BlockDACG.cpp

// @HEADER
// *****************************************************************************
//                 Anasazi: Block Eigensolvers Package
//
// Copyright 2004 NTESS and the Anasazi contributors.
// SPDX-License-Identifier: BSD-3-Clause
// *****************************************************************************
// @HEADER
 
// This software is a result of the research described in the report
//
//     "A comparison of algorithms for modal analysis in the absence
//     of a sparse direct method", P. Arbenz, R. Lehoucq, and U. Hetmaniuk,
//     Sandia National Laboratories, Technical report SAND2003-1028J.
//
// It is based on the Epetra, AztecOO, and ML packages defined in the Trilinos
// framework ( http://trilinos.org/ ).
 
#include "BlockDACG.h"
 
 
BlockDACG::BlockDACG(const Epetra_Comm &_Comm, const Epetra_Operator *KK,
                     const Epetra_Operator *PP, int _blk,
                     double _tol, int _maxIter, int _verb) :
           myVerify(_Comm),
           callBLAS(),
           callFortran(),
           modalTool(_Comm),
           mySort(),
           MyComm(_Comm),
           K(KK),
           M(0),
           Prec(PP),
           MyWatch(_Comm),
           tolEigenSolve(_tol),
           maxIterEigenSolve(_maxIter),
           blockSize(_blk),
           normWeight(0),
           verbose(_verb),
           historyCount(0),
           resHistory(0),
           memRequested(0.0),
           highMem(0.0),
           massOp(0),
           numRestart(0),
           outerIter(0),
           precOp(0),
           residual(0),
           stifOp(0),
           timeLocalProj(0.0),
           timeLocalSolve(0.0),
           timeLocalUpdate(0.0),
           timeMassOp(0.0),
           timeNorm(0.0),
           timeOrtho(0.0),
           timeOuterLoop(0.0),
           timePostProce(0.0),
           timePrecOp(0.0),
           timeResidual(0.0),
           timeRestart(0.0),
           timeSearchP(0.0),
           timeStifOp(0.0)
           {
 
}
 
 
BlockDACG::BlockDACG(const Epetra_Comm &_Comm, const Epetra_Operator *KK,
                     const Epetra_Operator *MM, const Epetra_Operator *PP, int _blk,
                     double _tol, int _maxIter, int _verb,
                     double *_weight) :
           myVerify(_Comm),
           callBLAS(),
           callFortran(),
           modalTool(_Comm),
           mySort(),
           MyComm(_Comm),
           K(KK),
           M(MM),
           Prec(PP),
           MyWatch(_Comm),
           tolEigenSolve(_tol),
           maxIterEigenSolve(_maxIter),
           blockSize(_blk),
           normWeight(_weight),
           verbose(_verb),
           historyCount(0),
           resHistory(0),
           memRequested(0.0),
           highMem(0.0),
           massOp(0),
           numRestart(0),
           outerIter(0),
           precOp(0),
           residual(0),
           stifOp(0),
           timeLocalProj(0.0),
           timeLocalSolve(0.0),
           timeLocalUpdate(0.0),
           timeMassOp(0.0),
           timeNorm(0.0),
           timeOrtho(0.0),
           timeOuterLoop(0.0),
           timePostProce(0.0),
           timePrecOp(0.0),
           timeResidual(0.0),
           timeRestart(0.0),
           timeSearchP(0.0),
           timeStifOp(0.0)
           {
 
}
 
 
BlockDACG::~BlockDACG() {
 
  if (resHistory) {
    delete[] resHistory;
    resHistory = 0;
  }
 
}
 
 
int BlockDACG::solve(int numEigen, Epetra_MultiVector &Q, double *lambda) {
 
  return BlockDACG::reSolve(numEigen, Q, lambda);
}
 
 
int BlockDACG::reSolve(int numEigen, Epetra_MultiVector &Q, double *lambda, int startingEV) {
 
