sparsity_tools.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.md at
// the top level directory of deal.II.
//
// ---------------------------------------------------------------------


#include <deal.II/base/exceptions.h>
#include <deal.II/base/std_cxx14/memory.h>

#include <deal.II/lac/exceptions.h>
#include <deal.II/lac/sparsity_pattern.h>
#include <deal.II/lac/sparsity_tools.h>

#include <algorithm>
#include <functional>
#include <set>

#ifdef DEAL_II_WITH_MPI
#  include <deal.II/base/mpi.h>
#  include <deal.II/base/utilities.h>

#  include <deal.II/lac/block_sparsity_pattern.h>
#  include <deal.II/lac/dynamic_sparsity_pattern.h>
#endif

#ifdef DEAL_II_WITH_METIS
extern "C"
{
#  include <metis.h>
}
#endif

#ifdef DEAL_II_TRILINOS_WITH_ZOLTAN
#  include <zoltan_cpp.h>
#endif

#include <string>


DEAL_II_NAMESPACE_OPEN

namespace SparsityTools
{
  namespace
  {
    void
    partition_metis(const SparsityPattern &          sparsity_pattern,
                    const std::vector<unsigned int> &cell_weights,
                    const unsigned int               n_partitions,
                    std::vector<unsigned int> &      partition_indices)
    {
      // Make sure that METIS is actually
      // installed and detected
#ifndef DEAL_II_WITH_METIS
      (void)sparsity_pattern;
      (void)cell_weights;
      (void)n_partitions;
      (void)partition_indices;
      AssertThrow(false, ExcMETISNotInstalled());
#else

      // generate the data structures for
      // METIS. Note that this is particularly
      // simple, since METIS wants exactly our
      // compressed row storage format. we only
      // have to set up a few auxiliary arrays
      idx_t n    = static_cast<signed int>(sparsity_pattern.n_rows()),
            ncon = 1, // number of balancing constraints (should be >0)
        nparts =
          static_cast<int>(n_partitions), // number of subdomains to create
        dummy;                            // the numbers of edges cut by the
      // resulting partition

      // We can not partition n items into more than n parts. METIS will
      // generate non-sensical output (everything is owned by a single process)
      // and complain with a message (but won't return an error code!):
      // ***Cannot bisect a graph with 0 vertices!
      // ***You are trying to partition a graph into too many parts!
      nparts = std::min(n, nparts);

      // use default options for METIS
      idx_t options[METIS_NOPTIONS];
      METIS_SetDefaultOptions(options);

      // one more nuisance: we have to copy our own data to arrays that store
      // signed integers :-(
      std::vector<idx_t> int_rowstart(1);
      int_rowstart.reserve(sparsity_pattern.n_rows() + 1);
      std::vector<idx_t> int_colnums;
      int_colnums.reserve(sparsity_pattern.n_nonzero_elements());
      for (SparsityPattern::size_type row = 0; row < sparsity_pattern.n_rows();
           ++row)
        {
          for (SparsityPattern::iterator col = sparsity_pattern.begin(row);
               col < sparsity_pattern.end(row);
               ++col)
            int_colnums.push_back(col->column());
          int_rowstart.push_back(int_colnums.size());
        }

      std::vector<idx_t> int_partition_indices(sparsity_pattern.n_rows());

      // Setup cell weighting option
      std::vector<idx_t> int_cell_weights;
      if (cell_weights.size() > 0)
        {
          Assert(cell_weights.size() == sparsity_pattern.n_rows(),
                 ExcDimensionMismatch(cell_weights.size(),
                                      sparsity_pattern.n_rows()));
          int_cell_weights.resize(cell_weights.size());
          std::copy(cell_weights.begin(),
                    cell_weights.end(),
                    int_cell_weights.begin());
        }
      // Set a pointer to the optional cell weighting information.
      // METIS expects a null pointer if there are no weights to be considered.
      idx_t *const p_int_cell_weights =
        (cell_weights.size() > 0 ? int_cell_weights.data() : nullptr);


      // Make use of METIS' error code.
      int ierr;

      // Select which type of partitioning to create

      // Use recursive if the number of partitions is less than or equal to 8
      if (nparts <= 8)
        ierr = METIS_PartGraphRecursive(&n,
                                        &ncon,
                                        int_rowstart.data(),
                                        int_colnums.data(),
                                        p_int_cell_weights,
                                        nullptr,
                                        nullptr,
                                        &nparts,
                                        nullptr,
                                        nullptr,
                                        options,
                                        &dummy,
                                        int_partition_indices.data());

      // Otherwise use kway
      else
        ierr = METIS_PartGraphKway(&n,
                                   &ncon,
                                   int_rowstart.data(),
                                   int_colnums.data(),
                                   p_int_cell_weights,
                                   nullptr,
                                   nullptr,
                                   &nparts,
                                   nullptr,
                                   nullptr,
                                   options,
                                   &dummy,
                                   int_partition_indices.data());

      // If metis returns normally, an error code METIS_OK=1 is returned from
      // the above functions (see metish.h)
      AssertThrow(ierr == 1, ExcMETISError(ierr));

      // now copy back generated indices into the output array
      std::copy(int_partition_indices.begin(),
                int_partition_indices.end(),
                partition_indices.begin());
#endif
    }


