TpetraExt_MatrixMatrix_def.hpp

Go to the documentation of this file.

// @HEADER
// *****************************************************************************
//          Tpetra: Templated Linear Algebra Services Package
//
// Copyright 2008 NTESS and the Tpetra contributors.
// SPDX-License-Identifier: BSD-3-Clause
// *****************************************************************************
// @HEADER
 
#ifndef TPETRA_MATRIXMATRIX_DEF_HPP
#define TPETRA_MATRIXMATRIX_DEF_HPP
#include "TpetraExt_MatrixMatrix_fwd.hpp"
#include "KokkosSparse_Utils.hpp"
#include "Tpetra_ConfigDefs.hpp"
#include "TpetraExt_MatrixMatrix_decl.hpp"
#include "Teuchos_VerboseObject.hpp"
#include "Teuchos_Array.hpp"
#include "Tpetra_Util.hpp"
#include "Tpetra_CrsMatrix.hpp"
#include "Tpetra_BlockCrsMatrix.hpp"
#include "TpetraExt_MMHelpers_def.hpp"
#include "Tpetra_RowMatrixTransposer.hpp"
#include "Tpetra_Details_computeOffsets.hpp"
#include "Tpetra_Details_Behavior.hpp"
#include "Tpetra_Details_makeColMap.hpp"
#include "Tpetra_ConfigDefs.hpp"
#include "Tpetra_Map.hpp"
#include "Tpetra_Export.hpp"
#include "Tpetra_Import_Util.hpp"
#include "Tpetra_Import_Util2.hpp"
#include "Tpetra_Details_Profiling.hpp"
 
#include <algorithm>
#include <type_traits>
#include "Teuchos_FancyOStream.hpp"
 
#include "TpetraExt_MatrixMatrix_ExtraKernels_def.hpp"
#include "Tpetra_Details_getEntryOnHost.hpp"
 
#include "KokkosSparse_spgemm.hpp"
#include "KokkosSparse_spadd.hpp"
#include "Kokkos_Bitset.hpp"
 
#include <MatrixMarket_Tpetra.hpp>
 
/*********************************************************************************************************/
// Include the architecture-specific kernel partial specializations here
// NOTE: This needs to be outside all namespaces
#include "TpetraExt_MatrixMatrix_OpenMP.hpp"
#include "TpetraExt_MatrixMatrix_Cuda.hpp"
#include "TpetraExt_MatrixMatrix_HIP.hpp"
#include "TpetraExt_MatrixMatrix_SYCL.hpp"
 
namespace Tpetra {
 
namespace MatrixMatrix {
 
//
// This method forms the matrix-matrix product C = op(A) * op(B), where
// op(A) == A   if transposeA is false,
// op(A) == A^T if transposeA is true,
// and similarly for op(B).
//
template <class Scalar,
          class LocalOrdinal,
          class GlobalOrdinal,
          class Node>
void Multiply(
    const CrsMatrix<Scalar, LocalOrdinal, GlobalOrdinal, Node>& A,
    bool transposeA,
    const CrsMatrix<Scalar, LocalOrdinal, GlobalOrdinal, Node>& B,
    bool transposeB,
    CrsMatrix<Scalar, LocalOrdinal, GlobalOrdinal, Node>& C,
    bool call_FillComplete_on_result,
    const std::string& label,
    const Teuchos::RCP<Teuchos::ParameterList>& params) {
  using Teuchos::null;
  using Teuchos::RCP;
  using Teuchos::rcp;
  typedef Scalar SC;
  typedef LocalOrdinal LO;
  typedef GlobalOrdinal GO;
  typedef Node NO;
  typedef CrsMatrix<SC, LO, GO, NO> crs_matrix_type;
  typedef Import<LO, GO, NO> import_type;
  typedef CrsMatrixStruct<SC, LO, GO, NO> crs_matrix_struct_type;
  typedef Map<LO, GO, NO> map_type;
  typedef RowMatrixTransposer<SC, LO, GO, NO> transposer_type;
 
  RCP<Tpetra::Details::ProfilingRegion> MM = rcp(new Tpetra::Details::ProfilingRegion("TpetraExt: MMM: All Setup"));
 
  const std::string prefix = "TpetraExt::MatrixMatrix::Multiply(): ";
 
  // TEUCHOS_FUNC_TIME_MONITOR_DIFF("My Matrix Mult", mmm_multiply);
 
  // The input matrices A and B must both be fillComplete.
  TEUCHOS_TEST_FOR_EXCEPTION(!A.isFillComplete(), std::runtime_error, prefix << "Matrix A is not fill complete.");
  TEUCHOS_TEST_FOR_EXCEPTION(!B.isFillComplete(), std::runtime_error, prefix << "Matrix B is not fill complete.");
 
  // If transposeA is true, then Aprime will be the transpose of A
  // (computed explicitly via RowMatrixTransposer).  Otherwise, Aprime
  // will just be a pointer to A.
  RCP<const crs_matrix_type> Aprime = null;
  // If transposeB is true, then Bprime will be the transpose of B
  // (computed explicitly via RowMatrixTransposer).  Otherwise, Bprime
  // will just be a pointer to B.
  RCP<const crs_matrix_type> Bprime = null;
 
  // Is this a "clean" matrix?
  //
  // mfh 27 Sep 2016: Historically, if Epetra_CrsMatrix was neither
  // locally nor globally indexed, then it was empty.  I don't like
  // this, because the most straightforward implementation presumes
  // lazy allocation of indices.  However, historical precedent
  // demands that we keep around this predicate as a way to test
  // whether the matrix is empty.
  const bool newFlag = !C.getGraph()->isLocallyIndexed() && !C.getGraph()->isGloballyIndexed();
 
  bool use_optimized_ATB = false;
  if (transposeA && !transposeB && call_FillComplete_on_result && newFlag)
    use_optimized_ATB = true;
 
#ifdef USE_OLD_TRANSPOSE  // NOTE: For Grey Ballard's use.  Remove this later.
  use_optimized_ATB = false;
#endif
 
  using Teuchos::ParameterList;
  RCP<ParameterList> transposeParams(new ParameterList);
  transposeParams->set("sort", true);  // Kokkos Kernels spgemm requires inputs to be sorted
 
  if (!use_optimized_ATB && transposeA) {
    transposer_type transposer(rcpFromRef(A));
    Aprime = transposer.createTranspose(transposeParams);
  } else {
    Aprime = rcpFromRef(A);
  }
 
  if (transposeB) {
    transposer_type transposer(rcpFromRef(B));
    Bprime = transposer.createTranspose(transposeParams);
  } else {
    Bprime = rcpFromRef(B);
  }
 
  // Check size compatibility
  global_size_t numACols = A.getDomainMap()->getGlobalNumElements();
  global_size_t numBCols = B.getDomainMap()->getGlobalNumElements();
  global_size_t Aouter   = transposeA ? numACols : A.getGlobalNumRows();
  global_size_t Bouter   = transposeB ? B.getGlobalNumRows() : numBCols;
  global_size_t Ainner   = transposeA ? A.getGlobalNumRows() : numACols;
  global_size_t Binner   = transposeB ? numBCols : B.getGlobalNumRows();
  TEUCHOS_TEST_FOR_EXCEPTION(Ainner != Binner, std::runtime_error,
                             prefix << "ERROR, inner dimensions of op(A) and op(B) "
                                       "must match for matrix-matrix product. op(A) is "
                                    << Aouter << "x" << Ainner << ", op(B) is " << Binner << "x" << Bouter);
 
  // The result matrix C must at least have a row-map that reflects the correct
  // row-size. Don't check the number of columns because rectangular matrices
  // which were constructed with only one map can still end up having the
  // correct capacity and dimensions when filled.
  TEUCHOS_TEST_FOR_EXCEPTION(Aouter > C.getGlobalNumRows(), std::runtime_error,
                             prefix << "ERROR, dimensions of result C must "
                                       "match dimensions of op(A) * op(B). C has "
                                    << C.getGlobalNumRows()
                                    << " rows, should have at least " << Aouter << std::endl);
 
  // It doesn't matter whether C is already Filled or not. If it is already
  // Filled, it must have space allocated for the positions that will be
  // referenced in forming C = op(A)*op(B). If it doesn't have enough space,
  // we'll error out later when trying to store result values.
 
  // CGB: However, matrix must be in active-fill
  if (!C.isFillActive()) C.resumeFill();
 
  // We're going to need to import remotely-owned sections of A and/or B if
  // more than one processor is performing this run, depending on the scenario.
  int numProcs = A.getComm()->getSize();
 
  // Declare a couple of structs that will be used to hold views of the data
  // of A and B, to be used for fast access during the matrix-multiplication.
  crs_matrix_struct_type Aview;
  crs_matrix_struct_type Bview;
 
  RCP<const map_type> targetMap_A = Aprime->getRowMap();
  RCP<const map_type> targetMap_B = Bprime->getRowMap();
 
  {
    Tpetra::Details::ProfilingRegion r("TpetraExt: MMM: All I&X");
 
    // Now import any needed remote rows and populate the Aview struct
    // NOTE: We assert that an import isn't needed --- since we do the transpose
    // above to handle that.
    if (!use_optimized_ATB) {
      RCP<const import_type> dummyImporter;
      MMdetails::import_and_extract_views(*Aprime, targetMap_A, Aview, dummyImporter, true, label, params);
    }
 
    // We will also need local access to all rows of B that correspond to the
    // column-map of op(A).
    if (numProcs > 1)
      targetMap_B = Aprime->getColMap();
 
    // Import any needed remote rows and populate the Bview struct.
    if (!use_optimized_ATB)
      MMdetails::import_and_extract_views(*Bprime, targetMap_B, Bview, Aprime->getGraph()->getImporter(), Aprime->getGraph()->getImporter().is_null(), label, params);
 
  }  // stop MM_importExtract here
 
  // stop the setup timer, and start the multiply timer
  MM = Teuchos::null;
  MM = rcp(new Tpetra::Details::ProfilingRegion("TpetraExt: MMM: All Multiply"));
 
  // Call the appropriate method to perform the actual multiplication.
  if (use_optimized_ATB) {
    MMdetails::mult_AT_B_newmatrix(A, B, C, label, params);
 
  } else if (call_FillComplete_on_result && newFlag) {
    MMdetails::mult_A_B_newmatrix(Aview, Bview, C, label, params);
 
  } else if (call_FillComplete_on_result) {
    MMdetails::mult_A_B_reuse(Aview, Bview, C, label, params);
 
  } else {
    // mfh 27 Sep 2016: Is this the "slow" case?  This
    // "CrsWrapper_CrsMatrix" thing could perhaps be made to support
    // thread-parallel inserts, but that may take some effort.
    CrsWrapper_CrsMatrix<Scalar, LocalOrdinal, GlobalOrdinal, Node> crsmat(C);
 
    MMdetails::mult_A_B(Aview, Bview, crsmat, label, params);
  }
 
  MM = Teuchos::null;
  MM = rcp(new Tpetra::Details::ProfilingRegion("TpetraExt: MMM: All FillComplete"));
  if (call_FillComplete_on_result && !C.isFillComplete()) {
    // We'll call FillComplete on the C matrix before we exit, and give it a
    // domain-map and a range-map.
    // The domain-map will be the domain-map of B, unless
    // op(B)==transpose(B), in which case the range-map of B will be used.
    // The range-map will be the range-map of A, unless op(A)==transpose(A),
    // in which case the domain-map of A will be used.
    C.fillComplete(Bprime->getDomainMap(), Aprime->getRangeMap());
  }
}
 
//
// This method forms the matrix-matrix product C = op(A) * op(B), where
// op(A) == A and similarly for op(B). op(A) = A^T is not yet implemented.
//
template <class Scalar,
          class LocalOrdinal,
          class GlobalOrdinal,
          class Node>
void Multiply(
    const Teuchos::RCP<const BlockCrsMatrix<Scalar, LocalOrdinal, GlobalOrdinal, Node>>& A,
    bool transposeA,
    const Teuchos::RCP<const BlockCrsMatrix<Scalar, LocalOrdinal, GlobalOrdinal, Node>>& B,
    bool transposeB,
    Teuchos::RCP<BlockCrsMatrix<Scalar, LocalOrdinal, GlobalOrdinal, Node>>& C,
    const std::string& label) {
  using Teuchos::null;
  using Teuchos::RCP;
  using Teuchos::rcp;
  typedef Scalar SC;
  typedef LocalOrdinal LO;
  typedef GlobalOrdinal GO;
  typedef Node NO;
  typedef BlockCrsMatrixStruct<SC, LO, GO, NO> blockcrs_matrix_struct_type;
  typedef Map<LO, GO, NO> map_type;
  typedef Import<LO, GO, NO> import_type;
 
  std::string prefix = std::string("TpetraExt ") + label + std::string(": ");
 
  TEUCHOS_TEST_FOR_EXCEPTION(transposeA == true, std::runtime_error, prefix << "Matrix A cannot be transposed.");
  TEUCHOS_TEST_FOR_EXCEPTION(transposeB == true, std::runtime_error, prefix << "Matrix B cannot be transposed.");
 
  // Check size compatibility
  global_size_t numACols = A->getGlobalNumCols();
  global_size_t numBCols = B->getGlobalNumCols();
  global_size_t numARows = A->getGlobalNumRows();
  global_size_t numBRows = B->getGlobalNumRows();
 
  global_size_t Aouter = numARows;
  global_size_t Bouter = numBCols;
  global_size_t Ainner = numACols;
  global_size_t Binner = numBRows;
  TEUCHOS_TEST_FOR_EXCEPTION(Ainner != Binner, std::runtime_error,
                             prefix << "ERROR, inner dimensions of op(A) and op(B) "
                                       "must match for matrix-matrix product. op(A) is "
                                    << Aouter << "x" << Ainner << ", op(B) is " << Binner << "x" << Bouter);
 
  // We're going to need to import remotely-owned sections of A and/or B if
  // more than one processor is performing this run, depending on the scenario.
  int numProcs = A->getComm()->getSize();
 
  const LO blocksize = A->getBlockSize();
  TEUCHOS_TEST_FOR_EXCEPTION(blocksize != B->getBlockSize(), std::runtime_error,
                             prefix << "ERROR, Blocksizes do not match. A.blocksize = " << blocksize << ", B.blocksize = " << B->getBlockSize());
 
  // Declare a couple of structs that will be used to hold views of the data
  // of A and B, to be used for fast access during the matrix-multiplication.
  blockcrs_matrix_struct_type Aview(blocksize);
  blockcrs_matrix_struct_type Bview(blocksize);
 
  RCP<const map_type> targetMap_A = A->getRowMap();
  RCP<const map_type> targetMap_B = B->getRowMap();
 
  // Populate the Aview struct. No remotes are needed.
  RCP<const import_type> dummyImporter;
  MMdetails::import_and_extract_views(*A, targetMap_A, Aview, dummyImporter, true);
 
  // We will also need local access to all rows of B that correspond to the
  // column-map of op(A).
  if (numProcs > 1)
    targetMap_B = A->getColMap();
 
  // Import any needed remote rows and populate the Bview struct.
  MMdetails::import_and_extract_views(*B, targetMap_B, Bview, A->getGraph()->getImporter(),
                                      A->getGraph()->getImporter().is_null());
 
  // Call the appropriate method to perform the actual multiplication.
  MMdetails::mult_A_B_newmatrix(Aview, Bview, C);
}
 
template <class Scalar,
          class LocalOrdinal,
          class GlobalOrdinal,
          class Node>
void Jacobi(Scalar omega,
            const Vector<Scalar, LocalOrdinal, GlobalOrdinal, Node>& Dinv,
            const CrsMatrix<Scalar, LocalOrdinal, GlobalOrdinal, Node>& A,
            const CrsMatrix<Scalar, LocalOrdinal, GlobalOrdinal, Node>& B,
            CrsMatrix<Scalar, LocalOrdinal, GlobalOrdinal, Node>& C,
            bool call_FillComplete_on_result,
            const std::string& label,
            const Teuchos::RCP<Teuchos::ParameterList>& params) {
  using Teuchos::RCP;
  using Teuchos::rcp;
  typedef Scalar SC;
  typedef LocalOrdinal LO;
  typedef GlobalOrdinal GO;
  typedef Node NO;
  typedef Import<LO, GO, NO> import_type;
  typedef CrsMatrixStruct<SC, LO, GO, NO> crs_matrix_struct_type;
  typedef Map<LO, GO, NO> map_type;
  typedef CrsMatrix<SC, LO, GO, NO> crs_matrix_type;
 
  RCP<Tpetra::Details::ProfilingRegion> MM = rcp(new Tpetra::Details::ProfilingRegion("TpetraExt: Jacobi: All Setup"));
 
  const std::string prefix = "TpetraExt::MatrixMatrix::Jacobi(): ";
 
  // A and B should already be Filled.
  // Should we go ahead and call FillComplete() on them if necessary or error
  // out? For now, we choose to error out.
  TEUCHOS_TEST_FOR_EXCEPTION(!A.isFillComplete(), std::runtime_error, prefix << "Matrix A is not fill complete.");
  TEUCHOS_TEST_FOR_EXCEPTION(!B.isFillComplete(), std::runtime_error, prefix << "Matrix B is not fill complete.");
 
  RCP<const crs_matrix_type> Aprime = rcpFromRef(A);
  RCP<const crs_matrix_type> Bprime = rcpFromRef(B);
 
  // Now check size compatibility
  global_size_t numACols = A.getDomainMap()->getGlobalNumElements();
  global_size_t numBCols = B.getDomainMap()->getGlobalNumElements();
  global_size_t Aouter   = A.getGlobalNumRows();
  global_size_t Bouter   = numBCols;
  global_size_t Ainner   = numACols;
  global_size_t Binner   = B.getGlobalNumRows();
  TEUCHOS_TEST_FOR_EXCEPTION(Ainner != Binner, std::runtime_error,
                             prefix << "ERROR, inner dimensions of op(A) and op(B) "
                                       "must match for matrix-matrix product. op(A) is "
                                    << Aouter << "x" << Ainner << ", op(B) is " << Binner << "x" << Bouter);
 
  // The result matrix C must at least have a row-map that reflects the correct
  // row-size. Don't check the number of columns because rectangular matrices
  // which were constructed with only one map can still end up having the
  // correct capacity and dimensions when filled.
  TEUCHOS_TEST_FOR_EXCEPTION(Aouter > C.getGlobalNumRows(), std::runtime_error,
                             prefix << "ERROR, dimensions of result C must "
                                       "match dimensions of op(A) * op(B). C has "
                                    << C.getGlobalNumRows()
                                    << " rows, should have at least " << Aouter << std::endl);
 
  // It doesn't matter whether C is already Filled or not. If it is already
  // Filled, it must have space allocated for the positions that will be
  // referenced in forming C = op(A)*op(B). If it doesn't have enough space,
  // we'll error out later when trying to store result values.
 
  // CGB: However, matrix must be in active-fill
  TEUCHOS_TEST_FOR_EXCEPT(C.isFillActive() == false);
 
  // We're going to need to import remotely-owned sections of A and/or B if
  // more than one processor is performing this run, depending on the scenario.
  int numProcs = A.getComm()->getSize();
 
  // Declare a couple of structs that will be used to hold views of the data of
  // A and B, to be used for fast access during the matrix-multiplication.
  crs_matrix_struct_type Aview;
  crs_matrix_struct_type Bview;
 
  RCP<const map_type> targetMap_A = Aprime->getRowMap();
  RCP<const map_type> targetMap_B = Bprime->getRowMap();
 
  {
    Tpetra::Details::ProfilingRegion r("TpetraExt: Jacobi: All I&X");
    // Enable globalConstants by default
    // NOTE: the I&X routine sticks an importer on the paramlist as output, so we have to use a unique guy here
    RCP<Teuchos::ParameterList> importParams1 = Teuchos::rcp(new Teuchos::ParameterList);
    if (!params.is_null()) {
      importParams1->set("compute global constants", params->get("compute global constants: temporaries", false));
      int mm_optimization_core_count  = 0;
      auto slist                      = params->sublist("matrixmatrix: kernel params", false);
      mm_optimization_core_count      = slist.get("MM_TAFC_OptimizationCoreCount", ::Tpetra::Details::Behavior::TAFC_OptimizationCoreCount());
      int mm_optimization_core_count2 = params->get("MM_TAFC_OptimizationCoreCount", mm_optimization_core_count);
      if (mm_optimization_core_count2 < mm_optimization_core_count) mm_optimization_core_count = mm_optimization_core_count2;
      bool isMM              = slist.get("isMatrixMatrix_TransferAndFillComplete", false);
      bool overrideAllreduce = slist.get("MM_TAFC_OverrideAllreduceCheck", false);
      auto& ip1slist         = importParams1->sublist("matrixmatrix: kernel params", false);
      ip1slist.set("MM_TAFC_OptimizationCoreCount", mm_optimization_core_count);
      ip1slist.set("isMatrixMatrix_TransferAndFillComplete", isMM);
      ip1slist.set("MM_TAFC_OverrideAllreduceCheck", overrideAllreduce);
    }
 
    // Now import any needed remote rows and populate the Aview struct.
    RCP<const import_type> dummyImporter;
    MMdetails::import_and_extract_views(*Aprime, targetMap_A, Aview, dummyImporter, true, label, importParams1);
 
    // We will also need local access to all rows of B that correspond to the
    // column-map of op(A).
    if (numProcs > 1)
      targetMap_B = Aprime->getColMap();
 
    // Now import any needed remote rows and populate the Bview struct.
    // Enable globalConstants by default
    // NOTE: the I&X routine sticks an importer on the paramlist as output, so we have to use a unique guy here
    RCP<Teuchos::ParameterList> importParams2 = Teuchos::rcp(new Teuchos::ParameterList);
    if (!params.is_null()) {
      importParams2->set("compute global constants", params->get("compute global constants: temporaries", false));
 
      auto slist                      = params->sublist("matrixmatrix: kernel params", false);
      int mm_optimization_core_count  = slist.get("MM_TAFC_OptimizationCoreCount", ::Tpetra::Details::Behavior::TAFC_OptimizationCoreCount());
      bool isMM                       = slist.get("isMatrixMatrix_TransferAndFillComplete", false);
      bool overrideAllreduce          = slist.get("MM_TAFC_OverrideAllreduceCheck", false);
      auto& ip2slist                  = importParams2->sublist("matrixmatrix: kernel params", false);
      int mm_optimization_core_count2 = params->get("MM_TAFC_OptimizationCoreCount", mm_optimization_core_count);
      if (mm_optimization_core_count2 < mm_optimization_core_count) mm_optimization_core_count = mm_optimization_core_count2;
      ip2slist.set("MM_TAFC_OptimizationCoreCount", mm_optimization_core_count);
      ip2slist.set("isMatrixMatrix_TransferAndFillComplete", isMM);
      ip2slist.set("MM_TAFC_OverrideAllreduceCheck", overrideAllreduce);
    }
 
    MMdetails::import_and_extract_views(*Bprime, targetMap_B, Bview, Aprime->getGraph()->getImporter(), Aprime->getGraph()->getImporter().is_null(), label, importParams2);
  }
  MM = Teuchos::null;
  MM = rcp(new Tpetra::Details::ProfilingRegion("TpetraExt: Jacobi All Multiply"));
 
  // Now call the appropriate method to perform the actual multiplication.
  CrsWrapper_CrsMatrix<Scalar, LocalOrdinal, GlobalOrdinal, Node> crsmat(C);
 
  // Is this a "clean" matrix
  bool newFlag = !C.getGraph()->isLocallyIndexed() && !C.getGraph()->isGloballyIndexed();
 
  if (call_FillComplete_on_result && newFlag) {
    MMdetails::jacobi_A_B_newmatrix(omega, Dinv, Aview, Bview, C, label, params);
 
  } else if (call_FillComplete_on_result) {
    MMdetails::jacobi_A_B_reuse(omega, Dinv, Aview, Bview, C, label, params);
 
  } else {
    TEUCHOS_TEST_FOR_EXCEPTION(true, std::runtime_error, "jacobi_A_B_general not implemented");
  }
 
  if (!params.is_null()) {
    bool removeZeroEntries = params->get("remove zeros", false);
    if (removeZeroEntries) {
      typedef Teuchos::ScalarTraits<Scalar> STS;
      typename STS::magnitudeType threshold = params->get("remove zeros threshold", STS::magnitude(STS::zero()));
      removeCrsMatrixZeros(C, threshold);
    }
  }
}
 
template <class Scalar,
          class LocalOrdinal,
          class GlobalOrdinal,
          class Node>
void Add(
    const CrsMatrix<Scalar, LocalOrdinal, GlobalOrdinal, Node>& A,
    bool transposeA,
    Scalar scalarA,
    CrsMatrix<Scalar, LocalOrdinal, GlobalOrdinal, Node>& B,
    Scalar scalarB) {
  using Teuchos::Array;
  using Teuchos::null;
  using Teuchos::RCP;
  typedef Scalar SC;
  typedef LocalOrdinal LO;
  typedef GlobalOrdinal GO;
  typedef Node NO;
  typedef CrsMatrix<SC, LO, GO, NO> crs_matrix_type;
  typedef RowMatrixTransposer<SC, LO, GO, NO> transposer_type;
 
  const std::string prefix = "TpetraExt::MatrixMatrix::Add(): ";
 
