
193  {  {
194      switch(op)          switch(op)
195      {          {
196          case TRANS:          case TRANS:
197          return left->getShape();             {                    // for the scoping of variables
198      break;                  const DataTypes::ShapeType& s=left->getShape();
199      case TRACE:                  DataTypes::ShapeType sh;
200          return DataTypes::scalarShape;                  int rank=left->getRank();
201      break;                  if (axis_offset<0 || axis_offset>rank)
202          default:                  {
203      throw DataException("Programmer error - resultShape(left,op) can't compute shapes for operator "+opToString(op)+".");              stringstream e;
204      }              e << "Error - Data::transpose must have 0 <= axis_offset <= rank=" << rank;
205                throw DataException(e.str());
206            }
207            for (int i=0; i<rank; i++)
208                    {
209                       int index = (axis_offset+i)%rank;
210               sh.push_back(s[index]); // Append to new shape
211            }
212                    return sh;
213               }
214            break;
215            case TRACE:
216               {
217                    int rank=left->getRank();
218                    if (rank<2)
219                    {
220                       throw DataException("Trace can only be computed for objects with rank 2 or greater.");
221                    }
222                    if ((axis_offset>rank-2) || (axis_offset<0))
223                    {
224                       throw DataException("Trace: axis offset must lie between 0 and rank-2 inclusive.");
225                    }
226                    if (rank==2)
227                    {
228                       return DataTypes::scalarShape;
229                    }
230                    else if (rank==3)
231                    {
232                       DataTypes::ShapeType sh;
233                       if (axis_offset==0)
234                       {
235                            sh.push_back(left->getShape()[2]);
236                       }
237                       else         // offset==1
238                       {
239                            sh.push_back(left->getShape()[0]);
240                       }
241                       return sh;
242                    }
243                    else if (rank==4)
244                    {
245                       DataTypes::ShapeType sh;
246                       const DataTypes::ShapeType& s=left->getShape();
247                       if (axis_offset==0)
248                       {
249                            sh.push_back(s[2]);
250                            sh.push_back(s[3]);
251                       }
252                       else if (axis_offset==1)
253                       {
254                            sh.push_back(s[0]);
255                            sh.push_back(s[3]);
256                       }
257                       else         // offset==2
258                       {
259                            sh.push_back(s[0]);
260                            sh.push_back(s[1]);
261                       }
262                       return sh;
263                    }
264                    else            // unknown rank
265                    {
266                       throw DataException("Error - Data::trace can only be calculated for rank 2, 3 or 4 object.");
267                    }
268               }
269            break;
270            default:
271            throw DataException("Programmer error - resultShape(left,op) can't compute shapes for operator "+opToString(op)+".");
272            }
273  }  }
274    
275    DataTypes::ShapeType
276    SwapShape(DataAbstract_ptr left, const int axis0, const int axis1)
277    {
278         // This code taken from the Data.cpp swapaxes() method
279         // Some of the checks are probably redundant here
280         int axis0_tmp,axis1_tmp;
281         const DataTypes::ShapeType& s=left->getShape();
282         DataTypes::ShapeType out_shape;
283         // Here's the equivalent of python s_out=s[axis_offset:]+s[:axis_offset]
284         // which goes thru all shape vector elements starting with axis_offset (at index=rank wrap around to 0)
285         int rank=left->getRank();
286         if (rank<2) {
287            throw DataException("Error - Data::swapaxes argument must have at least rank 2.");
288         }
289         if (axis0<0 || axis0>rank-1) {
290            stringstream e;
291            e << "Error - Data::swapaxes: axis0 must be between 0 and rank-1=" << (rank-1);
292            throw DataException(e.str());
293         }
294         if (axis1<0 || axis1>rank-1) {
295            stringstream e;
296            e << "Error - Data::swapaxes: axis1 must be between 0 and rank-1=" << (rank-1);
297            throw DataException(e.str());
298         }
299         if (axis0 == axis1) {
300             throw DataException("Error - Data::swapaxes: axis indices must be different.");
301         }
302         if (axis0 > axis1) {
303             axis0_tmp=axis1;
304             axis1_tmp=axis0;
305         } else {
306             axis0_tmp=axis0;
307             axis1_tmp=axis1;
308         }
309         for (int i=0; i<rank; i++) {
310           if (i == axis0_tmp) {
311              out_shape.push_back(s[axis1_tmp]);
312           } else if (i == axis1_tmp) {
313              out_shape.push_back(s[axis0_tmp]);
314           } else {
315              out_shape.push_back(s[i]);
316           }
317         }
318        return out_shape;
319    }
320    
321    
322  // determine the output shape for the general tensor product operation  // determine the output shape for the general tensor product operation
323  // the additional parameters return information required later for the product  // the additional parameters return information required later for the product
324  // 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
325  DataTypes::ShapeType  DataTypes::ShapeType
326  GTPShape(DataAbstract_ptr left, DataAbstract_ptr right, int axis_offset, int transpose, int& SL, int& SM, int& SR)  GTPShape(DataAbstract_ptr left, DataAbstract_ptr right, int axis_offset, int transpose, int& SL, int& SM, int& SR)
327  {  {
328                
329    // Get rank and shape of inputs    // Get rank and shape of inputs
330    int rank0 = left->getRank();    int rank0 = left->getRank();
331    int rank1 = right->getRank();    int rank1 = right->getRank();
# Line 217  GTPShape(DataAbstract_ptr left, DataAbst Line 334  GTPShape(DataAbstract_ptr left, DataAbst
334    
335    // Prepare for the loops of the product and verify compatibility of shapes    // Prepare for the loops of the product and verify compatibility of shapes
336    int start0=0, start1=0;    int start0=0, start1=0;
337    if (transpose == 0)       {}    if (transpose == 0)           {}
338    else if (transpose == 1)  { start0 = axis_offset; }    else if (transpose == 1)      { start0 = axis_offset; }
339    else if (transpose == 2)  { start1 = rank1-axis_offset; }    else if (transpose == 2)      { start1 = rank1-axis_offset; }
340    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"); }
341    
342    if (rank0<axis_offset)    if (rank0<axis_offset)
343    {    {
344      throw DataException("DataLazy GeneralTensorProduct Constructor: Error - rank of left < axisoffset");          throw DataException("DataLazy GeneralTensorProduct Constructor: Error - rank of left < axisoffset");
345    }    }
346    
347    // Adjust the shapes for transpose    // Adjust the shapes for transpose
348    DataTypes::ShapeType tmpShape0(rank0);    // pre-sizing the vectors rather    DataTypes::ShapeType tmpShape0(rank0);        // pre-sizing the vectors rather
349    DataTypes::ShapeType tmpShape1(rank1);    // than using push_back    DataTypes::ShapeType tmpShape1(rank1);        // than using push_back
350    for (int i=0; i<rank0; i++)   { tmpShape0[i]=shape0[(i+start0)%rank0]; }    for (int i=0; i<rank0; i++)   { tmpShape0[i]=shape0[(i+start0)%rank0]; }
351    for (int i=0; i<rank1; i++)   { tmpShape1[i]=shape1[(i+start1)%rank1]; }    for (int i=0; i<rank1; i++)   { tmpShape1[i]=shape1[(i+start1)%rank1]; }
352    
353    // Prepare for the loops of the product    // Prepare for the loops of the product
354    SL=1, SM=1, SR=1;    SL=1, SM=1, SR=1;
355    for (int i=0; i<rank0-axis_offset; i++)   {    for (int i=0; i<rank0-axis_offset; i++)       {
356      SL *= tmpShape0[i];      SL *= tmpShape0[i];
357    }    }
358    for (int i=rank0-axis_offset; i<rank0; i++)   {    for (int i=rank0-axis_offset; i<rank0; i++)   {
359      if (tmpShape0[i] != tmpShape1[i-(rank0-axis_offset)]) {      if (tmpShape0[i] != tmpShape1[i-(rank0-axis_offset)]) {
360        throw DataException("C_GeneralTensorProduct: Error - incompatible shapes");        throw DataException("C_GeneralTensorProduct: Error - incompatible shapes");
361      }      }
362      SM *= tmpShape0[i];      SM *= tmpShape0[i];
363    }    }
364    for (int i=axis_offset; i<rank1; i++)     {    for (int i=axis_offset; i<rank1; i++)         {
365      SR *= tmpShape1[i];      SR *= tmpShape1[i];
366    }    }
367    
368    // Define the shape of the output (rank of shape is the sum of the loop ranges below)    // Define the shape of the output (rank of shape is the sum of the loop ranges below)
369    DataTypes::ShapeType shape2(rank0+rank1-2*axis_offset);      DataTypes::ShapeType shape2(rank0+rank1-2*axis_offset);      
370    {         // block to limit the scope of out_index    {                     // block to limit the scope of out_index
371       int out_index=0;       int out_index=0;
372       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
373       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
# Line 266  GTPShape(DataAbstract_ptr left, DataAbst Line 383  GTPShape(DataAbstract_ptr left, DataAbst
383    return shape2;    return shape2;
384  }  }
385    
386    }       // end anonymous namespace
387    
388  // determine the number of points in the result of "left op right"  void DataLazy::LazyNodeSetup()
 // 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)  
389  {  {
390     switch(getOpgroup(op))  #ifdef _OPENMP
391     {      int numthreads=omp_get_max_threads();
392     case G_IDENTITY: return 1;      m_samples_r.resize(numthreads*m_samplesize);
393     case G_BINARY: return max(left->getBuffsRequired(),right->getBuffsRequired()+1);      m_sampleids=new int[numthreads];
394     case G_UNARY:      for (int i=0;i<numthreads;++i)
395     case G_UNARY_P: return max(left->getBuffsRequired(),1);      {
396     case G_NP1OUT: return 1+max(left->getBuffsRequired(),1);          m_sampleids[i]=-1;  
397     case G_NP1OUT_P: return 1+max(left->getBuffsRequired(),1);      }
398     case G_TENSORPROD: return 1+max(left->getBuffsRequired(),right->getBuffsRequired()+1);  #else
399     default:      if (m_iscompl)
400      throw DataException("Programmer Error - attempt to calcBuffs() for operator "+opToString(op)+".");      {
401     }          m_samples_c.resize(m_samplesize);
402  }      }
403        else
404        {
405  }   // end anonymous namespace          m_samples_r.resize(m_samplesize);
406        }
407        m_sampleids=new int[1];
408        m_sampleids[0]=-1;
409  // Return a string representing the operation  #endif  // _OPENMP
 const std::string&  
 opToString(ES_optype op)  
 {  
   if (op<0 || op>=ES_opcount)  
   {  
     op=UNKNOWNOP;  
   }  
   return ES_opstrings[op];  
410  }  }
411    
412    
413    // Creates an identity node
414  DataLazy::DataLazy(DataAbstract_ptr p)  DataLazy::DataLazy(DataAbstract_ptr p)
415      : parent(p->getFunctionSpace(),p->getShape()),          : parent(p->getFunctionSpace(),p->getShape())
416      m_op(IDENTITY),          ,m_sampleids(0),
417      m_axis_offset(0),          m_samples_r(1)
     m_transpose(0),  
     m_SL(0), m_SM(0), m_SR(0)  
418  {  {
419     if (p->isLazy())     if (p->isLazy())
420     {     {
421      // I don't want identity of Lazy.          // I don't want identity of Lazy.
422      // Question: Why would that be so bad?          // Question: Why would that be so bad?