// Query functions unused if zoltan is not installed
#ifdef DEAL_II_TRILINOS_WITH_ZOLTAN
    // Query functions for partition_zoltan
    int
    get_number_of_objects(void *data, int *ierr)
    {
      SparsityPattern *graph = reinterpret_cast<SparsityPattern *>(data);

      *ierr = ZOLTAN_OK;

      return graph->n_rows();
    }


    void
    get_object_list(void *data,
                    int /*sizeGID*/,
                    int /*sizeLID*/,
                    ZOLTAN_ID_PTR globalID,
                    ZOLTAN_ID_PTR localID,
                    int /*wgt_dim*/,
                    float * /*obj_wgts*/,
                    int *ierr)
    {
      SparsityPattern *graph = reinterpret_cast<SparsityPattern *>(data);
      *ierr                  = ZOLTAN_OK;

      Assert(globalID != nullptr, ExcInternalError());
      Assert(localID != nullptr, ExcInternalError());

      // set global degrees of freedom
      auto n_dofs = graph->n_rows();

      for (unsigned int i = 0; i < n_dofs; i++)
        {
          globalID[i] = i;
          localID[i]  = i; // Same as global ids.
        }
    }


    void
    get_num_edges_list(void *data,
                       int /*sizeGID*/,
                       int /*sizeLID*/,
                       int           num_obj,
                       ZOLTAN_ID_PTR globalID,
                       ZOLTAN_ID_PTR /*localID*/,
                       int *numEdges,
                       int *ierr)
    {
      SparsityPattern *graph = reinterpret_cast<SparsityPattern *>(data);

      *ierr = ZOLTAN_OK;

      Assert(numEdges != nullptr, ExcInternalError());

      for (int i = 0; i < num_obj; ++i)
        {
          if (graph->exists(i, i)) // Check if diagonal element is present
            numEdges[i] = graph->row_length(globalID[i]) - 1;
          else
            numEdges[i] = graph->row_length(globalID[i]);
        }
    }



    void
    get_edge_list(void *data,
                  int /*sizeGID*/,
                  int /*sizeLID*/,
                  int num_obj,
                  ZOLTAN_ID_PTR /*globalID*/,
                  ZOLTAN_ID_PTR /*localID*/,
                  int * /*num_edges*/,
                  ZOLTAN_ID_PTR nborGID,
                  int *         nborProc,
                  int /*wgt_dim*/,
                  float * /*ewgts*/,
                  int *ierr)
    {
      SparsityPattern *graph = reinterpret_cast<SparsityPattern *>(data);
      *ierr                  = ZOLTAN_OK;

      ZOLTAN_ID_PTR nextNborGID  = nborGID;
      int *         nextNborProc = nborProc;

      // Loop through rows corresponding to indices in globalID implicitly
      for (SparsityPattern::size_type i = 0;
           i < static_cast<SparsityPattern::size_type>(num_obj);
           ++i)
        {
          // Loop through each column to find neighbours
          for (SparsityPattern::iterator col = graph->begin(i);
               col < graph->end(i);
               ++col)
            // Ignore diagonal entries. Not needed for partitioning.
            if (i != col->column())
              {
                Assert(nextNborGID != nullptr, ExcInternalError());
                Assert(nextNborProc != nullptr, ExcInternalError());

                *nextNborGID++  = col->column();
                *nextNborProc++ = 0; // All the vertices on processor 0
              }
        }
    }
#endif


    void
    partition_zoltan(const SparsityPattern &          sparsity_pattern,
                     const std::vector<unsigned int> &cell_weights,
                     const unsigned int               n_partitions,
                     std::vector<unsigned int> &      partition_indices)
    {
      // Make sure that ZOLTAN is actually
      // installed and detected
#ifndef DEAL_II_TRILINOS_WITH_ZOLTAN
      (void)sparsity_pattern;
      (void)cell_weights;
      (void)n_partitions;
      (void)partition_indices;
      AssertThrow(false, ExcZOLTANNotInstalled());
#else

      Assert(
        cell_weights.size() == 0,
        ExcMessage(
          "The cell weighting functionality for Zoltan has not yet been implemented."));
      (void)cell_weights;

      // MPI environment must have been initialized by this point.
      std::unique_ptr<Zoltan> zz =
        std_cxx14::make_unique<Zoltan>(MPI_COMM_SELF);

      // General parameters
      // DEBUG_LEVEL call must precede the call to LB_METHOD
      zz->Set_Param("DEBUG_LEVEL", "0"); // set level of debug info
      zz->Set_Param(
        "LB_METHOD",
        "GRAPH"); // graph based partition method (LB-load balancing)
      zz->Set_Param("NUM_LOCAL_PARTS",
                    std::to_string(n_partitions)); // set number of partitions

      // The PHG partitioner is a hypergraph partitioner that Zoltan could use
      // for graph partitioning.
      // If number of vertices in hyperedge divided by total vertices in
      // hypergraph exceeds PHG_EDGE_SIZE_THRESHOLD,
      // then the hyperedge will be omitted as such (dense) edges will likely
      // incur high communication costs regardless of the partition.
      // PHG_EDGE_SIZE_THRESHOLD value is raised to 0.5 from the default
      // value of 0.25 so that the PHG partitioner doesn't throw warning saying
      // "PHG_EDGE_SIZE_THRESHOLD is low ..." after removing all dense edges.
      // For instance, in two dimensions if the triangulation being partitioned
      // is two quadrilaterals sharing an edge and if PHG_EDGE_SIZE_THRESHOLD
      // value is set to 0.25, PHG will remove all the edges throwing the
      // above warning.
      zz->Set_Param("PHG_EDGE_SIZE_THRESHOLD", "0.5");

