EpetraExt_MatrixMatrix.cpp

Go to the documentation of this file.

//@HEADER
// ***********************************************************************
// 
//     EpetraExt: Epetra Extended - Linear Algebra Services Package
//                 Copyright (2001) Sandia Corporation
// 
// Under terms of Contract DE-AC04-94AL85000, there is a non-exclusive
// license for use of this work by or on behalf of the U.S. Government.
// 
// This library is free software; you can redistribute it and/or modify
// it under the terms of the GNU Lesser General Public License as
// published by the Free Software Foundation; either version 2.1 of the
// License, or (at your option) any later version.
//  
// This library is distributed in the hope that it will be useful, but
// WITHOUT ANY WARRANTY; without even the implied warranty of
// MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
// Lesser General Public License for more details.
//  
// You should have received a copy of the GNU Lesser General Public
// License along with this library; if not, write to the Free Software
// Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307
// USA
// Questions? Contact Michael A. Heroux (maherou@sandia.gov) 
// 
// ***********************************************************************
//@HEADER

#include <EpetraExt_ConfigDefs.h>
#include <EpetraExt_MatrixMatrix.h>

#include <EpetraExt_Transpose_RowMatrix.h>

#include <Epetra_Export.h>
#include <Epetra_Import.h>
#include <Epetra_Util.h>
#include <Epetra_Map.h>
#include <Epetra_Comm.h>
#include <Epetra_CrsMatrix.h>
#include <Epetra_Directory.h>
#include <Epetra_HashTable.h>
#include <Epetra_Distributor.h>

#ifdef HAVE_VECTOR
#include <vector>
#endif

namespace EpetraExt {

//
//Method for internal use... sparsedot forms a dot-product between two
//sparsely-populated 'vectors'.
//Important assumption: assumes the indices in u_ind and v_ind are sorted.
//
double sparsedot(double* u, int* u_ind, int u_len,
     double* v, int* v_ind, int v_len)
{
  double result = 0.0;

  int v_idx = 0;
  int u_idx = 0;

  while(v_idx < v_len && u_idx < u_len) {
    int ui = u_ind[u_idx];
    int vi = v_ind[v_idx];

    if (ui < vi) {
      ++u_idx;
    }
    else if (ui > vi) {
      ++v_idx;
    }
    else {
      result += u[u_idx++]*v[v_idx++];
    }
  }

  return(result);
}

//struct that holds views of the contents of a CrsMatrix. These
//contents may be a mixture of local and remote rows of the
//original matrix. 
class CrsMatrixStruct {
public:
  CrsMatrixStruct()
    : numRows(0), numEntriesPerRow(NULL), indices(NULL), values(NULL),
      remote(NULL), numRemote(0), rowMap(NULL), colMap(NULL),
      domainMap(NULL), importColMap(NULL), importMatrix(NULL)
  {}

  virtual ~CrsMatrixStruct()
  {
    deleteContents();
  }

  void deleteContents()
  {
    numRows = 0;
    delete [] numEntriesPerRow; numEntriesPerRow = NULL;
    delete [] indices; indices = NULL;
    delete [] values; values = NULL;
    delete [] remote; remote = NULL;
    numRemote = 0;
    delete importMatrix;
  }

  int numRows;
  int* numEntriesPerRow;
  int** indices;
  double** values;
  bool* remote;
  int numRemote;
  const Epetra_Map* origRowMap;
  const Epetra_Map* rowMap;
  const Epetra_Map* colMap;
  const Epetra_Map* domainMap;
  const Epetra_Map* importColMap;
  Epetra_CrsMatrix* importMatrix;
};

int dumpCrsMatrixStruct(const CrsMatrixStruct& M)
{
  cout << "proc " << M.rowMap->Comm().MyPID()<<endl;
  cout << "numRows: " << M.numRows<<endl;
  for(int i=0; i<M.numRows; ++i) {
    for(int j=0; j<M.numEntriesPerRow[i]; ++j) {
      if (M.remote[i]) {
  cout << "  *"<<M.rowMap->GID(i)<<"   "
       <<M.importColMap->GID(M.indices[i][j])<<"   "<<M.values[i][j]<<endl;
      }
      else {
  cout << "   "<<M.rowMap->GID(i)<<"   "
       <<M.colMap->GID(M.indices[i][j])<<"   "<<M.values[i][j]<<endl;
      }
    }
  }
  return(0);
}

//kernel method for computing the local portion of C = A*B
int mult_A_B(CrsMatrixStruct& Aview,
       CrsMatrixStruct& Bview,
       Epetra_CrsMatrix& C)
{
  int C_firstCol = Bview.colMap->MinLID();
  int C_lastCol = Bview.colMap->MaxLID();

  int C_firstCol_import = 0;
  int C_lastCol_import = -1;

  int* bcols = Bview.colMap->MyGlobalElements();
  int* bcols_import = NULL;
  if (Bview.importColMap != NULL) {
    C_firstCol_import = Bview.importColMap->MinLID();
    C_lastCol_import = Bview.importColMap->MaxLID();

    bcols_import = Bview.importColMap->MyGlobalElements();
  }

  int C_numCols = C_lastCol - C_firstCol + 1;
  int C_numCols_import = C_lastCol_import - C_firstCol_import + 1;

  if (C_numCols_import > C_numCols) C_numCols = C_numCols_import;
  double* dwork = new double[C_numCols];
  int* iwork = new int[C_numCols];

  double* C_row_i = dwork;
  int* C_cols = iwork;

  int C_row_i_length, i, j, k;