423      // Answer: We assume that the child of ID is something we can call getVector on          // Answer: We assume that the child of ID is something we can call getVector on
424      throw DataException("Programmer error - attempt to create identity from a DataLazy.");          throw DataException("Programmer error - attempt to create identity from a DataLazy.");
425     }     }
426     else     else
427     {     {
428      m_id=dynamic_pointer_cast<DataReady>(p);          DataReady_ptr dr=dynamic_pointer_cast<DataReady>(p);
429      if(p->isConstant()) {m_readytype='C';}          makeIdentity(dr);
430      else if(p->isExpanded()) {m_readytype='E';}  LAZYDEBUG(cout << "Wrapping " << dr.get() << " id=" << m_id.get() << endl;)
     else if (p->isTagged()) {m_readytype='T';}  
     else {throw DataException("Unknown DataReady instance in DataLazy constructor.");}  
431     }     }
    m_buffsRequired=1;  
    m_samplesize=getNumDPPSample()*getNoValues();  
    m_maxsamplesize=m_samplesize;  
432  LAZYDEBUG(cout << "(1)Lazy created with " << m_samplesize << endl;)  LAZYDEBUG(cout << "(1)Lazy created with " << m_samplesize << endl;)
433  }  }
434    
   
   
   
435  DataLazy::DataLazy(DataAbstract_ptr left, ES_optype op)  DataLazy::DataLazy(DataAbstract_ptr left, ES_optype op)
436      : parent(left->getFunctionSpace(),left->getShape()),          : parent(left->getFunctionSpace(),(getOpgroup(op)!=G_REDUCTION)?left->getShape():DataTypes::scalarShape),
437      m_op(op),          m_op(op),
438      m_axis_offset(0),          m_axis_offset(0),
439      m_transpose(0),          m_transpose(0),
440      m_SL(0), m_SM(0), m_SR(0)          m_SL(0), m_SM(0), m_SR(0)
441  {  {
442     if ((getOpgroup(op)!=G_UNARY) && (getOpgroup(op)!=G_NP1OUT))     if ((getOpgroup(op)!=G_UNARY) && (getOpgroup(op)!=G_NP1OUT) && (getOpgroup(op)!=G_REDUCTION))
443     {     {
444      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.");
445     }     }
446    
447     DataLazy_ptr lleft;     DataLazy_ptr lleft;
448     if (!left->isLazy())     if (!left->isLazy())
449     {     {
450      lleft=DataLazy_ptr(new DataLazy(left));          lleft=DataLazy_ptr(new DataLazy(left));
451     }     }
452     else     else
453     {     {
454      lleft=dynamic_pointer_cast<DataLazy>(left);          lleft=dynamic_pointer_cast<DataLazy>(left);
455     }     }
456     m_readytype=lleft->m_readytype;     m_readytype=lleft->m_readytype;
457     m_left=lleft;     m_left=lleft;
    m_buffsRequired=calcBuffs(m_left, m_right,m_op); // yeah m_right will be null at this point  
458     m_samplesize=getNumDPPSample()*getNoValues();     m_samplesize=getNumDPPSample()*getNoValues();
459     m_maxsamplesize=max(m_samplesize,m_left->getMaxSampleSize());     m_children=m_left->m_children+1;
460       m_height=m_left->m_height+1;
461       LazyNodeSetup();
462       SIZELIMIT
463  }  }
464    
465    
466  // In this constructor we need to consider interpolation  // In this constructor we need to consider interpolation
467  DataLazy::DataLazy(DataAbstract_ptr left, DataAbstract_ptr right, ES_optype op)  DataLazy::DataLazy(DataAbstract_ptr left, DataAbstract_ptr right, ES_optype op)
468      : parent(resultFS(left,right,op), resultShape(left,right,op)),          : parent(resultFS(left,right,op), resultShape(left,right,op)),
469      m_op(op),          m_op(op),
470      m_SL(0), m_SM(0), m_SR(0)          m_SL(0), m_SM(0), m_SR(0)
471  {  {
472    LAZYDEBUG(cout << "Forming operator with " << left.get() << " " << right.get() << endl;)
473     if ((getOpgroup(op)!=G_BINARY))     if ((getOpgroup(op)!=G_BINARY))
474     {     {
475      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.");
476     }     }
477    
478     if (getFunctionSpace()!=left->getFunctionSpace())    // left needs to be interpolated     if (getFunctionSpace()!=left->getFunctionSpace())    // left needs to be interpolated
479     {     {
480      FunctionSpace fs=getFunctionSpace();          FunctionSpace fs=getFunctionSpace();
481      Data ltemp(left);          Data ltemp(left);
482      Data tmp(ltemp,fs);          Data tmp(ltemp,fs);
483      left=tmp.borrowDataPtr();          left=tmp.borrowDataPtr();
484     }     }
485     if (getFunctionSpace()!=right->getFunctionSpace())   // right needs to be interpolated     if (getFunctionSpace()!=right->getFunctionSpace())   // right needs to be interpolated
486     {     {
487      Data tmp(Data(right),getFunctionSpace());          Data tmp(Data(right),getFunctionSpace());
488      right=tmp.borrowDataPtr();          right=tmp.borrowDataPtr();
489    LAZYDEBUG(cout << "Right interpolation required " << right.get() << endl;)
490     }     }
491     left->operandCheck(*right);     left->operandCheck(*right);
492    
493     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
494     {     {
495      m_left=dynamic_pointer_cast<DataLazy>(left);          m_left=dynamic_pointer_cast<DataLazy>(left);
496    LAZYDEBUG(cout << "Left is " << m_left->toString() << endl;)
497     }     }
498     else     else
499     {     {
500      m_left=DataLazy_ptr(new DataLazy(left));          m_left=DataLazy_ptr(new DataLazy(left));
501    LAZYDEBUG(cout << "Left " << left.get() << " wrapped " << m_left->m_id.get() << endl;)
502     }     }
503     if (right->isLazy())     if (right->isLazy())
504     {     {
505      m_right=dynamic_pointer_cast<DataLazy>(right);          m_right=dynamic_pointer_cast<DataLazy>(right);
506    LAZYDEBUG(cout << "Right is " << m_right->toString() << endl;)
507     }     }
508     else     else
509     {     {
510      m_right=DataLazy_ptr(new DataLazy(right));          m_right=DataLazy_ptr(new DataLazy(right));
511    LAZYDEBUG(cout << "Right " << right.get() << " wrapped " << m_right->m_id.get() << endl;)
512     }     }
513     char lt=m_left->m_readytype;     char lt=m_left->m_readytype;
514     char rt=m_right->m_readytype;     char rt=m_right->m_readytype;
515     if (lt=='E' || rt=='E')     if (lt=='E' || rt=='E')
516     {     {
517      m_readytype='E';          m_readytype='E';
518     }     }
519     else if (lt=='T' || rt=='T')     else if (lt=='T' || rt=='T')
520     {     {
521      m_readytype='T';          m_readytype='T';
522     }     }
523     else     else
524     {     {
525      m_readytype='C';          m_readytype='C';
526     }     }
527     m_samplesize=getNumDPPSample()*getNoValues();     m_samplesize=getNumDPPSample()*getNoValues();
528     m_maxsamplesize=max(max(m_samplesize,m_right->getMaxSampleSize()),m_left->getMaxSampleSize());       m_children=m_left->m_children+m_right->m_children+2;
529     m_buffsRequired=calcBuffs(m_left, m_right,m_op);     m_height=max(m_left->m_height,m_right->m_height)+1;
530       LazyNodeSetup();
531       SIZELIMIT
532  LAZYDEBUG(cout << "(3)Lazy created with " << m_samplesize << endl;)  LAZYDEBUG(cout << "(3)Lazy created with " << m_samplesize << endl;)
533  }  }
534    
535  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)
536      : parent(resultFS(left,right,op), GTPShape(left,right, axis_offset, transpose, m_SL,m_SM, m_SR)),          : parent(resultFS(left,right,op), GTPShape(left,right, axis_offset, transpose, m_SL,m_SM, m_SR)),
537      m_op(op),          m_op(op),
538      m_axis_offset(axis_offset),          m_axis_offset(axis_offset),
539      m_transpose(transpose)          m_transpose(transpose)
540  {  {
541     if ((getOpgroup(op)!=G_TENSORPROD))     if ((getOpgroup(op)!=G_TENSORPROD))
542     {     {
543      throw DataException("Programmer error - constructor DataLazy(left, right, op, ax, tr) will only process BINARY operations which require parameters.");          throw DataException("Programmer error - constructor DataLazy(left, right, op, ax, tr) will only process BINARY operations which require parameters.");
544     }     }
545     if ((transpose>2) || (transpose<0))     if ((transpose>2) || (transpose<0))
546     {     {
547      throw DataException("DataLazy GeneralTensorProduct constructor: Error - transpose should be 0, 1 or 2");          throw DataException("DataLazy GeneralTensorProduct constructor: Error - transpose should be 0, 1 or 2");
548     }     }
549     if (getFunctionSpace()!=left->getFunctionSpace())    // left needs to be interpolated     if (getFunctionSpace()!=left->getFunctionSpace())    // left needs to be interpolated
550     {     {
551      FunctionSpace fs=getFunctionSpace();          FunctionSpace fs=getFunctionSpace();
552      Data ltemp(left);          Data ltemp(left);
553      Data tmp(ltemp,fs);          Data tmp(ltemp,fs);
554      left=tmp.borrowDataPtr();          left=tmp.borrowDataPtr();
555     }     }
556     if (getFunctionSpace()!=right->getFunctionSpace())   // right needs to be interpolated     if (getFunctionSpace()!=right->getFunctionSpace())   // right needs to be interpolated
557     {     {
558      Data tmp(Data(right),getFunctionSpace());          Data tmp(Data(right),getFunctionSpace());
559      right=tmp.borrowDataPtr();          right=tmp.borrowDataPtr();
560     }     }
561     left->operandCheck(*right);  //    left->operandCheck(*right);
562    
563     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
564     {     {
565      m_left=dynamic_pointer_cast<DataLazy>(left);          m_left=dynamic_pointer_cast<DataLazy>(left);
566     }     }
567     else     else
568     {     {
569      m_left=DataLazy_ptr(new DataLazy(left));          m_left=DataLazy_ptr(new DataLazy(left));
570     }     }
571     if (right->isLazy())     if (right->isLazy())
572     {     {
573      m_right=dynamic_pointer_cast<DataLazy>(right);          m_right=dynamic_pointer_cast<DataLazy>(right);
574     }     }
575     else     else
576     {     {
577      m_right=DataLazy_ptr(new DataLazy(right));          m_right=DataLazy_ptr(new DataLazy(right));
578     }     }
579     char lt=m_left->m_readytype;     char lt=m_left->m_readytype;
580     char rt=m_right->m_readytype;     char rt=m_right->m_readytype;
581     if (lt=='E' || rt=='E')     if (lt=='E' || rt=='E')
582     {     {
583      m_readytype='E';          m_readytype='E';
584     }     }
585     else if (lt=='T' || rt=='T')     else if (lt=='T' || rt=='T')
586     {     {
587      m_readytype='T';          m_readytype='T';
588     }     }
589     else     else
590     {     {
591      m_readytype='C';          m_readytype='C';
592     }     }
593     m_samplesize=getNumDPPSample()*getNoValues();     m_samplesize=getNumDPPSample()*getNoValues();
594     m_maxsamplesize=max(max(m_samplesize,m_right->getMaxSampleSize()),m_left->getMaxSampleSize());       m_children=m_left->m_children+m_right->m_children+2;
595     m_buffsRequired=calcBuffs(m_left, m_right,m_op);     m_height=max(m_left->m_height,m_right->m_height)+1;
596       LazyNodeSetup();
597       SIZELIMIT
598  LAZYDEBUG(cout << "(4)Lazy created with " << m_samplesize << endl;)  LAZYDEBUG(cout << "(4)Lazy created with " << m_samplesize << endl;)
599  }  }
600    
601    
602  DataLazy::DataLazy(DataAbstract_ptr left, ES_optype op, int axis_offset)  DataLazy::DataLazy(DataAbstract_ptr left, ES_optype op, int axis_offset)
603      : parent(left->getFunctionSpace(), resultShape(left,op)),          : parent(left->getFunctionSpace(), resultShape(left,op, axis_offset)),
604      m_op(op),          m_op(op),
605      m_axis_offset(axis_offset),          m_axis_offset(axis_offset),
606      m_transpose(0),          m_transpose(0),
607      m_tol(0)          m_tol(0)
608  {  {
609     if ((getOpgroup(op)!=G_NP1OUT_P))     if ((getOpgroup(op)!=G_NP1OUT_P))
610     {     {
611      throw DataException("Programmer error - constructor DataLazy(left, op, ax) will only process UNARY operations which require parameters.");          throw DataException("Programmer error - constructor DataLazy(left, op, ax) will only process UNARY operations which require parameters.");
612     }     }
613     DataLazy_ptr lleft;     DataLazy_ptr lleft;
614     if (!left->isLazy())     if (!left->isLazy())
615     {     {
616      lleft=DataLazy_ptr(new DataLazy(left));          lleft=DataLazy_ptr(new DataLazy(left));
617     }     }
618     else     else
619     {     {
620      lleft=dynamic_pointer_cast<DataLazy>(left);          lleft=dynamic_pointer_cast<DataLazy>(left);
621     }     }
622     m_readytype=lleft->m_readytype;     m_readytype=lleft->m_readytype;
623     m_left=lleft;     m_left=lleft;
    m_buffsRequired=calcBuffs(m_left, m_right,m_op); // yeah m_right will be null at this point  
624     m_samplesize=getNumDPPSample()*getNoValues();     m_samplesize=getNumDPPSample()*getNoValues();
625     m_maxsamplesize=max(m_samplesize,m_left->getMaxSampleSize());     m_children=m_left->m_children+1;