  TEUCHOS_TEST_FOR_EXCEPTION(!A.isFillComplete(), std::runtime_error,
                             prefix << "ERROR, input matrix A.isFillComplete() is false; it is required to be true. "
                                       "(Result matrix B is not required to be isFillComplete()).");
  TEUCHOS_TEST_FOR_EXCEPTION(B.isFillComplete(), std::runtime_error,
                             prefix << "ERROR, input matrix B must not be fill complete!");
  TEUCHOS_TEST_FOR_EXCEPTION(B.isStaticGraph(), std::runtime_error,
                             prefix << "ERROR, input matrix B must not have static graph!");
  TEUCHOS_TEST_FOR_EXCEPTION(B.isLocallyIndexed(), std::runtime_error,
                             prefix << "ERROR, input matrix B must not be locally indexed!");
 
  using Teuchos::ParameterList;
  RCP<ParameterList> transposeParams(new ParameterList);
  transposeParams->set("sort", false);
 
  RCP<const crs_matrix_type> Aprime = null;
  if (transposeA) {
    transposer_type transposer(rcpFromRef(A));
    Aprime = transposer.createTranspose(transposeParams);
  } else {
    Aprime = rcpFromRef(A);
  }
 
  size_t a_numEntries;
  typename crs_matrix_type::nonconst_global_inds_host_view_type a_inds("a_inds", A.getLocalMaxNumRowEntries());
  typename crs_matrix_type::nonconst_values_host_view_type a_vals("a_vals", A.getLocalMaxNumRowEntries());
  GO row;
 
  if (scalarB != Teuchos::ScalarTraits<SC>::one())
    B.scale(scalarB);
 
  size_t numMyRows = B.getLocalNumRows();
  if (scalarA != Teuchos::ScalarTraits<SC>::zero()) {
    for (LO i = 0; (size_t)i < numMyRows; ++i) {
      row = B.getRowMap()->getGlobalElement(i);
      Aprime->getGlobalRowCopy(row, a_inds, a_vals, a_numEntries);
 
      if (scalarA != Teuchos::ScalarTraits<SC>::one()) {
        for (size_t j = 0; j < a_numEntries; ++j)
          a_vals[j] *= scalarA;
      }
      B.insertGlobalValues(row, a_numEntries, reinterpret_cast<Scalar*>(a_vals.data()), a_inds.data());
    }
  }
}
 
template <class Scalar,
          class LocalOrdinal,
          class GlobalOrdinal,
          class Node>
Teuchos::RCP<CrsMatrix<Scalar, LocalOrdinal, GlobalOrdinal, Node>>
add(const Scalar& alpha,
    const bool transposeA,
    const CrsMatrix<Scalar, LocalOrdinal, GlobalOrdinal, Node>& A,
    const Scalar& beta,
    const bool transposeB,
    const CrsMatrix<Scalar, LocalOrdinal, GlobalOrdinal, Node>& B,
    const Teuchos::RCP<const Map<LocalOrdinal, GlobalOrdinal, Node>>& domainMap,
    const Teuchos::RCP<const Map<LocalOrdinal, GlobalOrdinal, Node>>& rangeMap,
    const Teuchos::RCP<Teuchos::ParameterList>& params) {
  using Teuchos::ParameterList;
  using Teuchos::RCP;
  using Teuchos::rcp;
  using Teuchos::rcpFromRef;
  typedef CrsMatrix<Scalar, LocalOrdinal, GlobalOrdinal, Node> crs_matrix_type;
  if (!params.is_null()) {
    TEUCHOS_TEST_FOR_EXCEPTION(
        params->isParameter("Call fillComplete") && !params->get<bool>("Call fillComplete"),
        std::invalid_argument,
        "Tpetra::MatrixMatrix::add(): this version of add() always calls fillComplete\n"
        "on the result, but you explicitly set 'Call fillComplete' = false in the parameter list. Don't set this explicitly.");
    params->set("Call fillComplete", true);
  }
  // If transposeB, must compute B's explicit transpose to
  // get the correct row map for C.
  RCP<const crs_matrix_type> Brcp = rcpFromRef(B);
  if (transposeB) {
    RowMatrixTransposer<Scalar, LocalOrdinal, GlobalOrdinal, Node> transposer(Brcp);
    Brcp = transposer.createTranspose();
  }
  // Check that A,B are fillComplete before getting B's column map
  TEUCHOS_TEST_FOR_EXCEPTION(!A.isFillComplete() || !Brcp->isFillComplete(), std::invalid_argument,
                             "TpetraExt::MatrixMatrix::add(): A and B must both be fill complete.");
  RCP<crs_matrix_type> C = rcp(new crs_matrix_type(Brcp->getRowMap(), 0));
  // this version of add() always fill completes the result, no matter what is in params on input
  add(alpha, transposeA, A, beta, false, *Brcp, *C, domainMap, rangeMap, params);
  return C;
}
 
// This functor does the same thing as CrsGraph::convertColumnIndicesFromGlobalToLocal,
// but since the spadd() output is always packed there is no need for a separate
// numRowEntries here.
//
template <class LO, class GO, class LOView, class GOView, class LocalMap>
struct ConvertGlobalToLocalFunctor {
  ConvertGlobalToLocalFunctor(LOView& lids_, const GOView& gids_, const LocalMap localColMap_)
    : lids(lids_)
    , gids(gids_)
    , localColMap(localColMap_) {}
 
  KOKKOS_FUNCTION void operator()(const GO i) const {
    lids(i) = localColMap.getLocalElement(gids(i));
  }
 
  LOView lids;
  const GOView gids;
  const LocalMap localColMap;
};
 
template <class Scalar,
          class LocalOrdinal,
          class GlobalOrdinal,
          class Node>
void add(const Scalar& alpha,
         const bool transposeA,
         const CrsMatrix<Scalar, LocalOrdinal, GlobalOrdinal, Node>& A,
         const Scalar& beta,
         const bool transposeB,
         const CrsMatrix<Scalar, LocalOrdinal, GlobalOrdinal, Node>& B,
         CrsMatrix<Scalar, LocalOrdinal, GlobalOrdinal, Node>& C,
         const Teuchos::RCP<const Map<LocalOrdinal, GlobalOrdinal, Node>>& domainMap,
         const Teuchos::RCP<const Map<LocalOrdinal, GlobalOrdinal, Node>>& rangeMap,
         const Teuchos::RCP<Teuchos::ParameterList>& params) {
  using Teuchos::RCP;
  using Teuchos::rcp;
  using Teuchos::rcp_dynamic_cast;
  using Teuchos::rcp_implicit_cast;
  using Teuchos::rcpFromRef;
  using Teuchos::TimeMonitor;
  using SC               = Scalar;
  using LO               = LocalOrdinal;
  using GO               = GlobalOrdinal;
  using NO               = Node;
  using crs_matrix_type  = CrsMatrix<SC, LO, GO, NO>;
  using crs_graph_type   = CrsGraph<LO, GO, NO>;
  using map_type         = Map<LO, GO, NO>;
  using transposer_type  = RowMatrixTransposer<SC, LO, GO, NO>;
  using import_type      = Import<LO, GO, NO>;
  using export_type      = Export<LO, GO, NO>;
  using exec_space       = typename crs_graph_type::execution_space;
  using AddKern          = MMdetails::AddKernels<SC, LO, GO, NO>;
  const char* prefix_mmm = "TpetraExt::MatrixMatrix::add: ";
  constexpr bool debug   = false;
 
  RCP<Tpetra::Details::ProfilingRegion> MM = rcp(new Tpetra::Details::ProfilingRegion("TpetraExt: add: Transpose"));
 
  if (debug) {
    std::ostringstream os;
    os << "Proc " << A.getMap()->getComm()->getRank() << ": "
       << "TpetraExt::MatrixMatrix::add" << std::endl;
    std::cerr << os.str();
  }
 
  TEUCHOS_TEST_FOR_EXCEPTION(C.isLocallyIndexed() || C.isGloballyIndexed(), std::invalid_argument,
                             prefix_mmm << "C must be a 'new' matrix (neither locally nor globally indexed).");
  TEUCHOS_TEST_FOR_EXCEPTION(!A.isFillComplete() || !B.isFillComplete(), std::invalid_argument,
                             prefix_mmm << "A and B must both be fill complete.");
#ifdef HAVE_TPETRA_DEBUG
  // The matrices don't have domain or range Maps unless they are fill complete.
  if (A.isFillComplete() && B.isFillComplete()) {
    const bool domainMapsSame =
        (!transposeA && !transposeB &&
         !A.getDomainMap()->locallySameAs(*B.getDomainMap())) ||
        (!transposeA && transposeB &&
         !A.getDomainMap()->isSameAs(*B.getRangeMap())) ||
        (transposeA && !transposeB &&
         !A.getRangeMap()->isSameAs(*B.getDomainMap()));
    TEUCHOS_TEST_FOR_EXCEPTION(domainMapsSame, std::invalid_argument,
                               prefix_mmm << "The domain Maps of Op(A) and Op(B) are not the same.");
 
    const bool rangeMapsSame =
        (!transposeA && !transposeB &&
         !A.getRangeMap()->isSameAs(*B.getRangeMap())) ||
        (!transposeA && transposeB &&
         !A.getRangeMap()->isSameAs(*B.getDomainMap())) ||
        (transposeA && !transposeB &&
         !A.getDomainMap()->isSameAs(*B.getRangeMap()));
    TEUCHOS_TEST_FOR_EXCEPTION(rangeMapsSame, std::invalid_argument,
                               prefix_mmm << "The range Maps of Op(A) and Op(B) are not the same.");
  }
#endif  // HAVE_TPETRA_DEBUG
 
  using Teuchos::ParameterList;
  // Form the explicit transpose of A if necessary.
  RCP<const crs_matrix_type> Aprime = rcpFromRef(A);
  if (transposeA) {
    transposer_type transposer(Aprime);
    Aprime = transposer.createTranspose();
  }
 
#ifdef HAVE_TPETRA_DEBUG
  TEUCHOS_TEST_FOR_EXCEPTION(Aprime.is_null(), std::logic_error,
                             prefix_mmm << "Failed to compute Op(A). "
                                           "Please report this bug to the Tpetra developers.");
#endif  // HAVE_TPETRA_DEBUG
 
  // Form the explicit transpose of B if necessary.
  RCP<const crs_matrix_type> Bprime = rcpFromRef(B);
  if (transposeB) {
    if (debug) {
      std::ostringstream os;
      os << "Proc " << A.getMap()->getComm()->getRank() << ": "
         << "Form explicit xpose of B" << std::endl;
      std::cerr << os.str();
    }
    transposer_type transposer(Bprime);
    Bprime = transposer.createTranspose();
  }
#ifdef HAVE_TPETRA_DEBUG
  TEUCHOS_TEST_FOR_EXCEPTION(Bprime.is_null(), std::logic_error,
                             prefix_mmm << "Failed to compute Op(B). Please report this bug to the Tpetra developers.");
  TEUCHOS_TEST_FOR_EXCEPTION(
      !Aprime->isFillComplete() || !Bprime->isFillComplete(), std::invalid_argument,
      prefix_mmm << "Aprime and Bprime must both be fill complete.  "
                    "Please report this bug to the Tpetra developers.");
#endif  // HAVE_TPETRA_DEBUG
  RCP<const map_type> CDomainMap = domainMap;
  RCP<const map_type> CRangeMap  = rangeMap;
  if (CDomainMap.is_null()) {
    CDomainMap = Bprime->getDomainMap();
  }
  if (CRangeMap.is_null()) {
    CRangeMap = Bprime->getRangeMap();
  }
  assert(!(CDomainMap.is_null()));
  assert(!(CRangeMap.is_null()));
  typedef typename AddKern::values_array values_array;
  typedef typename AddKern::row_ptrs_array row_ptrs_array;
  typedef typename AddKern::col_inds_array col_inds_array;
  bool AGraphSorted = Aprime->getCrsGraph()->isSorted();
  bool BGraphSorted = Bprime->getCrsGraph()->isSorted();
  values_array vals;
  row_ptrs_array rowptrs;
  col_inds_array colinds;
 
  MM = Teuchos::null;
  MM = rcp(new Tpetra::Details::ProfilingRegion("TpetraExt: add: rowmap check/import"));
 
  if (!(Aprime->getRowMap()->isSameAs(*(Bprime->getRowMap())))) {
    // import Aprime into Bprime's row map so the local matrices have same # of rows
    auto import = rcp(new import_type(Aprime->getRowMap(), Bprime->getRowMap()));
    // cbl do not set
    // parameterlist "isMatrixMatrix_TransferAndFillComplete" true here as
    // this import _may_ take the form of a transfer. In practice it would be unlikely,
    // but the general case is not so forgiving.
    Aprime = importAndFillCompleteCrsMatrix<crs_matrix_type>(Aprime, *import, Bprime->getDomainMap(), Bprime->getRangeMap());
  }
  bool matchingColMaps           = Aprime->getColMap()->isSameAs(*(Bprime->getColMap()));
  bool sorted                    = AGraphSorted && BGraphSorted;
  RCP<const import_type> Cimport = Teuchos::null;
  RCP<export_type> Cexport       = Teuchos::null;
  bool doFillComplete            = true;
  if (Teuchos::nonnull(params) && params->isParameter("Call fillComplete")) {
    doFillComplete = params->get<bool>("Call fillComplete");
  }
  auto Alocal     = Aprime->getLocalMatrixDevice();
  auto Blocal     = Bprime->getLocalMatrixDevice();
  LO numLocalRows = Alocal.numRows();
  if (numLocalRows == 0) {
    // KokkosKernels spadd assumes rowptrs.extent(0) + 1 == nrows,
    // but an empty Tpetra matrix is allowed to have rowptrs.extent(0) == 0.
    // Handle this case now
    //(without interfering with collective operations, since it's possible for
    // some ranks to have 0 local rows and others not).
    rowptrs = row_ptrs_array("C rowptrs", 0);
  }
  auto Acolmap = Aprime->getColMap();
  auto Bcolmap = Bprime->getColMap();
  if (!matchingColMaps) {
    using global_col_inds_array = typename AddKern::global_col_inds_array;
    MM                          = Teuchos::null;
    MM                          = rcp(new Tpetra::Details::ProfilingRegion("TpetraExt: add: mismatched col map full kernel"));
 
    // use kernel that converts col indices in both A and B to common domain map before adding
    auto AlocalColmap = Acolmap->getLocalMap();
    auto BlocalColmap = Bcolmap->getLocalMap();
    global_col_inds_array globalColinds;
    if (debug) {
      std::ostringstream os;
      os << "Proc " << A.getMap()->getComm()->getRank() << ": "
         << "Call AddKern::convertToGlobalAndAdd(...)" << std::endl;
      std::cerr << os.str();
    }
    AddKern::convertToGlobalAndAdd(
        Alocal, alpha, Blocal, beta, AlocalColmap, BlocalColmap,
        vals, rowptrs, globalColinds);
    if (debug) {
      std::ostringstream os;
      os << "Proc " << A.getMap()->getComm()->getRank() << ": "
         << "Finished AddKern::convertToGlobalAndAdd(...)" << std::endl;
      std::cerr << os.str();
    }
    MM = Teuchos::null;
    MM = rcp(new Tpetra::Details::ProfilingRegion("TpetraExt: add: Constructing graph"));
 
    RCP<const map_type> CcolMap;
    Tpetra::Details::makeColMap<LocalOrdinal, GlobalOrdinal, Node>(CcolMap, CDomainMap, globalColinds);
    C.replaceColMap(CcolMap);
    col_inds_array localColinds("C colinds", globalColinds.extent(0));
    Kokkos::parallel_for(Kokkos::RangePolicy<exec_space>(0, globalColinds.extent(0)),
                         ConvertGlobalToLocalFunctor<LocalOrdinal, GlobalOrdinal,
                                                     col_inds_array, global_col_inds_array,
                                                     typename map_type::local_map_type>(localColinds, globalColinds, CcolMap->getLocalMap()));
    KokkosSparse::sort_crs_matrix<exec_space, row_ptrs_array, col_inds_array, values_array>(rowptrs, localColinds, vals);
    C.setAllValues(rowptrs, localColinds, vals);
    C.fillComplete(CDomainMap, CRangeMap, params);
    if (!doFillComplete)
      C.resumeFill();
  } else {
    // Aprime, Bprime and C all have the same column maps
    auto Avals    = Alocal.values;
    auto Bvals    = Blocal.values;
    auto Arowptrs = Alocal.graph.row_map;
    auto Browptrs = Blocal.graph.row_map;
    auto Acolinds = Alocal.graph.entries;
    auto Bcolinds = Blocal.graph.entries;
    if (sorted) {
      // use sorted kernel
      MM = Teuchos::null;
      MM = rcp(new Tpetra::Details::ProfilingRegion("TpetraExt: add: sorted entries full kernel"));
      if (debug) {
        std::ostringstream os;
        os << "Proc " << A.getMap()->getComm()->getRank() << ": "
           << "Call AddKern::addSorted(...)" << std::endl;
        std::cerr << os.str();
      }
      AddKern::addSorted(Avals, Arowptrs, Acolinds, alpha, Bvals, Browptrs, Bcolinds, beta, Aprime->getGlobalNumCols(), vals, rowptrs, colinds);
    } else {
      // use unsorted kernel
      MM = Teuchos::null;
      MM = rcp(new Tpetra::Details::ProfilingRegion("TpetraExt: add: unsorted entries full kernel"));
 
      if (debug) {
        std::ostringstream os;
        os << "Proc " << A.getMap()->getComm()->getRank() << ": "
           << "Call AddKern::addUnsorted(...)" << std::endl;
        std::cerr << os.str();
      }
      AddKern::addUnsorted(Avals, Arowptrs, Acolinds, alpha, Bvals, Browptrs, Bcolinds, beta, Aprime->getGlobalNumCols(), vals, rowptrs, colinds);
    }
    // Bprime col map works as C's row map, since Aprime and Bprime have the same colmaps.
    RCP<const map_type> Ccolmap = Bcolmap;
    C.replaceColMap(Ccolmap);
    C.setAllValues(rowptrs, colinds, vals);
    if (doFillComplete) {
      MM = Teuchos::null;
      MM = rcp(new Tpetra::Details::ProfilingRegion("TpetraExt: add: expertStaticFillComplete"));
      if (!CDomainMap->isSameAs(*Ccolmap)) {
        if (debug) {
          std::ostringstream os;
          os << "Proc " << A.getMap()->getComm()->getRank() << ": "
             << "Create Cimport" << std::endl;
          std::cerr << os.str();
        }
        Cimport = rcp(new import_type(CDomainMap, Ccolmap));
      }
      if (!C.getRowMap()->isSameAs(*CRangeMap)) {
        if (debug) {
          std::ostringstream os;
          os << "Proc " << A.getMap()->getComm()->getRank() << ": "
             << "Create Cexport" << std::endl;
          std::cerr << os.str();
        }
        Cexport = rcp(new export_type(C.getRowMap(), CRangeMap));
      }
 
      if (debug) {
        std::ostringstream os;
        os << "Proc " << A.getMap()->getComm()->getRank() << ": "
           << "Call C->expertStaticFillComplete(...)" << std::endl;
        std::cerr << os.str();
      }
      C.expertStaticFillComplete(CDomainMap, CRangeMap, Cimport, Cexport, params);
    }
  }
}
 
// This version of Add takes C as RCP&, so C may be null on input (in this case,
// it is allocated and constructed in this function).
template <class Scalar,
          class LocalOrdinal,
          class GlobalOrdinal,
          class Node>
void Add(
    const CrsMatrix<Scalar, LocalOrdinal, GlobalOrdinal, Node>& A,
    bool transposeA,
    Scalar scalarA,
    const CrsMatrix<Scalar, LocalOrdinal, GlobalOrdinal, Node>& B,
    bool transposeB,
    Scalar scalarB,
    Teuchos::RCP<CrsMatrix<Scalar, LocalOrdinal, GlobalOrdinal, Node>>& C) {
  using std::endl;
  using Teuchos::Array;
  using Teuchos::ArrayRCP;
  using Teuchos::ArrayView;
  using Teuchos::RCP;
  using Teuchos::rcp;
  using Teuchos::rcp_dynamic_cast;
  using Teuchos::rcpFromRef;
  using Teuchos::tuple;
  //  typedef typename ArrayView<const Scalar>::size_type size_type;
  typedef Teuchos::ScalarTraits<Scalar> STS;
  typedef Map<LocalOrdinal, GlobalOrdinal, Node> map_type;
  //  typedef Import<LocalOrdinal, GlobalOrdinal, Node>                         import_type;
  //  typedef RowGraph<LocalOrdinal, GlobalOrdinal, Node>                       row_graph_type;
  //  typedef CrsGraph<LocalOrdinal, GlobalOrdinal, Node>                       crs_graph_type;
  typedef CrsMatrix<Scalar, LocalOrdinal, GlobalOrdinal, Node> crs_matrix_type;
  typedef RowMatrixTransposer<Scalar, LocalOrdinal, GlobalOrdinal, Node> transposer_type;
 
  std::string prefix = "TpetraExt::MatrixMatrix::Add(): ";
 
  TEUCHOS_TEST_FOR_EXCEPTION(
      !A.isFillComplete() || !B.isFillComplete(), std::invalid_argument,
      prefix << "A and B must both be fill complete before calling this function.");
 
  if (C.is_null()) {
    TEUCHOS_TEST_FOR_EXCEPTION(!A.haveGlobalConstants(), std::logic_error,
                               prefix << "C is null (must be allocated), but A.haveGlobalConstants() is false. "
                                         "Please report this bug to the Tpetra developers.");
    TEUCHOS_TEST_FOR_EXCEPTION(!B.haveGlobalConstants(), std::logic_error,
                               prefix << "C is null (must be allocated), but B.haveGlobalConstants() is false. "
                                         "Please report this bug to the Tpetra developers.");
  }
 
#ifdef HAVE_TPETRA_DEBUG
  {
    const bool domainMapsSame =
        (!transposeA && !transposeB && !A.getDomainMap()->isSameAs(*(B.getDomainMap()))) ||
        (!transposeA && transposeB && !A.getDomainMap()->isSameAs(*(B.getRangeMap()))) ||
        (transposeA && !transposeB && !A.getRangeMap()->isSameAs(*(B.getDomainMap())));
    TEUCHOS_TEST_FOR_EXCEPTION(domainMapsSame, std::invalid_argument,
                               prefix << "The domain Maps of Op(A) and Op(B) are not the same.");
 
    const bool rangeMapsSame =
        (!transposeA && !transposeB && !A.getRangeMap()->isSameAs(*(B.getRangeMap()))) ||
        (!transposeA && transposeB && !A.getRangeMap()->isSameAs(*(B.getDomainMap()))) ||
        (transposeA && !transposeB && !A.getDomainMap()->isSameAs(*(B.getRangeMap())));
    TEUCHOS_TEST_FOR_EXCEPTION(rangeMapsSame, std::invalid_argument,
                               prefix << "The range Maps of Op(A) and Op(B) are not the same.");
  }
#endif  // HAVE_TPETRA_DEBUG
 
  using Teuchos::ParameterList;
  RCP<ParameterList> transposeParams(new ParameterList);
  transposeParams->set("sort", false);
 
  // Form the explicit transpose of A if necessary.
  RCP<const crs_matrix_type> Aprime;
  if (transposeA) {
    transposer_type theTransposer(rcpFromRef(A));
    Aprime = theTransposer.createTranspose(transposeParams);
  } else {
    Aprime = rcpFromRef(A);
  }
 
#ifdef HAVE_TPETRA_DEBUG
  TEUCHOS_TEST_FOR_EXCEPTION(Aprime.is_null(), std::logic_error,
                             prefix << "Failed to compute Op(A). Please report this bug to the Tpetra developers.");
#endif  // HAVE_TPETRA_DEBUG
 
  // Form the explicit transpose of B if necessary.
  RCP<const crs_matrix_type> Bprime;
  if (transposeB) {
    transposer_type theTransposer(rcpFromRef(B));
    Bprime = theTransposer.createTranspose(transposeParams);
  } else {
    Bprime = rcpFromRef(B);
  }
 
#ifdef HAVE_TPETRA_DEBUG
  TEUCHOS_TEST_FOR_EXCEPTION(Bprime.is_null(), std::logic_error,
                             prefix << "Failed to compute Op(B). Please report this bug to the Tpetra developers.");
#endif  // HAVE_TPETRA_DEBUG
 
  bool CwasFillComplete = false;
 
  // Allocate or zero the entries of the result matrix.
  if (!C.is_null()) {
    CwasFillComplete = C->isFillComplete();
    if (CwasFillComplete)
      C->resumeFill();
    C->setAllToScalar(STS::zero());
  } else {
    // FIXME (mfh 08 May 2013) When I first looked at this method, I
    // noticed that C was being given the row Map of Aprime (the
    // possibly transposed version of A).  Is this what we want?
 