  //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.

  bool C_filled = C.Filled();

  //loop over the rows of A.
  for(i=0; i<Aview.numRows; ++i) {

    //only navigate the local portion of Aview... (It's probable that we
    //imported more of A than we need for A*B, because other cases like A^T*B 
    //need the extra rows.)
    if (Aview.remote[i]) {
      continue;
    }

    int* Aindices_i = Aview.indices[i];
    double* Aval_i  = Aview.values[i];

    int global_row = Aview.rowMap->GID(i);

    //loop across the i-th row of A and for each corresponding row
    //in B, loop across colums 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.

    for(k=0; k<Aview.numEntriesPerRow[i]; ++k) {
      int Ak = Bview.rowMap->LID(Aview.colMap->GID(Aindices_i[k]));
      double Aval = Aval_i[k];

      int* Bcol_inds = Bview.indices[Ak];
      double* Bvals_k = Bview.values[Ak];

      C_row_i_length = 0;

      if (Bview.remote[Ak]) {
  for(j=0; j<Bview.numEntriesPerRow[Ak]; ++j) {
    C_row_i[C_row_i_length] = Aval*Bvals_k[j];
          C_cols[C_row_i_length++] = bcols_import[Bcol_inds[j]];
  }
      }
      else {
  for(j=0; j<Bview.numEntriesPerRow[Ak]; ++j) {
    C_row_i[C_row_i_length] = Aval*Bvals_k[j];
          C_cols[C_row_i_length++] = bcols[Bcol_inds[j]];
  }
      }

      //
      //Now put the C_row_i values into C.
      //

      int err = C_filled ?
          C.SumIntoGlobalValues(global_row, C_row_i_length, C_row_i, C_cols)
          :
          C.InsertGlobalValues(global_row, C_row_i_length, C_row_i, C_cols);
 
      if (err < 0) {
        return(err);
      }
      if (err > 0) {
        if (C_filled) {
          //C is Filled, and doesn't
          //have all the necessary nonzero locations.
          return(err);
        }
      }
    }
  }

  delete [] dwork;
  delete [] iwork;

  return(0);
}

//kernel method for computing the local portion of C = A*B^T
int mult_A_Btrans(CrsMatrixStruct& Aview,
      CrsMatrixStruct& Bview,
      Epetra_CrsMatrix& C)
{
  int i, j, k;
  int returnValue = 0;

  int maxlen = 0;
  for(i=0; i<Aview.numRows; ++i) {
    if (Aview.numEntriesPerRow[i] > maxlen) maxlen = Aview.numEntriesPerRow[i];
  }
  for(i=0; i<Bview.numRows; ++i) {
    if (Bview.numEntriesPerRow[i] > maxlen) maxlen = Bview.numEntriesPerRow[i];
  }

  //cout << "Aview: " << endl;
  //dumpCrsMatrixStruct(Aview);

  //cout << "Bview: " << endl;
  //dumpCrsMatrixStruct(Bview);

  int numBcols = Bview.colMap->NumMyElements();
  int numBrows = Bview.numRows;

  int iworklen = maxlen*2 + numBcols;
  int* iwork = new int[iworklen];

  int* bcols = iwork+maxlen*2;
  int* bgids = Bview.colMap->MyGlobalElements();
  double* bvals = new double[maxlen*2];
  double* avals = bvals+maxlen;

  int max_all_b = Bview.colMap->MaxAllGID();
  int min_all_b = Bview.colMap->MinAllGID();

  //bcols will hold the GIDs from B's column-map for fast access
  //during the computations below
  for(i=0; i<numBcols; ++i) {
    int blid = Bview.colMap->LID(bgids[i]);
    bcols[blid] = bgids[i];
  }

  //next create arrays indicating the first and last column-index in
  //each row of B, so that we can know when to skip certain rows below.
  //This will provide a large performance gain for banded matrices, and
  //a somewhat smaller gain for *most* other matrices.
  int* b_firstcol = new int[2*numBrows];
  int* b_lastcol = b_firstcol+numBrows;
  int temp;
  for(i=0; i<numBrows; ++i) {
    b_firstcol[i] = max_all_b;
    b_lastcol[i] = min_all_b;

    int Blen_i = Bview.numEntriesPerRow[i];
    if (Blen_i < 1) continue;
    int* Bindices_i = Bview.indices[i];

    if (Bview.remote[i]) {
      for(k=0; k<Blen_i; ++k) {
        temp = Bview.importColMap->GID(Bindices_i[k]);
        if (temp < b_firstcol[i]) b_firstcol[i] = temp;
        if (temp > b_lastcol[i]) b_lastcol[i] = temp;
      }
    }
    else {
      for(k=0; k<Blen_i; ++k) {
        temp = bcols[Bindices_i[k]];
        if (temp < b_firstcol[i]) b_firstcol[i] = temp;
        if (temp > b_lastcol[i]) b_lastcol[i] = temp;
      }
    }
  }

  Epetra_Util util;

  int* Aind = iwork;
  int* Bind = iwork+maxlen;

  bool C_filled = C.Filled();

  //To form C = A*B^T, we're going to execute this expression:
  //
  // C(i,j) = sum_k( A(i,k)*B(j,k) )
  //
  //This is the easiest case of all to code (easier than A*B, A^T*B, A^T*B^T).
  //But it requires the use of a 'sparsedot' function (we're simply forming
  //dot-products with row A_i and row B_j for all i and j).

