trilinos_precondition_muelu.cc

// ---------------------------------------------------------------------
//
// Copyright (C) 2008 - 2018 by the deal.II authors
//
// This file is part of the deal.II library.
//
// The deal.II library is free software; you can use it, redistribute
// it, and/or modify it under the terms of the GNU Lesser General
// Public License as published by the Free Software Foundation; either
// version 2.1 of the License, or (at your option) any later version.
// The full text of the license can be found in the file LICENSE at
// the top level of the deal.II distribution.
//
// ---------------------------------------------------------------------

#include <deal.II/lac/trilinos_index_access.h>
#include <deal.II/lac/trilinos_precondition.h>

#ifdef DEAL_II_WITH_TRILINOS
#if DEAL_II_TRILINOS_VERSION_GTE(11,14,0)

#  include <deal.II/lac/vector.h>
#  include <deal.II/lac/sparse_matrix.h>
#  include <deal.II/lac/trilinos_index_access.h>
#  include <deal.II/lac/trilinos_sparse_matrix.h>

#  include <Teuchos_ParameterList.hpp>
#  include <Teuchos_RCP.hpp>
#  include <Epetra_MultiVector.h>
#  include <ml_include.h>
#  include <ml_MultiLevelPreconditioner.h>

#  include <MueLu.hpp>
#  include <MueLu_EpetraOperator.hpp>
#  include <MueLu_MLParameterListInterpreter.hpp>

DEAL_II_NAMESPACE_OPEN

namespace TrilinosWrappers
{
  PreconditionAMGMueLu::AdditionalData::
  AdditionalData (const bool                             elliptic,
                  const unsigned int                     n_cycles,
                  const bool                             w_cycle,
                  const double                           aggregation_threshold,
                  const std::vector<std::vector<bool> > &constant_modes,
                  const unsigned int                     smoother_sweeps,
                  const unsigned int                     smoother_overlap,
                  const bool                             output_details,
                  const char                            *smoother_type,
                  const char                            *coarse_type)
    :
    elliptic (elliptic),
    n_cycles (n_cycles),
    w_cycle (w_cycle),
    aggregation_threshold (aggregation_threshold),
    constant_modes (constant_modes),
    smoother_sweeps (smoother_sweeps),
    smoother_overlap (smoother_overlap),
    output_details (output_details),
    smoother_type (smoother_type),
    coarse_type (coarse_type)
  {}



  PreconditionAMGMueLu::PreconditionAMGMueLu()
  {
#ifdef DEAL_II_WITH_64BIT_INDICES
    AssertThrow (false, ExcMessage("PreconditionAMGMueLu does not support 64bit-indices!"));
#endif
  }



  PreconditionAMGMueLu::~PreconditionAMGMueLu()
  {
    preconditioner.reset();
    trilinos_matrix.reset();
  }



  void
  PreconditionAMGMueLu::initialize (const SparseMatrix   &matrix,
                                    const AdditionalData &additional_data)
  {
    initialize(matrix.trilinos_matrix(), additional_data);
  }



  void
  PreconditionAMGMueLu::initialize (const Epetra_CrsMatrix &matrix,
                                    const AdditionalData   &additional_data)
  {
    // Build the AMG preconditioner.
    Teuchos::ParameterList parameter_list;

    if (additional_data.elliptic == true)
      ML_Epetra::SetDefaults("SA",parameter_list);
    else
      {
        ML_Epetra::SetDefaults("NSSA",parameter_list);
        parameter_list.set("aggregation: block scaling", true);
      }
    // MIS does not exist anymore, only choice are uncoupled and coupled. When using
    // uncoupled, aggregates cannot span multiple processes. When using coupled
    // aggregates can span multiple processes.
    parameter_list.set("aggregation: type", "Uncoupled");

    parameter_list.set("smoother: type", additional_data.smoother_type);
    parameter_list.set("coarse: type", additional_data.coarse_type);

    parameter_list.set("smoother: sweeps",
                       static_cast<int>(additional_data.smoother_sweeps));
    parameter_list.set("cycle applications",
                       static_cast<int>(additional_data.n_cycles));
    if (additional_data.w_cycle == true)
      parameter_list.set("prec type", "MGW");
    else
      parameter_list.set("prec type", "MGV");

    parameter_list.set("smoother: Chebyshev alpha",10.);
    parameter_list.set("smoother: ifpack overlap",
                       static_cast<int>(additional_data.smoother_overlap));
    parameter_list.set("aggregation: threshold",
                       additional_data.aggregation_threshold);
    parameter_list.set("coarse: max size", 2000);

    if (additional_data.output_details)
      parameter_list.set("ML output", 10);
    else
      parameter_list.set("ML output", 0);

    const Epetra_Map &domain_map = matrix.OperatorDomainMap();

    const size_type constant_modes_dimension =
      additional_data.constant_modes.size();
    Epetra_MultiVector distributed_constant_modes (domain_map,
                                                   constant_modes_dimension > 0 ?
                                                   constant_modes_dimension : 1);
    std::vector<double> dummy (constant_modes_dimension);

    if (constant_modes_dimension > 0)
      {
        const size_type n_rows = TrilinosWrappers::n_global_rows(matrix);
        const bool constant_modes_are_global =
          additional_data.constant_modes[0].size() == n_rows;
        const size_type n_relevant_rows =
          constant_modes_are_global ? n_rows : additional_data.constant_modes[0].size();
        const size_type my_size = domain_map.NumMyElements();
        if (constant_modes_are_global == false)
          Assert (n_relevant_rows == my_size,
                  ExcDimensionMismatch(n_relevant_rows, my_size));
        Assert (n_rows ==
                static_cast<size_type>(TrilinosWrappers::global_length(distributed_constant_modes)),
                ExcDimensionMismatch(n_rows,
                                     TrilinosWrappers::global_length(distributed_constant_modes)));