  // Computes the smallest eigenvalues and the corresponding eigenvectors
  // of the generalized eigenvalue problem
  // 
  //      K X = M X Lambda
  // 
  // using a Block Deflation Accelerated Conjugate Gradient algorithm.
  //
  // Note that if M is not specified, then  K X = X Lambda is solved.
  //
  // Ref: P. Arbenz & R. Lehoucq, "A comparison of algorithms for modal analysis in the
  // absence of a sparse direct method", SNL, Technical Report SAND2003-1028J
  // With the notations of this report, the coefficient beta is defined as 
  //                 diag( H^T_{k} G_{k} ) / diag( H^T_{k-1} G_{k-1} )
  // 
  // Input variables:
  // 
  // numEigen  (integer) = Number of eigenmodes requested
  // 
  // Q (Epetra_MultiVector) = Converged eigenvectors
  //                   The number of columns of Q must be equal to numEigen + blockSize.
  //                   The rows of Q are distributed across processors.
  //                   At exit, the first numEigen columns contain the eigenvectors requested.
  // 
  // lambda (array of doubles) = Converged eigenvalues
  //                   At input, it must be of size numEigen + blockSize.
  //                   At exit, the first numEigen locations contain the eigenvalues requested.
  //
  // startingEV (integer) = Number of existing converged eigenmodes
  //
  // Return information on status of computation
  // 
  // info >=   0 >> Number of converged eigenpairs at the end of computation
  // 
  // // Failure due to input arguments
  // 
  // info = -  1 >> The stiffness matrix K has not been specified.
  // info = -  2 >> The maps for the matrix K and the matrix M differ.
  // info = -  3 >> The maps for the matrix K and the preconditioner P differ.
  // info = -  4 >> The maps for the vectors and the matrix K differ.
  // info = -  5 >> Q is too small for the number of eigenvalues requested.
  // info = -  6 >> Q is too small for the computation parameters.
  //
  // info = - 10 >> Failure during the mass orthonormalization
  // 
  // info = - 20 >> Error in LAPACK during the local eigensolve
  //
  // info = - 30 >> MEMORY
  //
 
  // Check the input parameters
  
  if (numEigen <= startingEV) {
    return startingEV;
  }
 
  int info = myVerify.inputArguments(numEigen, K, M, Prec, Q, numEigen + blockSize);
  if (info < 0)
    return info;
 
  int myPid = MyComm.MyPID();
 
  // Get the weight for approximating the M-inverse norm
  Epetra_Vector *vectWeight = 0;
  if (normWeight) {
    vectWeight = new Epetra_Vector(View, Q.Map(), normWeight);
  }
 
  int knownEV = startingEV;
  int localVerbose = verbose*(myPid==0);
 
  // Define local block vectors
  //
  // MX = Working vectors (storing M*X if M is specified, else pointing to X)
  // KX = Working vectors (storing K*X)
  //
  // R = Residuals
  //
  // H = Preconditioned residuals
  //
  // P = Search directions
  // MP = Working vectors (storing M*P if M is specified, else pointing to P)
  // KP = Working vectors (storing K*P)
 
  int xr = Q.MyLength();
  Epetra_MultiVector X(View, Q, numEigen, blockSize);
  X.Random();
 
  int tmp;
  tmp = (M == 0) ? 5*blockSize*xr : 7*blockSize*xr;
 
  double *work1 = new (nothrow) double[tmp]; 
  if (work1 == 0) {
    if (vectWeight)
      delete vectWeight;
    info = -30;
    return info;
  }
  memRequested += sizeof(double)*tmp/(1024.0*1024.0);
 
  highMem = (highMem > currentSize()) ? highMem : currentSize();
 
  double *tmpD = work1;
 
  Epetra_MultiVector KX(View, Q.Map(), tmpD, xr, blockSize);
  tmpD = tmpD + xr*blockSize;
 
  Epetra_MultiVector MX(View, Q.Map(), (M) ? tmpD : X.Values(), xr, blockSize);
  tmpD = (M) ? tmpD + xr*blockSize : tmpD;
 
  Epetra_MultiVector R(View, Q.Map(), tmpD, xr, blockSize);
  tmpD = tmpD + xr*blockSize;
 
  Epetra_MultiVector H(View, Q.Map(), tmpD, xr, blockSize);
  tmpD = tmpD + xr*blockSize;
 
  Epetra_MultiVector P(View, Q.Map(), tmpD, xr, blockSize);
  tmpD = tmpD + xr*blockSize;
 
  Epetra_MultiVector KP(View, Q.Map(), tmpD, xr, blockSize);
  tmpD = tmpD + xr*blockSize;
 
