MueLu_AggregationPhase2bAlgorithm_kokkos_def.hpp

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#ifndef MUELU_AGGREGATIONPHASE2BALGORITHM_KOKKOS_DEF_HPP
#define MUELU_AGGREGATIONPHASE2BALGORITHM_KOKKOS_DEF_HPP

#ifdef HAVE_MUELU_KOKKOS_REFACTOR

#include <Teuchos_Comm.hpp>
#include <Teuchos_CommHelpers.hpp>

#include <Xpetra_Vector.hpp>

#include "MueLu_AggregationPhase2bAlgorithm_kokkos_decl.hpp"

#include "MueLu_Aggregates_kokkos.hpp"
#include "MueLu_Exceptions.hpp"
#include "MueLu_LWGraph_kokkos.hpp"
#include "MueLu_Monitor.hpp"

namespace MueLu {

  // Try to stick unaggregated nodes into a neighboring aggregate if they are
  // not already too big
  template <class LocalOrdinal, class GlobalOrdinal, class Node>
  void AggregationPhase2bAlgorithm_kokkos<LocalOrdinal, GlobalOrdinal, Node>::
  BuildAggregates(const ParameterList& params,
                  const LWGraph_kokkos& graph,
                  Aggregates_kokkos& aggregates,
                  Kokkos::View<unsigned*, typename LWGraph_kokkos::memory_space>& aggStat,
                  LO& numNonAggregatedNodes) const {

    if(params.get<bool>("aggregation: deterministic")) {
      Monitor m(*this, "BuildAggregatesDeterministic");
      BuildAggregatesDeterministic(params, graph, aggregates, aggStat, numNonAggregatedNodes);
    } else {
      Monitor m(*this, "BuildAggregatesRandom");
      BuildAggregatesRandom(params, graph, aggregates, aggStat, numNonAggregatedNodes);
    }

  } // BuildAggregates

  template <class LO, class GO, class Node>
  void AggregationPhase2bAlgorithm_kokkos<LO, GO, Node>::
  BuildAggregatesRandom(const ParameterList& params,
                        const LWGraph_kokkos& graph,
                        Aggregates_kokkos& aggregates,
                        Kokkos::View<unsigned*, typename LWGraph_kokkos::memory_space>& aggStat,
                        LO& numNonAggregatedNodes) const {
    using memory_space    = typename LWGraph_kokkos::memory_space;
    using execution_space = typename LWGraph_kokkos::execution_space;

    const LO  numRows = graph.GetNodeNumVertices();
    const int myRank  = graph.GetComm()->getRank();

    auto vertex2AggId           = aggregates.GetVertex2AggId()->template getLocalView<memory_space>();
    auto procWinner             = aggregates.GetProcWinner()  ->template getLocalView<memory_space>();
    auto colors                 = aggregates.GetGraphColors();
    const LO numColors          = aggregates.GetGraphNumColors();
    const LO numLocalAggregates = aggregates.GetNumAggregates();

    const LO defaultConnectWeight = 100;
    const LO penaltyConnectWeight = 10;

    Kokkos::View<LO*, memory_space> aggWeight    ("aggWeight",     numLocalAggregates);
    Kokkos::View<LO*, memory_space> connectWeight("connectWeight", numRows);
    Kokkos::View<LO*, memory_space> aggPenalties ("aggPenalties",  numLocalAggregates);

    Kokkos::deep_copy(connectWeight, defaultConnectWeight);

    // taw: by running the aggregation routine more than once there is a chance that also
    // non-aggregated nodes with a node distance of two are added to existing aggregates.
    // Assuming that the aggregate size is 3 in each direction running the algorithm only twice
    // should be sufficient.
    // lbv: If the prior phase of aggregation where run without specifying an aggregate size,
    // the distance 2 coloring and phase 1 aggregation actually guarantee that only one iteration
    // is needed to reach distance 2 neighbors.
    int maxIters = 2;
    int maxNodesPerAggregate = params.get<int>("aggregation: max agg size");
    if(maxNodesPerAggregate == std::numeric_limits<int>::max()) {maxIters = 1;}
    for (int iter = 0; iter < maxIters; ++iter) {
      for(LO color = 1; color <= numColors; ++color) {
        Kokkos::deep_copy(aggWeight, 0);