    // It is a precondition that Aprime and Bprime have the same domain and range maps.
    // However, they may have different row maps. In this case, it's difficult to
    // get a precise upper bound on the number of entries in each local row of C, so
    // just use the looser upper bound based on the max number of entries in any row of Aprime and Bprime.
    if (Aprime->getRowMap()->isSameAs(*Bprime->getRowMap())) {
      LocalOrdinal numLocalRows = Aprime->getLocalNumRows();
      Array<size_t> CmaxEntriesPerRow(numLocalRows);
      for (LocalOrdinal i = 0; i < numLocalRows; i++) {
        CmaxEntriesPerRow[i] = Aprime->getNumEntriesInLocalRow(i) + Bprime->getNumEntriesInLocalRow(i);
      }
      C = rcp(new crs_matrix_type(Aprime->getRowMap(), CmaxEntriesPerRow()));
    } else {
      // Note: above we checked that Aprime and Bprime have global constants, so it's safe to ask for max entries per row.
      C = rcp(new crs_matrix_type(Aprime->getRowMap(), Aprime->getGlobalMaxNumRowEntries() + Bprime->getGlobalMaxNumRowEntries()));
    }
  }
 
#ifdef HAVE_TPETRA_DEBUG
  TEUCHOS_TEST_FOR_EXCEPTION(Aprime.is_null(), std::logic_error,
                             prefix << "At this point, Aprime is null. Please report this bug to the Tpetra developers.");
  TEUCHOS_TEST_FOR_EXCEPTION(Bprime.is_null(), std::logic_error,
                             prefix << "At this point, Bprime is null. Please report this bug to the Tpetra developers.");
  TEUCHOS_TEST_FOR_EXCEPTION(C.is_null(), std::logic_error,
                             prefix << "At this point, C is null. Please report this bug to the Tpetra developers.");
#endif  // HAVE_TPETRA_DEBUG
 
  Array<RCP<const crs_matrix_type>> Mat =
      tuple<RCP<const crs_matrix_type>>(Aprime, Bprime);
  Array<Scalar> scalar = tuple<Scalar>(scalarA, scalarB);
 
  // do a loop over each matrix to add: A reordering might be more efficient
  for (int k = 0; k < 2; ++k) {
    typename crs_matrix_type::nonconst_global_inds_host_view_type Indices;
    typename crs_matrix_type::nonconst_values_host_view_type Values;
 
    // Loop over each locally owned row of the current matrix (either
    // Aprime or Bprime), and sum its entries into the corresponding
    // row of C.  This works regardless of whether Aprime or Bprime
    // has the same row Map as C, because both sumIntoGlobalValues and
    // insertGlobalValues allow summing resp. inserting into nonowned
    // rows of C.
#ifdef HAVE_TPETRA_DEBUG
    TEUCHOS_TEST_FOR_EXCEPTION(Mat[k].is_null(), std::logic_error,
                               prefix << "At this point, curRowMap is null. Please report this bug to the Tpetra developers.");
#endif  // HAVE_TPETRA_DEBUG
    RCP<const map_type> curRowMap = Mat[k]->getRowMap();
#ifdef HAVE_TPETRA_DEBUG
    TEUCHOS_TEST_FOR_EXCEPTION(curRowMap.is_null(), std::logic_error,
                               prefix << "At this point, curRowMap is null. Please report this bug to the Tpetra developers.");
#endif  // HAVE_TPETRA_DEBUG
 
    const size_t localNumRows = Mat[k]->getLocalNumRows();
    for (size_t i = 0; i < localNumRows; ++i) {
      const GlobalOrdinal globalRow = curRowMap->getGlobalElement(i);
      size_t numEntries             = Mat[k]->getNumEntriesInGlobalRow(globalRow);
      if (numEntries > 0) {
        if (numEntries > Indices.extent(0)) {
          Kokkos::resize(Indices, numEntries);
          Kokkos::resize(Values, numEntries);
        }
        Mat[k]->getGlobalRowCopy(globalRow, Indices, Values, numEntries);
 
        if (scalar[k] != STS::one()) {
          for (size_t j = 0; j < numEntries; ++j) {
            Values[j] *= scalar[k];
          }
        }
 
        if (CwasFillComplete) {
          size_t result = C->sumIntoGlobalValues(globalRow, numEntries,
                                                 reinterpret_cast<Scalar*>(Values.data()), Indices.data());
          TEUCHOS_TEST_FOR_EXCEPTION(result != numEntries, std::logic_error,
                                     prefix << "sumIntoGlobalValues failed to add entries from A or B into C.");
        } else {
          C->insertGlobalValues(globalRow, numEntries,
                                reinterpret_cast<Scalar*>(Values.data()), Indices.data());
        }
      }
    }
  }
  if (CwasFillComplete) {
    C->fillComplete(C->getDomainMap(),
                    C->getRangeMap());
  }
}
 
// This version of Add takes C as const RCP&, so C must not be null on input. Otherwise, its behavior is identical
// to the above version where C is RCP&.
template <class Scalar,
          class LocalOrdinal,
          class GlobalOrdinal,
          class Node>
void Add(
    const CrsMatrix<Scalar, LocalOrdinal, GlobalOrdinal, Node>& A,
    bool transposeA,
    Scalar scalarA,
    const CrsMatrix<Scalar, LocalOrdinal, GlobalOrdinal, Node>& B,
    bool transposeB,
    Scalar scalarB,
    const Teuchos::RCP<CrsMatrix<Scalar, LocalOrdinal, GlobalOrdinal, Node>>& C) {
  std::string prefix = "TpetraExt::MatrixMatrix::Add(): ";
 
  TEUCHOS_TEST_FOR_EXCEPTION(C.is_null(), std::invalid_argument,
                             prefix << "C must not be null");
 
  Teuchos::RCP<CrsMatrix<Scalar, LocalOrdinal, GlobalOrdinal, Node>> C_ = C;
  Add(A, transposeA, scalarA, B, transposeB, scalarB, C_);
}
 
}  // End namespace MatrixMatrix
 
namespace MMdetails {
 
/*********************************************************************************************************/
// template <class TransferType>
// void printMultiplicationStatistics(Teuchos::RCP<TransferType > Transfer, const std::string &label) {
//   if (Transfer.is_null())
//     return;
//
//   const Distributor & Distor                   = Transfer->getDistributor();
//   Teuchos::RCP<const Teuchos::Comm<int> > Comm = Transfer->getSourceMap()->getComm();
//
//   size_t rows_send   = Transfer->getNumExportIDs();
//   size_t rows_recv   = Transfer->getNumRemoteIDs();
//
//   size_t round1_send = Transfer->getNumExportIDs() * sizeof(size_t);
//   size_t round1_recv = Transfer->getNumRemoteIDs() * sizeof(size_t);
//   size_t num_send_neighbors = Distor.getNumSends();
//   size_t num_recv_neighbors = Distor.getNumReceives();
//   size_t round2_send, round2_recv;
//   Distor.getLastDoStatistics(round2_send,round2_recv);
//
//   int myPID    = Comm->getRank();
//   int NumProcs = Comm->getSize();
//
//   // Processor by processor statistics
//   //    printf("[%d] %s Statistics: neigh[s/r]=%d/%d rows[s/r]=%d/%d r1bytes[s/r]=%d/%d r2bytes[s/r]=%d/%d\n",
//   //    myPID, label.c_str(),num_send_neighbors,num_recv_neighbors,rows_send,rows_recv,round1_send,round1_recv,round2_send,round2_recv);
//
//   // Global statistics
//   size_t lstats[8] = {num_send_neighbors,num_recv_neighbors,rows_send,rows_recv,round1_send,round1_recv,round2_send,round2_recv};
//   size_t gstats_min[8], gstats_max[8];
//
//   double lstats_avg[8], gstats_avg[8];
//   for(int i=0; i<8; i++)
//     lstats_avg[i] = ((double)lstats[i])/NumProcs;
//
//   Teuchos::reduceAll(*Comm(),Teuchos::REDUCE_MIN,8,lstats,gstats_min);
//   Teuchos::reduceAll(*Comm(),Teuchos::REDUCE_MAX,8,lstats,gstats_max);
//   Teuchos::reduceAll(*Comm(),Teuchos::REDUCE_SUM,8,lstats_avg,gstats_avg);
//
//   if(!myPID) {
//     printf("%s Send Statistics[min/avg/max]: neigh=%d/%4.1f/%d rows=%d/%4.1f/%d round1=%d/%4.1f/%d round2=%d/%4.1f/%d\n", label.c_str(),
//            (int)gstats_min[0],gstats_avg[0],(int)gstats_max[0], (int)gstats_min[2],gstats_avg[2],(int)gstats_max[2],
//            (int)gstats_min[4],gstats_avg[4],(int)gstats_max[4], (int)gstats_min[6],gstats_avg[6],(int)gstats_max[6]);
//     printf("%s Recv Statistics[min/avg/max]: neigh=%d/%4.1f/%d rows=%d/%4.1f/%d round1=%d/%4.1f/%d round2=%d/%4.1f/%d\n", label.c_str(),
//            (int)gstats_min[1],gstats_avg[1],(int)gstats_max[1], (int)gstats_min[3],gstats_avg[3],(int)gstats_max[3],
//            (int)gstats_min[5],gstats_avg[5],(int)gstats_max[5], (int)gstats_min[7],gstats_avg[7],(int)gstats_max[7]);
//   }
// }
 
// Kernel method for computing the local portion of C = A*B
template <class Scalar,
          class LocalOrdinal,
          class GlobalOrdinal,
          class Node>
void mult_AT_B_newmatrix(
    const CrsMatrix<Scalar, LocalOrdinal, GlobalOrdinal, Node>& A,
    const CrsMatrix<Scalar, LocalOrdinal, GlobalOrdinal, Node>& B,
    CrsMatrix<Scalar, LocalOrdinal, GlobalOrdinal, Node>& C,
    const std::string& label,
    const Teuchos::RCP<Teuchos::ParameterList>& params) {
  using Teuchos::RCP;
  using Teuchos::rcp;
  typedef Scalar SC;
  typedef LocalOrdinal LO;
  typedef GlobalOrdinal GO;
  typedef Node NO;
  typedef CrsMatrixStruct<SC, LO, GO, NO> crs_matrix_struct_type;
  typedef RowMatrixTransposer<SC, LO, GO, NO> transposer_type;
 
  RCP<Tpetra::Details::ProfilingRegion> MM = rcp(new Tpetra::Details::ProfilingRegion("TpetraExt: MMM-T: Transpose"));
 
  /*************************************************************/
  /* 1) Local Transpose of A                                   */
  /*************************************************************/
  transposer_type transposer(rcpFromRef(A), label + std::string("XP: "));
 
  using Teuchos::ParameterList;
  RCP<ParameterList> transposeParams(new ParameterList);
  transposeParams->set("sort", true);  // Kokkos Kernels spgemm requires inputs to be sorted
  if (!params.is_null()) {
    transposeParams->set("compute global constants",
                         params->get("compute global constants: temporaries",
                                     false));
  }
  RCP<Tpetra::CrsMatrix<SC, LO, GO, NO>> Atrans =
      transposer.createTransposeLocal(transposeParams);
 
  /*************************************************************/
  /* 2/3) Call mult_A_B_newmatrix w/ fillComplete              */
  /*************************************************************/
  MM = Teuchos::null;
  MM = rcp(new Tpetra::Details::ProfilingRegion("TpetraExt: MMM-T: I&X"));
 
  // Get views, asserting that no import is required to speed up computation
  crs_matrix_struct_type Aview;
  crs_matrix_struct_type Bview;
  RCP<const Import<LO, GO, NO>> dummyImporter;
 
  // NOTE: the I&X routine sticks an importer on the paramlist as output, so we have to use a unique guy here
  RCP<Teuchos::ParameterList> importParams1(new ParameterList);
  if (!params.is_null()) {
    importParams1->set("compute global constants",
                       params->get("compute global constants: temporaries",
                                   false));
    auto slist             = params->sublist("matrixmatrix: kernel params", false);
    bool isMM              = slist.get("isMatrixMatrix_TransferAndFillComplete", false);
    bool overrideAllreduce = slist.get("MM_TAFC_OverrideAllreduceCheck", false);
    int mm_optimization_core_count =
        ::Tpetra::Details::Behavior::TAFC_OptimizationCoreCount();
    mm_optimization_core_count =
        slist.get("MM_TAFC_OptimizationCoreCount", mm_optimization_core_count);
    int mm_optimization_core_count2 =
        params->get("MM_TAFC_OptimizationCoreCount", mm_optimization_core_count);
    if (mm_optimization_core_count2 < mm_optimization_core_count) {
      mm_optimization_core_count = mm_optimization_core_count2;
    }
    auto& sip1 = importParams1->sublist("matrixmatrix: kernel params", false);
    sip1.set("MM_TAFC_OptimizationCoreCount", mm_optimization_core_count);
    sip1.set("isMatrixMatrix_TransferAndFillComplete", isMM);
    sip1.set("MM_TAFC_OverrideAllreduceCheck", overrideAllreduce);
  }
 
  MMdetails::import_and_extract_views(*Atrans, Atrans->getRowMap(),
                                      Aview, dummyImporter, true,
                                      label, importParams1);
 
  RCP<ParameterList> importParams2(new ParameterList);
  if (!params.is_null()) {
    importParams2->set("compute global constants",
                       params->get("compute global constants: temporaries",
                                   false));
    auto slist             = params->sublist("matrixmatrix: kernel params", false);
    bool isMM              = slist.get("isMatrixMatrix_TransferAndFillComplete", false);
    bool overrideAllreduce = slist.get("MM_TAFC_OverrideAllreduceCheck", false);
    int mm_optimization_core_count =
        ::Tpetra::Details::Behavior::TAFC_OptimizationCoreCount();
    mm_optimization_core_count =
        slist.get("MM_TAFC_OptimizationCoreCount",
                  mm_optimization_core_count);
    int mm_optimization_core_count2 =
        params->get("MM_TAFC_OptimizationCoreCount",
                    mm_optimization_core_count);
    if (mm_optimization_core_count2 < mm_optimization_core_count) {
      mm_optimization_core_count = mm_optimization_core_count2;
    }
    auto& sip2 = importParams2->sublist("matrixmatrix: kernel params", false);
    sip2.set("MM_TAFC_OptimizationCoreCount", mm_optimization_core_count);
    sip2.set("isMatrixMatrix_TransferAndFillComplete", isMM);
    sip2.set("MM_TAFC_OverrideAllreduceCheck", overrideAllreduce);
  }
 
  if (B.getRowMap()->isSameAs(*Atrans->getColMap())) {
    MMdetails::import_and_extract_views(B, B.getRowMap(), Bview, dummyImporter, true, label, importParams2);
  } else {
    MMdetails::import_and_extract_views(B, Atrans->getColMap(), Bview, dummyImporter, false, label, importParams2);
  }
 
  MM = Teuchos::null;
  MM = rcp(new Tpetra::Details::ProfilingRegion("TpetraExt: MMM-T: AB-core"));
 
  RCP<Tpetra::CrsMatrix<SC, LO, GO, NO>> Ctemp;
 
  // If Atrans has no Exporter, we can use C instead of having to create a temp matrix
  bool needs_final_export = !Atrans->getGraph()->getExporter().is_null();
  if (needs_final_export) {
    Ctemp = rcp(new Tpetra::CrsMatrix<SC, LO, GO, NO>(Atrans->getRowMap(), 0));
  } else {
    Ctemp = rcp(&C, false);
  }
 
  mult_A_B_newmatrix(Aview, Bview, *Ctemp, label, params);
 
  /*************************************************************/
  /* 4) exportAndFillComplete matrix                           */
  /*************************************************************/
  MM = Teuchos::null;
  MM = rcp(new Tpetra::Details::ProfilingRegion("TpetraExt: MMM-T: exportAndFillComplete"));
 
  RCP<Tpetra::CrsMatrix<SC, LO, GO, NO>> Crcp(&C, false);
 
  if (needs_final_export) {
    ParameterList labelList;
    labelList.set("Timer Label", label);
    if (!params.is_null()) {
      ParameterList& params_sublist    = params->sublist("matrixmatrix: kernel params", false);
      ParameterList& labelList_subList = labelList.sublist("matrixmatrix: kernel params", false);
      int mm_optimization_core_count   = ::Tpetra::Details::Behavior::TAFC_OptimizationCoreCount();
      mm_optimization_core_count       = params_sublist.get("MM_TAFC_OptimizationCoreCount", mm_optimization_core_count);
      int mm_optimization_core_count2  = params->get("MM_TAFC_OptimizationCoreCount", mm_optimization_core_count);
      if (mm_optimization_core_count2 < mm_optimization_core_count) mm_optimization_core_count = mm_optimization_core_count2;
      labelList_subList.set("MM_TAFC_OptimizationCoreCount", mm_optimization_core_count, "Core Count above which the optimized neighbor discovery is used");
      bool isMM              = params_sublist.get("isMatrixMatrix_TransferAndFillComplete", false);
      bool overrideAllreduce = params_sublist.get("MM_TAFC_OverrideAllreduceCheck", false);
 
      labelList_subList.set("isMatrixMatrix_TransferAndFillComplete", isMM,
                            "This parameter should be set to true only for MatrixMatrix operations: the optimization in Epetra that was ported to Tpetra does _not_ take into account the possibility that for any given source PID, a particular GID may not exist on the target PID: i.e. a transfer operation. A fix for this general case is in development.");
      labelList.set("compute global constants", params->get("compute global constants", true));
      labelList.set("MM_TAFC_OverrideAllreduceCheck", overrideAllreduce);
    }
    Ctemp->exportAndFillComplete(Crcp,
                                 *Ctemp->getGraph()->getExporter(),
                                 B.getDomainMap(),
                                 A.getDomainMap(),
                                 rcp(&labelList, false));
  }
#ifdef HAVE_TPETRA_MMM_STATISTICS
  printMultiplicationStatistics(Ctemp->getGraph()->getExporter(), label + std::string(" AT_B MMM"));
#endif
}
 
/*********************************************************************************************************/
// Kernel method for computing the local portion of C = A*B
template <class Scalar,
          class LocalOrdinal,
          class GlobalOrdinal,
          class Node>
void mult_A_B(
    CrsMatrixStruct<Scalar, LocalOrdinal, GlobalOrdinal, Node>& Aview,
    CrsMatrixStruct<Scalar, LocalOrdinal, GlobalOrdinal, Node>& Bview,
    CrsWrapper<Scalar, LocalOrdinal, GlobalOrdinal, Node>& C,
    const std::string& /* label */,
    const Teuchos::RCP<Teuchos::ParameterList>& /* params */) {
  using Teuchos::Array;
  using Teuchos::ArrayRCP;
  using Teuchos::ArrayView;
  using Teuchos::null;
  using Teuchos::OrdinalTraits;
 
  typedef Teuchos::ScalarTraits<Scalar> STS;
  // TEUCHOS_FUNC_TIME_MONITOR_DIFF("mult_A_B", mult_A_B);
  LocalOrdinal C_firstCol = Bview.colMap->getMinLocalIndex();
  LocalOrdinal C_lastCol  = Bview.colMap->getMaxLocalIndex();
 
  LocalOrdinal C_firstCol_import = OrdinalTraits<LocalOrdinal>::zero();
  LocalOrdinal C_lastCol_import  = OrdinalTraits<LocalOrdinal>::invalid();
 
  ArrayView<const GlobalOrdinal> bcols        = Bview.colMap->getLocalElementList();
  ArrayView<const GlobalOrdinal> bcols_import = null;
  if (Bview.importColMap != null) {
    C_firstCol_import = Bview.importColMap->getMinLocalIndex();
    C_lastCol_import  = Bview.importColMap->getMaxLocalIndex();
 
    bcols_import = Bview.importColMap->getLocalElementList();
  }
 
  size_t C_numCols = C_lastCol - C_firstCol +
                     OrdinalTraits<LocalOrdinal>::one();
  size_t C_numCols_import = C_lastCol_import - C_firstCol_import +
                            OrdinalTraits<LocalOrdinal>::one();
 
  if (C_numCols_import > C_numCols)
    C_numCols = C_numCols_import;
 
  Array<Scalar> dwork        = Array<Scalar>(C_numCols);
  Array<GlobalOrdinal> iwork = Array<GlobalOrdinal>(C_numCols);
  Array<size_t> iwork2       = Array<size_t>(C_numCols);
 
  Array<Scalar> C_row_i               = dwork;
  Array<GlobalOrdinal> C_cols         = iwork;
  Array<size_t> c_index               = iwork2;
  Array<GlobalOrdinal> combined_index = Array<GlobalOrdinal>(2 * C_numCols);
  Array<Scalar> combined_values       = Array<Scalar>(2 * C_numCols);
 
  size_t C_row_i_length, j, k, last_index;
 
  // Run through all the hash table lookups once and for all
  LocalOrdinal LO_INVALID = OrdinalTraits<LocalOrdinal>::invalid();
  Array<LocalOrdinal> Acol2Brow(Aview.colMap->getLocalNumElements(), LO_INVALID);
  Array<LocalOrdinal> Acol2Irow(Aview.colMap->getLocalNumElements(), LO_INVALID);
  if (Aview.colMap->isSameAs(*Bview.origMatrix->getRowMap())) {
    // Maps are the same: Use local IDs as the hash
    for (LocalOrdinal i = Aview.colMap->getMinLocalIndex(); i <=
                                                            Aview.colMap->getMaxLocalIndex();
         i++)
      Acol2Brow[i] = i;
  } else {
    // Maps are not the same:  Use the map's hash
    for (LocalOrdinal i = Aview.colMap->getMinLocalIndex(); i <=
                                                            Aview.colMap->getMaxLocalIndex();
         i++) {
      GlobalOrdinal GID = Aview.colMap->getGlobalElement(i);
      LocalOrdinal BLID = Bview.origMatrix->getRowMap()->getLocalElement(GID);
      if (BLID != LO_INVALID)
        Acol2Brow[i] = BLID;
      else
        Acol2Irow[i] = Bview.importMatrix->getRowMap()->getLocalElement(GID);
    }
  }
 
  // To form C = A*B we're going to execute this expression:
  //
  //  C(i,j) = sum_k( A(i,k)*B(k,j) )
  //
  // Our goal, of course, is to navigate the data in A and B once, without
  // performing searches for column-indices, etc.
  auto Arowptr = Aview.origMatrix->getLocalRowPtrsHost();
  auto Acolind = Aview.origMatrix->getLocalIndicesHost();
  auto Avals   = Aview.origMatrix->getLocalValuesHost(Tpetra::Access::ReadOnly);
  auto Browptr = Bview.origMatrix->getLocalRowPtrsHost();
  auto Bcolind = Bview.origMatrix->getLocalIndicesHost();
  auto Bvals   = Bview.origMatrix->getLocalValuesHost(Tpetra::Access::ReadOnly);
  decltype(Browptr) Irowptr;
  decltype(Bcolind) Icolind;
  decltype(Bvals) Ivals;
  if (!Bview.importMatrix.is_null()) {
    Irowptr = Bview.importMatrix->getLocalRowPtrsHost();
    Icolind = Bview.importMatrix->getLocalIndicesHost();
    Ivals   = Bview.importMatrix->getLocalValuesHost(Tpetra::Access::ReadOnly);
  }
 
  bool C_filled = C.isFillComplete();
 
  for (size_t i = 0; i < C_numCols; i++)
    c_index[i] = OrdinalTraits<size_t>::invalid();
 
  // Loop over the rows of A.
  size_t Arows = Aview.rowMap->getLocalNumElements();
  for (size_t i = 0; i < Arows; ++i) {
    // Only navigate the local portion of Aview... which is, thankfully, all of
    // A since this routine doesn't do transpose modes
    GlobalOrdinal global_row = Aview.rowMap->getGlobalElement(i);
 
    // Loop across the i-th row of A and for each corresponding row in B, loop
    // across columns and accumulate product A(i,k)*B(k,j) into our partial sum
    // quantities C_row_i. In other words, as we stride across B(k,:) we're
    // calculating updates for row i of the result matrix C.
    C_row_i_length = OrdinalTraits<size_t>::zero();
 
    for (k = Arowptr[i]; k < Arowptr[i + 1]; ++k) {
      LocalOrdinal Ak   = Acol2Brow[Acolind[k]];
      const Scalar Aval = Avals[k];
      if (Aval == STS::zero())
        continue;
 
      if (Ak == LO_INVALID)
        continue;
 
      for (j = Browptr[Ak]; j < Browptr[Ak + 1]; ++j) {
        LocalOrdinal col = Bcolind[j];
        // assert(col >= 0 && col < C_numCols);
 
        if (c_index[col] == OrdinalTraits<size_t>::invalid()) {
          // assert(C_row_i_length >= 0 && C_row_i_length < C_numCols);
          //  This has to be a +=  so insertGlobalValue goes out
          C_row_i[C_row_i_length] = Aval * Bvals[j];
          C_cols[C_row_i_length]  = col;
          c_index[col]            = C_row_i_length;
          C_row_i_length++;
 