626       m_height=m_left->m_height+1;
627       LazyNodeSetup();
628       SIZELIMIT
629  LAZYDEBUG(cout << "(5)Lazy created with " << m_samplesize << endl;)  LAZYDEBUG(cout << "(5)Lazy created with " << m_samplesize << endl;)
630  }  }
631    
632  DataLazy::DataLazy(DataAbstract_ptr left, ES_optype op, double tol)  DataLazy::DataLazy(DataAbstract_ptr left, ES_optype op, double tol)
633      : parent(left->getFunctionSpace(), left->getShape()),          : parent(left->getFunctionSpace(), left->getShape()),
634      m_op(op),          m_op(op),
635      m_axis_offset(0),          m_axis_offset(0),
636      m_transpose(0),          m_transpose(0),
637      m_tol(tol)          m_tol(tol)
638  {  {
639     if ((getOpgroup(op)!=G_UNARY_P))     if ((getOpgroup(op)!=G_UNARY_P))
640     {     {
641      throw DataException("Programmer error - constructor DataLazy(left, op, tol) will only process UNARY operations which require parameters.");          throw DataException("Programmer error - constructor DataLazy(left, op, tol) will only process UNARY operations which require parameters.");
642     }     }
643     DataLazy_ptr lleft;     DataLazy_ptr lleft;
644     if (!left->isLazy())     if (!left->isLazy())
645     {     {
646      lleft=DataLazy_ptr(new DataLazy(left));          lleft=DataLazy_ptr(new DataLazy(left));
647     }     }
648     else     else
649     {     {
650      lleft=dynamic_pointer_cast<DataLazy>(left);          lleft=dynamic_pointer_cast<DataLazy>(left);
651     }     }
652     m_readytype=lleft->m_readytype;     m_readytype=lleft->m_readytype;
653     m_left=lleft;     m_left=lleft;
    m_buffsRequired=calcBuffs(m_left, m_right,m_op); // yeah m_right will be null at this point  
654     m_samplesize=getNumDPPSample()*getNoValues();     m_samplesize=getNumDPPSample()*getNoValues();
655     m_maxsamplesize=max(m_samplesize,m_left->getMaxSampleSize());     m_children=m_left->m_children+1;
656       m_height=m_left->m_height+1;
657       LazyNodeSetup();
658       SIZELIMIT
659  LAZYDEBUG(cout << "(6)Lazy created with " << m_samplesize << endl;)  LAZYDEBUG(cout << "(6)Lazy created with " << m_samplesize << endl;)
660  }  }
661    
662  DataLazy::~DataLazy()  
663    DataLazy::DataLazy(DataAbstract_ptr left, ES_optype op, const int axis0, const int axis1)
664            : parent(left->getFunctionSpace(), SwapShape(left,axis0,axis1)),
665            m_op(op),
666            m_axis_offset(axis0),
667            m_transpose(axis1),
668            m_tol(0)
669  {  {
670       if ((getOpgroup(op)!=G_NP1OUT_2P))
671       {
672            throw DataException("Programmer error - constructor DataLazy(left, op, tol) will only process UNARY operations which require two integer parameters.");
673       }
674       DataLazy_ptr lleft;
675       if (!left->isLazy())
676       {
677            lleft=DataLazy_ptr(new DataLazy(left));
678       }
679       else
680       {
681            lleft=dynamic_pointer_cast<DataLazy>(left);
682       }
683       m_readytype=lleft->m_readytype;
684       m_left=lleft;
685       m_samplesize=getNumDPPSample()*getNoValues();
686       m_children=m_left->m_children+1;
687       m_height=m_left->m_height+1;
688       LazyNodeSetup();
689       SIZELIMIT
690    LAZYDEBUG(cout << "(7)Lazy created with " << m_samplesize << endl;)
691  }  }
692    
693    
694  int  namespace
 DataLazy::getBuffsRequired() const  
695  {  {
696      return m_buffsRequired;  
697        inline int max3(int a, int b, int c)
698        {
699            int t=(a>b?a:b);
700            return (t>c?t:c);
701    
702        }
703    }
704    
705    DataLazy::DataLazy(DataAbstract_ptr mask, DataAbstract_ptr left, DataAbstract_ptr right/*, double tol*/)
706            : parent(left->getFunctionSpace(), left->getShape()),
707            m_op(CONDEVAL),
708            m_axis_offset(0),
709            m_transpose(0),
710            m_tol(0)
711    {
712    
713       DataLazy_ptr lmask;
714       DataLazy_ptr lleft;
715       DataLazy_ptr lright;
716       if (!mask->isLazy())
717       {
718            lmask=DataLazy_ptr(new DataLazy(mask));
719       }
720       else
721       {
722            lmask=dynamic_pointer_cast<DataLazy>(mask);
723       }
724       if (!left->isLazy())
725       {
726            lleft=DataLazy_ptr(new DataLazy(left));
727       }
728       else
729       {
730            lleft=dynamic_pointer_cast<DataLazy>(left);
731       }
732       if (!right->isLazy())
733       {
734            lright=DataLazy_ptr(new DataLazy(right));
735       }
736       else
737       {
738            lright=dynamic_pointer_cast<DataLazy>(right);
739       }
740       m_readytype=lmask->m_readytype;
741       if ((lleft->m_readytype!=lright->m_readytype) || (lmask->m_readytype!=lleft->m_readytype))
742       {
743            throw DataException("Programmer Error - condEval arguments must have the same readytype");
744       }
745       m_left=lleft;
746       m_right=lright;
747       m_mask=lmask;
748       m_samplesize=getNumDPPSample()*getNoValues();
749       m_children=m_left->m_children+m_right->m_children+m_mask->m_children+1;
750       m_height=max3(m_left->m_height,m_right->m_height,m_mask->m_height)+1;
751       LazyNodeSetup();
752       SIZELIMIT
753    LAZYDEBUG(cout << "(8)Lazy created with " << m_samplesize << endl;)
754  }  }
755    
756    
757  size_t  
758  DataLazy::getMaxSampleSize() const  DataLazy::~DataLazy()
759  {  {
760      return m_maxsamplesize;     delete[] m_sampleids;
761  }  }
762    
763    
764  /*  /*
765    \brief Evaluates the expression using methods on Data.    \brief Evaluates the expression using methods on Data.
766    This does the work for the collapse method.    This does the work for the collapse method.
767    For reasons of efficiency do not call this method on DataExpanded nodes.    For reasons of efficiency do not call this method on DataExpanded nodes.
768  */  */
769  DataReady_ptr  DataReady_ptr
770  DataLazy::collapseToReady()  DataLazy::collapseToReady() const
771  {  {
772    if (m_readytype=='E')    if (m_readytype=='E')
773    { // this is more an efficiency concern than anything else    {     // this is more an efficiency concern than anything else
774      throw DataException("Programmer Error - do not use collapse on Expanded data.");      throw DataException("Programmer Error - do not use collapse on Expanded data.");
775    }    }
776    if (m_op==IDENTITY)    if (m_op==IDENTITY)
# Line 606  DataLazy::collapseToReady() Line 788  DataLazy::collapseToReady()
788    switch(m_op)    switch(m_op)
789    {    {
790      case ADD:      case ADD:
791      result=left+right;          result=left+right;
792      break;          break;
793      case SUB:            case SUB:          
794      result=left-right;          result=left-right;
795      break;          break;
796      case MUL:            case MUL:          
797      result=left*right;          result=left*right;
798      break;          break;
799      case DIV:            case DIV:          
800      result=left/right;          result=left/right;
801      break;          break;
802      case SIN:      case SIN:
803      result=left.sin();            result=left.sin();      
804      break;          break;
805      case COS:      case COS:
806      result=left.cos();          result=left.cos();
807      break;          break;
808      case TAN:      case TAN:
809      result=left.tan();          result=left.tan();
810      break;          break;
811      case ASIN:      case ASIN:
812      result=left.asin();          result=left.asin();
813      break;          break;
814      case ACOS:      case ACOS:
815      result=left.acos();          result=left.acos();
816      break;          break;
817      case ATAN:      case ATAN:
818      result=left.atan();          result=left.atan();
819      break;          break;
820      case SINH:      case SINH:
821      result=left.sinh();          result=left.sinh();
822      break;          break;
823      case COSH:      case COSH:
824      result=left.cosh();          result=left.cosh();
825      break;          break;
826      case TANH:      case TANH:
827      result=left.tanh();          result=left.tanh();
828      break;          break;
829      case ERF:      case ERF:
830      result=left.erf();          result=left.erf();
831      break;          break;
832     case ASINH:     case ASINH:
833      result=left.asinh();          result=left.asinh();
834      break;          break;
835     case ACOSH:     case ACOSH:
836      result=left.acosh();          result=left.acosh();
837      break;          break;
838     case ATANH:     case ATANH:
839      result=left.atanh();          result=left.atanh();
840      break;          break;
841      case LOG10:      case LOG10:
842      result=left.log10();          result=left.log10();
843      break;          break;
844      case LOG:      case LOG:
845      result=left.log();          result=left.log();
846      break;          break;
847      case SIGN:      case SIGN:
848      result=left.sign();          result=left.sign();
849      break;          break;
850      case ABS:      case ABS:
851      result=left.abs();          result=left.abs();
852      break;          break;
853      case NEG:      case NEG:
854      result=left.neg();          result=left.neg();
855      break;          break;
856      case POS:      case POS:
857      // it doesn't mean anything for delayed.          // it doesn't mean anything for delayed.
858      // it will just trigger a deep copy of the lazy object          // it will just trigger a deep copy of the lazy object
859      throw DataException("Programmer error - POS not supported for lazy data.");          throw DataException("Programmer error - POS not supported for lazy data.");
860      break;          break;
861      case EXP:      case EXP:
862      result=left.exp();          result=left.exp();
863      break;          break;
864      case SQRT:      case SQRT:
865      result=left.sqrt();          result=left.sqrt();
866      break;          break;
867      case RECIP:      case RECIP:
868      result=left.oneOver();          result=left.oneOver();
869      break;          break;
870      case GZ:      case GZ:
871      result=left.wherePositive();          result=left.wherePositive();
872      break;          break;
873      case LZ:      case LZ:
874      result=left.whereNegative();          result=left.whereNegative();
875      break;          break;
876      case GEZ:      case GEZ:
877      result=left.whereNonNegative();          result=left.whereNonNegative();
878      break;          break;
879      case LEZ:      case LEZ:
880      result=left.whereNonPositive();          result=left.whereNonPositive();
881      break;          break;
882      case NEZ:      case NEZ:
883      result=left.whereNonZero(m_tol);          result=left.whereNonZero(m_tol);
884      break;          break;
885      case EZ:      case EZ:
886      result=left.whereZero(m_tol);          result=left.whereZero(m_tol);
887      break;          break;
888      case SYM:      case SYM:
889      result=left.symmetric();          result=left.symmetric();
890      break;          break;
891      case NSYM:      case NSYM:
892      result=left.nonsymmetric();          result=left.antisymmetric();
893      break;          break;
894      case PROD:      case PROD:
895      result=C_GeneralTensorProduct(left,right,m_axis_offset, m_transpose);          result=C_GeneralTensorProduct(left,right,m_axis_offset, m_transpose);
896      break;          break;
897      case TRANS:      case TRANS:
898      result=left.transpose(m_axis_offset);          result=left.transpose(m_axis_offset);
899      break;          break;
900      case TRACE:      case TRACE:
901      result=left.trace(m_axis_offset);          result=left.trace(m_axis_offset);
902            break;
903        case SWAP:
904            result=left.swapaxes(m_axis_offset, m_transpose);
905            break;
906        case MINVAL:
907            result=left.minval();
908            break;
909        case MAXVAL:
910            result=left.minval();
911            break;
912        case HER:
913        result=left.hermitian();
914      break;      break;
915      default:      default:
916      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)+".");
917    }    }
918    return result.borrowReadyPtr();    return result.borrowReadyPtr();
919  }  }
# Line 731  DataLazy::collapseToReady() Line 925  DataLazy::collapseToReady()
925     the purpose of using DataLazy in the first place).     the purpose of using DataLazy in the first place).