        //the reduce counts how many nodes are aggregated by this phase,
        //which will then be subtracted from numNonAggregatedNodes
        LO numAggregated = 0;
        Kokkos::parallel_reduce("Aggregation Phase 2b: aggregates expansion",
                                Kokkos::RangePolicy<execution_space>(0, numRows),
                                KOKKOS_LAMBDA (const LO i, LO& tmpNumAggregated) {
                                  if (aggStat(i) != READY || colors(i) != color)
                                    return;

                                  auto neighOfINode = graph.getNeighborVertices(i);
                                  for (int j = 0; j < neighOfINode.length; j++) {
                                    LO neigh = neighOfINode(j);

                                    // We don't check (neigh != i), as it is covered by checking
                                    // (aggStat[neigh] == AGGREGATED)
                                    if (graph.isLocalNeighborVertex(neigh) &&
                                        aggStat(neigh) == AGGREGATED)
                                      Kokkos::atomic_add(&aggWeight(vertex2AggId(neigh, 0)),
                                                         connectWeight(neigh));
                                  }

                                  int bestScore   = -100000;
                                  int bestAggId   = -1;
                                  int bestConnect = -1;

                                  for (int j = 0; j < neighOfINode.length; j++) {
                                    LO neigh = neighOfINode(j);

                                    if (graph.isLocalNeighborVertex(neigh) &&
                                        aggStat(neigh) == AGGREGATED) {
                                      auto aggId = vertex2AggId(neigh, 0);
                                      int score = aggWeight(aggId) - aggPenalties(aggId);

                                      if (score > bestScore) {
                                        bestAggId   = aggId;
                                        bestScore   = score;
                                        bestConnect = connectWeight(neigh);

                                      } else if (aggId == bestAggId &&
                                                 connectWeight(neigh) > bestConnect) {
                                        bestConnect = connectWeight(neigh);
                                      }
                                    }
                                  }
                                  if (bestScore >= 0) {
                                    aggStat(i)         = AGGREGATED;
                                    vertex2AggId(i, 0) = bestAggId;
                                    procWinner(i, 0)   = myRank;

                                    Kokkos::atomic_add(&aggPenalties(bestAggId), 1);
                                    connectWeight(i) = bestConnect - penaltyConnectWeight;
                                    tmpNumAggregated++;
                                  }
                                }, numAggregated); //parallel_for
        numNonAggregatedNodes -= numAggregated;
      }
    } // loop over maxIters

  } // BuildAggregatesRandom



  template <class LO, class GO, class Node>
  void AggregationPhase2bAlgorithm_kokkos<LO, GO, Node>::
  BuildAggregatesDeterministic(const ParameterList& params,
                               const LWGraph_kokkos& graph,
                               Aggregates_kokkos& aggregates,
                               Kokkos::View<unsigned*, typename LWGraph_kokkos::memory_space>& aggStat,
                               LO& numNonAggregatedNodes) const {
    using memory_space    = typename LWGraph_kokkos::memory_space;
    using execution_space = typename LWGraph_kokkos::execution_space;

    const LO  numRows = graph.GetNodeNumVertices();
    const int myRank  = graph.GetComm()->getRank();

    auto vertex2AggId     = aggregates.GetVertex2AggId()->template getLocalView<memory_space>();
    auto procWinner       = aggregates.GetProcWinner()  ->template getLocalView<memory_space>();
    auto colors           = aggregates.GetGraphColors();
    const LO numColors    = aggregates.GetGraphNumColors();
    LO numLocalAggregates = aggregates.GetNumAggregates();

    const int defaultConnectWeight = 100;
    const int penaltyConnectWeight = 10;

    Kokkos::View<int*, memory_space> connectWeight    ("connectWeight",     numRows);
    Kokkos::View<int*, memory_space> aggWeight        ("aggWeight",         numLocalAggregates);
    Kokkos::View<int*, memory_space> aggPenaltyUpdates("aggPenaltyUpdates", numLocalAggregates);
    Kokkos::View<int*, memory_space> aggPenalties     ("aggPenalties",      numLocalAggregates);