      // Need a non-const object equal to sparsity_pattern
      SparsityPattern graph;
      graph.copy_from(sparsity_pattern);

      // Set query functions
      zz->Set_Num_Obj_Fn(get_number_of_objects, &graph);
      zz->Set_Obj_List_Fn(get_object_list, &graph);
      zz->Set_Num_Edges_Multi_Fn(get_num_edges_list, &graph);
      zz->Set_Edge_List_Multi_Fn(get_edge_list, &graph);

      // Variables needed by partition function
      int           changes           = 0;
      int           num_gid_entries   = 1;
      int           num_lid_entries   = 1;
      int           num_import        = 0;
      ZOLTAN_ID_PTR import_global_ids = nullptr;
      ZOLTAN_ID_PTR import_local_ids  = nullptr;
      int *         import_procs      = nullptr;
      int *         import_to_part    = nullptr;
      int           num_export        = 0;
      ZOLTAN_ID_PTR export_global_ids = nullptr;
      ZOLTAN_ID_PTR export_local_ids  = nullptr;
      int *         export_procs      = nullptr;
      int *         export_to_part    = nullptr;

      // call partitioner
      const int rc = zz->LB_Partition(changes,
                                      num_gid_entries,
                                      num_lid_entries,
                                      num_import,
                                      import_global_ids,
                                      import_local_ids,
                                      import_procs,
                                      import_to_part,
                                      num_export,
                                      export_global_ids,
                                      export_local_ids,
                                      export_procs,
                                      export_to_part);
      (void)rc;

      // check for error code in partitioner
      Assert(rc == ZOLTAN_OK, ExcInternalError());

      // By default, all indices belong to part 0. After zoltan partition
      // some are migrated to different part ID, which is stored in
      // export_to_part array.
      std::fill(partition_indices.begin(), partition_indices.end(), 0);

      // copy from export_to_part to partition_indices, whose part_ids != 0.
      for (int i = 0; i < num_export; i++)
        partition_indices[export_local_ids[i]] = export_to_part[i];
#endif
    }
  } // namespace


  void
  partition(const SparsityPattern &    sparsity_pattern,
            const unsigned int         n_partitions,
            std::vector<unsigned int> &partition_indices,
            const Partitioner          partitioner)
  {
    std::vector<unsigned int> cell_weights;

    // Call the other more general function
    partition(sparsity_pattern,
              cell_weights,
              n_partitions,
              partition_indices,
              partitioner);
  }


  void
  partition(const SparsityPattern &          sparsity_pattern,
            const std::vector<unsigned int> &cell_weights,
            const unsigned int               n_partitions,
            std::vector<unsigned int> &      partition_indices,
            const Partitioner                partitioner)
  {
    Assert(sparsity_pattern.n_rows() == sparsity_pattern.n_cols(),
           ExcNotQuadratic());
    Assert(sparsity_pattern.is_compressed(),
           SparsityPattern::ExcNotCompressed());

    Assert(n_partitions > 0, ExcInvalidNumberOfPartitions(n_partitions));
    Assert(partition_indices.size() == sparsity_pattern.n_rows(),
           ExcInvalidArraySize(partition_indices.size(),
                               sparsity_pattern.n_rows()));

    // check for an easy return
    if (n_partitions == 1 || (sparsity_pattern.n_rows() == 1))
      {
        std::fill_n(partition_indices.begin(), partition_indices.size(), 0U);
        return;
      }

    if (partitioner == Partitioner::metis)
      partition_metis(sparsity_pattern,
                      cell_weights,
                      n_partitions,
                      partition_indices);
    else if (partitioner == Partitioner::zoltan)
      partition_zoltan(sparsity_pattern,
                       cell_weights,
                       n_partitions,
                       partition_indices);
    else
      AssertThrow(false, ExcInternalError());
  }


  unsigned int
  color_sparsity_pattern(const SparsityPattern &    sparsity_pattern,
                         std::vector<unsigned int> &color_indices)
  {
    // Make sure that ZOLTAN is actually
    // installed and detected
#ifndef DEAL_II_TRILINOS_WITH_ZOLTAN
    (void)sparsity_pattern;
    (void)color_indices;
    AssertThrow(false, ExcZOLTANNotInstalled());
    return 0;
#else
    // coloring algorithm is run in serial by each processor.
    std::unique_ptr<Zoltan> zz = std_cxx14::make_unique<Zoltan>(MPI_COMM_SELF);

    // Coloring parameters
    // DEBUG_LEVEL must precede all other calls
    zz->Set_Param("DEBUG_LEVEL", "0");               // level of debug info
    zz->Set_Param("COLORING_PROBLEM", "DISTANCE-1"); // Standard coloring
    zz->Set_Param("NUM_GID_ENTRIES", "1"); // 1 entry represents global ID
    zz->Set_Param("NUM_LID_ENTRIES", "1"); // 1 entry represents local ID
    zz->Set_Param("OBJ_WEIGHT_DIM", "0");  // object weights not used
    zz->Set_Param("RECOLORING_NUM_OF_ITERATIONS", "0");