926  */  */
927  void  void
928  DataLazy::collapse()  DataLazy::collapse() const
929  {  {
930    if (m_op==IDENTITY)    if (m_op==IDENTITY)
931    {    {
932      return;          return;
933    }    }
934    if (m_readytype=='E')    if (m_readytype=='E')
935    { // this is more an efficiency concern than anything else    {     // this is more an efficiency concern than anything else
936      throw DataException("Programmer Error - do not use collapse on Expanded data.");      throw DataException("Programmer Error - do not use collapse on Expanded data.");
937    }    }
938    m_id=collapseToReady();    m_id=collapseToReady();
939    m_op=IDENTITY;    m_op=IDENTITY;
940  }  }
941    
942  /*  // The result will be stored in m_samples
943    \brief Compute the value of the expression (unary operation) for the given sample.  // The return value is a pointer to the DataVector, offset is the offset within the return value
944    \return Vector which stores the value of the subexpression for the given sample.  const DataTypes::RealVectorType*
945    \param v A vector to store intermediate results.  DataLazy::resolveNodeSample(int tid, int sampleNo, size_t& roffset) const
   \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::resolveUnary(ValueType& v, size_t offset, int sampleNo, size_t& roffset) const  
946  {  {
947      // we assume that any collapsing has been done before we get here  LAZYDEBUG(cout << "Resolve sample " << toString() << endl;)
948      // since we only have one argument we don't need to think about only          // collapse so we have a 'E' node or an IDENTITY for some other type
949      // processing single points.    if (m_readytype!='E' && m_op!=IDENTITY)
   if (m_readytype!='E')  
950    {    {
951      throw DataException("Programmer error - resolveUnary should only be called on expanded Data.");          collapse();
952    }    }
953    const ValueType* vleft=m_left->resolveSample(v,offset,sampleNo,roffset);    if (m_op==IDENTITY)  
   const double* left=&((*vleft)[roffset]);  
   double* result=&(v[offset]);  
   roffset=offset;  
   switch (m_op)  
954    {    {
955      case SIN:        const RealVectorType& vec=m_id->getVectorRO();
956      tensor_unary_operation<double (*)(double)>(m_samplesize, left, result, ::sin);      roffset=m_id->getPointOffset(sampleNo, 0);
957      break;  #ifdef LAZY_STACK_PROF
958      case COS:  int x;
959      tensor_unary_operation<double (*)(double)>(m_samplesize, left, result, ::cos);  if (&x<stackend[omp_get_thread_num()])
960      break;  {
961      case TAN:         stackend[omp_get_thread_num()]=&x;
962      tensor_unary_operation<double (*)(double)>(m_samplesize, left, result, ::tan);  }
     break;  
     case ASIN:  
     tensor_unary_operation<double (*)(double)>(m_samplesize, left, result, ::asin);  
     break;  
     case ACOS:  
     tensor_unary_operation<double (*)(double)>(m_samplesize, left, result, ::acos);  
     break;  
     case ATAN:  
     tensor_unary_operation<double (*)(double)>(m_samplesize, left, result, ::atan);  
     break;  
     case SINH:  
     tensor_unary_operation<double (*)(double)>(m_samplesize, left, result, ::sinh);  
     break;  
     case COSH:  
     tensor_unary_operation<double (*)(double)>(m_samplesize, left, result, ::cosh);  
     break;  
     case TANH:  
     tensor_unary_operation<double (*)(double)>(m_samplesize, left, result, ::tanh);  
     break;  
     case ERF:  
 #if defined (_WIN32) && !defined(__INTEL_COMPILER)  
     throw DataException("Error - Data:: erf function is not supported on _WIN32 platforms.");  
 #else  
     tensor_unary_operation(m_samplesize, left, result, ::erf);  
     break;  
963  #endif  #endif
964     case ASINH:      return &(vec);
965  #if defined (_WIN32) && !defined(__INTEL_COMPILER)    }
966      tensor_unary_operation(m_samplesize, left, result, escript::asinh_substitute);    if (m_readytype!='E')
967  #else    {
968      tensor_unary_operation(m_samplesize, left, result, ::asinh);      throw DataException("Programmer Error - Collapse did not produce an expanded node.");
969  #endif      }
970      break;    if (m_sampleids[tid]==sampleNo)
971     case ACOSH:    {
972  #if defined (_WIN32) && !defined(__INTEL_COMPILER)          roffset=tid*m_samplesize;
973      tensor_unary_operation(m_samplesize, left, result, escript::acosh_substitute);          return &(m_samples_r);            // sample is already resolved
974  #else    }
975      tensor_unary_operation(m_samplesize, left, result, ::acosh);    m_sampleids[tid]=sampleNo;
 #endif    
     break;  
    case ATANH:  
 #if defined (_WIN32) && !defined(__INTEL_COMPILER)  
     tensor_unary_operation(m_samplesize, left, result, escript::atanh_substitute);  
 #else  
     tensor_unary_operation(m_samplesize, left, result, ::atanh);  
 #endif    
     break;  
     case LOG10:  
     tensor_unary_operation<double (*)(double)>(m_samplesize, left, result, ::log10);  
     break;  
     case LOG:  
     tensor_unary_operation<double (*)(double)>(m_samplesize, left, result, ::log);  
     break;  
     case SIGN:  
     tensor_unary_operation(m_samplesize, left, result, escript::fsign);  
     break;  
     case ABS:  
     tensor_unary_operation<double (*)(double)>(m_samplesize, left, result, ::fabs);  
     break;  
     case NEG:  
     tensor_unary_operation(m_samplesize, left, result, negate<double>());  
     break;  
     case POS:  
     // it doesn't mean anything for delayed.  
     // it will just trigger a deep copy of the lazy object  
     throw DataException("Programmer error - POS not supported for lazy data.");  
     break;  
     case EXP:  
     tensor_unary_operation<double (*)(double)>(m_samplesize, left, result, ::exp);  
     break;  
     case SQRT:  
     tensor_unary_operation<double (*)(double)>(m_samplesize, left, result, ::sqrt);  
     break;  
     case RECIP:  
     tensor_unary_operation(m_samplesize, left, result, bind1st(divides<double>(),1.));  
     break;  
     case GZ:  
     tensor_unary_operation(m_samplesize, left, result, bind2nd(greater<double>(),0.0));  
     break;  
     case LZ:  
     tensor_unary_operation(m_samplesize, left, result, bind2nd(less<double>(),0.0));  
     break;  
     case GEZ:  
     tensor_unary_operation(m_samplesize, left, result, bind2nd(greater_equal<double>(),0.0));  
     break;  
     case LEZ:  
     tensor_unary_operation(m_samplesize, left, result, bind2nd(less_equal<double>(),0.0));  
     break;  
 // There are actually G_UNARY_P but I don't see a compelling reason to treat them differently  
     case NEZ:  
     tensor_unary_operation(m_samplesize, left, result, bind2nd(AbsGT(),m_tol));  
     break;  
     case EZ:  
     tensor_unary_operation(m_samplesize, left, result, bind2nd(AbsLTE(),m_tol));  
     break;  
976    
977      default:    switch (getOpgroup(m_op))
978      throw DataException("Programmer error - resolveUnary can not resolve operator "+opToString(m_op)+".");    {
979      case G_UNARY:
980      case G_UNARY_P: return resolveNodeUnary(tid, sampleNo, roffset);
981      case G_BINARY: return resolveNodeBinary(tid, sampleNo, roffset);
982      case G_NP1OUT: return resolveNodeNP1OUT(tid, sampleNo, roffset);
983      case G_NP1OUT_P: return resolveNodeNP1OUT_P(tid, sampleNo, roffset);
984      case G_TENSORPROD: return resolveNodeTProd(tid, sampleNo, roffset);
985      case G_NP1OUT_2P: return resolveNodeNP1OUT_2P(tid, sampleNo, roffset);
986      case G_REDUCTION: return resolveNodeReduction(tid, sampleNo, roffset);
987      case G_CONDEVAL: return resolveNodeCondEval(tid, sampleNo, roffset);
988      default:
989        throw DataException("Programmer Error - resolveSample does not know how to process "+opToString(m_op)+".");
990    }    }
   return &v;  
991  }  }
992    
993    const DataTypes::RealVectorType*
994    DataLazy::resolveNodeUnary(int tid, int sampleNo, size_t& roffset) const
995    {
996            // we assume that any collapsing has been done before we get here
997            // since we only have one argument we don't need to think about only
998            // processing single points.
999            // we will also know we won't get identity nodes
1000      if (m_readytype!='E')
1001      {
1002        throw DataException("Programmer error - resolveUnary should only be called on expanded Data.");
1003      }
1004      if (m_op==IDENTITY)
1005      {
1006        throw DataException("Programmer error - resolveNodeUnary should not be called on identity nodes.");
1007      }
1008      const DataTypes::RealVectorType* leftres=m_left->resolveNodeSample(tid, sampleNo, roffset);
1009      const double* left=&((*leftres)[roffset]);
1010      roffset=m_samplesize*tid;
1011      double* result=&(m_samples_r[roffset]);
1012      if (m_op==POS)
1013      {
1014        // this should be prevented earlier
1015        // operation is meaningless for lazy
1016            throw DataException("Programmer error - POS not supported for lazy data.");    
1017      }
1018      tensor_unary_array_operation(m_samplesize,
1019                                 left,
1020                                 result,
1021                                 m_op,
1022                                 m_tol);  
1023      return &(m_samples_r);
1024    }
1025    
1026    
1027    const DataTypes::RealVectorType*
1028    DataLazy::resolveNodeReduction(int tid, int sampleNo, size_t& roffset) const
1029    {
1030            // we assume that any collapsing has been done before we get here
1031            // since we only have one argument we don't need to think about only
1032            // processing single points.
1033            // we will also know we won't get identity nodes
1034      if (m_readytype!='E')
1035      {
1036        throw DataException("Programmer error - resolveUnary should only be called on expanded Data.");
1037      }
1038      if (m_op==IDENTITY)
1039      {
1040        throw DataException("Programmer error - resolveNodeUnary should not be called on identity nodes.");
1041      }
1042      size_t loffset=0;
1043      const DataTypes::RealVectorType* leftres=m_left->resolveNodeSample(tid, sampleNo, loffset);
1044    
1045      roffset=m_samplesize*tid;
1046      unsigned int ndpps=getNumDPPSample();
1047      unsigned int psize=DataTypes::noValues(m_left->getShape());
1048      double* result=&(m_samples_r[roffset]);
1049      switch (m_op)
1050      {
1051        case MINVAL:
1052            {
1053              for (unsigned int z=0;z<ndpps;++z)
1054              {
1055                FMin op;
1056                *result=escript::reductionOpVector(*leftres, m_left->getShape(), loffset, op, numeric_limits<double>::max());
1057                loffset+=psize;
1058                result++;
1059              }
1060            }
1061            break;
1062        case MAXVAL:
1063            {
1064              for (unsigned int z=0;z<ndpps;++z)
1065              {
1066              FMax op;
1067              *result=escript::reductionOpVector(*leftres, m_left->getShape(), loffset, op, numeric_limits<double>::max()*-1);
1068              loffset+=psize;
1069              result++;
1070              }
1071            }
1072            break;
1073        default:
1074            throw DataException("Programmer error - resolveUnary can not resolve operator "+opToString(m_op)+".");
1075      }
1076      return &(m_samples_r);
1077    }
1078    
1079    const DataTypes::RealVectorType*
1080  /*  DataLazy::resolveNodeNP1OUT(int tid, int sampleNo, size_t& roffset) const
   \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  
1081  {  {
1082      // we assume that any collapsing has been done before we get here          // we assume that any collapsing has been done before we get here
1083      // 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
1084      // processing single points.          // processing single points.