  //loop over the rows of A.
  for(i=0; i<Aview.numRows; ++i) {
    if (Aview.remote[i]) {
      continue;
    }

    int* Aindices_i = Aview.indices[i];
    double* Aval_i  = Aview.values[i];
    int A_len_i = Aview.numEntriesPerRow[i];
    if (A_len_i < 1) {
      continue;
    }

    for(k=0; k<A_len_i; ++k) {
      Aind[k] = Aview.colMap->GID(Aindices_i[k]);
      avals[k] = Aval_i[k];
    }

    util.Sort(true, A_len_i, Aind, 1, &avals, 0, NULL);

    int mina = Aind[0];
    int maxa = Aind[A_len_i-1];

    if (mina > max_all_b || maxa < min_all_b) {
      continue;
    }

    int global_row = Aview.rowMap->GID(i);

    //loop over the rows of B and form results C_ij = dot(A(i,:),B(j,:))
    for(j=0; j<Bview.numRows; ++j) {
      if (b_firstcol[j] > maxa || b_lastcol[j] < mina) {
        continue;
      }

      int* Bindices_j = Bview.indices[j];
      int B_len_j = Bview.numEntriesPerRow[j];
      if (B_len_j < 1) {
        continue;
      }

      int tmp, Blen = 0;

      if (Bview.remote[j]) {
        for(k=0; k<B_len_j; ++k) {
    tmp = Bview.importColMap->GID(Bindices_j[k]);
          if (tmp < mina || tmp > maxa) {
            continue;
          }

          bvals[Blen] = Bview.values[j][k];
          Bind[Blen++] = tmp;
  }
      }
      else {
        for(k=0; k<B_len_j; ++k) {
    tmp = bcols[Bindices_j[k]];
          if (tmp < mina || tmp > maxa) {
            continue;
          }

          bvals[Blen] = Bview.values[j][k];
          Bind[Blen++] = tmp;
  }
      }

      if (Blen < 1) {
        continue;
      }

      util.Sort(true, Blen, Bind, 1, &bvals, 0, NULL);

      double C_ij = sparsedot(avals, Aind, A_len_i,
            bvals, Bind, Blen);

      if (C_ij == 0.0) {
  continue;
      }
      int global_col = Bview.rowMap->GID(j);

      int err = C_filled ?
        C.SumIntoGlobalValues(global_row, 1, &C_ij, &global_col)
        :
        C.InsertGlobalValues(global_row, 1, &C_ij, &global_col);

      if (err < 0) {
  return(err);
      }
      if (err > 0) {
  if (C_filled) {
    //C.Filled()==true, and C doesn't have all the necessary nonzero
          //locations, or global_row or global_col is out of range (less
          //than 0 or non local).
          std::cerr << "EpetraExt::MatrixMatrix::Multiply Warning: failed "
              << "to insert value in result matrix at position "<<global_row
             <<"," <<global_col<<", possibly because result matrix has a "
             << "column-map that doesn't include column "<<global_col
             <<" on this proc." <<std::endl;
    return(err);
  }
      }
    }
  }

  delete [] iwork;
  delete [] bvals;
  delete [] b_firstcol;

  return(returnValue);
}

//kernel method for computing the local portion of C = A^T*B
int mult_Atrans_B(CrsMatrixStruct& Aview,
      CrsMatrixStruct& Bview,
      Epetra_CrsMatrix& C)
{
  int C_firstCol = Bview.colMap->MinLID();
  int C_lastCol = Bview.colMap->MaxLID();

  int C_firstCol_import = 0;
  int C_lastCol_import = -1;

  if (Bview.importColMap != NULL) {
    C_firstCol_import = Bview.importColMap->MinLID();
    C_lastCol_import = Bview.importColMap->MaxLID();
  }

  int C_numCols = C_lastCol - C_firstCol + 1;
  int C_numCols_import = C_lastCol_import - C_firstCol_import + 1;

  if (C_numCols_import > C_numCols) C_numCols = C_numCols_import;

  double* C_row_i = new double[C_numCols];
  int* C_colInds = new int[C_numCols];

  int i, j, k;

  for(j=0; j<C_numCols; ++j) {
    C_row_i[j] = 0.0;
    C_colInds[j] = 0;
  }

  //To form C = A^T*B, compute a series of outer-product updates.
  //
  // for (ith column of A^T) { 
  //   C_i = outer product of A^T(:,i) and B(i,:)
  // Where C_i is the ith matrix update,
  //       A^T(:,i) is the ith column of A^T, and
  //       B(i,:) is the ith row of B.
  //

  //dumpCrsMatrixStruct(Aview);
  //dumpCrsMatrixStruct(Bview);
  int localProc = Bview.colMap->Comm().MyPID();

  int* Arows = Aview.rowMap->MyGlobalElements();

  bool C_filled = C.Filled();

  //loop over the rows of A (which are the columns of A^T).
  for(i=0; i<Aview.numRows; ++i) {

    int* Aindices_i = Aview.indices[i];
    double* Aval_i  = Aview.values[i];

    //we'll need to get the row of B corresponding to Arows[i],
    //where Arows[i] is the GID of A's ith row.
    int Bi = Bview.rowMap->LID(Arows[i]);
    if (Bi<0) {
      cout << "mult_Atrans_B ERROR, proc "<<localProc<<" needs row "
     <<Arows[i]<<" of matrix B, but doesn't have it."<<endl;
      return(-1);
    }

    int* Bcol_inds = Bview.indices[Bi];
    double* Bvals_i = Bview.values[Bi];

    //for each column-index Aj in the i-th row of A, we'll update
    //global-row GID(Aj) of the result matrix C. In that row of C,
    //we'll update column-indices given by the column-indices in the
    //ith row of B that we're now holding (Bcol_inds).