    // Zoltan::Color function requires a non-const SparsityPattern object
    SparsityPattern graph;
    graph.copy_from(sparsity_pattern);

    // Set query functions required by coloring function
    zz->Set_Num_Obj_Fn(get_number_of_objects, &graph);
    zz->Set_Obj_List_Fn(get_object_list, &graph);
    zz->Set_Num_Edges_Multi_Fn(get_num_edges_list, &graph);
    zz->Set_Edge_List_Multi_Fn(get_edge_list, &graph);

    // Variables needed by coloring function
    int num_gid_entries = 1;
    const int num_objects = graph.n_rows();

    // Preallocate input variables. Element type fixed by ZOLTAN.
    std::vector<ZOLTAN_ID_TYPE> global_ids(num_objects);
    std::vector<int> color_exp(num_objects);

    // Set ids for which coloring needs to be done
    for (int i = 0; i < num_objects; i++)
      global_ids[i] = i;

    // Call ZOLTAN coloring algorithm
    int rc = zz->Color(num_gid_entries,
                       num_objects,
                       global_ids.data(),
                       color_exp.data());

    (void)rc;
    // Check for error code
    Assert(rc == ZOLTAN_OK, ExcInternalError());

    // Allocate and assign color indices
    color_indices.resize(num_objects);
    Assert(color_exp.size() == color_indices.size(),
           ExcDimensionMismatch(color_exp.size(), color_indices.size()));

    std::copy(color_exp.begin(), color_exp.end(), color_indices.begin());

    unsigned int n_colors =
      *(std::max_element(color_indices.begin(), color_indices.end()));
    return n_colors;
#endif
  }


  namespace internal
  {
    DynamicSparsityPattern::size_type
    find_unnumbered_starting_index(
      const DynamicSparsityPattern &                        sparsity,
      const std::vector<DynamicSparsityPattern::size_type> &new_indices)
    {
      DynamicSparsityPattern::size_type starting_point =
        numbers::invalid_size_type;
      DynamicSparsityPattern::size_type min_coordination = sparsity.n_rows();
      for (DynamicSparsityPattern::size_type row = 0; row < sparsity.n_rows();
           ++row)
        // look over all as-yet unnumbered indices
        if (new_indices[row] == numbers::invalid_size_type)
          {
            if (sparsity.row_length(row) < min_coordination)
              {
                min_coordination = sparsity.row_length(row);
                starting_point   = row;
              }
          }

      // now we still have to care for the case that no unnumbered dof has a
      // coordination number less than sparsity.n_rows(). this rather exotic
      // case only happens if we only have one cell, as far as I can see,
      // but there may be others as well.
      //
      // if that should be the case, we can chose an arbitrary dof as
      // starting point, e.g. the first unnumbered one
      if (starting_point == numbers::invalid_size_type)
        {
          for (DynamicSparsityPattern::size_type i = 0; i < new_indices.size();
               ++i)
            if (new_indices[i] == numbers::invalid_size_type)
              {
                starting_point = i;
                break;
              }

          Assert(starting_point != numbers::invalid_size_type,
                 ExcInternalError());
        }

      return starting_point;
    }
  } // namespace internal



  void
  reorder_Cuthill_McKee(
    const DynamicSparsityPattern &                        sparsity,
    std::vector<DynamicSparsityPattern::size_type> &      new_indices,
    const std::vector<DynamicSparsityPattern::size_type> &starting_indices)
  {
    Assert(sparsity.n_rows() == sparsity.n_cols(),
           ExcDimensionMismatch(sparsity.n_rows(), sparsity.n_cols()));
    Assert(sparsity.n_rows() == new_indices.size(),
           ExcDimensionMismatch(sparsity.n_rows(), new_indices.size()));
    Assert(starting_indices.size() <= sparsity.n_rows(),
           ExcMessage(
             "You can't specify more starting indices than there are rows"));
    Assert(sparsity.row_index_set().size() == 0 ||
             sparsity.row_index_set().size() == sparsity.n_rows(),
           ExcMessage(
             "Only valid for sparsity patterns which store all rows."));
    for (SparsityPattern::size_type i = 0; i < starting_indices.size(); ++i)
      Assert(starting_indices[i] < sparsity.n_rows(),
             ExcMessage("Invalid starting index: All starting indices need "
                        "to be between zero and the number of rows in the "
                        "sparsity pattern."));

    // store the indices of the dofs renumbered in the last round. Default to
    // starting points
    std::vector<DynamicSparsityPattern::size_type> last_round_dofs(
      starting_indices);

    // initialize the new_indices array with invalid values
    std::fill(new_indices.begin(),
              new_indices.end(),
              numbers::invalid_size_type);

    // if no starting indices were given: find dof with lowest coordination
    // number
    if (last_round_dofs.empty())
      last_round_dofs.push_back(
        internal::find_unnumbered_starting_index(sparsity, new_indices));

    // store next free dof index
    DynamicSparsityPattern::size_type next_free_number = 0;