1085    if (m_readytype!='E')    if (m_readytype!='E')
1086    {    {
1087      throw DataException("Programmer error - resolveNP1OUT should only be called on expanded Data.");      throw DataException("Programmer error - resolveNodeNP1OUT should only be called on expanded Data.");
1088    }    }
1089      // since we can't write the result over the input, we need a result offset further along    if (m_op==IDENTITY)
1090    size_t subroffset=roffset+m_samplesize;    {
1091    const ValueType* vleft=m_left->resolveSample(v,offset,sampleNo,subroffset);      throw DataException("Programmer error - resolveNodeNP1OUT should not be called on identity nodes.");
1092    roffset=offset;    }
1093      size_t subroffset;
1094      const RealVectorType* leftres=m_left->resolveNodeSample(tid, sampleNo, subroffset);
1095      roffset=m_samplesize*tid;
1096      size_t loop=0;
1097      size_t numsteps=(m_readytype=='E')?getNumDPPSample():1;
1098      size_t step=getNoValues();
1099      size_t offset=roffset;
1100    switch (m_op)    switch (m_op)
1101    {    {
1102      case SYM:      case SYM:
1103      DataMaths::symmetric(*vleft,m_left->getShape(),subroffset, v, getShape(), offset);          for (loop=0;loop<numsteps;++loop)
1104      break;          {
1105                escript::symmetric(*leftres,m_left->getShape(),subroffset, m_samples_r, getShape(), offset);
1106                subroffset+=step;
1107                offset+=step;
1108            }
1109            break;
1110      case NSYM:      case NSYM:
1111      DataMaths::nonsymmetric(*vleft,m_left->getShape(),subroffset, v, getShape(), offset);          for (loop=0;loop<numsteps;++loop)
1112      break;          {
1113                escript::antisymmetric(*leftres,m_left->getShape(),subroffset, m_samples_r, getShape(), offset);
1114                subroffset+=step;
1115                offset+=step;
1116            }
1117            break;
1118      default:      default:
1119      throw DataException("Programmer error - resolveNP1OUT can not resolve operator "+opToString(m_op)+".");          throw DataException("Programmer error - resolveNP1OUT can not resolve operator "+opToString(m_op)+".");
1120    }    }
1121    return &v;    return &m_samples_r;
1122  }  }
1123    
1124  /*  const DataTypes::RealVectorType*
1125    \brief Compute the value of the expression (unary operation) for the given sample.  DataLazy::resolveNodeNP1OUT_P(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_P(ValueType& v, size_t offset, int sampleNo, size_t& roffset) const  
1126  {  {
1127      // we assume that any collapsing has been done before we get here          // we assume that any collapsing has been done before we get here
1128      // 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
1129      // processing single points.          // processing single points.
1130    if (m_readytype!='E')    if (m_readytype!='E')
1131    {    {
1132      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.");
1133    }    }
1134      // since we can't write the result over the input, we need a result offset further along    if (m_op==IDENTITY)
1135    size_t subroffset=roffset+m_samplesize;    {
1136    const ValueType* vleft=m_left->resolveSample(v,offset,sampleNo,subroffset);      throw DataException("Programmer error - resolveNodeNP1OUT_P should not be called on identity nodes.");
1137    roffset=offset;    }
1138      size_t subroffset;
1139      size_t offset;
1140      const RealVectorType* leftres=m_left->resolveNodeSample(tid, sampleNo, subroffset);
1141      roffset=m_samplesize*tid;
1142      offset=roffset;
1143      size_t loop=0;
1144      size_t numsteps=(m_readytype=='E')?getNumDPPSample():1;
1145      size_t outstep=getNoValues();
1146      size_t instep=m_left->getNoValues();
1147    switch (m_op)    switch (m_op)
1148    {    {
1149      case TRACE:      case TRACE:
1150           DataMaths::trace(*vleft,m_left->getShape(),subroffset, v,getShape(),offset,m_axis_offset);          for (loop=0;loop<numsteps;++loop)
1151      break;          {
1152                escript::trace(*leftres,m_left->getShape(),subroffset, m_samples_r ,getShape(),offset,m_axis_offset);
1153                subroffset+=instep;
1154                offset+=outstep;
1155            }
1156            break;
1157      case TRANS:      case TRANS:
1158           DataMaths::transpose(*vleft,m_left->getShape(),subroffset, v,getShape(),offset,m_axis_offset);          for (loop=0;loop<numsteps;++loop)
1159      break;          {
1160                escript::transpose(*leftres,m_left->getShape(),subroffset, m_samples_r, getShape(),offset,m_axis_offset);
1161                subroffset+=instep;
1162                offset+=outstep;
1163            }
1164            break;
1165      default:      default:
1166      throw DataException("Programmer error - resolveNP1OUTP can not resolve operator "+opToString(m_op)+".");          throw DataException("Programmer error - resolveNP1OUTP can not resolve operator "+opToString(m_op)+".");
1167    }    }
1168    return &v;    return &m_samples_r;
1169  }  }
1170    
1171    
1172  #define PROC_OP(TYPE,X)                               \  const DataTypes::RealVectorType*
1173      for (int i=0;i<steps;++i,resultp+=resultStep) \  DataLazy::resolveNodeNP1OUT_2P(int tid, int sampleNo, size_t& roffset) const
1174      { \  {
1175         tensor_binary_operation< TYPE >(chunksize, &((*left)[lroffset]), &((*right)[rroffset]), resultp, X); \    if (m_readytype!='E')
1176         lroffset+=leftStep; \    {
1177         rroffset+=rightStep; \      throw DataException("Programmer error - resolveNodeNP1OUT_2P should only be called on expanded Data.");
1178      }    }
1179      if (m_op==IDENTITY)
1180      {
1181        throw DataException("Programmer error - resolveNodeNP1OUT_2P should not be called on identity nodes.");
1182      }
1183      size_t subroffset;
1184      size_t offset;
1185      const RealVectorType* leftres=m_left->resolveNodeSample(tid, sampleNo, subroffset);
1186      roffset=m_samplesize*tid;
1187      offset=roffset;
1188      size_t loop=0;
1189      size_t numsteps=(m_readytype=='E')?getNumDPPSample():1;
1190      size_t outstep=getNoValues();
1191      size_t instep=m_left->getNoValues();
1192      switch (m_op)
1193      {
1194        case SWAP:
1195            for (loop=0;loop<numsteps;++loop)
1196            {
1197                escript::swapaxes(*leftres,m_left->getShape(),subroffset, m_samples_r, getShape(),offset, m_axis_offset, m_transpose);
1198                subroffset+=instep;
1199                offset+=outstep;
1200            }
1201            break;
1202        default:
1203            throw DataException("Programmer error - resolveNodeNP1OUT2P can not resolve operator "+opToString(m_op)+".");
1204      }
1205      return &m_samples_r;
1206    }
1207    
1208    const DataTypes::RealVectorType*
1209    DataLazy::resolveNodeCondEval(int tid, int sampleNo, size_t& roffset) const
1210    {
1211      if (m_readytype!='E')
1212      {
1213        throw DataException("Programmer error - resolveNodeCondEval should only be called on expanded Data.");
1214      }
1215      if (m_op!=CONDEVAL)
1216      {
1217        throw DataException("Programmer error - resolveNodeCondEval should only be called on CONDEVAL nodes.");
1218      }
1219      size_t subroffset;
1220    
1221      const RealVectorType* maskres=m_mask->resolveNodeSample(tid, sampleNo, subroffset);
1222      const RealVectorType* srcres=0;
1223      if ((*maskres)[subroffset]>0)
1224      {
1225            srcres=m_left->resolveNodeSample(tid, sampleNo, subroffset);
1226      }
1227      else
1228      {
1229            srcres=m_right->resolveNodeSample(tid, sampleNo, subroffset);
1230      }
1231    
1232      // Now we need to copy the result
1233    
1234      roffset=m_samplesize*tid;
1235      for (int i=0;i<m_samplesize;++i)
1236      {
1237            m_samples_r[roffset+i]=(*srcres)[subroffset+i];  
1238      }
1239    
1240      return &m_samples_r;
1241    }
1242    
 /*  
   \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.  
 */  
1243  // 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
1244  // 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.
1245  // 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 1249  DataLazy::resolveNP1OUT_P(ValueType& v,
1249  // There is an additional complication when scalar operations are considered.  // There is an additional complication when scalar operations are considered.
1250  // For example, 2+Vector.  // For example, 2+Vector.
1251  // In this case each double within the point is treated individually  // In this case each double within the point is treated individually
1252  DataTypes::ValueType*  const DataTypes::RealVectorType*
1253  DataLazy::resolveBinary(ValueType& v,  size_t offset, int sampleNo, size_t& roffset) const  DataLazy::resolveNodeBinary(int tid, int sampleNo, size_t& roffset) const
1254  {  {
1255  LAZYDEBUG(cout << "Resolve binary: " << toString() << endl;)  LAZYDEBUG(cout << "Resolve binary: " << toString() << endl;)
1256    
1257    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
1258      // first work out which of the children are expanded          // first work out which of the children are expanded
1259    bool leftExp=(m_left->m_readytype=='E');    bool leftExp=(m_left->m_readytype=='E');
1260    bool rightExp=(m_right->m_readytype=='E');    bool rightExp=(m_right->m_readytype=='E');
1261    if (!leftExp && !rightExp)    if (!leftExp && !rightExp)
1262    {    {
1263      throw DataException("Programmer Error - please use collapse if neither argument has type 'E'.");          throw DataException("Programmer Error - please use collapse if neither argument has type 'E'.");
1264    }    }
1265    bool leftScalar=(m_left->getRank()==0);    bool leftScalar=(m_left->getRank()==0);
1266    bool rightScalar=(m_right->getRank()==0);    bool rightScalar=(m_right->getRank()==0);
1267    bool bigloops=((leftExp && rightExp) || (!leftExp && !rightExp)); // is processing in single step?    if ((m_left->getRank()!=m_right->getRank()) && (!leftScalar && !rightScalar))
1268    int steps=(bigloops?1:getNumDPPSample());    {
1269    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.");
1270    if (m_left->getRank()!=m_right->getRank())    // need to deal with scalar * ? ops    }
1271    {    size_t leftsize=m_left->getNoValues();
1272      if (!leftScalar && !rightScalar)    size_t rightsize=m_right->getNoValues();
1273      {    size_t chunksize=1;                   // how many doubles will be processed in one go
1274         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
1275      }    int rightstep=0;
1276      steps=getNumDPPSample()*max(m_left->getNoValues(),m_right->getNoValues());    int numsteps=0;               // total number of steps for the inner loop
1277      chunksize=1;    // for scalar    int oleftstep=0;      // the o variables refer to the outer loop
1278    }        int orightstep=0;     // The outer loop is only required in cases where there is an extended scalar
1279    int leftStep=((leftExp && (!rightExp || rightScalar))? m_right->getNoValues() : 0);    int onumsteps=1;
1280    int rightStep=((rightExp && (!leftExp || leftScalar))? m_left->getNoValues() : 0);    
1281    int resultStep=max(leftStep,rightStep);   // only one (at most) should be !=0    bool LES=(leftExp && leftScalar);     // Left is an expanded scalar
1282      // Get the values of sub-expressions    bool RES=(rightExp && rightScalar);
1283    const ValueType* left=m_left->resolveSample(v,offset,sampleNo,lroffset);    bool LS=(!leftExp && leftScalar);     // left is a single scalar
1284    const ValueType* right=m_right->resolveSample(v,offset+m_samplesize,sampleNo,rroffset); // Note    bool RS=(!rightExp && rightScalar);
1285      // the right child starts further along.    bool LN=(!leftExp && !leftScalar);    // left is a single non-scalar
1286    double* resultp=&(v[offset]);     // results are stored at the vector offset we recieved    bool RN=(!rightExp && !rightScalar);
1287      bool LEN=(leftExp && !leftScalar);    // left is an expanded non-scalar
1288      bool REN=(rightExp && !rightScalar);
1289    
1290      if ((LES && RES) || (LEN && REN))     // both are Expanded scalars or both are expanded non-scalars
1291      {
1292            chunksize=m_left->getNumDPPSample()*leftsize;
1293            leftstep=0;
1294            rightstep=0;
1295            numsteps=1;
1296      }
1297      else if (LES || RES)
1298      {
1299            chunksize=1;
1300            if (LES)                // left is an expanded scalar
1301            {
1302                    if (RS)
1303                    {
1304                       leftstep=1;
1305                       rightstep=0;
1306                       numsteps=m_left->getNumDPPSample();
1307                    }
1308                    else            // RN or REN
1309                    {
1310                       leftstep=0;
1311                       oleftstep=1;
1312                       rightstep=1;
1313                       orightstep=(RN ? -(int)rightsize : 0);
1314                       numsteps=rightsize;
1315                       onumsteps=m_left->getNumDPPSample();
1316                    }
1317            }
1318            else            // right is an expanded scalar