        } else {
          // static cast from impl_scalar_type to Scalar needed for complex
          C_row_i[c_index[col]] += Aval * static_cast<Scalar>(Bvals[j]);
        }
      }
    }
 
    for (size_t ii = 0; ii < C_row_i_length; ii++) {
      c_index[C_cols[ii]] = OrdinalTraits<size_t>::invalid();
      C_cols[ii]          = bcols[C_cols[ii]];
      combined_index[ii]  = C_cols[ii];
      combined_values[ii] = C_row_i[ii];
    }
    last_index = C_row_i_length;
 
    //
    // Now put the C_row_i values into C.
    //
    // We might have to revamp this later.
    C_row_i_length = OrdinalTraits<size_t>::zero();
 
    for (k = Arowptr[i]; k < Arowptr[i + 1]; ++k) {
      LocalOrdinal Ak   = Acol2Brow[Acolind[k]];
      const Scalar Aval = Avals[k];
      if (Aval == STS::zero())
        continue;
 
      if (Ak != LO_INVALID) continue;
 
      Ak = Acol2Irow[Acolind[k]];
      for (j = Irowptr[Ak]; j < Irowptr[Ak + 1]; ++j) {
        LocalOrdinal col = Icolind[j];
        // assert(col >= 0 && col < C_numCols);
 
        if (c_index[col] == OrdinalTraits<size_t>::invalid()) {
          // assert(C_row_i_length >= 0 && C_row_i_length < C_numCols);
          //  This has to be a +=  so insertGlobalValue goes out
          C_row_i[C_row_i_length] = Aval * Ivals[j];
          C_cols[C_row_i_length]  = col;
          c_index[col]            = C_row_i_length;
          C_row_i_length++;
 
        } else {
          // This has to be a +=  so insertGlobalValue goes out
          // static cast from impl_scalar_type to Scalar needed for complex
          C_row_i[c_index[col]] += Aval * static_cast<Scalar>(Ivals[j]);
        }
      }
    }
 
    for (size_t ii = 0; ii < C_row_i_length; ii++) {
      c_index[C_cols[ii]]         = OrdinalTraits<size_t>::invalid();
      C_cols[ii]                  = bcols_import[C_cols[ii]];
      combined_index[last_index]  = C_cols[ii];
      combined_values[last_index] = C_row_i[ii];
      last_index++;
    }
 
    // Now put the C_row_i values into C.
    // We might have to revamp this later.
    C_filled ? C.sumIntoGlobalValues(
                   global_row,
                   combined_index.view(OrdinalTraits<size_t>::zero(), last_index),
                   combined_values.view(OrdinalTraits<size_t>::zero(), last_index))
             : C.insertGlobalValues(
                   global_row,
                   combined_index.view(OrdinalTraits<size_t>::zero(), last_index),
                   combined_values.view(OrdinalTraits<size_t>::zero(), last_index));
  }
}
 
/*********************************************************************************************************/
template <class Scalar,
          class LocalOrdinal,
          class GlobalOrdinal,
          class Node>
void setMaxNumEntriesPerRow(CrsMatrixStruct<Scalar, LocalOrdinal, GlobalOrdinal, Node>& Mview) {
  typedef typename Teuchos::Array<Teuchos::ArrayView<const LocalOrdinal>>::size_type local_length_size;
  Mview.maxNumRowEntries = Teuchos::OrdinalTraits<local_length_size>::zero();
 
  if (Mview.indices.size() > Teuchos::OrdinalTraits<local_length_size>::zero()) {
    Mview.maxNumRowEntries = Mview.indices[0].size();
 
    for (local_length_size i = 1; i < Mview.indices.size(); ++i)
      if (Mview.indices[i].size() > Mview.maxNumRowEntries)
        Mview.maxNumRowEntries = Mview.indices[i].size();
  }
}
 
/*********************************************************************************************************/
template <class CrsMatrixType>
size_t C_estimate_nnz(CrsMatrixType& A, CrsMatrixType& B) {
  // Follows the NZ estimate in ML's ml_matmatmult.c
  size_t Aest = 100, Best = 100;
  if (A.getLocalNumEntries() >= A.getLocalNumRows())
    Aest = (A.getLocalNumRows() > 0) ? A.getLocalNumEntries() / A.getLocalNumRows() : 100;
  if (B.getLocalNumEntries() >= B.getLocalNumRows())
    Best = (B.getLocalNumRows() > 0) ? B.getLocalNumEntries() / B.getLocalNumRows() : 100;
 
  size_t nnzperrow = (size_t)(sqrt((double)Aest) + sqrt((double)Best) - 1);
  nnzperrow *= nnzperrow;
 
  return (size_t)(A.getLocalNumRows() * nnzperrow * 0.75 + 100);
}
 
/*********************************************************************************************************/
// Kernel method for computing the local portion of C = A*B for CrsMatrix
//
// mfh 27 Sep 2016: Currently, mult_AT_B_newmatrix() also calls this
// function, so this is probably the function we want to
// thread-parallelize.
template <class Scalar,
          class LocalOrdinal,
          class GlobalOrdinal,
          class Node>
void mult_A_B_newmatrix(
    CrsMatrixStruct<Scalar, LocalOrdinal, GlobalOrdinal, Node>& Aview,
    CrsMatrixStruct<Scalar, LocalOrdinal, GlobalOrdinal, Node>& Bview,
    CrsMatrix<Scalar, LocalOrdinal, GlobalOrdinal, Node>& C,
    const std::string& label,
    const Teuchos::RCP<Teuchos::ParameterList>& params) {
  using Teuchos::Array;
  using Teuchos::ArrayRCP;
  using Teuchos::ArrayView;
  using Teuchos::RCP;
  using Teuchos::rcp;
 
  // Tpetra typedefs
  typedef LocalOrdinal LO;
  typedef GlobalOrdinal GO;
  typedef Node NO;
  typedef Import<LO, GO, NO> import_type;
  typedef Map<LO, GO, NO> map_type;
 
  // Kokkos typedefs
  typedef typename map_type::local_map_type local_map_type;
  typedef typename Tpetra::CrsMatrix<Scalar, LocalOrdinal, GlobalOrdinal, Node>::local_matrix_device_type KCRS;
  typedef typename KCRS::StaticCrsGraphType graph_t;
  typedef typename graph_t::row_map_type::non_const_type lno_view_t;
  typedef typename NO::execution_space execution_space;
  typedef Kokkos::RangePolicy<execution_space, size_t> range_type;
  typedef Kokkos::View<LO*, typename lno_view_t::array_layout, typename lno_view_t::device_type> lo_view_t;
 
  Tpetra::Details::ProfilingRegion MM("TpetraExt: MMM: M5 Cmap");
 
  LO LO_INVALID = Teuchos::OrdinalTraits<LO>::invalid();
 
  // Build the final importer / column map, hash table lookups for C
  RCP<const import_type> Cimport;
  RCP<const map_type> Ccolmap;
  RCP<const import_type> Bimport = Bview.origMatrix->getGraph()->getImporter();
  RCP<const import_type> Iimport = Bview.importMatrix.is_null() ? Teuchos::null : Bview.importMatrix->getGraph()->getImporter();
  local_map_type Acolmap_local   = Aview.colMap->getLocalMap();
  local_map_type Browmap_local   = Bview.origMatrix->getRowMap()->getLocalMap();
  local_map_type Irowmap_local;
  if (!Bview.importMatrix.is_null()) Irowmap_local = Bview.importMatrix->getRowMap()->getLocalMap();
  local_map_type Bcolmap_local = Bview.origMatrix->getColMap()->getLocalMap();
  local_map_type Icolmap_local;
  if (!Bview.importMatrix.is_null()) Icolmap_local = Bview.importMatrix->getColMap()->getLocalMap();
 
  // mfh 27 Sep 2016: Bcol2Ccol is a table that maps from local column
  // indices of B, to local column indices of C.  (B and C have the
  // same number of columns.)  The kernel uses this, instead of
  // copying the entire input matrix B and converting its column
  // indices to those of C.
  lo_view_t Bcol2Ccol(Kokkos::ViewAllocateWithoutInitializing("Bcol2Ccol"), Bview.colMap->getLocalNumElements()), Icol2Ccol;
 
  if (Bview.importMatrix.is_null()) {
    // mfh 27 Sep 2016: B has no "remotes," so B and C have the same column Map.
    Cimport             = Bimport;
    Ccolmap             = Bview.colMap;
    const LO colMapSize = static_cast<LO>(Bview.colMap->getLocalNumElements());
    // Bcol2Ccol is trivial
    Kokkos::parallel_for(
        "Tpetra::mult_A_B_newmatrix::Bcol2Ccol_fill",
        Kokkos::RangePolicy<execution_space, LO>(0, colMapSize),
        KOKKOS_LAMBDA(const LO i) {
          Bcol2Ccol(i) = i;
        });
  } else {
    // mfh 27 Sep 2016: B has "remotes," so we need to build the
    // column Map of C, as well as C's Import object (from its domain
    // Map to its column Map).  C's column Map is the union of the
    // column Maps of (the local part of) B, and the "remote" part of
    // B.  Ditto for the Import.  We have optimized this "setUnion"
    // operation on Import objects and Maps.
 
    // Choose the right variant of setUnion
    if (!Bimport.is_null() && !Iimport.is_null()) {
      Cimport = Bimport->setUnion(*Iimport);
    } else if (!Bimport.is_null() && Iimport.is_null()) {
      Cimport = Bimport->setUnion();
    } else if (Bimport.is_null() && !Iimport.is_null()) {
      Cimport = Iimport->setUnion();
    } else {
      throw std::runtime_error("TpetraExt::MMM status of matrix importers is nonsensical");
    }
    Ccolmap = Cimport->getTargetMap();
 
    // FIXME (mfh 27 Sep 2016) This error check requires an all-reduce
    // in general.  We should get rid of it in order to reduce
    // communication costs of sparse matrix-matrix multiply.
    TEUCHOS_TEST_FOR_EXCEPTION(!Cimport->getSourceMap()->isSameAs(*Bview.origMatrix->getDomainMap()),
                               std::runtime_error, "Tpetra::MMM: Import setUnion messed with the DomainMap in an unfortunate way");
 
    // NOTE: This is not efficient and should be folded into setUnion
    //
    // mfh 27 Sep 2016: What the above comment means, is that the
    // setUnion operation on Import objects could also compute these
    // local index - to - local index look-up tables.
    Kokkos::resize(Icol2Ccol, Bview.importMatrix->getColMap()->getLocalNumElements());
    local_map_type Ccolmap_local = Ccolmap->getLocalMap();
    Kokkos::parallel_for(
        "Tpetra::mult_A_B_newmatrix::Bcol2Ccol_getGlobalElement", range_type(0, Bview.origMatrix->getColMap()->getLocalNumElements()), KOKKOS_LAMBDA(const LO i) {
          Bcol2Ccol(i) = Ccolmap_local.getLocalElement(Bcolmap_local.getGlobalElement(i));
        });
    Kokkos::parallel_for(
        "Tpetra::mult_A_B_newmatrix::Icol2Ccol_getGlobalElement", range_type(0, Bview.importMatrix->getColMap()->getLocalNumElements()), KOKKOS_LAMBDA(const LO i) {
          Icol2Ccol(i) = Ccolmap_local.getLocalElement(Icolmap_local.getGlobalElement(i));
        });
  }
 
  // Replace the column map
  //
  // mfh 27 Sep 2016: We do this because C was originally created
  // without a column Map.  Now we have its column Map.
  C.replaceColMap(Ccolmap);
 
  // mfh 27 Sep 2016: Construct tables that map from local column
  // indices of A, to local row indices of either B_local (the locally
  // owned part of B), or B_remote (the "imported" remote part of B).
  //
  // For column index Aik in row i of A, if the corresponding row of B
  // exists in the local part of B ("orig") (which I'll call B_local),
  // then targetMapToOrigRow[Aik] is the local index of that row of B.
  // Otherwise, targetMapToOrigRow[Aik] is "invalid" (a flag value).
  //
  // For column index Aik in row i of A, if the corresponding row of B
  // exists in the remote part of B ("Import") (which I'll call
  // B_remote), then targetMapToImportRow[Aik] is the local index of
  // that row of B.  Otherwise, targetMapToOrigRow[Aik] is "invalid"
  // (a flag value).
 
  // Run through all the hash table lookups once and for all
  lo_view_t targetMapToOrigRow(Kokkos::ViewAllocateWithoutInitializing("targetMapToOrigRow"), Aview.colMap->getLocalNumElements());
  lo_view_t targetMapToImportRow(Kokkos::ViewAllocateWithoutInitializing("targetMapToImportRow"), Aview.colMap->getLocalNumElements());
 
  Kokkos::parallel_for(
      "Tpetra::mult_A_B_newmatrix::construct_tables", range_type(Aview.colMap->getMinLocalIndex(), Aview.colMap->getMaxLocalIndex() + 1), KOKKOS_LAMBDA(const LO i) {
        GO aidx  = Acolmap_local.getGlobalElement(i);
        LO B_LID = Browmap_local.getLocalElement(aidx);
        if (B_LID != LO_INVALID) {
          targetMapToOrigRow(i)   = B_LID;
          targetMapToImportRow(i) = LO_INVALID;
        } else {
          LO I_LID                = Irowmap_local.getLocalElement(aidx);
          targetMapToOrigRow(i)   = LO_INVALID;
          targetMapToImportRow(i) = I_LID;
        }
      });
 
  // Call the actual kernel.  We'll rely on partial template specialization to call the correct one ---
  // Either the straight-up Tpetra code (SerialNode) or the KokkosKernels one (other NGP node types)
  KernelWrappers<Scalar, LocalOrdinal, GlobalOrdinal, Node, lo_view_t>::mult_A_B_newmatrix_kernel_wrapper(Aview, Bview, targetMapToOrigRow, targetMapToImportRow, Bcol2Ccol, Icol2Ccol, C, Cimport, label, params);
}
 
/*********************************************************************************************************/
// Kernel method for computing the local portion of C = A*B for BlockCrsMatrix
template <class Scalar,
          class LocalOrdinal,
          class GlobalOrdinal,
          class Node>
void mult_A_B_newmatrix(BlockCrsMatrixStruct<Scalar, LocalOrdinal, GlobalOrdinal, Node>& Aview,
                        BlockCrsMatrixStruct<Scalar, LocalOrdinal, GlobalOrdinal, Node>& Bview,
                        Teuchos::RCP<BlockCrsMatrix<Scalar, LocalOrdinal, GlobalOrdinal, Node>>& C) {
  using Teuchos::Array;
  using Teuchos::ArrayRCP;
  using Teuchos::ArrayView;
  using Teuchos::null;
  using Teuchos::RCP;
  using Teuchos::rcp;
 
  // Tpetra typedefs
  typedef LocalOrdinal LO;
  typedef GlobalOrdinal GO;
  typedef Node NO;
  typedef Import<LO, GO, NO> import_type;
  typedef Map<LO, GO, NO> map_type;
  typedef BlockCrsMatrix<Scalar, LocalOrdinal, GlobalOrdinal, Node> block_crs_matrix_type;
  typedef typename block_crs_matrix_type::crs_graph_type graph_t;
 
  // Kokkos typedefs
  typedef typename map_type::local_map_type local_map_type;
  typedef typename block_crs_matrix_type::local_matrix_device_type KBSR;
  typedef typename KBSR::device_type device_t;
  typedef typename KBSR::StaticCrsGraphType static_graph_t;
  typedef typename static_graph_t::row_map_type::non_const_type lno_view_t;
  typedef typename static_graph_t::entries_type::non_const_type lno_nnz_view_t;
  typedef typename KBSR::values_type::non_const_type scalar_view_t;
  typedef typename NO::execution_space execution_space;
  typedef Kokkos::RangePolicy<execution_space, size_t> range_type;
  typedef Kokkos::View<LO*, typename lno_view_t::array_layout, typename lno_view_t::device_type> lo_view_t;
 
  LO LO_INVALID = Teuchos::OrdinalTraits<LO>::invalid();
 
  // Build the final importer / column map, hash table lookups for C
  RCP<const import_type> Cimport;
  RCP<const map_type> Ccolmap;
  RCP<const import_type> Bimport = Bview.origMatrix->getGraph()->getImporter();
  RCP<const import_type> Iimport = Bview.importMatrix.is_null() ? Teuchos::null : Bview.importMatrix->getGraph()->getImporter();
  local_map_type Acolmap_local   = Aview.colMap->getLocalMap();
  local_map_type Browmap_local   = Bview.origMatrix->getRowMap()->getLocalMap();
  local_map_type Irowmap_local;
  if (!Bview.importMatrix.is_null()) Irowmap_local = Bview.importMatrix->getRowMap()->getLocalMap();
  local_map_type Bcolmap_local = Bview.origMatrix->getColMap()->getLocalMap();
  local_map_type Icolmap_local;
  if (!Bview.importMatrix.is_null()) Icolmap_local = Bview.importMatrix->getColMap()->getLocalMap();
 
  // Bcol2Ccol is a table that maps from local column
  // indices of B, to local column indices of C.  (B and C have the
  // same number of columns.)  The kernel uses this, instead of
  // copying the entire input matrix B and converting its column
  // indices to those of C.
  lo_view_t Bcol2Ccol(Kokkos::ViewAllocateWithoutInitializing("Bcol2Ccol"), Bview.colMap->getLocalNumElements()), Icol2Ccol;
 
  if (Bview.importMatrix.is_null()) {
    // mfh 27 Sep 2016: B has no "remotes," so B and C have the same column Map.
    Cimport             = Bimport;
    Ccolmap             = Bview.colMap;
    const LO colMapSize = static_cast<LO>(Bview.colMap->getLocalNumElements());
    // Bcol2Ccol is trivial
    Kokkos::parallel_for(
        "Tpetra::mult_A_B_newmatrix::Bcol2Ccol_fill",
        Kokkos::RangePolicy<execution_space, LO>(0, colMapSize),
        KOKKOS_LAMBDA(const LO i) {
          Bcol2Ccol(i) = i;
        });
  } else {
    // B has "remotes," so we need to build the
    // column Map of C, as well as C's Import object (from its domain
    // Map to its column Map).  C's column Map is the union of the
    // column Maps of (the local part of) B, and the "remote" part of
    // B.  Ditto for the Import.  We have optimized this "setUnion"
    // operation on Import objects and Maps.
 
    // Choose the right variant of setUnion
    if (!Bimport.is_null() && !Iimport.is_null()) {
      Cimport = Bimport->setUnion(*Iimport);
    } else if (!Bimport.is_null() && Iimport.is_null()) {
      Cimport = Bimport->setUnion();
    } else if (Bimport.is_null() && !Iimport.is_null()) {
      Cimport = Iimport->setUnion();
    } else {
      throw std::runtime_error("TpetraExt::MMM status of matrix importers is nonsensical");
    }
    Ccolmap = Cimport->getTargetMap();
 
    // NOTE: This is not efficient and should be folded into setUnion
    //
    // What the above comment means, is that the
    // setUnion operation on Import objects could also compute these
    // local index - to - local index look-up tables.
    Kokkos::resize(Icol2Ccol, Bview.importMatrix->getColMap()->getLocalNumElements());
    local_map_type Ccolmap_local = Ccolmap->getLocalMap();
    Kokkos::parallel_for(
        "Tpetra::mult_A_B_newmatrix::Bcol2Ccol_getGlobalElement",
        range_type(0, Bview.origMatrix->getColMap()->getLocalNumElements()),
        KOKKOS_LAMBDA(const LO i) {
          Bcol2Ccol(i) = Ccolmap_local.getLocalElement(Bcolmap_local.getGlobalElement(i));
        });
    Kokkos::parallel_for(
        "Tpetra::mult_A_B_newmatrix::Icol2Ccol_getGlobalElement",
        range_type(0, Bview.importMatrix->getColMap()->getLocalNumElements()),
        KOKKOS_LAMBDA(const LO i) {
          Icol2Ccol(i) = Ccolmap_local.getLocalElement(Icolmap_local.getGlobalElement(i));
        });
  }
 
  // Construct tables that map from local column
  // indices of A, to local row indices of either B_local (the locally
  // owned part of B), or B_remote (the "imported" remote part of B).
  //
  // For column index Aik in row i of A, if the corresponding row of B
  // exists in the local part of B ("orig") (which I'll call B_local),
  // then targetMapToOrigRow[Aik] is the local index of that row of B.
  // Otherwise, targetMapToOrigRow[Aik] is "invalid" (a flag value).
  //
  // For column index Aik in row i of A, if the corresponding row of B
  // exists in the remote part of B ("Import") (which I'll call
  // B_remote), then targetMapToImportRow[Aik] is the local index of
  // that row of B.  Otherwise, targetMapToOrigRow[Aik] is "invalid"
  // (a flag value).
 
  // Run through all the hash table lookups once and for all
  lo_view_t targetMapToOrigRow(Kokkos::ViewAllocateWithoutInitializing("targetMapToOrigRow"),
                               Aview.colMap->getLocalNumElements());
  lo_view_t targetMapToImportRow(Kokkos::ViewAllocateWithoutInitializing("targetMapToImportRow"),
                                 Aview.colMap->getLocalNumElements());
 
  Kokkos::parallel_for(
      "Tpetra::mult_A_B_newmatrix::construct_tables",
      range_type(Aview.colMap->getMinLocalIndex(), Aview.colMap->getMaxLocalIndex() + 1),
      KOKKOS_LAMBDA(const LO i) {
        GO aidx  = Acolmap_local.getGlobalElement(i);
        LO B_LID = Browmap_local.getLocalElement(aidx);
        if (B_LID != LO_INVALID) {
          targetMapToOrigRow(i)   = B_LID;
          targetMapToImportRow(i) = LO_INVALID;
        } else {
          LO I_LID                = Irowmap_local.getLocalElement(aidx);
          targetMapToOrigRow(i)   = LO_INVALID;
          targetMapToImportRow(i) = I_LID;
        }
      });
 
  // Create the KernelHandle
  using KernelHandle =
      KokkosKernels::Experimental::KokkosKernelsHandle<typename lno_view_t::const_value_type,
                                                       typename lno_nnz_view_t::const_value_type,
                                                       typename scalar_view_t::const_value_type,
                                                       typename device_t::execution_space,
                                                       typename device_t::memory_space,
                                                       typename device_t::memory_space>;
  int team_work_size = 16;  // Defaults to 16 as per Deveci 12/7/16 - csiefer
  std::string myalg("SPGEMM_KK_MEMORY");
  KokkosSparse::SPGEMMAlgorithm alg_enum = KokkosSparse::StringToSPGEMMAlgorithm(myalg);
 
  KernelHandle kh;
  kh.create_spgemm_handle(alg_enum);
  kh.set_team_work_size(team_work_size);
 
  // Get KokkosSparse::BsrMatrix for A and Bmerged (B and BImport)
  const KBSR Amat    = Aview.origMatrix->getLocalMatrixDevice();
  const KBSR Bmerged = Tpetra::MMdetails::merge_matrices(Aview, Bview,
                                                         targetMapToOrigRow, targetMapToImportRow,
                                                         Bcol2Ccol, Icol2Ccol,
                                                         Ccolmap.getConst()->getLocalNumElements());
 
  RCP<graph_t> graphC;
  typename KBSR::values_type values;
  {
    // Call KokkosSparse routines to calculate Amat*Bmerged on device.
    // NOTE: Need to scope guard this since the BlockCrs constructor will need to copy the host graph
    KBSR Cmat;
    KokkosSparse::block_spgemm_symbolic(kh, Amat, false, Bmerged, false, Cmat);
    KokkosSparse::block_spgemm_numeric(kh, Amat, false, Bmerged, false, Cmat);
    kh.destroy_spgemm_handle();
 
    // Build Tpetra::BlockCrsMatrix from KokkosSparse::BsrMatrix
    graphC = rcp(new graph_t(Cmat.graph, Aview.origMatrix->getRowMap(), Ccolmap.getConst()));
    values = Cmat.values;
  }
  C = rcp(new block_crs_matrix_type(*graphC, values, Aview.blocksize));
}
 
/*********************************************************************************************************/
// AB NewMatrix Kernel wrappers (Default non-threaded version for CrsMatrix)
template <class Scalar,
          class LocalOrdinal,
          class GlobalOrdinal,
          class Node,
          class LocalOrdinalViewType>
void KernelWrappers<Scalar, LocalOrdinal, GlobalOrdinal, Node, LocalOrdinalViewType>::mult_A_B_newmatrix_kernel_wrapper(CrsMatrixStruct<Scalar, LocalOrdinal, GlobalOrdinal, Node>& Aview,
                                                                                                                        CrsMatrixStruct<Scalar, LocalOrdinal, GlobalOrdinal, Node>& Bview,
                                                                                                                        const LocalOrdinalViewType& targetMapToOrigRow,
                                                                                                                        const LocalOrdinalViewType& targetMapToImportRow,
                                                                                                                        const LocalOrdinalViewType& Bcol2Ccol,
                                                                                                                        const LocalOrdinalViewType& Icol2Ccol,
                                                                                                                        CrsMatrix<Scalar, LocalOrdinal, GlobalOrdinal, Node>& C,
                                                                                                                        Teuchos::RCP<const Import<LocalOrdinal, GlobalOrdinal, Node>> Cimport,
                                                                                                                        const std::string& label,
                                                                                                                        const Teuchos::RCP<Teuchos::ParameterList>& params) {
  using Teuchos::Array;
  using Teuchos::ArrayRCP;
  using Teuchos::ArrayView;
  using Teuchos::RCP;
  using Teuchos::rcp;
 