  Epetra_MultiVector MP(View, Q.Map(), (M) ? tmpD : P.Values(), xr, blockSize);
 
  // Define arrays
  //
  // theta = Store the local eigenvalues (size: 2*blockSize)
  // normR = Store the norm of residuals (size: blockSize)
  //
  // oldHtR = Store the previous H_i^T*R_i    (size: blockSize)
  // currentHtR = Store the current H_i^T*R_i (size: blockSize)
  //
  // MM = Local mass matrix              (size: 2*blockSize x 2*blockSize)
  // KK = Local stiffness matrix         (size: 2*blockSize x 2*blockSize)
  //
  // S = Local eigenvectors              (size: 2*blockSize x 2*blockSize)
 
  int lwork2;
  lwork2 = 5*blockSize + 12*blockSize*blockSize;
  double *work2 = new (nothrow) double[lwork2];
  if (work2 == 0) {
    if (vectWeight)
      delete vectWeight;
    delete[] work1;
    info = -30;
    return info;
  }
 
  highMem = (highMem > currentSize()) ? highMem : currentSize();
 
  tmpD = work2;
 
  double *theta = tmpD;
  tmpD = tmpD + 2*blockSize;
 
  double *normR = tmpD;
  tmpD = tmpD + blockSize;
 
  double *oldHtR = tmpD;
  tmpD = tmpD + blockSize;
  
  double *currentHtR = tmpD;
  tmpD = tmpD + blockSize;
  memset(currentHtR, 0, blockSize*sizeof(double));
  
  double *MM = tmpD;
  tmpD = tmpD + 4*blockSize*blockSize;
 
  double *KK = tmpD;
  tmpD = tmpD + 4*blockSize*blockSize;
 
  double *S = tmpD;
 
  memRequested += sizeof(double)*lwork2/(1024.0*1024.0);
 
  // Define an array to store the residuals history
  if (localVerbose > 2) {
    resHistory = new (nothrow) double[maxIterEigenSolve*blockSize];
    if (resHistory == 0) {
      if (vectWeight)
        delete vectWeight;
      delete[] work1;
      delete[] work2;
      info = -30;
      return info;
    }
    historyCount = 0;
  }
 
  // Miscellaneous definitions
 
  bool reStart = false;
  numRestart = 0;
 
  int localSize;
  int twoBlocks = 2*blockSize;
  int nFound = blockSize;
  int i, j;
 
  if (localVerbose > 0) {
    cout << endl;
    cout << " *|* Problem: ";
    if (M) 
      cout << "K*Q = M*Q D ";
    else
      cout << "K*Q = Q D ";
    if (Prec)
      cout << " with preconditioner";
    cout << endl;
    cout << " *|* Algorithm = DACG (block version)" << endl;
    cout << " *|* Size of blocks = " << blockSize << endl;
    cout << " *|* Number of requested eigenvalues = " << numEigen << endl;
    cout.precision(2);
    cout.setf(ios::scientific, ios::floatfield);
    cout << " *|* Tolerance for convergence = " << tolEigenSolve << endl;
    cout << " *|* Norm used for convergence: ";
    if (normWeight)
      cout << "weighted L2-norm with user-provided weights" << endl;
    else
      cout << "L^2-norm" << endl;
    if (startingEV > 0)
      cout << " *|* Input converged eigenvectors = " << startingEV << endl;
    cout << "\n -- Start iterations -- \n";
  }
 
  timeOuterLoop -= MyWatch.WallTime();
  for (outerIter = 1; outerIter <= maxIterEigenSolve; ++outerIter) {
 
    highMem = (highMem > currentSize()) ? highMem : currentSize();
 
    if ((outerIter == 1) || (reStart == true)) {
 
      reStart = false;
      localSize = blockSize;
 
      if (nFound > 0) {
 
        Epetra_MultiVector X2(View, X, blockSize-nFound, nFound);
        Epetra_MultiVector MX2(View, MX, blockSize-nFound, nFound);
        Epetra_MultiVector KX2(View, KX, blockSize-nFound, nFound);
 