1319            {
1320                    if (LS)
1321                    {
1322                       rightstep=1;
1323                       leftstep=0;
1324                       numsteps=m_right->getNumDPPSample();
1325                    }
1326                    else
1327                    {
1328                       rightstep=0;
1329                       orightstep=1;
1330                       leftstep=1;
1331                       oleftstep=(LN ? -(int)leftsize : 0);
1332                       numsteps=leftsize;
1333                       onumsteps=m_right->getNumDPPSample();
1334                    }
1335            }
1336      }
1337      else  // this leaves (LEN, RS), (LEN, RN) and their transposes
1338      {
1339            if (LEN)        // and Right will be a single value
1340            {
1341                    chunksize=rightsize;
1342                    leftstep=rightsize;
1343                    rightstep=0;
1344                    numsteps=m_left->getNumDPPSample();
1345                    if (RS)
1346                    {
1347                       numsteps*=leftsize;
1348                    }
1349            }
1350            else    // REN
1351            {
1352                    chunksize=leftsize;
1353                    rightstep=leftsize;
1354                    leftstep=0;
1355                    numsteps=m_right->getNumDPPSample();
1356                    if (LS)
1357                    {
1358                       numsteps*=rightsize;
1359                    }
1360            }
1361      }
1362    
1363      int resultStep=max(leftstep,rightstep);       // only one (at most) should be !=0
1364            // Get the values of sub-expressions
1365      const RealVectorType* left=m_left->resolveNodeSample(tid,sampleNo,lroffset);      
1366      const RealVectorType* right=m_right->resolveNodeSample(tid,sampleNo,rroffset);
1367    LAZYDEBUG(cout << "Post sub calls in " << toString() << endl;)
1368    LAZYDEBUG(cout << "shapes=" << DataTypes::shapeToString(m_left->getShape()) << "," << DataTypes::shapeToString(m_right->getShape()) << endl;)
1369    LAZYDEBUG(cout << "chunksize=" << chunksize << endl << "leftstep=" << leftstep << " rightstep=" << rightstep;)
1370    LAZYDEBUG(cout << " numsteps=" << numsteps << endl << "oleftstep=" << oleftstep << " orightstep=" << orightstep;)
1371    LAZYDEBUG(cout << "onumsteps=" << onumsteps << endl;)
1372    LAZYDEBUG(cout << " DPPS=" << m_left->getNumDPPSample() << "," <<m_right->getNumDPPSample() << endl;)
1373    LAZYDEBUG(cout << "" << LS << RS << LN << RN << LES << RES <<LEN << REN <<   endl;)
1374    
1375    LAZYDEBUG(cout << "Left res["<< lroffset<< "]=" << (*left)[lroffset] << endl;)
1376    LAZYDEBUG(cout << "Right res["<< rroffset<< "]=" << (*right)[rroffset] << endl;)
1377    
1378    
1379      roffset=m_samplesize*tid;
1380      double* resultp=&(m_samples_r[roffset]);                // results are stored at the vector offset we received
1381    switch(m_op)    switch(m_op)
1382    {    {
1383      case ADD:      case ADD:
1384          PROC_OP(NO_ARG,plus<double>());          //PROC_OP(NO_ARG,plus<double>());
1385      break;        escript::binaryOpVectorLazyHelper<real_t, real_t, real_t>(resultp,
1386                 &(*left)[0],
1387                 &(*right)[0],
1388                 chunksize,
1389                 onumsteps,
1390                 numsteps,
1391                 resultStep,
1392                 leftstep,
1393                 rightstep,
1394                 oleftstep,
1395                 orightstep,
1396                 lroffset,
1397                 rroffset,
1398                 escript::ES_optype::ADD);  
1399            break;
1400      case SUB:      case SUB:
1401      PROC_OP(NO_ARG,minus<double>());        escript::binaryOpVectorLazyHelper<real_t, real_t, real_t>(resultp,
1402      break;               &(*left)[0],
1403                 &(*right)[0],
1404                 chunksize,
1405                 onumsteps,
1406                 numsteps,
1407                 resultStep,
1408                 leftstep,
1409                 rightstep,
1410                 oleftstep,
1411                 orightstep,
1412                 lroffset,
1413                 rroffset,
1414                 escript::ES_optype::SUB);        
1415            //PROC_OP(NO_ARG,minus<double>());
1416            break;
1417      case MUL:      case MUL:
1418      PROC_OP(NO_ARG,multiplies<double>());          //PROC_OP(NO_ARG,multiplies<double>());
1419      break;        escript::binaryOpVectorLazyHelper<real_t, real_t, real_t>(resultp,
1420                 &(*left)[0],
1421                 &(*right)[0],
1422                 chunksize,
1423                 onumsteps,
1424                 numsteps,
1425                 resultStep,
1426                 leftstep,
1427                 rightstep,
1428                 oleftstep,
1429                 orightstep,
1430                 lroffset,
1431                 rroffset,
1432                 escript::ES_optype::MUL);        
1433            break;
1434      case DIV:      case DIV:
1435      PROC_OP(NO_ARG,divides<double>());          //PROC_OP(NO_ARG,divides<double>());
1436      break;        escript::binaryOpVectorLazyHelper<real_t, real_t, real_t>(resultp,
1437                 &(*left)[0],
1438                 &(*right)[0],
1439                 chunksize,
1440                 onumsteps,
1441                 numsteps,
1442                 resultStep,
1443                 leftstep,
1444                 rightstep,
1445                 oleftstep,
1446                 orightstep,
1447                 lroffset,
1448                 rroffset,
1449                 escript::ES_optype::DIV);        
1450            break;
1451      case POW:      case POW:
1452         PROC_OP(double (double,double),::pow);         //PROC_OP(double (double,double),::pow);
1453      break;        escript::binaryOpVectorLazyHelper<real_t, real_t, real_t>(resultp,
1454                 &(*left)[0],
1455                 &(*right)[0],
1456                 chunksize,
1457                 onumsteps,
1458                 numsteps,
1459                 resultStep,
1460                 leftstep,
1461                 rightstep,
1462                 oleftstep,
1463                 orightstep,
1464                 lroffset,
1465                 rroffset,
1466                 escript::ES_optype::POW);        
1467            break;
1468      default:      default:
1469      throw DataException("Programmer error - resolveBinary can not resolve operator "+opToString(m_op)+".");          throw DataException("Programmer error - resolveBinary can not resolve operator "+opToString(m_op)+".");
1470    }    }
1471    roffset=offset;    LAZYDEBUG(cout << "Result res[" << roffset<< "]" << m_samples_r[roffset] << endl;)
1472    return &v;    return &m_samples_r;
1473  }  }
1474    
1475    
 /*  
   \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.  
 */  
1476  // 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
1477  // 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.
1478  // unlike the other resolve helpers, we must treat these datapoints separately.  // unlike the other resolve helpers, we must treat these datapoints separately.
1479  DataTypes::ValueType*  const DataTypes::RealVectorType*
1480  DataLazy::resolveTProd(ValueType& v,  size_t offset, int sampleNo, size_t& roffset) const  DataLazy::resolveNodeTProd(int tid, int sampleNo, size_t& roffset) const
1481  {  {
1482  LAZYDEBUG(cout << "Resolve TensorProduct: " << toString() << endl;)  LAZYDEBUG(cout << "Resolve TensorProduct: " << toString() << endl;)
1483    
1484    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
1485      // first work out which of the children are expanded          // first work out which of the children are expanded
1486    bool leftExp=(m_left->m_readytype=='E');    bool leftExp=(m_left->m_readytype=='E');
1487    bool rightExp=(m_right->m_readytype=='E');    bool rightExp=(m_right->m_readytype=='E');
1488    int steps=getNumDPPSample();    int steps=getNumDPPSample();
1489    int leftStep=((leftExp && !rightExp)? m_right->getNoValues() : 0);    int leftStep=(leftExp? m_left->getNoValues() : 0);            // do not have scalars as input to this method
1490    int rightStep=((rightExp && !leftExp)? m_left->getNoValues() : 0);    int rightStep=(rightExp?m_right->getNoValues() : 0);
1491    int resultStep=max(leftStep,rightStep);   // only one (at most) should be !=0  
1492      // Get the values of sub-expressions (leave a gap of one sample for the result).    int resultStep=getNoValues();
1493    const ValueType* left=m_left->resolveSample(v,offset+m_samplesize,sampleNo,lroffset);    roffset=m_samplesize*tid;
1494    const ValueType* right=m_right->resolveSample(v,offset+2*m_samplesize,sampleNo,rroffset);    size_t offset=roffset;
1495    double* resultp=&(v[offset]);     // results are stored at the vector offset we recieved  
1496      const RealVectorType* left=m_left->resolveNodeSample(tid, sampleNo, lroffset);
1497    
1498      const RealVectorType* right=m_right->resolveNodeSample(tid, sampleNo, rroffset);
1499    
1500    LAZYDEBUG(cerr << "[Left shape]=" << DataTypes::shapeToString(m_left->getShape()) << "\n[Right shape]=" << DataTypes::shapeToString(m_right->getShape()) << " result=" <<DataTypes::shapeToString(getShape()) <<  endl;
1501    cout << getNoValues() << endl;)
1502    
1503    
1504    LAZYDEBUG(cerr << "Post sub calls: " << toString() << endl;)
1505    LAZYDEBUG(cout << "LeftExp=" << leftExp << " rightExp=" << rightExp << endl;)
1506    LAZYDEBUG(cout << "LeftR=" << m_left->getRank() << " rightExp=" << m_right->getRank() << endl;)
1507    LAZYDEBUG(cout << "LeftSize=" << m_left->getNoValues() << " RightSize=" << m_right->getNoValues() << endl;)
1508    LAZYDEBUG(cout << "m_samplesize=" << m_samplesize << endl;)
1509    LAZYDEBUG(cout << "outputshape=" << DataTypes::shapeToString(getShape()) << endl;)
1510    LAZYDEBUG(cout << "DPPS=" << m_right->getNumDPPSample() <<"."<<endl;)
1511    
1512      double* resultp=&(m_samples_r[offset]);         // results are stored at the vector offset we received
1513    switch(m_op)    switch(m_op)
1514    {    {
1515      case PROD:      case PROD:
1516      for (int i=0;i<steps;++i,resultp+=resultStep)          for (int i=0;i<steps;++i,resultp+=resultStep)
1517      {          {
1518            const double *ptr_0 = &((*left)[lroffset]);            const double *ptr_0 = &((*left)[lroffset]);
1519            const double *ptr_1 = &((*right)[rroffset]);            const double *ptr_1 = &((*right)[rroffset]);
1520            matrix_matrix_product(m_SL, m_SM, m_SR, ptr_0, ptr_1, resultp, m_transpose);  
1521        lroffset+=leftStep;  LAZYDEBUG(cout << DataTypes::pointToString(*left, m_left->getShape(),lroffset,"LEFT") << endl;)
1522        rroffset+=rightStep;  LAZYDEBUG(cout << DataTypes::pointToString(*right,m_right->getShape(),rroffset, "RIGHT") << endl;)
1523      }  
1524      break;            matrix_matrix_product(m_SL, m_SM, m_SR, ptr_0, ptr_1, resultp, m_transpose);
1525    
1526              lroffset+=leftStep;
1527              rroffset+=rightStep;
1528            }
1529            break;
1530      default:      default:
1531      throw DataException("Programmer error - resolveTProduct can not resolve operator "+opToString(m_op)+".");          throw DataException("Programmer error - resolveTProduct can not resolve operator "+opToString(m_op)+".");
1532    }    }
1533    roffset=offset;    roffset=offset;
1534    return &v;    return &m_samples_r;
1535  }  }
1536    
1537    
1538    const DataTypes::RealVectorType*
1539    DataLazy::resolveSample(int sampleNo, size_t& roffset) const
1540    {
1541    #ifdef _OPENMP
1542            int tid=omp_get_thread_num();
1543    #else
1544            int tid=0;
1545    #endif
1546    
1547  /*  #ifdef LAZY_STACK_PROF
1548    \brief Compute the value of the expression for the given sample.          stackstart[tid]=&tid;
1549    \return Vector which stores the value of the subexpression for the given sample.          stackend[tid]=&tid;
1550    \param v A vector to store intermediate results.          const DataTypes::RealVectorType* r=resolveNodeSample(tid, sampleNo, roffset);
1551    \param offset Index in v to begin storing results.          size_t d=(size_t)stackstart[tid]-(size_t)stackend[tid];
1552    \param sampleNo Sample number to evaluate.          #pragma omp critical
1553    \param roffset (output parameter) the offset in the return vector where the result begins.          if (d>maxstackuse)
1554            {
1555    cout << "Max resolve Stack use " << d << endl;
1556                    maxstackuse=d;
1557            }
1558            return r;
1559    #else
1560            return resolveNodeSample(tid, sampleNo, roffset);
1561    #endif
1562    }
1563    
1564    The return value will be an existing vector so do not deallocate it.  
1565  */  // This needs to do the work of the identity constructor
1566  // the vector and the offset are a place where the method could write its data if it wishes  void
1567  // it is not obligated to do so. For example, if it has its own storage already, it can use that.  DataLazy::resolveToIdentity()
 // Hence the return value to indicate where the data is actually stored.  