    // enumerate the first round dofs
    for (DynamicSparsityPattern::size_type i = 0; i != last_round_dofs.size();
         ++i)
      new_indices[last_round_dofs[i]] = next_free_number++;

    // now do as many steps as needed to renumber all dofs
    while (true)
      {
        // store the indices of the dofs to be renumbered in the next round
        std::vector<DynamicSparsityPattern::size_type> next_round_dofs;

        // find all neighbors of the dofs numbered in the last round
        for (DynamicSparsityPattern::size_type i = 0;
             i < last_round_dofs.size();
             ++i)
          for (DynamicSparsityPattern::iterator j =
                 sparsity.begin(last_round_dofs[i]);
               j < sparsity.end(last_round_dofs[i]);
               ++j)
            next_round_dofs.push_back(j->column());

        // sort dof numbers
        std::sort(next_round_dofs.begin(), next_round_dofs.end());

        // delete multiple entries
        std::vector<DynamicSparsityPattern::size_type>::iterator end_sorted;
        end_sorted =
          std::unique(next_round_dofs.begin(), next_round_dofs.end());
        next_round_dofs.erase(end_sorted, next_round_dofs.end());

        // eliminate dofs which are already numbered
        for (int s = next_round_dofs.size() - 1; s >= 0; --s)
          if (new_indices[next_round_dofs[s]] != numbers::invalid_size_type)
            next_round_dofs.erase(next_round_dofs.begin() + s);

        // check whether there are any new dofs in the list. if there are
        // none, then we have completely numbered the current component of the
        // graph. check if there are as yet unnumbered components of the graph
        // that we would then have to do next
        if (next_round_dofs.empty())
          {
            if (std::find(new_indices.begin(),
                          new_indices.end(),
                          numbers::invalid_size_type) == new_indices.end())
              // no unnumbered indices, so we can leave now
              break;

            // otherwise find a valid starting point for the next component of
            // the graph and continue with numbering that one. we only do so
            // if no starting indices were provided by the user (see the
            // documentation of this function) so produce an error if we got
            // here and starting indices were given
            Assert(starting_indices.empty(),
                   ExcMessage("The input graph appears to have more than one "
                              "component, but as stated in the documentation "
                              "we only want to reorder such graphs if no "
                              "starting indices are given. The function was "
                              "called with starting indices, however."))

              next_round_dofs.push_back(
                internal::find_unnumbered_starting_index(sparsity,
                                                         new_indices));
          }



        // store for each coordination number the dofs with these coordination
        // number
        std::multimap<DynamicSparsityPattern::size_type, int>
          dofs_by_coordination;

        // find coordination number for each of these dofs
        for (std::vector<DynamicSparsityPattern::size_type>::iterator s =
               next_round_dofs.begin();
             s != next_round_dofs.end();
             ++s)
          {
            const DynamicSparsityPattern::size_type coordination =
              sparsity.row_length(*s);

            // insert this dof at its coordination number
            const std::pair<const DynamicSparsityPattern::size_type, int>
              new_entry(coordination, *s);
            dofs_by_coordination.insert(new_entry);
          }

        // assign new DoF numbers to the elements of the present front:
        std::multimap<DynamicSparsityPattern::size_type, int>::iterator i;
        for (i = dofs_by_coordination.begin(); i != dofs_by_coordination.end();
             ++i)
          new_indices[i->second] = next_free_number++;

        // after that: copy this round's dofs for the next round
        last_round_dofs = next_round_dofs;
      }

    // test for all indices numbered. this mostly tests whether the
    // front-marching-algorithm (which Cuthill-McKee actually is) has reached
    // all points.
    Assert((std::find(new_indices.begin(),
                      new_indices.end(),
                      numbers::invalid_size_type) == new_indices.end()) &&
             (next_free_number == sparsity.n_rows()),
           ExcInternalError());
  }



  namespace internal
  {
    void
    reorder_hierarchical(
      const DynamicSparsityPattern &                  connectivity,
      std::vector<DynamicSparsityPattern::size_type> &renumbering)
    {
      AssertDimension(connectivity.n_rows(), connectivity.n_cols());
      AssertDimension(connectivity.n_rows(), renumbering.size());
      Assert(connectivity.row_index_set().size() == 0 ||
               connectivity.row_index_set().size() == connectivity.n_rows(),
             ExcMessage(
               "Only valid for sparsity patterns which store all rows."));

      std::vector<types::global_dof_index> touched_nodes(
        connectivity.n_rows(), numbers::invalid_dof_index);
      std::vector<unsigned int>         row_lengths(connectivity.n_rows());
      std::set<types::global_dof_index> current_neighbors;
      std::vector<std::vector<types::global_dof_index>> groups;

      // First collect the number of neighbors for each node. We use this
      // field to find next nodes with the minimum number of non-touched
      // neighbors in the field n_remaining_neighbors, so we will count down
      // on this field. We also cache the row lengths because we need this
      // data frequently and getting it from the sparsity pattern is more
      // expensive.
      for (types::global_dof_index row = 0; row < connectivity.n_rows(); ++row)
        {
          row_lengths[row] = connectivity.row_length(row);
          Assert(row_lengths[row] > 0, ExcInternalError());
        }
      std::vector<unsigned int> n_remaining_neighbors(row_lengths);