    //First create a list of GIDs for the column-indices
    //that we'll be updating.

    int Blen = Bview.numEntriesPerRow[Bi];
    if (Bview.remote[Bi]) {
      for(j=0; j<Blen; ++j) {
        C_colInds[j] = Bview.importColMap->GID(Bcol_inds[j]);
      }
    }
    else {
      for(j=0; j<Blen; ++j) {
        C_colInds[j] = Bview.colMap->GID(Bcol_inds[j]);
      }
    }

    //loop across the i-th row of A (column of A^T)
    for(j=0; j<Aview.numEntriesPerRow[i]; ++j) {

      int Aj = Aindices_i[j];
      double Aval = Aval_i[j];

      int global_row;
      if (Aview.remote[i]) {
  global_row = Aview.importColMap->GID(Aj);
      }
      else {
  global_row = Aview.colMap->GID(Aj);
      }

      if (!C.RowMap().MyGID(global_row)) {
        continue;
      }

      for(k=0; k<Blen; ++k) {
        C_row_i[k] = Aval*Bvals_i[k];
      }

      //
      //Now add this row-update to C.
      //

      int err = C_filled ?
        C.SumIntoGlobalValues(global_row, Blen, C_row_i, C_colInds)
        :
        C.InsertGlobalValues(global_row, Blen, C_row_i, C_colInds);

      if (err < 0) {
        return(err);
      }
      if (err > 0) {
        if (C_filled) {
          //C is Filled, and doesn't have all the necessary nonzero locations.
          return(err);
        }
      }
    }
  }

  delete [] C_row_i;
  delete [] C_colInds;

  return(0);
}

//kernel method for computing the local portion of C = A^T*B^T
int mult_Atrans_Btrans(CrsMatrixStruct& Aview,
           CrsMatrixStruct& Bview,
           Epetra_CrsMatrix& C)
{
  int C_firstCol = Aview.rowMap->MinLID();
  int C_lastCol = Aview.rowMap->MaxLID();

  int C_firstCol_import = 0;
  int C_lastCol_import = -1;

  if (Aview.importColMap != NULL) {
    C_firstCol_import = Aview.importColMap->MinLID();
    C_lastCol_import = Aview.importColMap->MaxLID();
  }

  int C_numCols = C_lastCol - C_firstCol + 1;
  int C_numCols_import = C_lastCol_import - C_firstCol_import + 1;

  if (C_numCols_import > C_numCols) C_numCols = C_numCols_import;

  double* dwork = new double[C_numCols];
  int* iwork = new int[C_numCols];

  double* C_col_j = dwork;
  int* C_inds = iwork;

  //cout << "Aview: " << endl;
  //dumpCrsMatrixStruct(Aview);

  //cout << "Bview: " << endl;
  //dumpCrsMatrixStruct(Bview);


  int i, j, k;

  for(j=0; j<C_numCols; ++j) {
    C_col_j[j] = 0.0;
    C_inds[j] = -1;
  }

  int* A_col_inds = Aview.colMap->MyGlobalElements();
  int* A_col_inds_import = Aview.importColMap ?
    Aview.importColMap->MyGlobalElements() : 0;

  const Epetra_Map* Crowmap = &(C.RowMap());

  //To form C = A^T*B^T, we're going to execute this expression:
  //
  // C(i,j) = sum_k( A(k,i)*B(j,k) )
  //
  //Our goal, of course, is to navigate the data in A and B once, without
  //performing searches for column-indices, etc. In other words, we avoid
  //column-wise operations like the plague...

  int* Brows = Bview.rowMap->MyGlobalElements();

  //loop over the rows of B
  for(j=0; j<Bview.numRows; ++j) {
    int* Bindices_j = Bview.indices[j];
    double* Bvals_j = Bview.values[j];

    int global_col = Brows[j];

    //loop across columns in the j-th row of B and for each corresponding
    //row in A, loop across columns and accumulate product
    //A(k,i)*B(j,k) into our partial sum quantities in C_col_j. In other
    //words, as we stride across B(j,:), we use selected rows in A to
    //calculate updates for column j of the result matrix C.

    for(k=0; k<Bview.numEntriesPerRow[j]; ++k) {

      int bk = Bindices_j[k];
      double Bval = Bvals_j[k];

      int global_k;
      if (Bview.remote[j]) {
  global_k = Bview.importColMap->GID(bk);
      }
      else {
  global_k = Bview.colMap->GID(bk);
      }

      //get the corresponding row in A
      int ak = Aview.rowMap->LID(global_k);
      if (ak<0) {
  continue;
      }

      int* Aindices_k = Aview.indices[ak];
      double* Avals_k = Aview.values[ak];

      int C_len = 0;

      if (Aview.remote[ak]) {
  for(i=0; i<Aview.numEntriesPerRow[ak]; ++i) {
    C_col_j[C_len] = Avals_k[i]*Bval;
          C_inds[C_len++] = A_col_inds_import[Aindices_k[i]];
  }
      }
      else {
  for(i=0; i<Aview.numEntriesPerRow[ak]; ++i) {
    C_col_j[C_len] = Avals_k[i]*Bval;
          C_inds[C_len++] = A_col_inds[Aindices_k[i]];
  }
      }