  Tpetra::Details::ProfilingRegion MM("TpetraExt: MMM: Newmatrix SerialCore");
 
  // Lots and lots of typedefs
  typedef typename Tpetra::CrsMatrix<Scalar, LocalOrdinal, GlobalOrdinal, Node>::local_matrix_host_type KCRS;
  typedef typename KCRS::StaticCrsGraphType graph_t;
  typedef typename graph_t::row_map_type::const_type c_lno_view_t;
  typedef typename graph_t::row_map_type::non_const_type lno_view_t;
  typedef typename graph_t::entries_type::non_const_type lno_nnz_view_t;
  typedef typename KCRS::values_type::non_const_type scalar_view_t;
 
  typedef Scalar SC;
  typedef LocalOrdinal LO;
  typedef GlobalOrdinal GO;
  typedef Node NO;
  typedef Map<LO, GO, NO> map_type;
  const size_t ST_INVALID = Teuchos::OrdinalTraits<LO>::invalid();
  const LO LO_INVALID     = Teuchos::OrdinalTraits<LO>::invalid();
  const SC SC_ZERO        = Teuchos::ScalarTraits<Scalar>::zero();
 
  // Sizes
  RCP<const map_type> Ccolmap = C.getColMap();
  size_t m                    = Aview.origMatrix->getLocalNumRows();
  size_t n                    = Ccolmap->getLocalNumElements();
  size_t b_max_nnz_per_row    = Bview.origMatrix->getLocalMaxNumRowEntries();
 
  // Grab the  Kokkos::SparseCrsMatrices & inner stuff
  const KCRS& Amat = Aview.origMatrix->getLocalMatrixHost();
  const KCRS& Bmat = Bview.origMatrix->getLocalMatrixHost();
 
  c_lno_view_t Arowptr = Amat.graph.row_map, Browptr = Bmat.graph.row_map;
  const lno_nnz_view_t Acolind = Amat.graph.entries, Bcolind = Bmat.graph.entries;
  const scalar_view_t Avals = Amat.values, Bvals = Bmat.values;
 
  c_lno_view_t Irowptr;
  lno_nnz_view_t Icolind;
  scalar_view_t Ivals;
  if (!Bview.importMatrix.is_null()) {
    auto lclB         = Bview.importMatrix->getLocalMatrixHost();
    Irowptr           = lclB.graph.row_map;
    Icolind           = lclB.graph.entries;
    Ivals             = lclB.values;
    b_max_nnz_per_row = std::max(b_max_nnz_per_row, Bview.importMatrix->getLocalMaxNumRowEntries());
  }
 
  // Classic csr assembly (low memory edition)
  //
  // mfh 27 Sep 2016: C_estimate_nnz does not promise an upper bound.
  // The method loops over rows of A, and may resize after processing
  // each row.  Chris Siefert says that this reflects experience in
  // ML; for the non-threaded case, ML found it faster to spend less
  // effort on estimation and risk an occasional reallocation.
  size_t CSR_alloc = std::max(C_estimate_nnz(*Aview.origMatrix, *Bview.origMatrix), n);
  lno_view_t Crowptr(Kokkos::ViewAllocateWithoutInitializing("Crowptr"), m + 1);
  lno_nnz_view_t Ccolind(Kokkos::ViewAllocateWithoutInitializing("Ccolind"), CSR_alloc);
  scalar_view_t Cvals(Kokkos::ViewAllocateWithoutInitializing("Cvals"), CSR_alloc);
 
  // mfh 27 Sep 2016: The c_status array is an implementation detail
  // of the local sparse matrix-matrix multiply routine.
 
  // The status array will contain the index into colind where this entry was last deposited.
  //   c_status[i] <  CSR_ip - not in the row yet
  //   c_status[i] >= CSR_ip - this is the entry where you can find the data
  // We start with this filled with INVALID's indicating that there are no entries yet.
  // Sadly, this complicates the code due to the fact that size_t's are unsigned.
  size_t INVALID = Teuchos::OrdinalTraits<size_t>::invalid();
  std::vector<size_t> c_status(n, ST_INVALID);
 
  // mfh 27 Sep 2016: Here is the local sparse matrix-matrix multiply
  // routine.  The routine computes C := A * (B_local + B_remote).
  //
  // For column index Aik in row i of A, targetMapToOrigRow[Aik] tells
  // you whether the corresponding row of B belongs to B_local
  // ("orig") or B_remote ("Import").
 
  // For each row of A/C
  size_t CSR_ip = 0, OLD_ip = 0;
  for (size_t i = 0; i < m; i++) {
    // mfh 27 Sep 2016: m is the number of rows in the input matrix A
    // on the calling process.
    Crowptr[i] = CSR_ip;
 
    // mfh 27 Sep 2016: For each entry of A in the current row of A
    for (size_t k = Arowptr[i]; k < Arowptr[i + 1]; k++) {
      LO Aik        = Acolind[k];  // local column index of current entry of A
      const SC Aval = Avals[k];    // value of current entry of A
      if (Aval == SC_ZERO)
        continue;  // skip explicitly stored zero values in A
 
      if (targetMapToOrigRow[Aik] != LO_INVALID) {
        // mfh 27 Sep 2016: If the entry of targetMapToOrigRow
        // corresponding to the current entry of A is populated, then
        // the corresponding row of B is in B_local (i.e., it lives on
        // the calling process).
 
        // Local matrix
        size_t Bk = static_cast<size_t>(targetMapToOrigRow[Aik]);
 
        // mfh 27 Sep 2016: Go through all entries in that row of B_local.
        for (size_t j = Browptr[Bk]; j < Browptr[Bk + 1]; ++j) {
          LO Bkj = Bcolind[j];
          LO Cij = Bcol2Ccol[Bkj];
 
          if (c_status[Cij] == INVALID || c_status[Cij] < OLD_ip) {
            // New entry
            c_status[Cij]   = CSR_ip;
            Ccolind[CSR_ip] = Cij;
            Cvals[CSR_ip]   = Aval * Bvals[j];
            CSR_ip++;
 
          } else {
            Cvals[c_status[Cij]] += Aval * Bvals[j];
          }
        }
 
      } else {
        // mfh 27 Sep 2016: If the entry of targetMapToOrigRow
        // corresponding to the current entry of A NOT populated (has
        // a flag "invalid" value), then the corresponding row of B is
        // in B_local (i.e., it lives on the calling process).
 
        // Remote matrix
        size_t Ik = static_cast<size_t>(targetMapToImportRow[Aik]);
        for (size_t j = Irowptr[Ik]; j < Irowptr[Ik + 1]; ++j) {
          LO Ikj = Icolind[j];
          LO Cij = Icol2Ccol[Ikj];
 
          if (c_status[Cij] == INVALID || c_status[Cij] < OLD_ip) {
            // New entry
            c_status[Cij]   = CSR_ip;
            Ccolind[CSR_ip] = Cij;
            Cvals[CSR_ip]   = Aval * Ivals[j];
            CSR_ip++;
          } else {
            Cvals[c_status[Cij]] += Aval * Ivals[j];
          }
        }
      }
    }
 
    // Resize for next pass if needed
    if (i + 1 < m && CSR_ip + std::min(n, (Arowptr[i + 2] - Arowptr[i + 1]) * b_max_nnz_per_row) > CSR_alloc) {
      CSR_alloc *= 2;
      Kokkos::resize(Ccolind, CSR_alloc);
      Kokkos::resize(Cvals, CSR_alloc);
    }
    OLD_ip = CSR_ip;
  }
 
  Crowptr[m] = CSR_ip;
 
  // Downward resize
  Kokkos::resize(Ccolind, CSR_ip);
  Kokkos::resize(Cvals, CSR_ip);
 
  {
    Tpetra::Details::ProfilingRegion MM3("TpetraExt: MMM: Newmatrix Final Sort");
 
    // Final sort & set of CRS arrays
    if (params.is_null() || params->get("sort entries", true))
      Import_Util::sortCrsEntries(Crowptr, Ccolind, Cvals);
    C.setAllValues(Crowptr, Ccolind, Cvals);
  }
 
  Tpetra::Details::ProfilingRegion MM4("TpetraExt: MMM: Newmatrix ESCC");
  {
    // Final FillComplete
    //
    // mfh 27 Sep 2016: So-called "expert static fill complete" bypasses
    // Import (from domain Map to column Map) construction (which costs
    // lots of communication) by taking the previously constructed
    // Import object.  We should be able to do this without interfering
    // with the implementation of the local part of sparse matrix-matrix
    // multply above.
    RCP<Teuchos::ParameterList> labelList = rcp(new Teuchos::ParameterList);
    labelList->set("Timer Label", label);
    if (!params.is_null()) labelList->set("compute global constants", params->get("compute global constants", true));
    RCP<const Export<LO, GO, NO>> dummyExport;
    C.expertStaticFillComplete(Bview.origMatrix->getDomainMap(), Aview.origMatrix->getRangeMap(), Cimport, dummyExport, labelList);
  }
}
/*********************************************************************************************************/
// Kernel method for computing the local portion of C = A*B (reuse)
template <class Scalar,
          class LocalOrdinal,
          class GlobalOrdinal,
          class Node>
void mult_A_B_reuse(
    CrsMatrixStruct<Scalar, LocalOrdinal, GlobalOrdinal, Node>& Aview,
    CrsMatrixStruct<Scalar, LocalOrdinal, GlobalOrdinal, Node>& Bview,
    CrsMatrix<Scalar, LocalOrdinal, GlobalOrdinal, Node>& C,
    const std::string& label,
    const Teuchos::RCP<Teuchos::ParameterList>& params) {
  using Teuchos::Array;
  using Teuchos::ArrayRCP;
  using Teuchos::ArrayView;
  using Teuchos::RCP;
  using Teuchos::rcp;
 
  // Tpetra typedefs
  typedef LocalOrdinal LO;
  typedef GlobalOrdinal GO;
  typedef Node NO;
  typedef Import<LO, GO, NO> import_type;
  typedef Map<LO, GO, NO> map_type;
 
  // Kokkos typedefs
  typedef typename map_type::local_map_type local_map_type;
  typedef typename Tpetra::CrsMatrix<Scalar, LocalOrdinal, GlobalOrdinal, Node>::local_matrix_device_type KCRS;
  typedef typename KCRS::StaticCrsGraphType graph_t;
  typedef typename graph_t::row_map_type::non_const_type lno_view_t;
  typedef typename NO::execution_space execution_space;
  typedef Kokkos::RangePolicy<execution_space, size_t> range_type;
  typedef Kokkos::View<LO*, typename lno_view_t::array_layout, typename lno_view_t::device_type> lo_view_t;
 
  Tpetra::Details::ProfilingRegion MM("TpetraExt: MMM: Reuse Cmap");
  (void)label;
 
  LO LO_INVALID = Teuchos::OrdinalTraits<LO>::invalid();
 
  // Grab all the maps
  RCP<const import_type> Cimport = C.getGraph()->getImporter();
  RCP<const map_type> Ccolmap    = C.getColMap();
  local_map_type Acolmap_local   = Aview.colMap->getLocalMap();
  local_map_type Browmap_local   = Bview.origMatrix->getRowMap()->getLocalMap();
  local_map_type Irowmap_local;
  if (!Bview.importMatrix.is_null()) Irowmap_local = Bview.importMatrix->getRowMap()->getLocalMap();
  local_map_type Bcolmap_local = Bview.origMatrix->getColMap()->getLocalMap();
  local_map_type Icolmap_local;
  if (!Bview.importMatrix.is_null()) Icolmap_local = Bview.importMatrix->getColMap()->getLocalMap();
  local_map_type Ccolmap_local = Ccolmap->getLocalMap();
 
  // Build the final importer / column map, hash table lookups for C
  lo_view_t Bcol2Ccol(Kokkos::ViewAllocateWithoutInitializing("Bcol2Ccol"), Bview.colMap->getLocalNumElements()), Icol2Ccol;
  {
    // Bcol2Col may not be trivial, as Ccolmap is compressed during fillComplete in newmatrix
    // So, column map of C may be a strict subset of the column map of B
    Kokkos::parallel_for(
        range_type(0, Bview.origMatrix->getColMap()->getLocalNumElements()), KOKKOS_LAMBDA(const LO i) {
          Bcol2Ccol(i) = Ccolmap_local.getLocalElement(Bcolmap_local.getGlobalElement(i));
        });
 
    if (!Bview.importMatrix.is_null()) {
      TEUCHOS_TEST_FOR_EXCEPTION(!Cimport->getSourceMap()->isSameAs(*Bview.origMatrix->getDomainMap()),
                                 std::runtime_error, "Tpetra::MMM: Import setUnion messed with the DomainMap in an unfortunate way");
 
      Kokkos::resize(Icol2Ccol, Bview.importMatrix->getColMap()->getLocalNumElements());
      Kokkos::parallel_for(
          range_type(0, Bview.importMatrix->getColMap()->getLocalNumElements()), KOKKOS_LAMBDA(const LO i) {
            Icol2Ccol(i) = Ccolmap_local.getLocalElement(Icolmap_local.getGlobalElement(i));
          });
    }
  }
 
  // Run through all the hash table lookups once and for all
  lo_view_t targetMapToOrigRow(Kokkos::ViewAllocateWithoutInitializing("targetMapToOrigRow"), Aview.colMap->getLocalNumElements());
  lo_view_t targetMapToImportRow(Kokkos::ViewAllocateWithoutInitializing("targetMapToImportRow"), Aview.colMap->getLocalNumElements());
  Kokkos::parallel_for(
      range_type(Aview.colMap->getMinLocalIndex(), Aview.colMap->getMaxLocalIndex() + 1), KOKKOS_LAMBDA(const LO i) {
        GO aidx  = Acolmap_local.getGlobalElement(i);
        LO B_LID = Browmap_local.getLocalElement(aidx);
        if (B_LID != LO_INVALID) {
          targetMapToOrigRow(i)   = B_LID;
          targetMapToImportRow(i) = LO_INVALID;
        } else {
          LO I_LID                = Irowmap_local.getLocalElement(aidx);
          targetMapToOrigRow(i)   = LO_INVALID;
          targetMapToImportRow(i) = I_LID;
        }
      });
 
  // Call the actual kernel.  We'll rely on partial template specialization to call the correct one ---
  // Either the straight-up Tpetra code (SerialNode) or the KokkosKernels one (other NGP node types)
  KernelWrappers<Scalar, LocalOrdinal, GlobalOrdinal, Node, lo_view_t>::mult_A_B_reuse_kernel_wrapper(Aview, Bview, targetMapToOrigRow, targetMapToImportRow, Bcol2Ccol, Icol2Ccol, C, Cimport, label, params);
}
 
/*********************************************************************************************************/
template <class Scalar,
          class LocalOrdinal,
          class GlobalOrdinal,
          class Node,
          class LocalOrdinalViewType>
void KernelWrappers<Scalar, LocalOrdinal, GlobalOrdinal, Node, LocalOrdinalViewType>::mult_A_B_reuse_kernel_wrapper(CrsMatrixStruct<Scalar, LocalOrdinal, GlobalOrdinal, Node>& Aview,
                                                                                                                    CrsMatrixStruct<Scalar, LocalOrdinal, GlobalOrdinal, Node>& Bview,
                                                                                                                    const LocalOrdinalViewType& targetMapToOrigRow,
                                                                                                                    const LocalOrdinalViewType& targetMapToImportRow,
                                                                                                                    const LocalOrdinalViewType& Bcol2Ccol,
                                                                                                                    const LocalOrdinalViewType& Icol2Ccol,
                                                                                                                    CrsMatrix<Scalar, LocalOrdinal, GlobalOrdinal, Node>& C,
                                                                                                                    Teuchos::RCP<const Import<LocalOrdinal, GlobalOrdinal, Node>> /* Cimport */,
                                                                                                                    const std::string& label,
                                                                                                                    const Teuchos::RCP<Teuchos::ParameterList>& /* params */) {
  using Teuchos::RCP;
  using Teuchos::rcp;
 
  // Lots and lots of typedefs
  typedef typename Tpetra::CrsMatrix<Scalar, LocalOrdinal, GlobalOrdinal, Node>::local_matrix_host_type KCRS;
  typedef typename KCRS::StaticCrsGraphType graph_t;
  typedef typename graph_t::row_map_type::const_type c_lno_view_t;
  typedef typename graph_t::entries_type::non_const_type lno_nnz_view_t;
  typedef typename KCRS::values_type::non_const_type scalar_view_t;
 
  typedef Scalar SC;
  typedef LocalOrdinal LO;
  typedef GlobalOrdinal GO;
  typedef Node NO;
  typedef Map<LO, GO, NO> map_type;
  const size_t ST_INVALID = Teuchos::OrdinalTraits<LO>::invalid();
  const LO LO_INVALID     = Teuchos::OrdinalTraits<LO>::invalid();
  const SC SC_ZERO        = Teuchos::ScalarTraits<Scalar>::zero();
 
  Tpetra::Details::ProfilingRegion MM("TpetraExt: MMM: Reuse SerialCore");
  (void)label;
 
  // Sizes
  RCP<const map_type> Ccolmap = C.getColMap();
  size_t m                    = Aview.origMatrix->getLocalNumRows();
  size_t n                    = Ccolmap->getLocalNumElements();
 
  // Grab the  Kokkos::SparseCrsMatrices & inner stuff
  const KCRS& Amat = Aview.origMatrix->getLocalMatrixHost();
  const KCRS& Bmat = Bview.origMatrix->getLocalMatrixHost();
  const KCRS& Cmat = C.getLocalMatrixHost();
 
  c_lno_view_t Arowptr = Amat.graph.row_map, Browptr = Bmat.graph.row_map, Crowptr = Cmat.graph.row_map;
  const lno_nnz_view_t Acolind = Amat.graph.entries, Bcolind = Bmat.graph.entries, Ccolind = Cmat.graph.entries;
  const scalar_view_t Avals = Amat.values, Bvals = Bmat.values;
  scalar_view_t Cvals = Cmat.values;
 
  c_lno_view_t Irowptr;
  lno_nnz_view_t Icolind;
  scalar_view_t Ivals;
  if (!Bview.importMatrix.is_null()) {
    auto lclB = Bview.importMatrix->getLocalMatrixHost();
    Irowptr   = lclB.graph.row_map;
    Icolind   = lclB.graph.entries;
    Ivals     = lclB.values;
  }
 
  // Classic csr assembly (low memory edition)
  // mfh 27 Sep 2016: The c_status array is an implementation detail
  // of the local sparse matrix-matrix multiply routine.
 
  // The status array will contain the index into colind where this entry was last deposited.
  //   c_status[i] <  CSR_ip - not in the row yet
  //   c_status[i] >= CSR_ip - this is the entry where you can find the data
  // We start with this filled with INVALID's indicating that there are no entries yet.
  // Sadly, this complicates the code due to the fact that size_t's are unsigned.
  std::vector<size_t> c_status(n, ST_INVALID);
 
  // For each row of A/C
  size_t CSR_ip = 0, OLD_ip = 0;
  for (size_t i = 0; i < m; i++) {
    // First fill the c_status array w/ locations where we're allowed to
    // generate nonzeros for this row
    OLD_ip = Crowptr[i];
    CSR_ip = Crowptr[i + 1];
    for (size_t k = OLD_ip; k < CSR_ip; k++) {
      c_status[Ccolind[k]] = k;
 
      // Reset values in the row of C
      Cvals[k] = SC_ZERO;
    }
 
    for (size_t k = Arowptr[i]; k < Arowptr[i + 1]; k++) {
      LO Aik        = Acolind[k];
      const SC Aval = Avals[k];
      if (Aval == SC_ZERO)
        continue;
 
      if (targetMapToOrigRow[Aik] != LO_INVALID) {
        // Local matrix
        size_t Bk = static_cast<size_t>(targetMapToOrigRow[Aik]);
 
        for (size_t j = Browptr[Bk]; j < Browptr[Bk + 1]; ++j) {
          LO Bkj = Bcolind[j];
          LO Cij = Bcol2Ccol[Bkj];
 
          TEUCHOS_TEST_FOR_EXCEPTION(c_status[Cij] < OLD_ip || c_status[Cij] >= CSR_ip,
                                     std::runtime_error, "Trying to insert a new entry (" << i << "," << Cij << ") into a static graph "
                                                                                          << "(c_status = " << c_status[Cij] << " of [" << OLD_ip << "," << CSR_ip << "))");
 
          Cvals[c_status[Cij]] += Aval * Bvals[j];
        }
 
      } else {
        // Remote matrix
        size_t Ik = static_cast<size_t>(targetMapToImportRow[Aik]);
        for (size_t j = Irowptr[Ik]; j < Irowptr[Ik + 1]; ++j) {
          LO Ikj = Icolind[j];
          LO Cij = Icol2Ccol[Ikj];
 
          TEUCHOS_TEST_FOR_EXCEPTION(c_status[Cij] < OLD_ip || c_status[Cij] >= CSR_ip,
                                     std::runtime_error, "Trying to insert a new entry (" << i << "," << Cij << ") into a static graph "
                                                                                          << "(c_status = " << c_status[Cij] << " of [" << OLD_ip << "," << CSR_ip << "))");
 
          Cvals[c_status[Cij]] += Aval * Ivals[j];
        }
      }
    }
  }
 
  {
    Tpetra::Details::ProfilingRegion MM3("TpetraExt: MMM: Reuse ESFC");
    C.fillComplete(C.getDomainMap(), C.getRangeMap());
  }
}
 
/*********************************************************************************************************/
// Kernel method for computing the local portion of C = (I-omega D^{-1} A)*B
template <class Scalar,
          class LocalOrdinal,
          class GlobalOrdinal,
          class Node>
void jacobi_A_B_newmatrix(
    Scalar omega,
    const Vector<Scalar, LocalOrdinal, GlobalOrdinal, Node>& Dinv,
    CrsMatrixStruct<Scalar, LocalOrdinal, GlobalOrdinal, Node>& Aview,
    CrsMatrixStruct<Scalar, LocalOrdinal, GlobalOrdinal, Node>& Bview,
    CrsMatrix<Scalar, LocalOrdinal, GlobalOrdinal, Node>& C,
    const std::string& label,
    const Teuchos::RCP<Teuchos::ParameterList>& params) {
  using Teuchos::Array;
  using Teuchos::ArrayRCP;
  using Teuchos::ArrayView;
  using Teuchos::RCP;
  using Teuchos::rcp;
  //  typedef Scalar            SC;
  typedef LocalOrdinal LO;
  typedef GlobalOrdinal GO;
  typedef Node NO;
 
  typedef Import<LO, GO, NO> import_type;
  typedef Map<LO, GO, NO> map_type;
  typedef typename map_type::local_map_type local_map_type;
 
  // All of the Kokkos typedefs
  typedef typename Tpetra::CrsMatrix<Scalar, LocalOrdinal, GlobalOrdinal, Node>::local_matrix_device_type KCRS;
  typedef typename KCRS::StaticCrsGraphType graph_t;
  typedef typename graph_t::row_map_type::non_const_type lno_view_t;
  typedef typename NO::execution_space execution_space;
  typedef Kokkos::RangePolicy<execution_space, size_t> range_type;
  typedef Kokkos::View<LO*, typename lno_view_t::array_layout, typename lno_view_t::device_type> lo_view_t;
 
  Tpetra::Details::ProfilingRegion MM3("TpetraExt: Jacobi: M5 Cmap");
 
  LO LO_INVALID = Teuchos::OrdinalTraits<LO>::invalid();
 
  // Build the final importer / column map, hash table lookups for C
  RCP<const import_type> Cimport;
  RCP<const map_type> Ccolmap;
  RCP<const import_type> Bimport = Bview.origMatrix->getGraph()->getImporter();
  RCP<const import_type> Iimport = Bview.importMatrix.is_null() ? Teuchos::null : Bview.importMatrix->getGraph()->getImporter();
  local_map_type Acolmap_local   = Aview.colMap->getLocalMap();
  local_map_type Browmap_local   = Bview.origMatrix->getRowMap()->getLocalMap();
  local_map_type Irowmap_local;
  if (!Bview.importMatrix.is_null()) Irowmap_local = Bview.importMatrix->getRowMap()->getLocalMap();
  local_map_type Bcolmap_local = Bview.origMatrix->getColMap()->getLocalMap();
  local_map_type Icolmap_local;
  if (!Bview.importMatrix.is_null()) Icolmap_local = Bview.importMatrix->getColMap()->getLocalMap();
 
  // mfh 27 Sep 2016: Bcol2Ccol is a table that maps from local column
  // indices of B, to local column indices of C.  (B and C have the
  // same number of columns.)  The kernel uses this, instead of
  // copying the entire input matrix B and converting its column
  // indices to those of C.
  lo_view_t Bcol2Ccol(Kokkos::ViewAllocateWithoutInitializing("Bcol2Ccol"), Bview.colMap->getLocalNumElements()), Icol2Ccol;
 
  if (Bview.importMatrix.is_null()) {
    // mfh 27 Sep 2016: B has no "remotes," so B and C have the same column Map.
    Cimport = Bimport;
    Ccolmap = Bview.colMap;
    // Bcol2Ccol is trivial
    // Bcol2Ccol is trivial
 
    Kokkos::RangePolicy<execution_space, LO> range(0, static_cast<LO>(Bview.colMap->getLocalNumElements()));
    Kokkos::parallel_for(
        range, KOKKOS_LAMBDA(const size_t i) {
          Bcol2Ccol(i) = static_cast<LO>(i);
        });
  } else {
    // mfh 27 Sep 2016: B has "remotes," so we need to build the
    // column Map of C, as well as C's Import object (from its domain
    // Map to its column Map).  C's column Map is the union of the
    // column Maps of (the local part of) B, and the "remote" part of
    // B.  Ditto for the Import.  We have optimized this "setUnion"
    // operation on Import objects and Maps.
 
