Fixed parallelization inconsistency in Optimizer and Reconstructor

Signed-off-by: Dmitry I. Lyakh <quant4me@gmail.com>

src/exatn/optimizer.cpp

 /** ExaTN:: Variational optimizer of a closed symmetric tensor network expansion functional
-REVISION: 2021/10/02
+REVISION: 2021/10/18
 
 Copyright (C) 2018-2021 Dmitry I. Lyakh (Liakh)
 Copyright (C) 2018-2021 Oak Ridge National Laboratory (UT-Battelle) **/
@@ -22,7 +22,12 @@ TensorNetworkOptimizer::TensorNetworkOptimizer(std::shared_ptr<TensorOperator> t
                                                double tolerance):
  tensor_operator_(tensor_operator), vector_expansion_(vector_expansion),
  max_iterations_(DEFAULT_MAX_ITERATIONS), micro_iterations_(DEFAULT_MICRO_ITERATIONS),
- epsilon_(DEFAULT_LEARN_RATE), tolerance_(tolerance), parallel_(true)
+ epsilon_(DEFAULT_LEARN_RATE), tolerance_(tolerance),
+#ifdef MPI_ENABLED
+ parallel_(true)
+#else
+ parallel_(false)
+#endif
 {
  if(!vector_expansion_->isKet()){
   std::cout << "#ERROR(exatn:TensorNetworkOptimizer): The tensor network vector expansion must be a ket!"
@@ -85,7 +90,8 @@ bool TensorNetworkOptimizer::optimize_sd(const ProcessGroup & process_group)
 
  unsigned int local_rank; //local process rank within the process group
  if(!process_group.rankIsIn(exatn::getProcessRank(),&local_rank)) return true; //process is not in the group: Do nothing
- const auto num_procs = process_group.getSize();
+ unsigned int num_procs = 1;
+ if(parallel_) num_procs = process_group.getSize();
 
  if(TensorNetworkOptimizer::focus >= 0){
   if(getProcessRank() != TensorNetworkOptimizer::focus) TensorNetworkOptimizer::debug = 0;

src/exatn/reconstructor.cpp

 /** ExaTN:: Reconstructs an approximate tensor network expansion for a given tensor network expansion
-REVISION: 2021/10/02
+REVISION: 2021/10/18
 
 Copyright (C) 2018-2021 Dmitry I. Lyakh (Liakh)
 Copyright (C) 2018-2021 Oak Ridge National Laboratory (UT-Battelle) **/
@@ -22,7 +22,12 @@ TensorNetworkReconstructor::TensorNetworkReconstructor(std::shared_ptr<TensorExp
                                                        std::shared_ptr<TensorExpansion> approximant,
                                                        double tolerance):
  expansion_(expansion), approximant_(approximant),
- max_iterations_(DEFAULT_MAX_ITERATIONS), epsilon_(DEFAULT_LEARN_RATE), tolerance_(tolerance), parallel_(true),
+ max_iterations_(DEFAULT_MAX_ITERATIONS), epsilon_(DEFAULT_LEARN_RATE), tolerance_(tolerance),
+#ifdef MPI_ENABLED
+ parallel_(true),
+#else
+ parallel_(false),
+#endif
  input_norm_(0.0), output_norm_(0.0), residual_norm_(0.0), fidelity_(0.0)
 {
  if(!expansion_->isKet()){
@@ -126,7 +131,8 @@ bool TensorNetworkReconstructor::reconstruct_sd(const ProcessGroup & process_gro
 {
  unsigned int local_rank; //local process rank within the process group
  if(!process_group.rankIsIn(exatn::getProcessRank(),&local_rank)) return true; //process is not in the group: Do nothing
- const auto num_procs = process_group.getSize();
+ unsigned int num_procs = 1;
+ if(parallel_) num_procs = process_group.getSize();
 
  assert(residual_norm != nullptr);
  assert(fidelity != nullptr);

src/exatn/tests/NumServerTester.cpp

@@ -1646,10 +1646,10 @@ TEST(NumServerTester, IsingTNO)
   }
 
   //Reconstruct the Ising Hamiltonian as a tensor network operator:
-  success = exatn::balanceNormalizeNorm2Sync(*ham_expansion,1.0,1.0,false); assert(success);
+  success = exatn::normalizeNorm2Sync(*ham_expansion,1.0); assert(success);
   success = exatn::balanceNorm2Sync(*ham_tno_expansion,1.0,true); assert(success);
   ham_tno_expansion->conjugate();
-  exatn::TensorNetworkReconstructor::resetDebugLevel(1); //debug
+  exatn::TensorNetworkReconstructor::resetDebugLevel(1,0); //debug
   exatn::TensorNetworkReconstructor reconstructor(ham_expansion,ham_tno_expansion,1e-4);
   success = exatn::sync(); assert(success);
   double residual_norm, fidelity;