      //Now loop across the C_col_j values and put non-zeros into C.

      for(i=0; i < C_len ; ++i) {
  if (C_col_j[i] == 0.0) continue;

  int global_row = C_inds[i];
  if (!Crowmap->MyGID(global_row)) {
    continue;
  }

  int err = C.SumIntoGlobalValues(global_row, 1, &(C_col_j[i]), &global_col);

  if (err < 0) {
    return(err);
  }
  else {
          if (err > 0) {
      err = C.InsertGlobalValues(global_row, 1, &(C_col_j[i]), &global_col);
      if (err < 0) {
              return(err);
            }
    }
  }
      }

    }
  }

  delete [] dwork;
  delete [] iwork;

  return(0);
}

int import_and_extract_views(const Epetra_CrsMatrix& M,
           const Epetra_Map& targetMap,
           CrsMatrixStruct& Mview)
{
  //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();

  const Epetra_Map& Mrowmap = M.RowMap();

  int numProcs = Mrowmap.Comm().NumProc();

  Mview.numRows = targetMap.NumMyElements();

  int* Mrows = targetMap.MyGlobalElements();

  if (Mview.numRows > 0) {
    Mview.numEntriesPerRow = new int[Mview.numRows];
    Mview.indices = new int*[Mview.numRows];
    Mview.values = new double*[Mview.numRows];
    Mview.remote = new bool[Mview.numRows];
  }

  Mview.numRemote = 0;

  int i;
  for(i=0; i<Mview.numRows; ++i) {
    int mlid = Mrowmap.LID(Mrows[i]);
    if (mlid < 0) {
      Mview.remote[i] = true;
      ++Mview.numRemote;
    }
    else {
      EPETRA_CHK_ERR( M.ExtractMyRowView(mlid, Mview.numEntriesPerRow[i],
           Mview.values[i], Mview.indices[i]) );
      Mview.remote[i] = false;
    }
  }

  Mview.origRowMap = &(M.RowMap());
  Mview.rowMap = &targetMap;
  Mview.colMap = &(M.ColMap());
  Mview.domainMap = &(M.DomainMap());
  Mview.importColMap = NULL;

  if (numProcs < 2) {
    if (Mview.numRemote > 0) {
      cerr << "EpetraExt::MatrixMatrix::Multiply ERROR, numProcs < 2 but "
     << "attempting to import remote matrix rows."<<endl;
      return(-1);
    }

    //If only one processor we don't need to import any remote rows, so return.
    return(0);
  }

  //
  //Now we will import the needed remote rows of M, if the global maximum
  //value of numRemote is greater than 0.
  //

  int globalMaxNumRemote = 0;
  Mrowmap.Comm().MaxAll(&Mview.numRemote, &globalMaxNumRemote, 1);

  if (globalMaxNumRemote > 0) {
    //Create a map that describes the remote rows of M that we need.

    int* MremoteRows = Mview.numRemote>0 ? new int[Mview.numRemote] : NULL;
    int offset = 0;
    for(i=0; i<Mview.numRows; ++i) {
      if (Mview.remote[i]) {
  MremoteRows[offset++] = Mrows[i];
      }
    }

    Epetra_Map MremoteRowMap(-1, Mview.numRemote, MremoteRows,
           Mrowmap.IndexBase(), Mrowmap.Comm());

    //Create an importer with target-map MremoteRowMap and 
    //source-map Mrowmap.
    Epetra_Import importer(MremoteRowMap, Mrowmap);

    //Now create a new matrix into which we can import the remote rows of M
    //that we need.
    Mview.importMatrix = new Epetra_CrsMatrix(Copy, MremoteRowMap, 1);

    EPETRA_CHK_ERR( Mview.importMatrix->Import(M, importer, Insert) );

    EPETRA_CHK_ERR( Mview.importMatrix->FillComplete(M.DomainMap(), M.RangeMap()) );

    //Finally, use the freshly imported data to fill in the gaps in our views
    //of rows of M.
    for(i=0; i<Mview.numRows; ++i) {
      if (Mview.remote[i]) {
  int importLID = MremoteRowMap.LID(Mrows[i]);
  EPETRA_CHK_ERR( Mview.importMatrix->ExtractMyRowView(importLID,
              Mview.numEntriesPerRow[i],
              Mview.values[i],
              Mview.indices[i]) );
      }
    }

    Mview.importColMap = &(Mview.importMatrix->ColMap());

    delete [] MremoteRows;
  }

  return(0);
}

int distribute_list(const Epetra_Comm& Comm,
                    int lenSendList,
                    const int* sendList,
                    int& maxSendLen,
                    int*& recvList)
{
  maxSendLen = 0; 
  Comm.MaxAll(&lenSendList, &maxSendLen, 1);
  int numProcs = Comm.NumProc();
  recvList = new int[numProcs*maxSendLen];
  int* send = new int[maxSendLen];
  for(int i=0; i<lenSendList; ++i) {
    send[i] = sendList[i];
  }

  Comm.GatherAll(send, recvList, maxSendLen);
  delete [] send;

  return(0);
}

Epetra_Map* create_map_from_imported_rows(const Epetra_Map* map,
            int totalNumSend,
            int* sendRows,
            int numProcs,
            int* numSendPerProc)
{
  //Perform sparse all-to-all communication to send the row-GIDs
  //in sendRows to appropriate processors according to offset
  //information in numSendPerProc.
  //Then create and return a map containing the rows that we
  //received on the local processor.