        // Apply the mass matrix to X
        timeMassOp -= MyWatch.WallTime();
        if (M)
          M->Apply(X2, MX2);
        timeMassOp += MyWatch.WallTime();
        massOp += nFound;
 
        if (knownEV > 0) {
          // Orthonormalize X against the known eigenvectors with Gram-Schmidt
          // Note: Use R as a temporary work space
          Epetra_MultiVector copyQ(View, Q, 0, knownEV);
          timeOrtho -= MyWatch.WallTime();
          info = modalTool.massOrthonormalize(X, MX, M, copyQ, nFound, 0, R.Values());
          timeOrtho += MyWatch.WallTime();
          // Exit the code if the orthogonalization did not succeed
          if (info < 0) {
            info = -10;
            delete[] work1;
            delete[] work2;
            if (vectWeight)
              delete vectWeight;
            return info;
          }
        }
 
        // Apply the stiffness matrix to X
        timeStifOp -= MyWatch.WallTime();
        K->Apply(X2, KX2);
        timeStifOp += MyWatch.WallTime();
        stifOp += nFound;
 
      } // if (nFound > 0)
 
    } // if ((outerIter == 1) || (reStart == true))
    else {
 
      // Apply the preconditioner on the residuals
      if (Prec != 0) {
        timePrecOp -= MyWatch.WallTime();
        Prec->ApplyInverse(R, H);
        timePrecOp += MyWatch.WallTime();
        precOp += blockSize;
      }
      else {
        memcpy(H.Values(), R.Values(), xr*blockSize*sizeof(double));
      }
 
      // Compute the product H^T*R
      timeSearchP -= MyWatch.WallTime();      
      memcpy(oldHtR, currentHtR, blockSize*sizeof(double));
      H.Dot(R, currentHtR);
      // Define the new search directions
      if (localSize == blockSize) {
        P.Scale(-1.0, H);
        localSize = twoBlocks;
      } // if (localSize == blockSize)
      else {
        bool hasZeroDot = false;
        for (j = 0; j < blockSize; ++j) {
          if (oldHtR[j] == 0.0) {
            hasZeroDot = true;
            break; 
          }
          callBLAS.SCAL(xr, currentHtR[j]/oldHtR[j], P.Values() + j*xr);
        }
        if (hasZeroDot == true) {
          // Restart the computation when there is a null dot product
          if (localVerbose > 0) {
            cout << endl;
            cout << " !! Null dot product -- Restart the search space !!\n";
            cout << endl;
          }
          if (blockSize == 1) {
            X.Random();
            nFound = blockSize;
          }
          else {
            Epetra_MultiVector Xinit(View, X, j, blockSize-j);
            Xinit.Random();
            nFound = blockSize - j;
          } // if (blockSize == 1)
          reStart = true;
          numRestart += 1;
          info = 0;
          continue;
        }
        callBLAS.AXPY(xr*blockSize, -1.0, H.Values(), P.Values());
      } // if (localSize == blockSize)
      timeSearchP += MyWatch.WallTime();
 
      // Apply the mass matrix on P
      timeMassOp -= MyWatch.WallTime();
      if (M)
        M->Apply(P, MP);
      timeMassOp += MyWatch.WallTime();
      massOp += blockSize;
 
      if (knownEV > 0) {
        // Orthogonalize P against the known eigenvectors
        // Note: Use R as a temporary work space
        Epetra_MultiVector copyQ(View, Q, 0, knownEV);
        timeOrtho -= MyWatch.WallTime();
        modalTool.massOrthonormalize(P, MP, M, copyQ, blockSize, 1, R.Values());
        timeOrtho += MyWatch.WallTime();
      }
 
      // Apply the stiffness matrix to P
      timeStifOp -= MyWatch.WallTime();
      K->Apply(P, KP);
      timeStifOp += MyWatch.WallTime();
      stifOp += blockSize;
 
    } // if ((outerIter == 1) || (reStart == true))
 