    Kokkos::deep_copy(connectWeight, defaultConnectWeight);

    // We do this cycle twice.
    // I don't know why, but ML does it too
    // taw: by running the aggregation routine more than once there is a chance that also
    // non-aggregated nodes with a node distance of two are added to existing aggregates.
    // Assuming that the aggregate size is 3 in each direction running the algorithm only twice
    // should be sufficient.
    int maxIters = 2;
    int maxNodesPerAggregate = params.get<int>("aggregation: max agg size");
    if(maxNodesPerAggregate == std::numeric_limits<int>::max()) {maxIters = 1;}
    for (int iter = 0; iter < maxIters; ++iter) {
      for(LO color = 1; color <= numColors; color++) {
        Kokkos::deep_copy(aggWeight, 0);

        //the reduce counts how many nodes are aggregated by this phase,
        //which will then be subtracted from numNonAggregatedNodes
        LO numAggregated = 0;
        Kokkos::parallel_for("Aggregation Phase 2b: updating agg weights",
          Kokkos::RangePolicy<execution_space>(0, numRows),
          KOKKOS_LAMBDA (const LO i)
          {
            if (aggStat(i) != READY || colors(i) != color)
              return;
            auto neighOfINode = graph.getNeighborVertices(i);
            for (int j = 0; j < neighOfINode.length; j++) {
              LO neigh = neighOfINode(j);
              // We don't check (neigh != i), as it is covered by checking
              // (aggStat[neigh] == AGGREGATED)
              if (graph.isLocalNeighborVertex(neigh) &&
                  aggStat(neigh) == AGGREGATED)
              Kokkos::atomic_add(&aggWeight(vertex2AggId(neigh, 0)),
                  connectWeight(neigh));
            }
          });
        execution_space().fence();
        Kokkos::parallel_reduce("Aggregation Phase 2b: aggregates expansion",
          Kokkos::RangePolicy<execution_space>(0, numRows),
          KOKKOS_LAMBDA (const LO i, LO& tmpNumAggregated)
          {
            if (aggStat(i) != READY || colors(i) != color)
              return;
            int bestScore   = -100000;
            int bestAggId   = -1;
            int bestConnect = -1;

            auto neighOfINode = graph.getNeighborVertices(i);
            for (int j = 0; j < neighOfINode.length; j++) {
              LO neigh = neighOfINode(j);

              if (graph.isLocalNeighborVertex(neigh) &&
                  aggStat(neigh) == AGGREGATED) {
                auto aggId = vertex2AggId(neigh, 0);
                int score = aggWeight(aggId) - aggPenalties(aggId);

                if (score > bestScore) {
                  bestAggId   = aggId;
                  bestScore   = score;
                  bestConnect = connectWeight(neigh);

                } else if (aggId == bestAggId &&
                    connectWeight(neigh) > bestConnect) {
                  bestConnect = connectWeight(neigh);
                }
              }
            }
            if (bestScore >= 0) {
              aggStat(i)         = AGGREGATED;
              vertex2AggId(i, 0) = bestAggId;
              procWinner(i, 0)   = myRank;

              Kokkos::atomic_add(&aggPenaltyUpdates(bestAggId), 1);
              connectWeight(i) = bestConnect - penaltyConnectWeight;
              tmpNumAggregated++;
            }
          }, numAggregated); //parallel_reduce
        execution_space().fence();
        Kokkos::parallel_for("Aggregation Phase 2b: updating agg penalties",
          Kokkos::RangePolicy<execution_space>(0, numLocalAggregates),
          KOKKOS_LAMBDA (const LO agg)
          {
            aggPenalties(agg) += aggPenaltyUpdates(agg);
            aggPenaltyUpdates(agg) = 0;
          });
        numNonAggregatedNodes -= numAggregated;
      }
    } // loop over k
  } // BuildAggregatesDeterministic
} // end namespace

#endif // HAVE_MUELU_KOKKOS_REFACTOR
#endif // MUELU_AGGREGATIONPHASE2BALGORITHM_KOKKOS_DEF_HPP