        (void)n_relevant_rows;
        (void)global_length;

        // Reshape null space as a contiguous vector of doubles so that
        // Trilinos can read from it.
        for (size_type d=0; d<constant_modes_dimension; ++d)
          for (size_type row=0; row<my_size; ++row)
            {
              TrilinosWrappers::types::int_type global_row_id =
                constant_modes_are_global ? TrilinosWrappers::global_index(domain_map,row) : row;
              distributed_constant_modes[d][row] =
                additional_data.constant_modes[d][global_row_id];
            }

        parameter_list.set("null space: type", "pre-computed");
        parameter_list.set("null space: dimension",
                           distributed_constant_modes.NumVectors());
        if (my_size > 0)
          parameter_list.set("null space: vectors",
                             distributed_constant_modes.Values());
        // We need to set a valid pointer to data even if there is no data on
        // the current processor. Therefore, pass a dummy in that case
        else
          parameter_list.set("null space: vectors",
                             dummy.data());
      }

    initialize (matrix, parameter_list);
  }



  void
  PreconditionAMGMueLu::initialize (const SparseMatrix           &matrix,
                                    Teuchos::ParameterList &muelu_parameters)
  {
    initialize(matrix.trilinos_matrix(), muelu_parameters);
  }



  void
  PreconditionAMGMueLu::initialize (const Epetra_CrsMatrix       &matrix,
                                    Teuchos::ParameterList &muelu_parameters)
  {
    // We cannot use MueLu::CreateEpetraOperator directly because, we cannot
    // transfer ownership of MueLu::EpetraOperator from Teuchos::RCP to
    // std::shared_ptr.

    // For now, just use serial node, i.e. no multithreaing or GPU.
    typedef KokkosClassic::DefaultNode::DefaultNodeType node;
    preconditioner.reset ();

    // Cast matrix into a MueLu::Matrix. The constness needs to be cast away.
    // MueLu uses Teuchos::RCP which are Trilinos version of std::shared_ptr.
    Teuchos::RCP<Epetra_CrsMatrix> rcp_matrix = Teuchos::rcpFromRef(
                                                  *(const_cast<Epetra_CrsMatrix *>(&matrix)));
    Teuchos::RCP<Xpetra::CrsMatrix<double,int,int,node> > muelu_crs_matrix =
      Teuchos::rcp(new Xpetra::EpetraCrsMatrix (rcp_matrix));
    Teuchos::RCP<Xpetra::Matrix<double,int,int,node> > muelu_matrix =
      Teuchos::rcp(new Xpetra::CrsMatrixWrap<double,int,int,node> (muelu_crs_matrix));

    // Create the multigrid hierarchy using ML parameters.
    Teuchos::RCP<MueLu::HierarchyManager<double,int,int,node> > hierarchy_factory;
    hierarchy_factory = Teuchos::rcp(
                          new MueLu::MLParameterListInterpreter<double,int,int,node> (muelu_parameters));
    Teuchos::RCP<MueLu::Hierarchy<double,int,int,node> > hierarchy = hierarchy_factory->CreateHierarchy();
    hierarchy->GetLevel(0)->Set("A",muelu_matrix);
    hierarchy_factory->SetupHierarchy(*hierarchy);

    // MueLu::EpetraOperator is just a wrapper around a "standard"
    // Epetra_Operator.
    preconditioner = std::make_shared<MueLu::EpetraOperator>(hierarchy);
  }



  template <typename number>
  void
  PreconditionAMGMueLu::
  initialize (const ::SparseMatrix<number> &deal_ii_sparse_matrix,
              const AdditionalData                 &additional_data,
              const double                          drop_tolerance,
              const ::SparsityPattern      *use_this_sparsity)
  {
    preconditioner.reset();
    const size_type n_rows = deal_ii_sparse_matrix.m();

    // Init Epetra Matrix using an
    // equidistributed map; avoid
    // storing the nonzero
    // elements.
    vector_distributor = std::make_shared<Epetra_Map>
                         (static_cast<TrilinosWrappers::types::int_type>(n_rows),
                          0, communicator);

    if (trilinos_matrix.get() == nullptr)
      trilinos_matrix = std::make_shared<SparseMatrix>();

    trilinos_matrix->reinit (*vector_distributor, *vector_distributor,
                             deal_ii_sparse_matrix, drop_tolerance, true,
                             use_this_sparsity);

    initialize (*trilinos_matrix, additional_data);
  }



  void PreconditionAMGMueLu::clear ()
  {
    PreconditionBase::clear();
    trilinos_matrix.reset();
  }



  PreconditionAMGMueLu::size_type
  PreconditionAMGMueLu::memory_consumption() const
  {
    unsigned int memory = sizeof(*this);

    // todo: find a way to read out ML's data
    // sizes
    if (trilinos_matrix.get() != nullptr)
      memory += trilinos_matrix->memory_consumption();
    return memory;
  }



  // explicit instantiations
  template void PreconditionAMGMueLu::initialize (const ::SparseMatrix<double> &,
                                                  const AdditionalData &, const double,
                                                  const ::SparsityPattern *);
  template void PreconditionAMGMueLu::initialize (const ::SparseMatrix<float> &,
                                                  const AdditionalData &, const double,
                                                  const ::SparsityPattern *);

}

DEAL_II_NAMESPACE_CLOSE

#endif // DEAL_II_TRILINOS_VERSION_GTE(11,14,0)
#endif // DEAL_II_WITH_TRILINOS