      // This outer loop is typically traversed only once, unless the global
      // graph is not connected
      while (true)
        {
          // Find cell with the minimal number of neighbors (typically a
          // corner node when based on FEM meshes). If no cell is left, we are
          // done. Together with the outer while loop, this loop can possibly
          // be of quadratic complexity in the number of disconnected
          // partitions, i.e. up to connectivity.n_rows() in the worst case,
          // but that is not the usual use case of this loop and thus not
          // optimized for.
          std::pair<types::global_dof_index, types::global_dof_index>
            min_neighbors(numbers::invalid_dof_index,
                          numbers::invalid_dof_index);
          for (types::global_dof_index i = 0; i < touched_nodes.size(); ++i)
            if (touched_nodes[i] == numbers::invalid_dof_index)
              if (row_lengths[i] < min_neighbors.second)
                {
                  min_neighbors = std::make_pair(i, n_remaining_neighbors[i]);
                  if (n_remaining_neighbors[i] <= 1)
                    break;
                }
          if (min_neighbors.first == numbers::invalid_dof_index)
            break;

          Assert(min_neighbors.second > 0, ExcInternalError());

          current_neighbors.clear();
          current_neighbors.insert(min_neighbors.first);
          while (!current_neighbors.empty())
            {
              // Find node with minimum number of untouched neighbors among the
              // next set of possible neighbors
              min_neighbors = std::make_pair(numbers::invalid_dof_index,
                                             numbers::invalid_dof_index);
              for (std::set<types::global_dof_index>::iterator it =
                     current_neighbors.begin();
                   it != current_neighbors.end();
                   ++it)
                {
                  Assert(touched_nodes[*it] == numbers::invalid_dof_index,
                         ExcInternalError());
                  if (n_remaining_neighbors[*it] < min_neighbors.second)
                    min_neighbors =
                      std::make_pair(*it, n_remaining_neighbors[*it]);
                }

              // Among the set of nodes with the minimal number of neighbors,
              // choose the one with the largest number of touched neighbors,
              // i.e., the one with the largest row length
              const types::global_dof_index best_row_length =
                min_neighbors.second;
              for (std::set<types::global_dof_index>::iterator it =
                     current_neighbors.begin();
                   it != current_neighbors.end();
                   ++it)
                if (n_remaining_neighbors[*it] == best_row_length)
                  if (row_lengths[*it] > min_neighbors.second)
                    min_neighbors = std::make_pair(*it, row_lengths[*it]);

              // Add the pivot and all direct neighbors of the pivot node not
              // yet touched to the list of new entries.
              groups.emplace_back();
              std::vector<types::global_dof_index> &next_group = groups.back();

              next_group.push_back(min_neighbors.first);
              touched_nodes[min_neighbors.first] = groups.size() - 1;
              for (DynamicSparsityPattern::iterator it =
                     connectivity.begin(min_neighbors.first);
                   it != connectivity.end(min_neighbors.first);
                   ++it)
                if (touched_nodes[it->column()] == numbers::invalid_dof_index)
                  {
                    next_group.push_back(it->column());
                    touched_nodes[it->column()] = groups.size() - 1;
                  }

              // Add all neighbors of the current list not yet touched to the
              // set of possible next pivots. The added node is no longer a
              // valid neighbor (here we assume symmetry of the
              // connectivity). Delete the entries of the current list from
              // the set of possible next pivots.
              for (unsigned int i = 0; i < next_group.size(); ++i)
                {
                  for (DynamicSparsityPattern::iterator it =
                         connectivity.begin(next_group[i]);
                       it != connectivity.end(next_group[i]);
                       ++it)
                    {
                      if (touched_nodes[it->column()] ==
                          numbers::invalid_dof_index)
                        current_neighbors.insert(it->column());
                      n_remaining_neighbors[it->column()]--;
                    }
                  current_neighbors.erase(next_group[i]);
                }
            }
        }

      // Sanity check: for all nodes, there should not be any neighbors left
      for (types::global_dof_index row = 0; row < connectivity.n_rows(); ++row)
        Assert(n_remaining_neighbors[row] == 0, ExcInternalError());

      // If the number of groups is smaller than the number of nodes, we
      // continue by recursively calling this method
      if (groups.size() < connectivity.n_rows())
        {
          // Form the connectivity of the groups
          DynamicSparsityPattern connectivity_next(groups.size(),
                                                   groups.size());
          for (types::global_dof_index i = 0; i < groups.size(); ++i)
            for (types::global_dof_index col = 0; col < groups[i].size(); ++col)
              for (DynamicSparsityPattern::iterator it =
                     connectivity.begin(groups[i][col]);
                   it != connectivity.end(groups[i][col]);
                   ++it)
                connectivity_next.add(i, touched_nodes[it->column()]);

          // Recursively call the reordering
          std::vector<types::global_dof_index> renumbering_next(groups.size());
          reorder_hierarchical(connectivity_next, renumbering_next);