    // Choose the right variant of setUnion
    if (!Bimport.is_null() && !Iimport.is_null()) {
      Cimport = Bimport->setUnion(*Iimport);
      Ccolmap = Cimport->getTargetMap();
 
    } else if (!Bimport.is_null() && Iimport.is_null()) {
      Cimport = Bimport->setUnion();
 
    } else if (Bimport.is_null() && !Iimport.is_null()) {
      Cimport = Iimport->setUnion();
 
    } else
      throw std::runtime_error("TpetraExt::Jacobi status of matrix importers is nonsensical");
 
    Ccolmap = Cimport->getTargetMap();
 
    TEUCHOS_TEST_FOR_EXCEPTION(!Cimport->getSourceMap()->isSameAs(*Bview.origMatrix->getDomainMap()),
                               std::runtime_error, "Tpetra:Jacobi Import setUnion messed with the DomainMap in an unfortunate way");
 
    // NOTE: This is not efficient and should be folded into setUnion
    //
    // mfh 27 Sep 2016: What the above comment means, is that the
    // setUnion operation on Import objects could also compute these
    // local index - to - local index look-up tables.
    Kokkos::resize(Icol2Ccol, Bview.importMatrix->getColMap()->getLocalNumElements());
    local_map_type Ccolmap_local = Ccolmap->getLocalMap();
    Kokkos::parallel_for(
        range_type(0, Bview.origMatrix->getColMap()->getLocalNumElements()), KOKKOS_LAMBDA(const LO i) {
          Bcol2Ccol(i) = Ccolmap_local.getLocalElement(Bcolmap_local.getGlobalElement(i));
        });
    Kokkos::parallel_for(
        range_type(0, Bview.importMatrix->getColMap()->getLocalNumElements()), KOKKOS_LAMBDA(const LO i) {
          Icol2Ccol(i) = Ccolmap_local.getLocalElement(Icolmap_local.getGlobalElement(i));
        });
  }
 
  // Replace the column map
  //
  // mfh 27 Sep 2016: We do this because C was originally created
  // without a column Map.  Now we have its column Map.
  C.replaceColMap(Ccolmap);
 
  // mfh 27 Sep 2016: Construct tables that map from local column
  // indices of A, to local row indices of either B_local (the locally
  // owned part of B), or B_remote (the "imported" remote part of B).
  //
  // For column index Aik in row i of A, if the corresponding row of B
  // exists in the local part of B ("orig") (which I'll call B_local),
  // then targetMapToOrigRow[Aik] is the local index of that row of B.
  // Otherwise, targetMapToOrigRow[Aik] is "invalid" (a flag value).
  //
  // For column index Aik in row i of A, if the corresponding row of B
  // exists in the remote part of B ("Import") (which I'll call
  // B_remote), then targetMapToImportRow[Aik] is the local index of
  // that row of B.  Otherwise, targetMapToOrigRow[Aik] is "invalid"
  // (a flag value).
 
  // Run through all the hash table lookups once and for all
  lo_view_t targetMapToOrigRow(Kokkos::ViewAllocateWithoutInitializing("targetMapToOrigRow"), Aview.colMap->getLocalNumElements());
  lo_view_t targetMapToImportRow(Kokkos::ViewAllocateWithoutInitializing("targetMapToImportRow"), Aview.colMap->getLocalNumElements());
  Kokkos::parallel_for(
      range_type(Aview.colMap->getMinLocalIndex(), Aview.colMap->getMaxLocalIndex() + 1), KOKKOS_LAMBDA(const LO i) {
        GO aidx  = Acolmap_local.getGlobalElement(i);
        LO B_LID = Browmap_local.getLocalElement(aidx);
        if (B_LID != LO_INVALID) {
          targetMapToOrigRow(i)   = B_LID;
          targetMapToImportRow(i) = LO_INVALID;
        } else {
          LO I_LID                = Irowmap_local.getLocalElement(aidx);
          targetMapToOrigRow(i)   = LO_INVALID;
          targetMapToImportRow(i) = I_LID;
        }
      });
 
  // Call the actual kernel.  We'll rely on partial template specialization to call the correct one ---
  // Either the straight-up Tpetra code (SerialNode) or the KokkosKernels one (other NGP node types)
  KernelWrappers2<Scalar, LocalOrdinal, GlobalOrdinal, Node, lo_view_t>::jacobi_A_B_newmatrix_kernel_wrapper(omega, Dinv, Aview, Bview, targetMapToOrigRow, targetMapToImportRow, Bcol2Ccol, Icol2Ccol, C, Cimport, label, params);
}
 
/*********************************************************************************************************/
// Jacobi AB NewMatrix Kernel wrappers (Default non-threaded version)
// Kernel method for computing the local portion of C = (I-omega D^{-1} A)*B
 
template <class Scalar,
          class LocalOrdinal,
          class GlobalOrdinal,
          class Node,
          class LocalOrdinalViewType>
void KernelWrappers2<Scalar, LocalOrdinal, GlobalOrdinal, Node, LocalOrdinalViewType>::jacobi_A_B_newmatrix_kernel_wrapper(Scalar omega,
                                                                                                                           const Vector<Scalar, LocalOrdinal, GlobalOrdinal, Node>& Dinv,
                                                                                                                           CrsMatrixStruct<Scalar, LocalOrdinal, GlobalOrdinal, Node>& Aview,
                                                                                                                           CrsMatrixStruct<Scalar, LocalOrdinal, GlobalOrdinal, Node>& Bview,
                                                                                                                           const LocalOrdinalViewType& targetMapToOrigRow,
                                                                                                                           const LocalOrdinalViewType& targetMapToImportRow,
                                                                                                                           const LocalOrdinalViewType& Bcol2Ccol,
                                                                                                                           const LocalOrdinalViewType& Icol2Ccol,
                                                                                                                           CrsMatrix<Scalar, LocalOrdinal, GlobalOrdinal, Node>& C,
                                                                                                                           Teuchos::RCP<const Import<LocalOrdinal, GlobalOrdinal, Node>> Cimport,
                                                                                                                           const std::string& label,
                                                                                                                           const Teuchos::RCP<Teuchos::ParameterList>& params) {
  Tpetra::Details::ProfilingRegion MM("TpetraExt: Jacobi: Newmatrix SerialCore");
 
  using Teuchos::Array;
  using Teuchos::ArrayRCP;
  using Teuchos::ArrayView;
  using Teuchos::RCP;
  using Teuchos::rcp;
 
  // Lots and lots of typedefs
  typedef typename Tpetra::CrsMatrix<Scalar, LocalOrdinal, GlobalOrdinal, Node>::local_matrix_host_type KCRS;
  typedef typename KCRS::StaticCrsGraphType graph_t;
  typedef typename graph_t::row_map_type::const_type c_lno_view_t;
  typedef typename graph_t::row_map_type::non_const_type lno_view_t;
  typedef typename graph_t::entries_type::non_const_type lno_nnz_view_t;
  typedef typename KCRS::values_type::non_const_type scalar_view_t;
 
  // Jacobi-specific
  typedef typename scalar_view_t::memory_space scalar_memory_space;
 
  typedef Scalar SC;
  typedef LocalOrdinal LO;
  typedef GlobalOrdinal GO;
  typedef Node NO;
 
  typedef Map<LO, GO, NO> map_type;
  size_t ST_INVALID = Teuchos::OrdinalTraits<LO>::invalid();
  LO LO_INVALID     = Teuchos::OrdinalTraits<LO>::invalid();
 
  // Sizes
  RCP<const map_type> Ccolmap = C.getColMap();
  size_t m                    = Aview.origMatrix->getLocalNumRows();
  size_t n                    = Ccolmap->getLocalNumElements();
  size_t b_max_nnz_per_row    = Bview.origMatrix->getLocalMaxNumRowEntries();
 
  // Grab the  Kokkos::SparseCrsMatrices & inner stuff
  const KCRS& Amat = Aview.origMatrix->getLocalMatrixHost();
  const KCRS& Bmat = Bview.origMatrix->getLocalMatrixHost();
 
  c_lno_view_t Arowptr = Amat.graph.row_map, Browptr = Bmat.graph.row_map;
  const lno_nnz_view_t Acolind = Amat.graph.entries, Bcolind = Bmat.graph.entries;
  const scalar_view_t Avals = Amat.values, Bvals = Bmat.values;
 
  c_lno_view_t Irowptr;
  lno_nnz_view_t Icolind;
  scalar_view_t Ivals;
  if (!Bview.importMatrix.is_null()) {
    auto lclB         = Bview.importMatrix->getLocalMatrixHost();
    Irowptr           = lclB.graph.row_map;
    Icolind           = lclB.graph.entries;
    Ivals             = lclB.values;
    b_max_nnz_per_row = std::max(b_max_nnz_per_row, Bview.importMatrix->getLocalMaxNumRowEntries());
  }
 
  // Jacobi-specific inner stuff
  auto Dvals =
      Dinv.template getLocalView<scalar_memory_space>(Access::ReadOnly);
 
  // Teuchos::ArrayView::operator[].
  // The status array will contain the index into colind where this entry was last deposited.
  // c_status[i] < CSR_ip - not in the row yet.
  // c_status[i] >= CSR_ip, this is the entry where you can find the data
  // We start with this filled with INVALID's indicating that there are no entries yet.
  // Sadly, this complicates the code due to the fact that size_t's are unsigned.
  size_t INVALID = Teuchos::OrdinalTraits<size_t>::invalid();
  Array<size_t> c_status(n, ST_INVALID);
 
  // Classic csr assembly (low memory edition)
  //
  // mfh 27 Sep 2016: C_estimate_nnz does not promise an upper bound.
  // The method loops over rows of A, and may resize after processing
  // each row.  Chris Siefert says that this reflects experience in
  // ML; for the non-threaded case, ML found it faster to spend less
  // effort on estimation and risk an occasional reallocation.
  size_t CSR_alloc = std::max(C_estimate_nnz(*Aview.origMatrix, *Bview.origMatrix), n);
  lno_view_t Crowptr(Kokkos::ViewAllocateWithoutInitializing("Crowptr"), m + 1);
  lno_nnz_view_t Ccolind(Kokkos::ViewAllocateWithoutInitializing("Ccolind"), CSR_alloc);
  scalar_view_t Cvals(Kokkos::ViewAllocateWithoutInitializing("Cvals"), CSR_alloc);
  size_t CSR_ip = 0, OLD_ip = 0;
 
  const SC SC_ZERO = Teuchos::ScalarTraits<Scalar>::zero();
 
  // mfh 27 Sep 2016: Here is the local sparse matrix-matrix multiply
  // routine.  The routine computes
  //
  // C := (I - omega * D^{-1} * A) * (B_local + B_remote)).
  //
  // This corresponds to one sweep of (weighted) Jacobi.
  //
  // For column index Aik in row i of A, targetMapToOrigRow[Aik] tells
  // you whether the corresponding row of B belongs to B_local
  // ("orig") or B_remote ("Import").
 
  // For each row of A/C
  for (size_t i = 0; i < m; i++) {
    // mfh 27 Sep 2016: m is the number of rows in the input matrix A
    // on the calling process.
    Crowptr[i]        = CSR_ip;
    SC minusOmegaDval = -omega * Dvals(i, 0);
 
    // Entries of B
    for (size_t j = Browptr[i]; j < Browptr[i + 1]; j++) {
      Scalar Bval = Bvals[j];
      if (Bval == SC_ZERO)
        continue;
      LO Bij = Bcolind[j];
      LO Cij = Bcol2Ccol[Bij];
 
      // Assume no repeated entries in B
      c_status[Cij]   = CSR_ip;
      Ccolind[CSR_ip] = Cij;
      Cvals[CSR_ip]   = Bvals[j];
      CSR_ip++;
    }
 
    // Entries of -omega * Dinv * A * B
    for (size_t k = Arowptr[i]; k < Arowptr[i + 1]; k++) {
      LO Aik        = Acolind[k];
      const SC Aval = Avals[k];
      if (Aval == SC_ZERO)
        continue;
 
      if (targetMapToOrigRow[Aik] != LO_INVALID) {
        // Local matrix
        size_t Bk = static_cast<size_t>(targetMapToOrigRow[Aik]);
 
        for (size_t j = Browptr[Bk]; j < Browptr[Bk + 1]; ++j) {
          LO Bkj = Bcolind[j];
          LO Cij = Bcol2Ccol[Bkj];
 
          if (c_status[Cij] == INVALID || c_status[Cij] < OLD_ip) {
            // New entry
            c_status[Cij]   = CSR_ip;
            Ccolind[CSR_ip] = Cij;
            Cvals[CSR_ip]   = minusOmegaDval * Aval * Bvals[j];
            CSR_ip++;
 
          } else {
            Cvals[c_status[Cij]] += minusOmegaDval * Aval * Bvals[j];
          }
        }
 
      } else {
        // Remote matrix
        size_t Ik = static_cast<size_t>(targetMapToImportRow[Aik]);
        for (size_t j = Irowptr[Ik]; j < Irowptr[Ik + 1]; ++j) {
          LO Ikj = Icolind[j];
          LO Cij = Icol2Ccol[Ikj];
 
          if (c_status[Cij] == INVALID || c_status[Cij] < OLD_ip) {
            // New entry
            c_status[Cij]   = CSR_ip;
            Ccolind[CSR_ip] = Cij;
            Cvals[CSR_ip]   = minusOmegaDval * Aval * Ivals[j];
            CSR_ip++;
          } else {
            Cvals[c_status[Cij]] += minusOmegaDval * Aval * Ivals[j];
          }
        }
      }
    }
 
    // Resize for next pass if needed
    if (i + 1 < m && CSR_ip + std::min(n, (Arowptr[i + 2] - Arowptr[i + 1] + 1) * b_max_nnz_per_row) > CSR_alloc) {
      CSR_alloc *= 2;
      Kokkos::resize(Ccolind, CSR_alloc);
      Kokkos::resize(Cvals, CSR_alloc);
    }
    OLD_ip = CSR_ip;
  }
  Crowptr[m] = CSR_ip;
 
  // Downward resize
  Kokkos::resize(Ccolind, CSR_ip);
  Kokkos::resize(Cvals, CSR_ip);
 
  {
    Tpetra::Details::ProfilingRegion MM2("TpetraExt: Jacobi: Newmatrix Final Sort");
 
    // Replace the column map
    //
    // mfh 27 Sep 2016: We do this because C was originally created
    // without a column Map.  Now we have its column Map.
    C.replaceColMap(Ccolmap);
 
    // Final sort & set of CRS arrays
    //
    // TODO (mfh 27 Sep 2016) Will the thread-parallel "local" sparse
    // matrix-matrix multiply routine sort the entries for us?
    // Final sort & set of CRS arrays
    if (params.is_null() || params->get("sort entries", true))
      Import_Util::sortCrsEntries(Crowptr, Ccolind, Cvals);
    C.setAllValues(Crowptr, Ccolind, Cvals);
  }
  {
    Tpetra::Details::ProfilingRegion MM3("TpetraExt: Jacobi: Newmatrix ESFC");
 
    // Final FillComplete
    //
    // mfh 27 Sep 2016: So-called "expert static fill complete" bypasses
    // Import (from domain Map to column Map) construction (which costs
    // lots of communication) by taking the previously constructed
    // Import object.  We should be able to do this without interfering
    // with the implementation of the local part of sparse matrix-matrix
    // multply above
    RCP<Teuchos::ParameterList> labelList = rcp(new Teuchos::ParameterList);
    labelList->set("Timer Label", label);
    if (!params.is_null()) labelList->set("compute global constants", params->get("compute global constants", true));
    RCP<const Export<LO, GO, NO>> dummyExport;
    C.expertStaticFillComplete(Bview.origMatrix->getDomainMap(), Aview.origMatrix->getRangeMap(), Cimport, dummyExport, labelList);
  }
}
 
/*********************************************************************************************************/
// Kernel method for computing the local portion of C = (I-omega D^{-1} A)*B
template <class Scalar,
          class LocalOrdinal,
          class GlobalOrdinal,
          class Node>
void jacobi_A_B_reuse(
    Scalar omega,
    const Vector<Scalar, LocalOrdinal, GlobalOrdinal, Node>& Dinv,
    CrsMatrixStruct<Scalar, LocalOrdinal, GlobalOrdinal, Node>& Aview,
    CrsMatrixStruct<Scalar, LocalOrdinal, GlobalOrdinal, Node>& Bview,
    CrsMatrix<Scalar, LocalOrdinal, GlobalOrdinal, Node>& C,
    const std::string& label,
    const Teuchos::RCP<Teuchos::ParameterList>& params) {
  using Teuchos::Array;
  using Teuchos::ArrayRCP;
  using Teuchos::ArrayView;
  using Teuchos::RCP;
  using Teuchos::rcp;
 
  typedef LocalOrdinal LO;
  typedef GlobalOrdinal GO;
  typedef Node NO;
 
  typedef Import<LO, GO, NO> import_type;
  typedef Map<LO, GO, NO> map_type;
 
  // Kokkos typedefs
  typedef typename map_type::local_map_type local_map_type;
  typedef typename Tpetra::CrsMatrix<Scalar, LocalOrdinal, GlobalOrdinal, Node>::local_matrix_device_type KCRS;
  typedef typename KCRS::StaticCrsGraphType graph_t;
  typedef typename graph_t::row_map_type::non_const_type lno_view_t;
  typedef typename NO::execution_space execution_space;
  typedef Kokkos::RangePolicy<execution_space, size_t> range_type;
  typedef Kokkos::View<LO*, typename lno_view_t::array_layout, typename lno_view_t::device_type> lo_view_t;
 
  RCP<const import_type> Cimport = C.getGraph()->getImporter();
  lo_view_t Bcol2Ccol, Icol2Ccol;
  lo_view_t targetMapToOrigRow;
  lo_view_t targetMapToImportRow;
  {
    Tpetra::Details::ProfilingRegion MM("TpetraExt: Jacobi: Reuse Cmap");
 
    LO LO_INVALID = Teuchos::OrdinalTraits<LO>::invalid();
 
    // Grab all the maps
    RCP<const map_type> Ccolmap  = C.getColMap();
    local_map_type Acolmap_local = Aview.colMap->getLocalMap();
    local_map_type Browmap_local = Bview.origMatrix->getRowMap()->getLocalMap();
    local_map_type Irowmap_local;
    if (!Bview.importMatrix.is_null()) Irowmap_local = Bview.importMatrix->getRowMap()->getLocalMap();
    local_map_type Bcolmap_local = Bview.origMatrix->getColMap()->getLocalMap();
    local_map_type Icolmap_local;
    if (!Bview.importMatrix.is_null()) Icolmap_local = Bview.importMatrix->getColMap()->getLocalMap();
    local_map_type Ccolmap_local = Ccolmap->getLocalMap();
 
    // Build the final importer / column map, hash table lookups for C
    Bcol2Ccol = lo_view_t(Kokkos::ViewAllocateWithoutInitializing("Bcol2Ccol"), Bview.colMap->getLocalNumElements());
    {
      // Bcol2Col may not be trivial, as Ccolmap is compressed during fillComplete in newmatrix
      // So, column map of C may be a strict subset of the column map of B
      Kokkos::parallel_for(
          range_type(0, Bview.origMatrix->getColMap()->getLocalNumElements()), KOKKOS_LAMBDA(const LO i) {
            Bcol2Ccol(i) = Ccolmap_local.getLocalElement(Bcolmap_local.getGlobalElement(i));
          });
 
      if (!Bview.importMatrix.is_null()) {
        TEUCHOS_TEST_FOR_EXCEPTION(!Cimport->getSourceMap()->isSameAs(*Bview.origMatrix->getDomainMap()),
                                   std::runtime_error, "Tpetra::Jacobi: Import setUnion messed with the DomainMap in an unfortunate way");
 
        Kokkos::resize(Icol2Ccol, Bview.importMatrix->getColMap()->getLocalNumElements());
        Kokkos::parallel_for(
            range_type(0, Bview.importMatrix->getColMap()->getLocalNumElements()), KOKKOS_LAMBDA(const LO i) {
              Icol2Ccol(i) = Ccolmap_local.getLocalElement(Icolmap_local.getGlobalElement(i));
            });
      }
    }
 
    // Run through all the hash table lookups once and for all
    targetMapToOrigRow   = lo_view_t(Kokkos::ViewAllocateWithoutInitializing("targetMapToOrigRow"), Aview.colMap->getLocalNumElements());
    targetMapToImportRow = lo_view_t(Kokkos::ViewAllocateWithoutInitializing("targetMapToImportRow"), Aview.colMap->getLocalNumElements());
    Kokkos::parallel_for(
        range_type(Aview.colMap->getMinLocalIndex(), Aview.colMap->getMaxLocalIndex() + 1), KOKKOS_LAMBDA(const LO i) {
          GO aidx  = Acolmap_local.getGlobalElement(i);
          LO B_LID = Browmap_local.getLocalElement(aidx);
          if (B_LID != LO_INVALID) {
            targetMapToOrigRow(i)   = B_LID;
            targetMapToImportRow(i) = LO_INVALID;
          } else {
            LO I_LID                = Irowmap_local.getLocalElement(aidx);
            targetMapToOrigRow(i)   = LO_INVALID;
            targetMapToImportRow(i) = I_LID;
          }
        });
  }
 
  // Call the actual kernel.  We'll rely on partial template specialization to call the correct one ---
  // Either the straight-up Tpetra code (SerialNode) or the KokkosKernels one (other NGP node types)
  KernelWrappers2<Scalar, LocalOrdinal, GlobalOrdinal, Node, lo_view_t>::jacobi_A_B_reuse_kernel_wrapper(omega, Dinv, Aview, Bview, targetMapToOrigRow, targetMapToImportRow, Bcol2Ccol, Icol2Ccol, C, Cimport, label, params);
}
 
/*********************************************************************************************************/
template <class Scalar,
          class LocalOrdinal,
          class GlobalOrdinal,
          class Node,
          class LocalOrdinalViewType>
void KernelWrappers2<Scalar, LocalOrdinal, GlobalOrdinal, Node, LocalOrdinalViewType>::jacobi_A_B_reuse_kernel_wrapper(Scalar omega,
                                                                                                                       const Vector<Scalar, LocalOrdinal, GlobalOrdinal, Node>& Dinv,
                                                                                                                       CrsMatrixStruct<Scalar, LocalOrdinal, GlobalOrdinal, Node>& Aview,
                                                                                                                       CrsMatrixStruct<Scalar, LocalOrdinal, GlobalOrdinal, Node>& Bview,
                                                                                                                       const LocalOrdinalViewType& targetMapToOrigRow,
                                                                                                                       const LocalOrdinalViewType& targetMapToImportRow,
                                                                                                                       const LocalOrdinalViewType& Bcol2Ccol,
                                                                                                                       const LocalOrdinalViewType& Icol2Ccol,
                                                                                                                       CrsMatrix<Scalar, LocalOrdinal, GlobalOrdinal, Node>& C,
                                                                                                                       Teuchos::RCP<const Import<LocalOrdinal, GlobalOrdinal, Node>> /* Cimport */,
                                                                                                                       const std::string& label,
                                                                                                                       const Teuchos::RCP<Teuchos::ParameterList>& /* params */) {
  Tpetra::Details::ProfilingRegion MM2("TpetraExt: Jacobi: Reuse Serial Core");
  (void)label;
 
  using Teuchos::RCP;
  using Teuchos::rcp;
 