  Epetra_Distributor* distributor = map->Comm().CreateDistributor();

  int* sendPIDs = totalNumSend>0 ? new int[totalNumSend] : NULL;
  int offset = 0;
  for(int i=0; i<numProcs; ++i) {
    for(int j=0; j<numSendPerProc[i]; ++j) {
      sendPIDs[offset++] = i;
    }
  }

  int numRecv = 0;
  int err = distributor->CreateFromSends(totalNumSend, sendPIDs,
           true, numRecv);
  assert( err == 0 );

  char* c_recv_objs = numRecv>0 ? new char[numRecv*sizeof(int)] : NULL;
  int num_c_recv = numRecv*(int)sizeof(int);

  err = distributor->Do(reinterpret_cast<char*>(sendRows),
      (int)sizeof(int), num_c_recv, c_recv_objs);
  assert( err == 0 );

  int* recvRows = reinterpret_cast<int*>(c_recv_objs);

  //Now create a map with the rows we've received from other processors.
  Epetra_Map* import_rows = new Epetra_Map(-1, numRecv, recvRows,
             map->IndexBase(), map->Comm());

  delete [] c_recv_objs;
  delete [] sendPIDs;

  delete distributor;

  return( import_rows );
}

int form_map_union(const Epetra_Map* map1,
       const Epetra_Map* map2,
       const Epetra_Map*& mapunion)
{
  //form the union of two maps

  if (map1 == NULL) {
    mapunion = new Epetra_Map(*map2);
    return(0);
  }

  if (map2 == NULL) {
    mapunion = new Epetra_Map(*map1);
    return(0);
  }

  int map1_len       = map1->NumMyElements();
  int* map1_elements = map1->MyGlobalElements();
  int map2_len       = map2->NumMyElements();
  int* map2_elements = map2->MyGlobalElements();

  int* union_elements = new int[map1_len+map2_len];

  int map1_offset = 0, map2_offset = 0, union_offset = 0;

  while(map1_offset < map1_len && map2_offset < map2_len) {
    int map1_elem = map1_elements[map1_offset];
    int map2_elem = map2_elements[map2_offset];

    if (map1_elem < map2_elem) {
      union_elements[union_offset++] = map1_elem;
      ++map1_offset;
    }
    else if (map1_elem > map2_elem) {
      union_elements[union_offset++] = map2_elem;
      ++map2_offset;
    }
    else {
      union_elements[union_offset++] = map1_elem;
      ++map1_offset;
      ++map2_offset;
    }
  }

  int i;
  for(i=map1_offset; i<map1_len; ++i) {
    union_elements[union_offset++] = map1_elements[i];
  }

  for(i=map2_offset; i<map2_len; ++i) {
    union_elements[union_offset++] = map2_elements[i];
  }

  mapunion = new Epetra_Map(-1, union_offset, union_elements,
          map1->IndexBase(), map1->Comm());

  delete [] union_elements;

  return(0);
}

Epetra_Map* find_rows_containing_cols(const Epetra_CrsMatrix& M,
                                      const Epetra_Map* colmap)
{
  //The goal of this function is to find all rows in the matrix M that contain
  //column-indices which are in 'colmap'. A map containing those rows is
  //returned.

  int numProcs = colmap->Comm().NumProc();
  int localProc = colmap->Comm().MyPID();

  if (numProcs < 2) {
    Epetra_Map* result_map = NULL;

    int err = form_map_union(&(M.RowMap()), NULL,
                             (const Epetra_Map*&)result_map);
    if (err != 0) {
      return(NULL);
    }
    return(result_map);
  }

  int MnumRows = M.NumMyRows();
  int numCols = colmap->NumMyElements();

  int* iwork = new int[numCols+2*numProcs+numProcs*MnumRows];
  int iworkOffset = 0;

  int* cols = &(iwork[iworkOffset]); iworkOffset += numCols;

  cols[0] = numCols;
  colmap->MyGlobalElements( &(cols[1]) );

  //cols are not necessarily sorted at this point, so we'll make sure
  //they are sorted.
  Epetra_Util util;
  util.Sort(true, numCols, &(cols[1]), 0, NULL, 0, NULL);

  int* all_proc_cols = NULL;
  
  int max_num_cols;
  distribute_list(colmap->Comm(), numCols+1, cols, max_num_cols, all_proc_cols);

  const Epetra_CrsGraph& Mgraph = M.Graph();
  const Epetra_Map& Mrowmap = M.RowMap();
  const Epetra_Map& Mcolmap = M.ColMap();
  int MminMyLID = Mrowmap.MinLID();

  int* procNumCols = &(iwork[iworkOffset]); iworkOffset += numProcs;
  int* procNumRows = &(iwork[iworkOffset]); iworkOffset += numProcs;
  int* procRows_1D = &(iwork[iworkOffset]);
  int** procCols = new int*[numProcs];
  int** procRows = new int*[numProcs];
  int i, err;
  int offset = 0;
  for(i=0; i<numProcs; ++i) {
    procNumCols[i] = all_proc_cols[offset];
    procCols[i] = &(all_proc_cols[offset+1]);
    offset += max_num_cols;

    procNumRows[i] = 0;
    procRows[i] = &(procRows_1D[i*MnumRows]);
  }

  int* Mindices;