 // Regardless, the storage should be assumed to be used, even if it isn't.  
   
 // the roffset is the offset within the returned vector where the data begins  
 const DataTypes::ValueType*  
 DataLazy::resolveSample(ValueType& v, size_t offset, int sampleNo, size_t& roffset)  
1568  {  {
1569  LAZYDEBUG(cout << "Resolve sample " << toString() << endl;)     if (m_op==IDENTITY)
1570      // collapse so we have a 'E' node or an IDENTITY for some other type          return;
1571    if (m_readytype!='E' && m_op!=IDENTITY)     DataReady_ptr p=resolveNodeWorker();
1572       makeIdentity(p);
1573    }
1574    
1575    void DataLazy::makeIdentity(const DataReady_ptr& p)
1576    {
1577       m_op=IDENTITY;
1578       m_axis_offset=0;
1579       m_transpose=0;
1580       m_SL=m_SM=m_SR=0;
1581       m_children=m_height=0;
1582       m_id=p;
1583       if(p->isConstant()) {m_readytype='C';}
1584       else if(p->isExpanded()) {m_readytype='E';}
1585       else if (p->isTagged()) {m_readytype='T';}
1586       else {throw DataException("Unknown DataReady instance in convertToIdentity constructor.");}
1587       m_samplesize=p->getNumDPPSample()*p->getNoValues();
1588       m_left.reset();
1589       m_right.reset();
1590       m_iscompl=p->isComplex();
1591    }
1592    
1593    
1594    DataReady_ptr
1595    DataLazy::resolve()
1596    {
1597        resolveToIdentity();
1598        return m_id;
1599    }
1600    
1601    
1602    /* This is really a static method but I think that caused problems in windows */
1603    void
1604    DataLazy::resolveGroupWorker(std::vector<DataLazy*>& dats)
1605    {
1606      if (dats.empty())
1607    {    {
1608      collapse();          return;
1609    }    }
1610    if (m_op==IDENTITY)      vector<DataLazy*> work;
1611    {    FunctionSpace fs=dats[0]->getFunctionSpace();
1612      const ValueType& vec=m_id->getVector();    bool match=true;
1613      if (m_readytype=='C')    for (int i=dats.size()-1;i>=0;--i)
1614      {    {
1615      roffset=0;          if (dats[i]->m_readytype!='E')
1616      return &(vec);          {
1617      }                  dats[i]->collapse();
1618      roffset=m_id->getPointOffset(sampleNo, 0);          }
1619      return &(vec);          if (dats[i]->m_op!=IDENTITY)
1620            {
1621                    work.push_back(dats[i]);
1622                    if (fs!=dats[i]->getFunctionSpace())
1623                    {
1624                            match=false;
1625                    }
1626            }
1627      }
1628      if (work.empty())
1629      {
1630            return;         // no work to do
1631      }
1632      if (match)    // all functionspaces match.  Yes I realise this is overly strict
1633      {             // it is possible that dats[0] is one of the objects which we discarded and
1634                    // all the other functionspaces match.
1635            vector<DataExpanded*> dep;
1636            vector<RealVectorType*> vecs;
1637            for (int i=0;i<work.size();++i)
1638            {
1639                    dep.push_back(new DataExpanded(fs,work[i]->getShape(), RealVectorType(work[i]->getNoValues())));
1640                    vecs.push_back(&(dep[i]->getVectorRW()));
1641            }
1642            int totalsamples=work[0]->getNumSamples();
1643            const RealVectorType* res=0; // Storage for answer
1644            int sample;
1645            #pragma omp parallel private(sample, res)
1646            {
1647                size_t roffset=0;
1648                #pragma omp for schedule(static)
1649                for (sample=0;sample<totalsamples;++sample)
1650                {
1651                    roffset=0;
1652                    int j;
1653                    for (j=work.size()-1;j>=0;--j)
1654                    {
1655    #ifdef _OPENMP
1656                        res=work[j]->resolveNodeSample(omp_get_thread_num(),sample,roffset);
1657    #else
1658                        res=work[j]->resolveNodeSample(0,sample,roffset);
1659    #endif
1660                        RealVectorType::size_type outoffset=dep[j]->getPointOffset(sample,0);
1661                        memcpy(&((*vecs[j])[outoffset]),&((*res)[roffset]),work[j]->m_samplesize*sizeof(RealVectorType::ElementType));
1662                    }
1663                }
1664            }
1665            // Now we need to load the new results as identity ops into the lazy nodes
1666            for (int i=work.size()-1;i>=0;--i)
1667            {
1668                work[i]->makeIdentity(REFCOUNTNS::dynamic_pointer_cast<DataReady>(dep[i]->getPtr()));
1669            }
1670    }    }
1671    if (m_readytype!='E')    else  // functionspaces do not match
1672    {    {
1673      throw DataException("Programmer Error - Collapse did not produce an expanded node.");          for (int i=0;i<work.size();++i)
1674    }          {
1675    switch (getOpgroup(m_op))                  work[i]->resolveToIdentity();
1676    {          }
   case G_UNARY:  
   case G_UNARY_P: return resolveUnary(v, offset,sampleNo,roffset);  
   case G_BINARY: return resolveBinary(v, offset,sampleNo,roffset);  
   case G_NP1OUT: return resolveNP1OUT(v, offset, sampleNo,roffset);  
   case G_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)+".");  
1677    }    }
1678  }  }
1679    
1680    
1681  // To simplify the memory management, all threads operate on one large vector, rather than one each.  
1682  // Each sample is evaluated independently and copied into the result DataExpanded.  // This version of resolve uses storage in each node to hold results
1683  DataReady_ptr  DataReady_ptr
1684  DataLazy::resolve()  DataLazy::resolveNodeWorker()
1685  {  {
1686      if (m_readytype!='E')         // if the whole sub-expression is Constant or Tagged, then evaluate it normally
 LAZYDEBUG(cout << "Sample size=" << m_samplesize << endl;)  
 LAZYDEBUG(cout << "Buffers=" << m_buffsRequired << endl;)  
   
   if (m_readytype!='E')     // if the whole sub-expression is Constant or Tagged, then evaluate it normally  
1687    {    {
1688      collapse();      collapse();
1689    }    }
1690    if (m_op==IDENTITY)       // So a lazy expression of Constant or Tagged data will be returned here.    if (m_op==IDENTITY)           // So a lazy expression of Constant or Tagged data will be returned here.
1691    {    {
1692      return m_id;      return m_id;
1693    }    }
1694      // 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'
1695    size_t threadbuffersize=m_maxsamplesize*(max(1,m_buffsRequired)); // Each thread needs to have enough    DataExpanded* result=new DataExpanded(getFunctionSpace(),getShape(),  RealVectorType(getNoValues()));
1696      // storage to evaluate its expression    RealVectorType& resvec=result->getVectorRW();
   int numthreads=1;  
 #ifdef _OPENMP  
   numthreads=getNumberOfThreads();  
 #endif  
   ValueType v(numthreads*threadbuffersize);  
 LAZYDEBUG(cout << "Buffer created with size=" << v.size() << endl;)  
   DataExpanded* result=new DataExpanded(getFunctionSpace(),getShape(),  ValueType(getNoValues()));  
   ValueType& resvec=result->getVector();  
1697    DataReady_ptr resptr=DataReady_ptr(result);    DataReady_ptr resptr=DataReady_ptr(result);
1698    
1699    int sample;    int sample;
   size_t outoffset;     // offset in the output data  
1700    int totalsamples=getNumSamples();    int totalsamples=getNumSamples();
1701    const ValueType* res=0;   // Vector storing the answer    const RealVectorType* res=0;       // Storage for answer
1702    size_t resoffset=0;       // where in the vector to find the answer  LAZYDEBUG(cout << "Total number of samples=" <<totalsamples << endl;)
1703    #pragma omp parallel for private(sample,resoffset,outoffset,res) schedule(static)    #pragma omp parallel private(sample,res)
1704    for (sample=0;sample<totalsamples;++sample)    {
1705    {          size_t roffset=0;
1706  LAZYDEBUG(cout << "################################# " << sample << endl;)  #ifdef LAZY_STACK_PROF
1707            stackstart[omp_get_thread_num()]=&roffset;
1708            stackend[omp_get_thread_num()]=&roffset;
1709    #endif
1710            #pragma omp for schedule(static)
1711            for (sample=0;sample<totalsamples;++sample)
1712            {
1713                    roffset=0;
1714  #ifdef _OPENMP  #ifdef _OPENMP
1715      res=resolveSample(v,threadbuffersize*omp_get_thread_num(),sample,resoffset);                  res=resolveNodeSample(omp_get_thread_num(),sample,roffset);
1716  #else  #else
1717      res=resolveSample(v,0,sample,resoffset);   // res would normally be v, but not if its a single IDENTITY op.                  res=resolveNodeSample(0,sample,roffset);
1718  #endif  #endif
1719  LAZYDEBUG(cerr << "-------------------------------- " << endl;)  LAZYDEBUG(cout << "Sample #" << sample << endl;)
1720      outoffset=result->getPointOffset(sample,0);  LAZYDEBUG(cout << "Final res[" << roffset<< "]=" << (*res)[roffset] << (*res)[roffset]<< endl; )
1721  LAZYDEBUG(cerr << "offset=" << outoffset << endl;)                  RealVectorType::size_type outoffset=result->getPointOffset(sample,0);
1722      for (unsigned int i=0;i<m_samplesize;++i,++outoffset,++resoffset)   // copy values into the output vector                  memcpy(&(resvec[outoffset]),&((*res)[roffset]),m_samplesize*sizeof(RealVectorType::ElementType));
1723      {          }
1724      resvec[outoffset]=(*res)[resoffset];    }
1725      }  #ifdef LAZY_STACK_PROF
1726  LAZYDEBUG(cerr << "*********************************" << endl;)    for (int i=0;i<getNumberOfThreads();++i)
1727      {
1728            size_t r=((size_t)stackstart[i] - (size_t)stackend[i]);
1729    //      cout << i << " " << stackstart[i] << " .. " << stackend[i] << " = " <<  r << endl;
1730            if (r>maxstackuse)
1731            {
1732                    maxstackuse=r;
1733            }
1734    }    }
1735      cout << "Max resolve Stack use=" << maxstackuse << endl;
1736    #endif
1737    return resptr;    return resptr;
1738  }  }
1739    
# Line 1224  std::string Line 1741  std::string
1741  DataLazy::toString() const  DataLazy::toString() const
1742  {  {
1743    ostringstream oss;    ostringstream oss;
1744    oss << "Lazy Data:";    oss << "Lazy Data: [depth=" << m_height<< "] ";
1745    intoString(oss);    switch (escriptParams.getLazyStrFmt())
1746      {
1747      case 1:       // tree format
1748            oss << endl;
1749            intoTreeString(oss,"");
1750            break;
1751      case 2:       // just the depth
1752            break;
1753      default:
1754            intoString(oss);
1755            break;
1756      }
1757    return oss.str();    return oss.str();
1758  }  }
1759    
# Line 1233  DataLazy::toString() const Line 1761  DataLazy::toString() const
1761  void  void
1762  DataLazy::intoString(ostringstream& oss) const  DataLazy::intoString(ostringstream& oss) const
1763  {  {
1764    //    oss << "[" << m_children <<";"<<m_height <<"]";
1765    switch (getOpgroup(m_op))    switch (getOpgroup(m_op))
1766    {    {
1767    case G_IDENTITY:    case G_IDENTITY:
1768      if (m_id->isExpanded())          if (m_id->isExpanded())
1769      {          {
1770         oss << "E";             oss << "E";
1771      }          }
1772      else if (m_id->isTagged())          else if (m_id->isTagged())