    // Form "local" mass and stiffness matrices
    // Note: Use S as a temporary workspace
    timeLocalProj -= MyWatch.WallTime();
    modalTool.localProjection(blockSize, blockSize, xr, X.Values(), xr, KX.Values(), xr,
                    KK, localSize, S);
    modalTool.localProjection(blockSize, blockSize, xr, X.Values(), xr, MX.Values(), xr,
                    MM, localSize, S);
    if (localSize > blockSize) {
      modalTool.localProjection(blockSize, blockSize, xr, X.Values(), xr, KP.Values(), xr,
                      KK + blockSize*localSize, localSize, S);
      modalTool.localProjection(blockSize, blockSize, xr, P.Values(), xr, KP.Values(), xr,
                      KK + blockSize*localSize + blockSize, localSize, S);
      modalTool.localProjection(blockSize, blockSize, xr, X.Values(), xr, MP.Values(), xr,
                      MM + blockSize*localSize, localSize, S);
      modalTool.localProjection(blockSize, blockSize, xr, P.Values(), xr, MP.Values(), xr,
                      MM + blockSize*localSize + blockSize, localSize, S);
    } // if (localSize > blockSize)
    timeLocalProj += MyWatch.WallTime();
 
    // Perform a spectral decomposition
    timeLocalSolve -= MyWatch.WallTime();
    int nevLocal = localSize;
    info = modalTool.directSolver(localSize, KK, localSize, MM, localSize, nevLocal,
                                  S, localSize, theta, localVerbose,
                                  (blockSize == 1) ? 1: 0);
    timeLocalSolve += MyWatch.WallTime();
 
    if (info < 0) {
      // Stop when spectral decomposition has a critical failure
      break;
    }
 
    // Check for restarting
    if ((theta[0] < 0.0) || (nevLocal < blockSize)) {
      if (localVerbose > 0) {
        cout << " Iteration " << outerIter;
        cout << "- Failure for spectral decomposition - RESTART with new random search\n";
      }
      if (blockSize == 1) {
        X.Random();
        nFound = blockSize;
      }
      else {
        Epetra_MultiVector Xinit(View, X, 1, blockSize-1);
        Xinit.Random();
        nFound = blockSize - 1;
      } // if (blockSize == 1)
      reStart = true;
      numRestart += 1;
      info = 0;
      continue;
    } // if ((theta[0] < 0.0) || (nevLocal < blockSize))
 
    if ((localSize == twoBlocks) && (nevLocal == blockSize)) {
      for (j = 0; j < nevLocal; ++j)
        memcpy(S + j*blockSize, S + j*twoBlocks, blockSize*sizeof(double));
      localSize = blockSize;
    }
 
    // Check the direction of eigenvectors
    // Note: This sign check is important for convergence
    for (j = 0; j < nevLocal; ++j) {
      double coeff = S[j + j*localSize];
      if (coeff < 0.0)
        callBLAS.SCAL(localSize, -1.0, S + j*localSize);
    }
 
    // Compute the residuals
    timeResidual -= MyWatch.WallTime();
    callBLAS.GEMM('N', 'N', xr, blockSize, blockSize, 1.0, KX.Values(), xr,
                  S, localSize, 0.0, R.Values(), xr);
    if (localSize == twoBlocks) {
      callBLAS.GEMM('N', 'N', xr, blockSize, blockSize, 1.0, KP.Values(), xr,
                    S + blockSize, localSize, 1.0, R.Values(), xr);
    }
    for (j = 0; j < blockSize; ++j)
      callBLAS.SCAL(localSize, theta[j], S + j*localSize);
    callBLAS.GEMM('N', 'N', xr, blockSize, blockSize, -1.0, MX.Values(), xr,
                  S, localSize, 1.0, R.Values(), xr);
    if (localSize == twoBlocks) {
      callBLAS.GEMM('N', 'N', xr, blockSize, blockSize, -1.0, MP.Values(), xr,
                  S + blockSize, localSize, 1.0, R.Values(), xr);
    }
    for (j = 0; j < blockSize; ++j)
      callBLAS.SCAL(localSize, 1.0/theta[j], S + j*localSize);
    timeResidual += MyWatch.WallTime();
    
    // Compute the norms of the residuals
    timeNorm -= MyWatch.WallTime();
    if (vectWeight)
      R.NormWeighted(*vectWeight, normR);
    else
      R.Norm2(normR);
    // Scale the norms of residuals with the eigenvalues
    // Count the converged eigenvectors
    nFound = 0;
    for (j = 0; j < blockSize; ++j) {
      normR[j] = (theta[j] == 0.0) ? normR[j] : normR[j]/theta[j];
      if (normR[j] < tolEigenSolve) 
        nFound += 1;
    }
    timeNorm += MyWatch.WallTime();
 