          // Renumber the indices group by group according to the incoming
          // ordering for the groups
          for (types::global_dof_index i = 0, count = 0; i < groups.size(); ++i)
            for (types::global_dof_index col = 0;
                 col < groups[renumbering_next[i]].size();
                 ++col, ++count)
              renumbering[count] = groups[renumbering_next[i]][col];
        }
      else
        {
          // All groups should have size one and no more recursion is possible,
          // so use the numbering of the groups
          for (types::global_dof_index i = 0, count = 0; i < groups.size(); ++i)
            for (types::global_dof_index col = 0; col < groups[i].size();
                 ++col, ++count)
              renumbering[count] = groups[i][col];
        }
    }
  } // namespace internal

  void
  reorder_hierarchical(
    const DynamicSparsityPattern &                  connectivity,
    std::vector<DynamicSparsityPattern::size_type> &renumbering)
  {
    // the internal renumbering keeps the numbering the wrong way around (but
    // we cannot invert the numbering inside that method because it is used
    // recursively), so invert it here
    internal::reorder_hierarchical(connectivity, renumbering);
    renumbering = Utilities::invert_permutation(renumbering);
  }



#ifdef DEAL_II_WITH_MPI
  void
  distribute_sparsity_pattern(
    DynamicSparsityPattern &                              dsp,
    const std::vector<DynamicSparsityPattern::size_type> &rows_per_cpu,
    const MPI_Comm &                                      mpi_comm,
    const IndexSet &                                      myrange)
  {
    const unsigned int myid = Utilities::MPI::this_mpi_process(mpi_comm);
    std::vector<DynamicSparsityPattern::size_type> start_index(
      rows_per_cpu.size() + 1);
    start_index[0] = 0;
    for (DynamicSparsityPattern::size_type i = 0; i < rows_per_cpu.size(); ++i)
      start_index[i + 1] = start_index[i] + rows_per_cpu[i];

    using map_vec_t = std::map<DynamicSparsityPattern::size_type,
                               std::vector<DynamicSparsityPattern::size_type>>;

    map_vec_t send_data;

    {
      unsigned int dest_cpu = 0;

      DynamicSparsityPattern::size_type n_local_rel_rows = myrange.n_elements();
      for (DynamicSparsityPattern::size_type row_idx = 0;
           row_idx < n_local_rel_rows;
           ++row_idx)
        {
          DynamicSparsityPattern::size_type row =
            myrange.nth_index_in_set(row_idx);

          // calculate destination CPU
          while (row >= start_index[dest_cpu + 1])
            ++dest_cpu;

          // skip myself
          if (dest_cpu == myid)
            {
              row_idx += rows_per_cpu[myid] - 1;
              continue;
            }

          DynamicSparsityPattern::size_type rlen = dsp.row_length(row);

          // skip empty lines
          if (!rlen)
            continue;

          // save entries
          std::vector<DynamicSparsityPattern::size_type> &dst =
            send_data[dest_cpu];

          dst.push_back(rlen); // number of entries
          dst.push_back(row);  // row index
          for (DynamicSparsityPattern::size_type c = 0; c < rlen; ++c)
            {
              // columns
              DynamicSparsityPattern::size_type column =
                dsp.column_number(row, c);
              dst.push_back(column);
            }
        }
    }

    unsigned int num_receive = 0;
    {
      std::vector<unsigned int> send_to;
      send_to.reserve(send_data.size());
      for (map_vec_t::iterator it = send_data.begin(); it != send_data.end();
           ++it)
        send_to.push_back(it->first);

      num_receive =
        Utilities::MPI::compute_point_to_point_communication_pattern(mpi_comm,
                                                                     send_to)
          .size();
    }

    std::vector<MPI_Request> requests(send_data.size());


    // send data
    {
      unsigned int idx = 0;
      for (map_vec_t::iterator it = send_data.begin(); it != send_data.end();
           ++it, ++idx)
        {
          const int ierr = MPI_Isend(&(it->second[0]),
                                     it->second.size(),
                                     DEAL_II_DOF_INDEX_MPI_TYPE,
                                     it->first,
                                     124,
                                     mpi_comm,
                                     &requests[idx]);
          AssertThrowMPI(ierr);
        }
    }

    {
      // receive
      std::vector<DynamicSparsityPattern::size_type> recv_buf;
      for (unsigned int index = 0; index < num_receive; ++index)
        {
          MPI_Status status;
          int        len;
          int ierr = MPI_Probe(MPI_ANY_SOURCE, MPI_ANY_TAG, mpi_comm, &status);
          AssertThrowMPI(ierr);
          Assert(status.MPI_TAG == 124, ExcInternalError());

          ierr = MPI_Get_count(&status, DEAL_II_DOF_INDEX_MPI_TYPE, &len);
          AssertThrowMPI(ierr);
          recv_buf.resize(len);
          ierr = MPI_Recv(recv_buf.data(),
                          len,
                          DEAL_II_DOF_INDEX_MPI_TYPE,
                          status.MPI_SOURCE,
                          status.MPI_TAG,
                          mpi_comm,
                          &status);
          AssertThrowMPI(ierr);

          std::vector<DynamicSparsityPattern::size_type>::const_iterator ptr =
            recv_buf.begin();
          std::vector<DynamicSparsityPattern::size_type>::const_iterator end =
            recv_buf.end();
          while (ptr != end)
            {
              DynamicSparsityPattern::size_type num = *(ptr++);
              Assert(ptr != end, ExcInternalError());
              DynamicSparsityPattern::size_type row = *(ptr++);
              for (unsigned int c = 0; c < num; ++c)
                {
                  Assert(ptr != end, ExcInternalError());
                  dsp.add(row, *ptr);
                  ++ptr;
                }
            }
          Assert(ptr == end, ExcInternalError());
        }
    }