1773      {          {
1774        oss << "T";            oss << "T";
1775      }          }
1776      else if (m_id->isConstant())          else if (m_id->isConstant())
1777      {          {
1778        oss << "C";            oss << "C";
1779      }          }
1780      else          else
1781      {          {
1782        oss << "?";            oss << "?";
1783      }          }
1784      oss << '@' << m_id.get();          oss << '@' << m_id.get();
1785      break;          break;
1786    case G_BINARY:    case G_BINARY:
1787      oss << '(';          oss << '(';
1788      m_left->intoString(oss);          m_left->intoString(oss);
1789      oss << ' ' << opToString(m_op) << ' ';          oss << ' ' << opToString(m_op) << ' ';
1790      m_right->intoString(oss);          m_right->intoString(oss);
1791      oss << ')';          oss << ')';
1792      break;          break;
1793    case G_UNARY:    case G_UNARY:
1794    case G_UNARY_P:    case G_UNARY_P:
1795    case G_NP1OUT:    case G_NP1OUT:
1796    case G_NP1OUT_P:    case G_NP1OUT_P:
1797      oss << opToString(m_op) << '(';    case G_REDUCTION:
1798      m_left->intoString(oss);          oss << opToString(m_op) << '(';
1799      oss << ')';          m_left->intoString(oss);
1800      break;          oss << ')';
1801            break;
1802    case G_TENSORPROD:    case G_TENSORPROD:
1803      oss << opToString(m_op) << '(';          oss << opToString(m_op) << '(';
1804      m_left->intoString(oss);          m_left->intoString(oss);
1805      oss << ", ";          oss << ", ";
1806      m_right->intoString(oss);          m_right->intoString(oss);
1807      oss << ')';          oss << ')';
1808      break;          break;
1809      case G_NP1OUT_2P:
1810            oss << opToString(m_op) << '(';
1811            m_left->intoString(oss);
1812            oss << ", " << m_axis_offset << ", " << m_transpose;
1813            oss << ')';
1814            break;
1815      case G_CONDEVAL:
1816            oss << opToString(m_op)<< '(' ;
1817            m_mask->intoString(oss);
1818            oss << " ? ";
1819            m_left->intoString(oss);
1820            oss << " : ";
1821            m_right->intoString(oss);
1822            oss << ')';
1823            break;
1824      default:
1825            oss << "UNKNOWN";
1826      }
1827    }
1828    
1829    
1830    void
1831    DataLazy::intoTreeString(ostringstream& oss, string indent) const
1832    {
1833      oss << '[' << m_rank << ':' << setw(3) << m_samplesize << "] " << indent;
1834      switch (getOpgroup(m_op))
1835      {
1836      case G_IDENTITY:
1837            if (m_id->isExpanded())
1838            {
1839               oss << "E";
1840            }
1841            else if (m_id->isTagged())
1842            {
1843              oss << "T";
1844            }
1845            else if (m_id->isConstant())
1846            {
1847              oss << "C";
1848            }
1849            else
1850            {
1851              oss << "?";
1852            }
1853            oss << '@' << m_id.get() << endl;
1854            break;
1855      case G_BINARY:
1856            oss << opToString(m_op) << endl;
1857            indent+='.';
1858            m_left->intoTreeString(oss, indent);
1859            m_right->intoTreeString(oss, indent);
1860            break;
1861      case G_UNARY:
1862      case G_UNARY_P:
1863      case G_NP1OUT:
1864      case G_NP1OUT_P:
1865      case G_REDUCTION:
1866            oss << opToString(m_op) << endl;
1867            indent+='.';
1868            m_left->intoTreeString(oss, indent);
1869            break;
1870      case G_TENSORPROD:
1871            oss << opToString(m_op) << endl;
1872            indent+='.';
1873            m_left->intoTreeString(oss, indent);
1874            m_right->intoTreeString(oss, indent);
1875            break;
1876      case G_NP1OUT_2P:
1877            oss << opToString(m_op) << ", " << m_axis_offset << ", " << m_transpose<< endl;
1878            indent+='.';
1879            m_left->intoTreeString(oss, indent);
1880            break;
1881    default:    default:
1882      oss << "UNKNOWN";          oss << "UNKNOWN";
1883    }    }
1884  }  }
1885    
1886    
1887  DataAbstract*  DataAbstract*
1888  DataLazy::deepCopy()  DataLazy::deepCopy() const
1889  {  {
1890    switch (getOpgroup(m_op))    switch (getOpgroup(m_op))
1891    {    {
1892    case G_IDENTITY:  return new DataLazy(m_id->deepCopy()->getPtr());    case G_IDENTITY:  return new DataLazy(m_id->deepCopy()->getPtr());
1893    case G_UNARY: return new DataLazy(m_left->deepCopy()->getPtr(),m_op);    case G_UNARY:
1894    case G_BINARY:    return new DataLazy(m_left->deepCopy()->getPtr(),m_right->deepCopy()->getPtr(),m_op);    case G_REDUCTION:      return new DataLazy(m_left->deepCopy()->getPtr(),m_op);
1895      case G_UNARY_P:       return new DataLazy(m_left->deepCopy()->getPtr(), m_op, m_tol);
1896      case G_BINARY:        return new DataLazy(m_left->deepCopy()->getPtr(),m_right->deepCopy()->getPtr(),m_op);
1897    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);
1898    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);
1899      case G_NP1OUT_P:   return new DataLazy(m_left->deepCopy()->getPtr(),m_op,  m_axis_offset);
1900      case G_NP1OUT_2P:  return new DataLazy(m_left->deepCopy()->getPtr(), m_op, m_axis_offset, m_transpose);
1901    default:    default:
1902      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)+".");
1903    }    }
1904  }  }
1905    
1906    // For this, we don't care what op you were doing because the answer is now zero
1907    DataAbstract*
1908    DataLazy::zeroedCopy() const
1909    {
1910      return new DataLazy(m_id->zeroedCopy()->getPtr());
1911    }
1912    
1913  // There is no single, natural interpretation of getLength on DataLazy.  // There is no single, natural interpretation of getLength on DataLazy.
1914  // Instances of DataReady can look at the size of their vectors.  // Instances of DataReady can look at the size of their vectors.
# Line 1303  DataLazy::deepCopy() Line 1916  DataLazy::deepCopy()
1916  // or it could be some function of the lengths of the DataReady instances which  // or it could be some function of the lengths of the DataReady instances which
1917  // form part of the expression.  // form part of the expression.
1918  // Rather than have people making assumptions, I have disabled the method.  // Rather than have people making assumptions, I have disabled the method.
1919  DataTypes::ValueType::size_type  DataTypes::RealVectorType::size_type
1920  DataLazy::getLength() const  DataLazy::getLength() const
1921  {  {
1922    throw DataException("getLength() does not make sense for lazy data.");    throw DataException("getLength() does not make sense for lazy data.");
# Line 1318  DataLazy::getSlice(const DataTypes::Regi Line 1931  DataLazy::getSlice(const DataTypes::Regi
1931    
1932    
1933  // To do this we need to rely on our child nodes  // To do this we need to rely on our child nodes
1934  DataTypes::ValueType::size_type  DataTypes::RealVectorType::size_type
1935  DataLazy::getPointOffset(int sampleNo,  DataLazy::getPointOffset(int sampleNo,
1936                   int dataPointNo)                   int dataPointNo)
1937  {  {
1938    if (m_op==IDENTITY)    if (m_op==IDENTITY)
1939    {    {
1940      return m_id->getPointOffset(sampleNo,dataPointNo);          return m_id->getPointOffset(sampleNo,dataPointNo);
1941    }    }
1942    if (m_readytype!='E')    if (m_readytype!='E')
1943    {    {
1944      collapse();          collapse();
1945      return m_id->getPointOffset(sampleNo,dataPointNo);          return m_id->getPointOffset(sampleNo,dataPointNo);
1946    }    }
1947    // at this point we do not have an identity node and the expression will be Expanded    // at this point we do not have an identity node and the expression will be Expanded
1948    // so we only need to know which child to ask    // so we only need to know which child to ask
1949    if (m_left->m_readytype=='E')    if (m_left->m_readytype=='E')
1950    {    {
1951      return m_left->getPointOffset(sampleNo,dataPointNo);          return m_left->getPointOffset(sampleNo,dataPointNo);
1952    }    }
1953    else    else
1954    {    {
1955      return m_right->getPointOffset(sampleNo,dataPointNo);          return m_right->getPointOffset(sampleNo,dataPointNo);
1956    }    }
1957  }  }
1958    
1959  // To do this we need to rely on our child nodes  // To do this we need to rely on our child nodes
1960  DataTypes::ValueType::size_type  DataTypes::RealVectorType::size_type
1961  DataLazy::getPointOffset(int sampleNo,  DataLazy::getPointOffset(int sampleNo,
1962                   int dataPointNo) const                   int dataPointNo) const
1963  {  {
1964    if (m_op==IDENTITY)    if (m_op==IDENTITY)
1965    {    {
1966      return m_id->getPointOffset(sampleNo,dataPointNo);          return m_id->getPointOffset(sampleNo,dataPointNo);
1967    }    }
1968    if (m_readytype=='E')    if (m_readytype=='E')
1969    {    {
# Line 1358  DataLazy::getPointOffset(int sampleNo, Line 1971  DataLazy::getPointOffset(int sampleNo,
1971      // so we only need to know which child to ask      // so we only need to know which child to ask
1972      if (m_left->m_readytype=='E')      if (m_left->m_readytype=='E')
1973      {      {
1974      return m_left->getPointOffset(sampleNo,dataPointNo);          return m_left->getPointOffset(sampleNo,dataPointNo);
1975      }      }
1976      else      else
1977      {      {
1978      return m_right->getPointOffset(sampleNo,dataPointNo);          return m_right->getPointOffset(sampleNo,dataPointNo);
1979      }      }
1980    }    }
1981    if (m_readytype=='C')    if (m_readytype=='C')
1982    {    {
1983      return m_left->getPointOffset(sampleNo,dataPointNo); // which child doesn't matter          return m_left->getPointOffset(sampleNo,dataPointNo); // which child doesn't matter
1984    }    }
1985    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).");
1986  }  }
1987    
1988  // It would seem that DataTagged will need to be treated differently since even after setting all tags  
1989  // 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.  
1990  void  void
1991  DataLazy::setToZero()  DataLazy::setToZero()
1992  {  {
1993    DataTypes::ValueType v(getNoValues(),0);  //   DataTypes::RealVectorType v(getNoValues(),0);
1994    m_id=DataReady_ptr(new DataConstant(getFunctionSpace(),getShape(),v));  //   m_id=DataReady_ptr(new DataConstant(getFunctionSpace(),getShape(),v));
1995    m_op=IDENTITY;  //   m_op=IDENTITY;
1996    m_right.reset();    //   m_right.reset();  
1997    m_left.reset();  //   m_left.reset();
1998    m_readytype='C';  //   m_readytype='C';
1999    m_buffsRequired=1;  //   m_buffsRequired=1;
2000    
2001      (void)privdebug;  // to stop the compiler complaining about unused privdebug
2002      throw DataException("Programmer error - setToZero not supported for DataLazy (DataLazy objects should be read only).");
2003    }
2004    
2005    bool
2006    DataLazy::actsExpanded() const
2007    {
2008            return (m_readytype=='E');
2009  }  }
2010    
2011  }   // end namespace  } // end namespace
2012    

Legend:
Removed from v.2147  
changed lines
  Added in v.6512

  ViewVC Help
Powered by ViewVC 1.1.26