    // Store the residual history
    if (localVerbose > 2) {
      memcpy(resHistory + historyCount*blockSize, normR, blockSize*sizeof(double));
      historyCount += 1;
    }
 
    // Print information on current iteration
    if (localVerbose > 0) {
      cout << " Iteration " << outerIter << " - Number of converged eigenvectors ";
      cout << knownEV + nFound << endl;
    }
 
    if (localVerbose > 1) {
      cout << endl;
      cout.precision(2);
      cout.setf(ios::scientific, ios::floatfield);
      for (i=0; i<blockSize; ++i) {
        cout << " Iteration " << outerIter << " - Scaled Norm of Residual " << i;
        cout << " = " << normR[i] << endl;
      }
      cout << endl;
      cout.precision(2);
      for (i=0; i<blockSize; ++i) {
        cout << " Iteration " << outerIter << " - Ritz eigenvalue " << i;
        cout.setf((fabs(theta[i]) < 0.01) ? ios::scientific : ios::fixed, ios::floatfield);
        cout << " = " << theta[i] << endl;
      }
      cout << endl;
    }
 
    if (nFound == 0) {
      // Update the spaces
      // Note: Use H as a temporary work space
      timeLocalUpdate -= MyWatch.WallTime();
      memcpy(H.Values(), X.Values(), xr*blockSize*sizeof(double));
      callBLAS.GEMM('N', 'N', xr, blockSize, blockSize, 1.0, H.Values(), xr, S, localSize,
                    0.0, X.Values(), xr);
      memcpy(H.Values(), KX.Values(), xr*blockSize*sizeof(double));
      callBLAS.GEMM('N', 'N', xr, blockSize, blockSize, 1.0, H.Values(), xr, S, localSize,
                    0.0, KX.Values(), xr);
      if (M) {
        memcpy(H.Values(), MX.Values(), xr*blockSize*sizeof(double));
        callBLAS.GEMM('N', 'N', xr, blockSize, blockSize, 1.0, H.Values(), xr, S, localSize,
                      0.0, MX.Values(), xr);
      }
      if (localSize == twoBlocks) {
        callBLAS.GEMM('N', 'N', xr, blockSize, blockSize, 1.0, P.Values(), xr,
                      S + blockSize, localSize, 1.0, X.Values(), xr);
        callBLAS.GEMM('N', 'N', xr, blockSize, blockSize, 1.0, KP.Values(), xr,
                      S + blockSize, localSize, 1.0, KX.Values(), xr);
        if (M) {
          callBLAS.GEMM('N', 'N', xr, blockSize, blockSize, 1.0, MP.Values(), xr,
                      S + blockSize, localSize, 1.0, MX.Values(), xr);
        }
      } // if (localSize == twoBlocks)
      timeLocalUpdate += MyWatch.WallTime();
      // When required, monitor some orthogonalities
      if (verbose > 2) {
        if (knownEV == 0) {
          accuracyCheck(&X, &MX, &R, 0, (localSize>blockSize) ? &P : 0);
        }
        else {
          Epetra_MultiVector copyQ(View, Q, 0, knownEV);
          accuracyCheck(&X, &MX, &R, &copyQ, (localSize>blockSize) ? &P : 0);
        }
      } // if (verbose > 2)
      continue;
    } // if (nFound == 0)
 
    // Order the Ritz eigenvectors by putting the converged vectors at the beginning
    int firstIndex = blockSize;
    for (j = 0; j < blockSize; ++j) {
      if (normR[j] >= tolEigenSolve) {
        firstIndex = j;
        break;
      }
    } // for (j = 0; j < blockSize; ++j)
    while (firstIndex < nFound) {
      for (j = firstIndex; j < blockSize; ++j) {
        if (normR[j] < tolEigenSolve) {
          // Swap the j-th and firstIndex-th position
          callFortran.SWAP(localSize, S + j*localSize, 1, S + firstIndex*localSize, 1);
          callFortran.SWAP(1, theta + j, 1, theta + firstIndex, 1);
          callFortran.SWAP(1, normR + j, 1, normR + firstIndex, 1);
          break;
        }
      } // for (j = firstIndex; j < blockSize; ++j)
      for (j = 0; j < blockSize; ++j) {
        if (normR[j] >= tolEigenSolve) {
          firstIndex = j;
          break;
        }
      } // for (j = 0; j < blockSize; ++j)
    } // while (firstIndex < nFound)
 