  for(int row=0; row<MnumRows; ++row) {
    int localRow = MminMyLID+row;
    int globalRow = Mrowmap.GID(localRow);
    int MnumCols;
    err = Mgraph.ExtractMyRowView(localRow, MnumCols, Mindices);
    if (err != 0) {
      cerr << "proc "<<localProc<<", error in Mgraph.ExtractMyRowView, row "
           <<localRow<<endl;
      return(NULL);
    }

    for(int j=0; j<MnumCols; ++j) {
      int colGID = Mcolmap.GID(Mindices[j]);

      for(int p=0; p<numProcs; ++p) {
        if (p==localProc) continue;

        int insertPoint;
        int foundOffset = Epetra_Util_binary_search(colGID, procCols[p],
                                                    procNumCols[p], insertPoint);
        if (foundOffset > -1) {
          int numRowsP = procNumRows[p];
          int* prows = procRows[p];
          if (numRowsP < 1 || prows[numRowsP-1] < globalRow) {
            prows[numRowsP] = globalRow;
            procNumRows[p]++;
          }
        }
      }
    }
  }

  //Now make the contents of procRows occupy a contiguous section
  //of procRows_1D.
  offset = procNumRows[0];
  for(i=1; i<numProcs; ++i) {
    for(int j=0; j<procNumRows[i]; ++j) {
      procRows_1D[offset++] = procRows[i][j];
    }
  }

  int totalNumSend = offset;
  //Next we will do a sparse all-to-all communication to send the lists of rows
  //to the appropriate processors, and create a map with the rows we've received
  //from other processors.
  Epetra_Map* recvd_rows =
    create_map_from_imported_rows(&Mrowmap, totalNumSend,
                                  procRows_1D, numProcs, procNumRows);

  Epetra_Map* result_map = NULL;

  err = form_map_union(&(M.RowMap()), recvd_rows, (const Epetra_Map*&)result_map);
  if (err != 0) {
    return(NULL);
  }

  delete [] iwork;
  delete [] procCols;
  delete [] procRows;
  delete [] all_proc_cols;
  delete recvd_rows;

  return(result_map);
}

int MatrixMatrix::Multiply(const Epetra_CrsMatrix& A,
         bool transposeA,
         const Epetra_CrsMatrix& B,
         bool transposeB,
         Epetra_CrsMatrix& C,
                           bool call_FillComplete_on_result)
{
  //
  //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).
  //

  //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.)
  if (!A.Filled() || !B.Filled()) {
    EPETRA_CHK_ERR(-1);
  }

  //We're going to refer to the different combinations of op(A) and op(B)
  //as scenario 1 through 4.

  int scenario = 1;//A*B
  if (transposeB && !transposeA) scenario = 2;//A*B^T
  if (transposeA && !transposeB) scenario = 3;//A^T*B
  if (transposeA && transposeB)  scenario = 4;//A^T*B^T

  //now check size compatibility
  int Aouter = transposeA ? A.NumGlobalCols() : A.NumGlobalRows();
  int Bouter = transposeB ? B.NumGlobalRows() : B.NumGlobalCols();
  int Ainner = transposeA ? A.NumGlobalRows() : A.NumGlobalCols();
  int Binner = transposeB ? B.NumGlobalCols() : B.NumGlobalRows();
  if (Ainner != Binner) {
    cerr << "MatrixMatrix::Multiply: 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<<endl;
    return(-1);
  }

  //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.
  if (Aouter > C.NumGlobalRows()) {
    cerr << "MatrixMatrix::Multiply: ERROR, dimensions of result C must "
         << "match dimensions of op(A) * op(B). C has "<<C.NumGlobalRows()
         << " rows, should have at least "<<Aouter << endl;
    return(-1);
  }

  //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.

  //We're going to need to import remotely-owned sections of A and/or B
  //if more than 1 processor is performing this run, depending on the scenario.
  int numProcs = A.Comm().NumProc();

  //If we are to use the transpose of A and/or B, we'll need to be able to 
  //access, on the local processor, all rows that contain column-indices in
  //the domain-map.
  const Epetra_Map* domainMap_A = &(A.DomainMap());
  const Epetra_Map* domainMap_B = &(B.DomainMap());

  const Epetra_Map* rowmap_A = &(A.RowMap());
  const Epetra_Map* rowmap_B = &(B.RowMap());

  //Declare some 'work-space' maps which may be created depending on
  //the scenario, and which will be deleted before exiting this function.
  const Epetra_Map* workmap1 = NULL;
  const Epetra_Map* workmap2 = NULL;
  const Epetra_Map* mapunion1 = NULL;

  //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.
  CrsMatrixStruct Aview;
  CrsMatrixStruct Bview;

  const Epetra_Map* targetMap_A = rowmap_A;
  const Epetra_Map* targetMap_B = rowmap_B;

  if (numProcs > 1) {
    //If op(A) = A^T, find all rows of A that contain column-indices in the
    //local portion of the domain-map. (We'll import any remote rows
    //that fit this criteria onto the local processor.)
    if (transposeA) {
      workmap1 = find_rows_containing_cols(A, domainMap_A);
      targetMap_A = workmap1;
    }
  }

  //Now import any needed remote rows and populate the Aview struct.
  EPETRA_CHK_ERR( import_and_extract_views(A, *targetMap_A, Aview) );