    // complete all sends, so that we can safely destroy the buffers.
    if (requests.size())
      {
        const int ierr =
          MPI_Waitall(requests.size(), requests.data(), MPI_STATUSES_IGNORE);
        AssertThrowMPI(ierr);
      }
  }

  void
  distribute_sparsity_pattern(BlockDynamicSparsityPattern &dsp,
                              const std::vector<IndexSet> &owned_set_per_cpu,
                              const MPI_Comm &             mpi_comm,
                              const IndexSet &             myrange)
  {
    const unsigned int myid = Utilities::MPI::this_mpi_process(mpi_comm);

    using map_vec_t =
      std::map<BlockDynamicSparsityPattern::size_type,
               std::vector<BlockDynamicSparsityPattern::size_type>>;
    map_vec_t send_data;

    {
      unsigned int dest_cpu = 0;

      BlockDynamicSparsityPattern::size_type n_local_rel_rows =
        myrange.n_elements();
      for (BlockDynamicSparsityPattern::size_type row_idx = 0;
           row_idx < n_local_rel_rows;
           ++row_idx)
        {
          BlockDynamicSparsityPattern::size_type row =
            myrange.nth_index_in_set(row_idx);

          // calculate destination CPU, note that we start the search
          // at last destination cpu, because even if the owned ranges
          // are not contiguous, they hopefully consist of large blocks
          while (!owned_set_per_cpu[dest_cpu].is_element(row))
            {
              ++dest_cpu;
              if (dest_cpu == owned_set_per_cpu.size()) // wrap around
                dest_cpu = 0;
            }

          // skip myself
          if (dest_cpu == myid)
            continue;

          BlockDynamicSparsityPattern::size_type rlen = dsp.row_length(row);

          // skip empty lines
          if (!rlen)
            continue;

          // save entries
          std::vector<BlockDynamicSparsityPattern::size_type> &dst =
            send_data[dest_cpu];

          dst.push_back(rlen); // number of entries
          dst.push_back(row);  // row index
          for (BlockDynamicSparsityPattern::size_type c = 0; c < rlen; ++c)
            {
              // columns
              BlockDynamicSparsityPattern::size_type column =
                dsp.column_number(row, c);
              dst.push_back(column);
            }
        }
    }

    unsigned int num_receive = 0;
    {
      std::vector<unsigned int> send_to;
      send_to.reserve(send_data.size());
      for (map_vec_t::iterator it = send_data.begin(); it != send_data.end();
           ++it)
        send_to.push_back(it->first);

      num_receive =
        Utilities::MPI::compute_point_to_point_communication_pattern(mpi_comm,
                                                                     send_to)
          .size();
    }

    std::vector<MPI_Request> requests(send_data.size());


    // send data
    {
      unsigned int idx = 0;
      for (map_vec_t::iterator it = send_data.begin(); it != send_data.end();
           ++it, ++idx)
        {
          const int ierr = MPI_Isend(&(it->second[0]),
                                     it->second.size(),
                                     DEAL_II_DOF_INDEX_MPI_TYPE,
                                     it->first,
                                     124,
                                     mpi_comm,
                                     &requests[idx]);
          AssertThrowMPI(ierr);
        }
    }

    {
      // receive
      std::vector<BlockDynamicSparsityPattern::size_type> recv_buf;
      for (unsigned int index = 0; index < num_receive; ++index)
        {
          MPI_Status status;
          int        len;
          int ierr = MPI_Probe(MPI_ANY_SOURCE, MPI_ANY_TAG, mpi_comm, &status);
          AssertThrowMPI(ierr);
          Assert(status.MPI_TAG == 124, ExcInternalError());

          ierr = MPI_Get_count(&status, DEAL_II_DOF_INDEX_MPI_TYPE, &len);
          AssertThrowMPI(ierr);
          recv_buf.resize(len);
          ierr = MPI_Recv(recv_buf.data(),
                          len,
                          DEAL_II_DOF_INDEX_MPI_TYPE,
                          status.MPI_SOURCE,
                          status.MPI_TAG,
                          mpi_comm,
                          &status);
          AssertThrowMPI(ierr);

          std::vector<BlockDynamicSparsityPattern::size_type>::const_iterator
            ptr = recv_buf.begin();
          std::vector<BlockDynamicSparsityPattern::size_type>::const_iterator
            end = recv_buf.end();
          while (ptr != end)
            {
              BlockDynamicSparsityPattern::size_type num = *(ptr++);
              Assert(ptr != end, ExcInternalError());
              BlockDynamicSparsityPattern::size_type row = *(ptr++);
              for (unsigned int c = 0; c < num; ++c)
                {
                  Assert(ptr != end, ExcInternalError());
                  dsp.add(row, *ptr);
                  ++ptr;
                }
            }
          Assert(ptr == end, ExcInternalError());
        }
    }

    // complete all sends, so that we can safely destroy the buffers.
    if (requests.size())
      {
        const int ierr =
          MPI_Waitall(requests.size(), requests.data(), MPI_STATUSES_IGNORE);
        AssertThrowMPI(ierr);
      }
  }
#endif
} // namespace SparsityTools

DEAL_II_NAMESPACE_CLOSE