  // Lots and lots of typedefs
  typedef typename Tpetra::CrsMatrix<Scalar, LocalOrdinal, GlobalOrdinal, Node>::local_matrix_host_type KCRS;
  typedef typename KCRS::StaticCrsGraphType graph_t;
  typedef typename graph_t::row_map_type::const_type c_lno_view_t;
  typedef typename graph_t::entries_type::non_const_type lno_nnz_view_t;
  typedef typename KCRS::values_type::non_const_type scalar_view_t;
  typedef typename scalar_view_t::memory_space scalar_memory_space;
 
  typedef Scalar SC;
  typedef LocalOrdinal LO;
  typedef GlobalOrdinal GO;
  typedef Node NO;
  typedef Map<LO, GO, NO> map_type;
  const size_t ST_INVALID = Teuchos::OrdinalTraits<LO>::invalid();
  const LO LO_INVALID     = Teuchos::OrdinalTraits<LO>::invalid();
  const SC SC_ZERO        = Teuchos::ScalarTraits<Scalar>::zero();
 
  // Sizes
  RCP<const map_type> Ccolmap = C.getColMap();
  size_t m                    = Aview.origMatrix->getLocalNumRows();
  size_t n                    = Ccolmap->getLocalNumElements();
 
  // Grab the  Kokkos::SparseCrsMatrices & inner stuff
  const KCRS& Amat = Aview.origMatrix->getLocalMatrixHost();
  const KCRS& Bmat = Bview.origMatrix->getLocalMatrixHost();
  const KCRS& Cmat = C.getLocalMatrixHost();
 
  c_lno_view_t Arowptr = Amat.graph.row_map, Browptr = Bmat.graph.row_map, Crowptr = Cmat.graph.row_map;
  const lno_nnz_view_t Acolind = Amat.graph.entries, Bcolind = Bmat.graph.entries, Ccolind = Cmat.graph.entries;
  const scalar_view_t Avals = Amat.values, Bvals = Bmat.values;
  scalar_view_t Cvals = Cmat.values;
 
  c_lno_view_t Irowptr;
  lno_nnz_view_t Icolind;
  scalar_view_t Ivals;
  if (!Bview.importMatrix.is_null()) {
    auto lclB = Bview.importMatrix->getLocalMatrixHost();
    Irowptr   = lclB.graph.row_map;
    Icolind   = lclB.graph.entries;
    Ivals     = lclB.values;
  }
 
  // Jacobi-specific inner stuff
  auto Dvals =
      Dinv.template getLocalView<scalar_memory_space>(Access::ReadOnly);
 
  // The status array will contain the index into colind where this entry was last deposited.
  //   c_status[i] <  CSR_ip - not in the row yet
  //   c_status[i] >= CSR_ip - this is the entry where you can find the data
  // We start with this filled with INVALID's indicating that there are no entries yet.
  // Sadly, this complicates the code due to the fact that size_t's are unsigned.
  std::vector<size_t> c_status(n, ST_INVALID);
 
  // For each row of A/C
  size_t CSR_ip = 0, OLD_ip = 0;
  for (size_t i = 0; i < m; i++) {
    // First fill the c_status array w/ locations where we're allowed to
    // generate nonzeros for this row
    OLD_ip = Crowptr[i];
    CSR_ip = Crowptr[i + 1];
    for (size_t k = OLD_ip; k < CSR_ip; k++) {
      c_status[Ccolind[k]] = k;
 
      // Reset values in the row of C
      Cvals[k] = SC_ZERO;
    }
 
    SC minusOmegaDval = -omega * Dvals(i, 0);
 
    // Entries of B
    for (size_t j = Browptr[i]; j < Browptr[i + 1]; j++) {
      Scalar Bval = Bvals[j];
      if (Bval == SC_ZERO)
        continue;
      LO Bij = Bcolind[j];
      LO Cij = Bcol2Ccol[Bij];
 
      TEUCHOS_TEST_FOR_EXCEPTION(c_status[Cij] < OLD_ip || c_status[Cij] >= CSR_ip,
                                 std::runtime_error, "Trying to insert a new entry into a static graph");
 
      Cvals[c_status[Cij]] = Bvals[j];
    }
 
    // Entries of -omega * Dinv * A * B
    for (size_t k = Arowptr[i]; k < Arowptr[i + 1]; k++) {
      LO Aik        = Acolind[k];
      const SC Aval = Avals[k];
      if (Aval == SC_ZERO)
        continue;
 
      if (targetMapToOrigRow[Aik] != LO_INVALID) {
        // Local matrix
        size_t Bk = static_cast<size_t>(targetMapToOrigRow[Aik]);
 
        for (size_t j = Browptr[Bk]; j < Browptr[Bk + 1]; ++j) {
          LO Bkj = Bcolind[j];
          LO Cij = Bcol2Ccol[Bkj];
 
          TEUCHOS_TEST_FOR_EXCEPTION(c_status[Cij] < OLD_ip || c_status[Cij] >= CSR_ip,
                                     std::runtime_error, "Trying to insert a new entry into a static graph");
 
          Cvals[c_status[Cij]] += minusOmegaDval * Aval * Bvals[j];
        }
 
      } else {
        // Remote matrix
        size_t Ik = static_cast<size_t>(targetMapToImportRow[Aik]);
        for (size_t j = Irowptr[Ik]; j < Irowptr[Ik + 1]; ++j) {
          LO Ikj = Icolind[j];
          LO Cij = Icol2Ccol[Ikj];
 
          TEUCHOS_TEST_FOR_EXCEPTION(c_status[Cij] < OLD_ip || c_status[Cij] >= CSR_ip,
                                     std::runtime_error, "Trying to insert a new entry into a static graph");
 
          Cvals[c_status[Cij]] += minusOmegaDval * Aval * Ivals[j];
        }
      }
    }
  }
 
  {
    Tpetra::Details::ProfilingRegion MM3("TpetraExt: Jacobi: Reuse ESFC");
    C.fillComplete(C.getDomainMap(), C.getRangeMap());
  }
}
 
/*********************************************************************************************************/
template <class Scalar,
          class LocalOrdinal,
          class GlobalOrdinal,
          class Node>
void import_and_extract_views(
    const CrsMatrix<Scalar, LocalOrdinal, GlobalOrdinal, Node>& A,
    Teuchos::RCP<const Map<LocalOrdinal, GlobalOrdinal, Node>> targetMap,
    CrsMatrixStruct<Scalar, LocalOrdinal, GlobalOrdinal, Node>& Aview,
    Teuchos::RCP<const Import<LocalOrdinal, GlobalOrdinal, Node>> prototypeImporter,
    bool userAssertsThereAreNoRemotes,
    const std::string& label,
    const Teuchos::RCP<Teuchos::ParameterList>& params) {
  using Teuchos::Array;
  using Teuchos::ArrayView;
  using Teuchos::null;
  using Teuchos::RCP;
  using Teuchos::rcp;
 
  typedef Scalar SC;
  typedef LocalOrdinal LO;
  typedef GlobalOrdinal GO;
  typedef Node NO;
 
  typedef Map<LO, GO, NO> map_type;
  typedef Import<LO, GO, NO> import_type;
  typedef CrsMatrix<SC, LO, GO, NO> crs_matrix_type;
 
  RCP<Tpetra::Details::ProfilingRegion> MM = rcp(new Tpetra::Details::ProfilingRegion("TpetraExt: MMM: I&X Alloc"));
 
  // The goal of this method is to populate the 'Aview' struct with views of the
  // rows of A, including all rows that correspond to elements in 'targetMap'.
  //
  // If targetMap includes local elements that correspond to remotely-owned rows
  // of A, then those remotely-owned rows will be imported into
  // 'Aview.importMatrix', and views of them will be included in 'Aview'.
  Aview.deleteContents();
 
  Aview.origMatrix = rcp(&A, false);
  // trigger creation of int-typed row pointer array for use in TPLs, but don't actually need it here
  Aview.origMatrix->getApplyHelper();
  Aview.origRowMap           = A.getRowMap();
  Aview.rowMap               = targetMap;
  Aview.colMap               = A.getColMap();
  Aview.domainMap            = A.getDomainMap();
  Aview.importColMap         = null;
  RCP<const map_type> rowMap = A.getRowMap();
  const int numProcs         = rowMap->getComm()->getSize();
 
  // Short circuit if the user swears there are no remotes (or if we're in serial)
  if (userAssertsThereAreNoRemotes || numProcs < 2)
    return;
 
  RCP<const import_type> importer;
  if (params != null && params->isParameter("importer")) {
    importer = params->get<RCP<const import_type>>("importer");
 
  } else {
    MM = Teuchos::null;
    MM = rcp(new Tpetra::Details::ProfilingRegion("TpetraExt: MMM: I&X RemoteMap"));
 
    // Mark each row in targetMap as local or remote, and go ahead and get a view
    // for the local rows
    RCP<const map_type> remoteRowMap;
    size_t numRemote = 0;
    int mode         = 0;
    if (!prototypeImporter.is_null() &&
        prototypeImporter->getSourceMap()->isSameAs(*rowMap) &&
        prototypeImporter->getTargetMap()->isSameAs(*targetMap)) {
      // We have a valid prototype importer --- ask it for the remotes
      Tpetra::Details::ProfilingRegion MM2("TpetraExt: MMM: I&X RemoteMap: Mode1");
 
      ArrayView<const LO> remoteLIDs = prototypeImporter->getRemoteLIDs();
      numRemote                      = prototypeImporter->getNumRemoteIDs();
 
      Array<GO> remoteRows(numRemote);
      for (size_t i = 0; i < numRemote; i++)
        remoteRows[i] = targetMap->getGlobalElement(remoteLIDs[i]);
 
      remoteRowMap = rcp(new map_type(Teuchos::OrdinalTraits<global_size_t>::invalid(), remoteRows(),
                                      rowMap->getIndexBase(), rowMap->getComm()));
      mode         = 1;
 
    } else if (prototypeImporter.is_null()) {
      // No prototype importer --- count the remotes the hard way
      Tpetra::Details::ProfilingRegion MM2("TpetraExt: MMM: I&X RemoteMap: Mode2");
 
      ArrayView<const GO> rows = targetMap->getLocalElementList();
      size_t numRows           = targetMap->getLocalNumElements();
 
      Array<GO> remoteRows(numRows);
      for (size_t i = 0; i < numRows; ++i) {
        const LO mlid = rowMap->getLocalElement(rows[i]);
 
        if (mlid == Teuchos::OrdinalTraits<LO>::invalid())
          remoteRows[numRemote++] = rows[i];
      }
      remoteRows.resize(numRemote);
      remoteRowMap = rcp(new map_type(Teuchos::OrdinalTraits<global_size_t>::invalid(), remoteRows(),
                                      rowMap->getIndexBase(), rowMap->getComm()));
      mode         = 2;
 
    } else {
      // PrototypeImporter is bad.  But if we're in serial that's OK.
      mode = 3;
    }
 
    if (numProcs < 2) {
      TEUCHOS_TEST_FOR_EXCEPTION(numRemote > 0, std::runtime_error,
                                 "MatrixMatrix::import_and_extract_views ERROR, numProcs < 2 but attempting to import remote matrix rows.");
      // If only one processor we don't need to import any remote rows, so return.
      return;
    }
 
    //
    // Now we will import the needed remote rows of A, if the global maximum
    // value of numRemote is greater than 0.
    //
    MM = Teuchos::null;
    MM = rcp(new Tpetra::Details::ProfilingRegion("TpetraExt: MMM: I&X Collective-0"));
 
    global_size_t globalMaxNumRemote = 0;
    Teuchos::reduceAll(*(rowMap->getComm()), Teuchos::REDUCE_MAX, (global_size_t)numRemote, Teuchos::outArg(globalMaxNumRemote));
 
    if (globalMaxNumRemote > 0) {
      MM = Teuchos::null;
      MM = rcp(new Tpetra::Details::ProfilingRegion("TpetraExt: MMM: I&X Import-2"));
 
      // Create an importer with target-map remoteRowMap and source-map rowMap.
      if (mode == 1)
        importer = prototypeImporter->createRemoteOnlyImport(remoteRowMap);
      else if (mode == 2)
        importer = rcp(new import_type(rowMap, remoteRowMap));
      else
        throw std::runtime_error("prototypeImporter->SourceMap() does not match A.getRowMap()!");
    }
 
    if (params != null)
      params->set("importer", importer);
  }
 
  if (importer != null) {
    MM = Teuchos::null;
    MM = rcp(new Tpetra::Details::ProfilingRegion("TpetraExt: MMM: I&X Import-3"));
 
    // Now create a new matrix into which we can import the remote rows of A that we need.
    Teuchos::ParameterList labelList;
    labelList.set("Timer Label", label);
    auto& labelList_subList = labelList.sublist("matrixmatrix: kernel params", false);
 
    bool isMM                      = true;
    bool overrideAllreduce         = false;
    int mm_optimization_core_count = ::Tpetra::Details::Behavior::TAFC_OptimizationCoreCount();
    // Minor speedup tweak - avoid computing the global constants
    Teuchos::ParameterList params_sublist;
    if (!params.is_null()) {
      labelList.set("compute global constants", params->get("compute global constants", false));
      params_sublist                  = params->sublist("matrixmatrix: kernel params", false);
      mm_optimization_core_count      = params_sublist.get("MM_TAFC_OptimizationCoreCount", mm_optimization_core_count);
      int mm_optimization_core_count2 = params->get("MM_TAFC_OptimizationCoreCount", mm_optimization_core_count);
      if (mm_optimization_core_count2 < mm_optimization_core_count) mm_optimization_core_count = mm_optimization_core_count2;
      isMM              = params_sublist.get("isMatrixMatrix_TransferAndFillComplete", false);
      overrideAllreduce = params_sublist.get("MM_TAFC_OverrideAllreduceCheck", false);
    }
    labelList_subList.set("isMatrixMatrix_TransferAndFillComplete", isMM);
    labelList_subList.set("MM_TAFC_OptimizationCoreCount", mm_optimization_core_count);
    labelList_subList.set("MM_TAFC_OverrideAllreduceCheck", overrideAllreduce);
 
    Aview.importMatrix = Tpetra::importAndFillCompleteCrsMatrix<crs_matrix_type>(rcpFromRef(A), *importer,
                                                                                 A.getDomainMap(), importer->getTargetMap(), rcpFromRef(labelList));
    // trigger creation of int-typed row pointer array for use in TPLs, but don't actually need it here
    Aview.importMatrix->getApplyHelper();
 
#if 0
    // Disabled code for dumping input matrices
    static int count=0;
    char str[80];
    sprintf(str,"import_matrix.%d.dat",count);
    Tpetra::MatrixMarket::Writer<crs_matrix_type>::writeSparseFile(str,Aview.importMatrix);
    count++;
#endif
 
#ifdef HAVE_TPETRA_MMM_STATISTICS
    printMultiplicationStatistics(importer, label + std::string(" I&X MMM"));
#endif
 
    MM = Teuchos::null;
    MM = rcp(new Tpetra::Details::ProfilingRegion("TpetraExt: MMM: I&X Import-4"));
 
    // Save the column map of the imported matrix, so that we can convert indices back to global for arithmetic later
    Aview.importColMap = Aview.importMatrix->getColMap();
    MM                 = Teuchos::null;
  }
}
 
/*********************************************************************************************************/
template <class Scalar,
          class LocalOrdinal,
          class GlobalOrdinal,
          class Node>
void import_and_extract_views(
    const BlockCrsMatrix<Scalar, LocalOrdinal, GlobalOrdinal, Node>& M,
    Teuchos::RCP<const Map<LocalOrdinal, GlobalOrdinal, Node>> targetMap,
    BlockCrsMatrixStruct<Scalar, LocalOrdinal, GlobalOrdinal, Node>& Mview,
    Teuchos::RCP<const Import<LocalOrdinal, GlobalOrdinal, Node>> prototypeImporter,
    bool userAssertsThereAreNoRemotes) {
  using Teuchos::Array;
  using Teuchos::ArrayView;
  using Teuchos::null;
  using Teuchos::RCP;
  using Teuchos::rcp;
 
  typedef Scalar SC;
  typedef LocalOrdinal LO;
  typedef GlobalOrdinal GO;
  typedef Node NO;
 
  typedef Map<LO, GO, NO> map_type;
  typedef Import<LO, GO, NO> import_type;
  typedef BlockCrsMatrix<SC, LO, GO, NO> blockcrs_matrix_type;
 
  // The goal of this method is to populate the 'Mview' struct with views of the
  // rows of M, including all rows that correspond to elements in 'targetMap'.
  //
  // If targetMap includes local elements that correspond to remotely-owned rows
  // of M, then those remotely-owned rows will be imported into
  // 'Mview.importMatrix', and views of them will be included in 'Mview'.
  Mview.deleteContents();
 
  Mview.origMatrix = rcp(&M, false);
  // trigger creation of int-typed row pointer array for use in TPLs, but don't actually need it here
  Mview.origMatrix->getApplyHelper();
  Mview.origRowMap           = M.getRowMap();
  Mview.rowMap               = targetMap;
  Mview.colMap               = M.getColMap();
  Mview.importColMap         = null;
  RCP<const map_type> rowMap = M.getRowMap();
  const int numProcs         = rowMap->getComm()->getSize();
 
  // Short circuit if the user swears there are no remotes (or if we're in serial)
  if (userAssertsThereAreNoRemotes || numProcs < 2) return;
 
  // Mark each row in targetMap as local or remote, and go ahead and get a view
  // for the local rows
  RCP<const map_type> remoteRowMap;
  size_t numRemote = 0;
  int mode         = 0;
  if (!prototypeImporter.is_null() &&
      prototypeImporter->getSourceMap()->isSameAs(*rowMap) &&
      prototypeImporter->getTargetMap()->isSameAs(*targetMap)) {
    // We have a valid prototype importer --- ask it for the remotes
    ArrayView<const LO> remoteLIDs = prototypeImporter->getRemoteLIDs();
    numRemote                      = prototypeImporter->getNumRemoteIDs();
 
    Array<GO> remoteRows(numRemote);
    for (size_t i = 0; i < numRemote; i++)
      remoteRows[i] = targetMap->getGlobalElement(remoteLIDs[i]);
 
    remoteRowMap = rcp(new map_type(Teuchos::OrdinalTraits<global_size_t>::invalid(), remoteRows(),
                                    rowMap->getIndexBase(), rowMap->getComm()));
    mode         = 1;
 
  } else if (prototypeImporter.is_null()) {
    // No prototype importer --- count the remotes the hard way
    ArrayView<const GO> rows = targetMap->getLocalElementList();
    size_t numRows           = targetMap->getLocalNumElements();
 
    Array<GO> remoteRows(numRows);
    for (size_t i = 0; i < numRows; ++i) {
      const LO mlid = rowMap->getLocalElement(rows[i]);
 
      if (mlid == Teuchos::OrdinalTraits<LO>::invalid())
        remoteRows[numRemote++] = rows[i];
    }
    remoteRows.resize(numRemote);
    remoteRowMap = rcp(new map_type(Teuchos::OrdinalTraits<global_size_t>::invalid(), remoteRows(),
                                    rowMap->getIndexBase(), rowMap->getComm()));
    mode         = 2;
 
  } else {
    // PrototypeImporter is bad.  But if we're in serial that's OK.
    mode = 3;
  }
 
  if (numProcs < 2) {
    TEUCHOS_TEST_FOR_EXCEPTION(numRemote > 0, std::runtime_error,
                               "MatrixMatrix::import_and_extract_views ERROR, numProcs < 2 but attempting to import remote matrix rows.");
    // If only one processor we don't need to import any remote rows, so return.
    return;
  }
 
  // Now we will import the needed remote rows of M, if the global maximum
  // value of numRemote is greater than 0.
  global_size_t globalMaxNumRemote = 0;
  Teuchos::reduceAll(*(rowMap->getComm()), Teuchos::REDUCE_MAX, (global_size_t)numRemote, Teuchos::outArg(globalMaxNumRemote));
 
  RCP<const import_type> importer;
 
  if (globalMaxNumRemote > 0) {
    // Create an importer with target-map remoteRowMap and source-map rowMap.
    if (mode == 1)
      importer = prototypeImporter->createRemoteOnlyImport(remoteRowMap);
    else if (mode == 2)
      importer = rcp(new import_type(rowMap, remoteRowMap));
    else
      throw std::runtime_error("prototypeImporter->SourceMap() does not match M.getRowMap()!");
  }
 
  if (importer != null) {
    // Get import matrix
    // TODO: create the int-typed row-pointer here
    Mview.importMatrix = Tpetra::importAndFillCompleteBlockCrsMatrix<blockcrs_matrix_type>(rcpFromRef(M), *importer);
    // trigger creation of int-typed row pointer array for use in TPLs, but don't actually need it here
    Mview.importMatrix->getApplyHelper();
    // Save the column map of the imported matrix, so that we can convert indices
    // back to global for arithmetic later
    Mview.importColMap = Mview.importMatrix->getColMap();
  }
}
 
/*********************************************************************************************************/
// This only merges matrices that look like B & Bimport, aka, they have no overlapping rows
template <class Scalar, class LocalOrdinal, class GlobalOrdinal, class Node, class LocalOrdinalViewType>
const typename Tpetra::CrsMatrix<Scalar, LocalOrdinal, GlobalOrdinal, Node>::local_matrix_device_type
merge_matrices(CrsMatrixStruct<Scalar, LocalOrdinal, GlobalOrdinal, Node>& Aview,
               CrsMatrixStruct<Scalar, LocalOrdinal, GlobalOrdinal, Node>& Bview,
               const LocalOrdinalViewType& Acol2Brow,
               const LocalOrdinalViewType& Acol2Irow,
               const LocalOrdinalViewType& Bcol2Ccol,
               const LocalOrdinalViewType& Icol2Ccol,
               const size_t mergedNodeNumCols) {
  using Teuchos::RCP;
  typedef typename Tpetra::CrsMatrix<Scalar, LocalOrdinal, GlobalOrdinal, Node>::local_matrix_device_type KCRS;
  typedef typename KCRS::StaticCrsGraphType graph_t;
  typedef typename graph_t::row_map_type::non_const_type lno_view_t;
  typedef typename graph_t::entries_type::non_const_type lno_nnz_view_t;
  typedef typename KCRS::values_type::non_const_type scalar_view_t;
  // Grab the  Kokkos::SparseCrsMatrices
  const KCRS& Ak = Aview.origMatrix->getLocalMatrixDevice();
  const KCRS& Bk = Bview.origMatrix->getLocalMatrixDevice();
 
  // We need to do this dance if either (a) We have Bimport or (b) We don't A's colMap is not the same as B's rowMap
  if (!Bview.importMatrix.is_null() || (Bview.importMatrix.is_null() && (&*Aview.origMatrix->getGraph()->getColMap() != &*Bview.origMatrix->getGraph()->getRowMap()))) {
    // We do have a Bimport
    // NOTE: We're going merge Borig and Bimport into a single matrix and reindex the columns *before* we multiply.
    // This option was chosen because we know we don't have any duplicate entries, so we can allocate once.
    RCP<const KCRS> Ik_;
    if (!Bview.importMatrix.is_null()) Ik_ = Teuchos::rcpFromRef<const KCRS>(Bview.importMatrix->getLocalMatrixDevice());
    const KCRS* Ik = Bview.importMatrix.is_null() ? 0 : &*Ik_;
    KCRS Iks;
    if (Ik != 0) Iks = *Ik;
    size_t merge_numrows = Ak.numCols();
    // The last entry of this at least, need to be initialized
    lno_view_t Mrowptr("Mrowptr", merge_numrows + 1);
 
    const LocalOrdinal LO_INVALID = Teuchos::OrdinalTraits<LocalOrdinal>::invalid();
 
    // Use a Kokkos::parallel_scan to build the rowptr
    typedef typename Node::execution_space execution_space;
    typedef Kokkos::RangePolicy<execution_space, size_t> range_type;
    Kokkos::parallel_scan(
        "Tpetra_MatrixMatrix_merge_matrices_buildRowptr", range_type(0, merge_numrows),
        KOKKOS_LAMBDA(const size_t i, size_t& update, const bool final) {
          if (final) Mrowptr(i) = update;
          // Get the row count
          size_t ct = 0;
          if (Acol2Brow(i) != LO_INVALID)
            ct = Bk.graph.row_map(Acol2Brow(i) + 1) - Bk.graph.row_map(Acol2Brow(i));
          else
            ct = Iks.graph.row_map(Acol2Irow(i) + 1) - Iks.graph.row_map(Acol2Irow(i));
          update += ct;
 
          if (final && i + 1 == merge_numrows)
            Mrowptr(i + 1) = update;
        });
 
    // Allocate nnz
    size_t merge_nnz = ::Tpetra::Details::getEntryOnHost(Mrowptr, merge_numrows);
    lno_nnz_view_t Mcolind(Kokkos::ViewAllocateWithoutInitializing("Mcolind"), merge_nnz);
    scalar_view_t Mvalues(Kokkos::ViewAllocateWithoutInitializing("Mvals"), merge_nnz);
 