  //We will also need local access to all rows of B that correspond to the
  //column-map of op(A).
  if (numProcs > 1) {
    const Epetra_Map* colmap_op_A = NULL;
    if (transposeA) {
      colmap_op_A = targetMap_A;
    }
    else {
      colmap_op_A = &(A.ColMap());
    }

    targetMap_B = colmap_op_A;

    //If op(B) = B^T, find all rows of B that contain column-indices in the
    //local-portion of the domain-map, or in the column-map of op(A).
    //We'll import any remote rows that fit this criteria onto the
    //local processor.
    if (transposeB) {
      EPETRA_CHK_ERR( form_map_union(colmap_op_A, domainMap_B, mapunion1) );
      workmap2 = find_rows_containing_cols(B, mapunion1);
      targetMap_B = workmap2;
    }
  }

  //Now import any needed remote rows and populate the Bview struct.
  EPETRA_CHK_ERR( import_and_extract_views(B, *targetMap_B, Bview) );

  //zero the result matrix before we start the calculations.
  EPETRA_CHK_ERR( C.PutScalar(0.0) );


  //Now call the appropriate method to perform the actual multiplication.

  switch(scenario) {
  case 1:    EPETRA_CHK_ERR( mult_A_B(Aview, Bview, C) );
    break;
  case 2:    EPETRA_CHK_ERR( mult_A_Btrans(Aview, Bview, C) );
    break;
  case 3:    EPETRA_CHK_ERR( mult_Atrans_B(Aview, Bview, C) );
    break;
  case 4:    EPETRA_CHK_ERR( mult_Atrans_Btrans(Aview, Bview, C) );
    break;
  }

  if (call_FillComplete_on_result) {
    //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.

    const Epetra_Map* domainmap =
      transposeB ? &(B.RangeMap()) : &(B.DomainMap());

    const Epetra_Map* rangemap =
      transposeA ? &(A.DomainMap()) : &(A.RangeMap());

    if (!C.Filled()) {
      EPETRA_CHK_ERR( C.FillComplete(*domainmap, *rangemap) );
    }
  }

  //Finally, delete the objects that were potentially created
  //during the course of importing remote sections of A and B.

  delete mapunion1; mapunion1 = NULL;
  delete workmap1; workmap1 = NULL;
  delete workmap2; workmap2 = NULL;

  return(0);
}

int MatrixMatrix::Add(const Epetra_CrsMatrix& A,
                      bool transposeA,
                      double scalarA,
                      Epetra_CrsMatrix& B,
                      double scalarB )
{
  //
  //This method forms the matrix-matrix sum B = scalarA * op(A) + scalarB * B, where

  //A should already be Filled. It doesn't matter whether B is
  //already Filled, but if it is, then its graph must already contain
  //all nonzero locations that will be referenced in forming the
  //sum.

  if (!A.Filled() ) {
    std::cerr << "EpetraExt::MatrixMatrix::Add ERROR, input matrix A.Filled() is false, it is required to be true. (Result matrix B is not required to be Filled())."<<std::endl;
    EPETRA_CHK_ERR(-1);
  }

  //explicit tranpose A formed as necessary
  Epetra_CrsMatrix * Aprime = 0;
  EpetraExt::RowMatrix_Transpose * Atrans = 0;
  if( transposeA )
  {
    Atrans = new EpetraExt::RowMatrix_Transpose();
    Aprime = &(dynamic_cast<Epetra_CrsMatrix&>(((*Atrans)(const_cast<Epetra_CrsMatrix&>(A)))));
  }
  else
    Aprime = const_cast<Epetra_CrsMatrix*>(&A);

  int MaxNumEntries = EPETRA_MAX( A.MaxNumEntries(), B.MaxNumEntries() );
  int A_NumEntries, B_NumEntries;
  int * A_Indices = new int[MaxNumEntries];
  double * A_Values = new double[MaxNumEntries];
  int* B_Indices;
  double* B_Values;

  int NumMyRows = B.NumMyRows();
  int Row, err;

  if( scalarA )
  {
    //Loop over B's rows and sum into
    for( int i = 0; i < NumMyRows; ++i )
    {
      Row = B.GRID(i);
      EPETRA_CHK_ERR( Aprime->ExtractGlobalRowCopy( Row, MaxNumEntries, A_NumEntries, A_Values, A_Indices ) );

      if (scalarB != 1.0) {
        if (!B.Filled()) {
          EPETRA_CHK_ERR( B.ExtractGlobalRowView( Row, B_NumEntries,
                                                  B_Values, B_Indices));
        }
        else {
          EPETRA_CHK_ERR( B.ExtractMyRowView( i, B_NumEntries,
                                              B_Values, B_Indices));
        }

        for(int jj=0; jj<B_NumEntries; ++jj) {
          B_Values[jj] = scalarB*B_Values[jj];
        }
      }

      if( scalarA != 1.0 ) {
        for( int j = 0; j < A_NumEntries; ++j ) A_Values[j] *= scalarA;
      }

      if( B.Filled() ) {//Sum In Values
        err = B.SumIntoGlobalValues( Row, A_NumEntries, A_Values, A_Indices );
        assert( err == 0 );
      }
      else {
        err = B.InsertGlobalValues( Row, A_NumEntries, A_Values, A_Indices );
        assert( err == 0 || err == 1 || err == 3 );
      }
    }
  }
  else {
    EPETRA_CHK_ERR( B.Scale(scalarB) );
  }

  delete [] A_Indices;
  delete [] A_Values;

  if( Atrans ) delete Atrans;

  return(0);
}

} // namespace EpetraExt

---