    // Use a Kokkos::parallel_for to fill the rowptr/colind arrays
    typedef Kokkos::RangePolicy<execution_space, size_t> range_type;
    Kokkos::parallel_for(
        "Tpetra_MatrixMatrix_merg_matrices_buildColindValues", range_type(0, merge_numrows), KOKKOS_LAMBDA(const size_t i) {
          if (Acol2Brow(i) != LO_INVALID) {
            size_t row   = Acol2Brow(i);
            size_t start = Bk.graph.row_map(row);
            for (size_t j = Mrowptr(i); j < Mrowptr(i + 1); j++) {
              Mvalues(j) = Bk.values(j - Mrowptr(i) + start);
              Mcolind(j) = Bcol2Ccol(Bk.graph.entries(j - Mrowptr(i) + start));
            }
          } else {
            size_t row   = Acol2Irow(i);
            size_t start = Iks.graph.row_map(row);
            for (size_t j = Mrowptr(i); j < Mrowptr(i + 1); j++) {
              Mvalues(j) = Iks.values(j - Mrowptr(i) + start);
              Mcolind(j) = Icol2Ccol(Iks.graph.entries(j - Mrowptr(i) + start));
            }
          }
        });
 
    KCRS newmat("CrsMatrix", merge_numrows, mergedNodeNumCols, merge_nnz, Mvalues, Mrowptr, Mcolind);
    return newmat;
  } else {
    // We don't have a Bimport (the easy case)
    return Bk;
  }
}  // end merge_matrices
 
/*********************************************************************************************************/
// This only merges matrices that look like B & Bimport, aka, they have no overlapping rows
template <class Scalar, class LocalOrdinal, class GlobalOrdinal, class Node, class LocalOrdinalViewType>
const typename Tpetra::BlockCrsMatrix<Scalar, LocalOrdinal, GlobalOrdinal, Node>::local_matrix_device_type
merge_matrices(BlockCrsMatrixStruct<Scalar, LocalOrdinal, GlobalOrdinal, Node>& Aview,
               BlockCrsMatrixStruct<Scalar, LocalOrdinal, GlobalOrdinal, Node>& Bview,
               const LocalOrdinalViewType& Acol2Brow,
               const LocalOrdinalViewType& Acol2Irow,
               const LocalOrdinalViewType& Bcol2Ccol,
               const LocalOrdinalViewType& Icol2Ccol,
               const size_t mergedNodeNumCols) {
  using Teuchos::RCP;
  typedef typename Tpetra::BlockCrsMatrix<Scalar, LocalOrdinal, GlobalOrdinal, Node>::local_matrix_device_type KBCRS;
  typedef typename KBCRS::StaticCrsGraphType graph_t;
  typedef typename graph_t::row_map_type::non_const_type lno_view_t;
  typedef typename graph_t::entries_type::non_const_type lno_nnz_view_t;
  typedef typename KBCRS::values_type::non_const_type scalar_view_t;
 
  // Grab the  KokkosSparse::BsrMatrix
  const KBCRS& Ak = Aview.origMatrix->getLocalMatrixDevice();
  const KBCRS& Bk = Bview.origMatrix->getLocalMatrixDevice();
 
  // We need to do this dance if either (a) We have Bimport or (b) A's colMap is not the same as B's rowMap
  if (!Bview.importMatrix.is_null() ||
      (Bview.importMatrix.is_null() &&
       (&*Aview.origMatrix->getGraph()->getColMap() != &*Bview.origMatrix->getGraph()->getRowMap()))) {
    // We do have a Bimport
    // NOTE: We're going merge Borig and Bimport into a single matrix and reindex the columns *before* we multiply.
    // This option was chosen because we know we don't have any duplicate entries, so we can allocate once.
    RCP<const KBCRS> Ik_;
    if (!Bview.importMatrix.is_null()) Ik_ = Teuchos::rcpFromRef<const KBCRS>(Bview.importMatrix->getLocalMatrixDevice());
    const KBCRS* Ik = Bview.importMatrix.is_null() ? 0 : &*Ik_;
    KBCRS Iks;
    if (Ik != 0) Iks = *Ik;
    size_t merge_numrows = Ak.numCols();
 
    // The last entry of this at least, need to be initialized
    lno_view_t Mrowptr("Mrowptr", merge_numrows + 1);
 
    const LocalOrdinal LO_INVALID = Teuchos::OrdinalTraits<LocalOrdinal>::invalid();
 
    // Use a Kokkos::parallel_scan to build the rowptr
    typedef typename Node::execution_space execution_space;
    typedef Kokkos::RangePolicy<execution_space, size_t> range_type;
    Kokkos::parallel_scan(
        "Tpetra_MatrixMatrix_merge_matrices_buildRowptr", range_type(0, merge_numrows),
        KOKKOS_LAMBDA(const size_t i, size_t& update, const bool final) {
          if (final) Mrowptr(i) = update;
          // Get the row count
          size_t ct = 0;
          if (Acol2Brow(i) != LO_INVALID)
            ct = Bk.graph.row_map(Acol2Brow(i) + 1) - Bk.graph.row_map(Acol2Brow(i));
          else
            ct = Iks.graph.row_map(Acol2Irow(i) + 1) - Iks.graph.row_map(Acol2Irow(i));
          update += ct;
 
          if (final && i + 1 == merge_numrows)
            Mrowptr(i + 1) = update;
        });
 
    // Allocate nnz
    size_t merge_nnz    = ::Tpetra::Details::getEntryOnHost(Mrowptr, merge_numrows);
    const int blocksize = Ak.blockDim();
    lno_nnz_view_t Mcolind(Kokkos::ViewAllocateWithoutInitializing("Mcolind"), merge_nnz);
    scalar_view_t Mvalues(Kokkos::ViewAllocateWithoutInitializing("Mvals"), merge_nnz * blocksize * blocksize);
 
    // Use a Kokkos::parallel_for to fill the rowptr/colind arrays
    typedef Kokkos::RangePolicy<execution_space, size_t> range_type;
    Kokkos::parallel_for(
        "Tpetra_MatrixMatrix_merg_matrices_buildColindValues", range_type(0, merge_numrows), KOKKOS_LAMBDA(const size_t i) {
          if (Acol2Brow(i) != LO_INVALID) {
            size_t row   = Acol2Brow(i);
            size_t start = Bk.graph.row_map(row);
            for (size_t j = Mrowptr(i); j < Mrowptr(i + 1); j++) {
              Mcolind(j) = Bcol2Ccol(Bk.graph.entries(j - Mrowptr(i) + start));
 
              for (int b = 0; b < blocksize * blocksize; ++b) {
                const int val_indx   = j * blocksize * blocksize + b;
                const int b_val_indx = (j - Mrowptr(i) + start) * blocksize * blocksize + b;
                Mvalues(val_indx)    = Bk.values(b_val_indx);
              }
            }
          } else {
            size_t row   = Acol2Irow(i);
            size_t start = Iks.graph.row_map(row);
            for (size_t j = Mrowptr(i); j < Mrowptr(i + 1); j++) {
              Mcolind(j) = Icol2Ccol(Iks.graph.entries(j - Mrowptr(i) + start));
 
              for (int b = 0; b < blocksize * blocksize; ++b) {
                const int val_indx   = j * blocksize * blocksize + b;
                const int b_val_indx = (j - Mrowptr(i) + start) * blocksize * blocksize + b;
                Mvalues(val_indx)    = Iks.values(b_val_indx);
              }
            }
          }
        });
 
    // Build and return merged KokkosSparse matrix
    KBCRS newmat("CrsMatrix", merge_numrows, mergedNodeNumCols, merge_nnz, Mvalues, Mrowptr, Mcolind, blocksize);
    return newmat;
  } else {
    // We don't have a Bimport (the easy case)
    return Bk;
  }
}  // end merge_matrices
 
/*********************************************************************************************************/
template <typename SC, typename LO, typename GO, typename NO>
void AddKernels<SC, LO, GO, NO>::
    addSorted(
        const typename MMdetails::AddKernels<SC, LO, GO, NO>::values_array& Avals,
        const typename MMdetails::AddKernels<SC, LO, GO, NO>::row_ptrs_array_const& Arowptrs,
        const typename MMdetails::AddKernels<SC, LO, GO, NO>::col_inds_array& Acolinds,
        const typename MMdetails::AddKernels<SC, LO, GO, NO>::impl_scalar_type scalarA,
        const typename MMdetails::AddKernels<SC, LO, GO, NO>::values_array& Bvals,
        const typename MMdetails::AddKernels<SC, LO, GO, NO>::row_ptrs_array_const& Browptrs,
        const typename MMdetails::AddKernels<SC, LO, GO, NO>::col_inds_array& Bcolinds,
        const typename MMdetails::AddKernels<SC, LO, GO, NO>::impl_scalar_type scalarB,
        GO numGlobalCols,
        typename MMdetails::AddKernels<SC, LO, GO, NO>::values_array& Cvals,
        typename MMdetails::AddKernels<SC, LO, GO, NO>::row_ptrs_array& Crowptrs,
        typename MMdetails::AddKernels<SC, LO, GO, NO>::col_inds_array& Ccolinds) {
  using Teuchos::rcp;
  using Teuchos::TimeMonitor;
  using AddKern = MMdetails::AddKernels<SC, LO, GO, NO>;
  TEUCHOS_TEST_FOR_EXCEPTION(Arowptrs.extent(0) != Browptrs.extent(0), std::runtime_error, "Can't add matrices with different numbers of rows.");
  auto nrows = Arowptrs.extent(0) - 1;
  Crowptrs   = row_ptrs_array(Kokkos::ViewAllocateWithoutInitializing("C row ptrs"), nrows + 1);
  typename AddKern::KKH handle;
  handle.create_spadd_handle(true);
  auto addHandle = handle.get_spadd_handle();
 
  auto MM = rcp(new Tpetra::Details::ProfilingRegion("TpetraExt: add: sorted symbolic"));
 
  KokkosSparse::spadd_symbolic(&handle,
                               nrows, numGlobalCols,
                               Arowptrs, Acolinds, Browptrs, Bcolinds, Crowptrs);
  // KokkosKernels requires values to be zeroed
  Cvals    = values_array("C values", addHandle->get_c_nnz());
  Ccolinds = col_inds_array(Kokkos::ViewAllocateWithoutInitializing("C colinds"), addHandle->get_c_nnz());
 
  MM = Teuchos::null;
  MM = rcp(new Tpetra::Details::ProfilingRegion("TpetraExt: add: sorted numeric"));
  KokkosSparse::spadd_numeric(&handle,
                              nrows, numGlobalCols,
                              Arowptrs, Acolinds, Avals, scalarA,
                              Browptrs, Bcolinds, Bvals, scalarB,
                              Crowptrs, Ccolinds, Cvals);
}
 
template <typename SC, typename LO, typename GO, typename NO>
void AddKernels<SC, LO, GO, NO>::
    addUnsorted(
        const typename MMdetails::AddKernels<SC, LO, GO, NO>::values_array& Avals,
        const typename MMdetails::AddKernels<SC, LO, GO, NO>::row_ptrs_array_const& Arowptrs,
        const typename MMdetails::AddKernels<SC, LO, GO, NO>::col_inds_array& Acolinds,
        const typename MMdetails::AddKernels<SC, LO, GO, NO>::impl_scalar_type scalarA,
        const typename MMdetails::AddKernels<SC, LO, GO, NO>::values_array& Bvals,
        const typename MMdetails::AddKernels<SC, LO, GO, NO>::row_ptrs_array_const& Browptrs,
        const typename MMdetails::AddKernels<SC, LO, GO, NO>::col_inds_array& Bcolinds,
        const typename MMdetails::AddKernels<SC, LO, GO, NO>::impl_scalar_type scalarB,
        GO numGlobalCols,
        typename MMdetails::AddKernels<SC, LO, GO, NO>::values_array& Cvals,
        typename MMdetails::AddKernels<SC, LO, GO, NO>::row_ptrs_array& Crowptrs,
        typename MMdetails::AddKernels<SC, LO, GO, NO>::col_inds_array& Ccolinds) {
  using Teuchos::rcp;
  using Teuchos::TimeMonitor;
  using AddKern = MMdetails::AddKernels<SC, LO, GO, NO>;
  TEUCHOS_TEST_FOR_EXCEPTION(Arowptrs.extent(0) != Browptrs.extent(0), std::runtime_error, "Can't add matrices with different numbers of rows.");
  auto nrows = Arowptrs.extent(0) - 1;
  Crowptrs   = row_ptrs_array(Kokkos::ViewAllocateWithoutInitializing("C row ptrs"), nrows + 1);
  typedef MMdetails::AddKernels<SC, LO, GO, NO> AddKern;
  typename AddKern::KKH handle;
  handle.create_spadd_handle(false);
  auto addHandle = handle.get_spadd_handle();
  auto MM        = rcp(new Tpetra::Details::ProfilingRegion("TpetraExt: add: unsorted symbolic"));
 
  KokkosSparse::spadd_symbolic(&handle,
                               nrows, numGlobalCols,
                               Arowptrs, Acolinds, Browptrs, Bcolinds, Crowptrs);
  // Cvals must be zeroed out
  Cvals    = values_array("C values", addHandle->get_c_nnz());
  Ccolinds = col_inds_array(Kokkos::ViewAllocateWithoutInitializing("C colinds"), addHandle->get_c_nnz());
  MM       = Teuchos::null;
  MM       = rcp(new Tpetra::Details::ProfilingRegion("TpetraExt: add: unsorted numeric"));
  KokkosSparse::spadd_numeric(&handle,
                              nrows, numGlobalCols,
                              Arowptrs, Acolinds, Avals, scalarA,
                              Browptrs, Bcolinds, Bvals, scalarB,
                              Crowptrs, Ccolinds, Cvals);
}
 
template <typename GO,
          typename LocalIndicesType,
          typename GlobalIndicesType,
          typename ColMapType>
struct ConvertLocalToGlobalFunctor {
  ConvertLocalToGlobalFunctor(
      const LocalIndicesType& colindsOrig_,
      const GlobalIndicesType& colindsConverted_,
      const ColMapType& colmap_)
    : colindsOrig(colindsOrig_)
    , colindsConverted(colindsConverted_)
    , colmap(colmap_) {}
  KOKKOS_INLINE_FUNCTION void
  operator()(const GO i) const {
    colindsConverted(i) = colmap.getGlobalElement(colindsOrig(i));
  }
  LocalIndicesType colindsOrig;
  GlobalIndicesType colindsConverted;
  ColMapType colmap;
};
 
template <typename SC, typename LO, typename GO, typename NO>
void MMdetails::AddKernels<SC, LO, GO, NO>::
    convertToGlobalAndAdd(
        const typename MMdetails::AddKernels<SC, LO, GO, NO>::KCRS& A,
        const typename MMdetails::AddKernels<SC, LO, GO, NO>::impl_scalar_type scalarA,
        const typename MMdetails::AddKernels<SC, LO, GO, NO>::KCRS& B,
        const typename MMdetails::AddKernels<SC, LO, GO, NO>::impl_scalar_type scalarB,
        const typename MMdetails::AddKernels<SC, LO, GO, NO>::local_map_type& AcolMap,
        const typename MMdetails::AddKernels<SC, LO, GO, NO>::local_map_type& BcolMap,
        typename MMdetails::AddKernels<SC, LO, GO, NO>::values_array& Cvals,
        typename MMdetails::AddKernels<SC, LO, GO, NO>::row_ptrs_array& Crowptrs,
        typename MMdetails::AddKernels<SC, LO, GO, NO>::global_col_inds_array& Ccolinds) {
  using Teuchos::rcp;
  using Teuchos::TimeMonitor;
  // Need to use a different KokkosKernelsHandle type than other versions,
  // since the ordinals are now GO
  using KKH_GO = KokkosKernels::Experimental::KokkosKernelsHandle<size_t, GO, impl_scalar_type,
                                                                  typename NO::execution_space, typename NO::memory_space, typename NO::memory_space>;
 
  const values_array Avals      = A.values;
  const values_array Bvals      = B.values;
  const col_inds_array Acolinds = A.graph.entries;
  const col_inds_array Bcolinds = B.graph.entries;
  auto Arowptrs                 = A.graph.row_map;
  auto Browptrs                 = B.graph.row_map;
  global_col_inds_array AcolindsConverted(Kokkos::ViewAllocateWithoutInitializing("A colinds (converted)"), Acolinds.extent(0));
  global_col_inds_array BcolindsConverted(Kokkos::ViewAllocateWithoutInitializing("B colinds (converted)"), Bcolinds.extent(0));
 
  auto MM = rcp(new Tpetra::Details::ProfilingRegion("TpetraExt: add: diff col map kernel: column map conversion"));
 
  ConvertLocalToGlobalFunctor<GO, col_inds_array, global_col_inds_array, local_map_type> convertA(Acolinds, AcolindsConverted, AcolMap);
  Kokkos::parallel_for("Tpetra_MatrixMatrix_convertColIndsA", range_type(0, Acolinds.extent(0)), convertA);
  ConvertLocalToGlobalFunctor<GO, col_inds_array, global_col_inds_array, local_map_type> convertB(Bcolinds, BcolindsConverted, BcolMap);
  Kokkos::parallel_for("Tpetra_MatrixMatrix_convertColIndsB", range_type(0, Bcolinds.extent(0)), convertB);
  KKH_GO handle;
  handle.create_spadd_handle(false);
  auto addHandle = handle.get_spadd_handle();
  MM             = Teuchos::null;
  MM             = rcp(new Tpetra::Details::ProfilingRegion("TpetraExt: add: diff col map kernel: unsorted symbolic"));
  auto nrows     = Arowptrs.extent(0) - 1;
  Crowptrs       = row_ptrs_array(Kokkos::ViewAllocateWithoutInitializing("C row ptrs"), nrows + 1);
  KokkosSparse::spadd_symbolic(&handle,
                               nrows, A.numCols(),
                               Arowptrs, AcolindsConverted, Browptrs, BcolindsConverted, Crowptrs);
  Cvals    = values_array("C values", addHandle->get_c_nnz());
  Ccolinds = global_col_inds_array(Kokkos::ViewAllocateWithoutInitializing("C colinds"), addHandle->get_c_nnz());
 
  MM = Teuchos::null;
  MM = rcp(new Tpetra::Details::ProfilingRegion("TpetraExt: add: diff col map kernel: unsorted numeric"));
  KokkosSparse::spadd_numeric(&handle,
                              nrows, A.numCols(),
                              Arowptrs, AcolindsConverted, Avals, scalarA,
                              Browptrs, BcolindsConverted, Bvals, scalarB,
                              Crowptrs, Ccolinds, Cvals);
}
 
}  // namespace MMdetails
 
}  // End namespace Tpetra
 
/*********************************************************************************************************/
//
// Explicit instantiation macro
//
// Must be expanded from within the Tpetra namespace!
//
namespace Tpetra {
 
#define TPETRA_MATRIXMATRIX_INSTANT(SCALAR, LO, GO, NODE)                                                                             \
  template void MatrixMatrix::Multiply(                                                                                               \
      const CrsMatrix<SCALAR, LO, GO, NODE>& A,                                                                                       \
      bool transposeA,                                                                                                                \
      const CrsMatrix<SCALAR, LO, GO, NODE>& B,                                                                                       \
      bool transposeB,                                                                                                                \
      CrsMatrix<SCALAR, LO, GO, NODE>& C,                                                                                             \
      bool call_FillComplete_on_result,                                                                                               \
      const std::string& label,                                                                                                       \
      const Teuchos::RCP<Teuchos::ParameterList>& params);                                                                            \
                                                                                                                                      \
  template void MatrixMatrix::Multiply(                                                                                               \
      const Teuchos::RCP<const BlockCrsMatrix<SCALAR, LO, GO, NODE>>& A,                                                              \
      bool transposeA,                                                                                                                \
      const Teuchos::RCP<const BlockCrsMatrix<SCALAR, LO, GO, NODE>>& B,                                                              \
      bool transposeB,                                                                                                                \
      Teuchos::RCP<BlockCrsMatrix<SCALAR, LO, GO, NODE>>& C,                                                                          \
      const std::string& label);                                                                                                      \
                                                                                                                                      \
  template void MatrixMatrix::Jacobi(                                                                                                 \
      SCALAR omega,                                                                                                                   \
      const Vector<SCALAR, LO, GO, NODE>& Dinv,                                                                                       \
      const CrsMatrix<SCALAR, LO, GO, NODE>& A,                                                                                       \
      const CrsMatrix<SCALAR, LO, GO, NODE>& B,                                                                                       \
      CrsMatrix<SCALAR, LO, GO, NODE>& C,                                                                                             \
      bool call_FillComplete_on_result,                                                                                               \
      const std::string& label,                                                                                                       \
      const Teuchos::RCP<Teuchos::ParameterList>& params);                                                                            \
                                                                                                                                      \
  template void MatrixMatrix::Add(                                                                                                    \
      const CrsMatrix<SCALAR, LO, GO, NODE>& A,                                                                                       \
      bool transposeA,                                                                                                                \
      SCALAR scalarA,                                                                                                                 \
      const CrsMatrix<SCALAR, LO, GO, NODE>& B,                                                                                       \
      bool transposeB,                                                                                                                \
      SCALAR scalarB,                                                                                                                 \
      Teuchos::RCP<CrsMatrix<SCALAR, LO, GO, NODE>>& C);                                                                              \
                                                                                                                                      \
  template void MatrixMatrix::Add(                                                                                                    \
      const CrsMatrix<SCALAR, LO, GO, NODE>& A,                                                                                       \
      bool transposeA,                                                                                                                \
      SCALAR scalarA,                                                                                                                 \
      const CrsMatrix<SCALAR, LO, GO, NODE>& B,                                                                                       \
      bool transposeB,                                                                                                                \
      SCALAR scalarB,                                                                                                                 \
      const Teuchos::RCP<CrsMatrix<SCALAR, LO, GO, NODE>>& C);                                                                        \
                                                                                                                                      \
  template void MatrixMatrix::Add(                                                                                                    \
      const CrsMatrix<SCALAR, LO, GO, NODE>& A,                                                                                       \
      bool transposeA,                                                                                                                \
      SCALAR scalarA,                                                                                                                 \
      CrsMatrix<SCALAR, LO, GO, NODE>& B,                                                                                             \
      SCALAR scalarB);                                                                                                                \
                                                                                                                                      \
  template Teuchos::RCP<CrsMatrix<SCALAR, LO, GO, NODE>>                                                                              \
  MatrixMatrix::add<SCALAR, LO, GO, NODE>(const SCALAR& alpha,                                                                        \
                                          const bool transposeA,                                                                      \
                                          const CrsMatrix<SCALAR, LO, GO, NODE>& A,                                                   \
                                          const SCALAR& beta,                                                                         \
                                          const bool transposeB,                                                                      \
                                          const CrsMatrix<SCALAR, LO, GO, NODE>& B,                                                   \
                                          const Teuchos::RCP<const Map<LO, GO, NODE>>& domainMap,                                     \
                                          const Teuchos::RCP<const Map<LO, GO, NODE>>& rangeMap,                                      \
                                          const Teuchos::RCP<Teuchos::ParameterList>& params);                                        \
                                                                                                                                      \
  template void                                                                                                                       \
  MatrixMatrix::add<SCALAR, LO, GO, NODE>(const SCALAR& alpha,                                                                        \
                                          const bool transposeA,                                                                      \
                                          const CrsMatrix<SCALAR, LO, GO, NODE>& A,                                                   \
                                          const SCALAR& beta,                                                                         \
                                          const bool transposeB,                                                                      \
                                          const CrsMatrix<SCALAR, LO, GO, NODE>& B,                                                   \
                                          CrsMatrix<SCALAR, LO, GO, NODE>& C,                                                         \
                                          const Teuchos::RCP<const Map<LO, GO, NODE>>& domainMap,                                     \
                                          const Teuchos::RCP<const Map<LO, GO, NODE>>& rangeMap,                                      \
                                          const Teuchos::RCP<Teuchos::ParameterList>& params);                                        \
                                                                                                                                      \
  template struct MMdetails::AddKernels<SCALAR, LO, GO, NODE>;                                                                        \
                                                                                                                                      \
  template void MMdetails::import_and_extract_views<SCALAR, LO, GO, NODE>(const CrsMatrix<SCALAR, LO, GO, NODE>& M,                   \
                                                                          Teuchos::RCP<const Map<LO, GO, NODE>> targetMap,            \
                                                                          CrsMatrixStruct<SCALAR, LO, GO, NODE>& Mview,               \
                                                                          Teuchos::RCP<const Import<LO, GO, NODE>> prototypeImporter, \
                                                                          bool userAssertsThereAreNoRemotes,                          \
                                                                          const std::string& label,                                   \
                                                                          const Teuchos::RCP<Teuchos::ParameterList>& params);        \
                                                                                                                                      \
  template void MMdetails::import_and_extract_views<SCALAR, LO, GO, NODE>(const BlockCrsMatrix<SCALAR, LO, GO, NODE>& M,              \
                                                                          Teuchos::RCP<const Map<LO, GO, NODE>> targetMap,            \
                                                                          BlockCrsMatrixStruct<SCALAR, LO, GO, NODE>& Mview,          \
                                                                          Teuchos::RCP<const Import<LO, GO, NODE>> prototypeImporter, \
                                                                          bool userAssertsThereAreNoRemotes);
}  // End namespace Tpetra
 
#endif  // TPETRA_MATRIXMATRIX_DEF_HPP