    // Copy the converged eigenvalues
    memcpy(lambda + knownEV, theta, nFound*sizeof(double));
 
    // Convergence test
    if (knownEV + nFound >= numEigen) {
      callBLAS.GEMM('N', 'N', xr, nFound, blockSize, 1.0, X.Values(), xr,
                    S, localSize, 0.0, R.Values(), xr);
      if (localSize > blockSize) {
        callBLAS.GEMM('N', 'N', xr, nFound, blockSize, 1.0, P.Values(), xr,
                      S + blockSize, localSize, 1.0, R.Values(), xr);
      }
      memcpy(Q.Values() + knownEV*xr, R.Values(), nFound*xr*sizeof(double));
      knownEV += nFound;
      if (localVerbose == 1) {
        cout << endl;
        cout.precision(2);
        cout.setf(ios::scientific, ios::floatfield);
        for (i=0; i<blockSize; ++i) {
          cout << " Iteration " << outerIter << " - Scaled Norm of Residual " << i;
          cout << " = " << normR[i] << endl;
        }
        cout << endl;
      }
      break;
    }
 
    // Store the converged eigenvalues and eigenvectors
    callBLAS.GEMM('N', 'N', xr, nFound, blockSize, 1.0, X.Values(), xr,
                  S, localSize, 0.0, Q.Values() + knownEV*xr, xr);
    if (localSize == twoBlocks) {
      callBLAS.GEMM('N', 'N', xr, nFound, blockSize, 1.0, P.Values(), xr,
                    S + blockSize, localSize, 1.0, Q.Values() + knownEV*xr, xr);
    }
    knownEV += nFound;
 
    // Define the restarting vectors
    timeRestart -= MyWatch.WallTime();
    int leftOver = (nevLocal < blockSize + nFound) ? nevLocal - nFound : blockSize;
    double *Snew = S + nFound*localSize;
    memcpy(H.Values(), X.Values(), blockSize*xr*sizeof(double));
    callBLAS.GEMM('N', 'N', xr, leftOver, blockSize, 1.0, H.Values(), xr,
                  Snew, localSize, 0.0, X.Values(), xr);
    memcpy(H.Values(), KX.Values(), blockSize*xr*sizeof(double));
    callBLAS.GEMM('N', 'N', xr, leftOver, blockSize, 1.0, H.Values(), xr,
                  Snew, localSize, 0.0, KX.Values(), xr);
    if (M) {
      memcpy(H.Values(), MX.Values(), blockSize*xr*sizeof(double));
      callBLAS.GEMM('N', 'N', xr, leftOver, blockSize, 1.0, H.Values(), xr,
                    Snew, localSize, 0.0, MX.Values(), xr);
    }
    if (localSize == twoBlocks) {
      callBLAS.GEMM('N', 'N', xr, leftOver, blockSize, 1.0, P.Values(), xr,
                    Snew+blockSize, localSize, 1.0, X.Values(), xr);
      callBLAS.GEMM('N', 'N', xr, leftOver, blockSize, 1.0, KP.Values(), xr,
                    Snew+blockSize, localSize, 1.0, KX.Values(), xr);
      if (M) {
        callBLAS.GEMM('N', 'N', xr, leftOver, blockSize, 1.0, MP.Values(), xr,
                      Snew+blockSize, localSize, 1.0, MX.Values(), xr);
      }
    } // if (localSize == twoBlocks)
    if (nevLocal < blockSize + nFound) {
      // Put new random vectors at the end of the block
      Epetra_MultiVector Xtmp(View, X, leftOver, blockSize - leftOver);
      Xtmp.Random();
    }
    else {
      nFound = 0;
    } // if (nevLocal < blockSize + nFound)
    reStart = true;
    timeRestart += MyWatch.WallTime();
 
  } // for (outerIter = 1; outerIter <= maxIterEigenSolve; ++outerIter)
  timeOuterLoop += MyWatch.WallTime();
  highMem = (highMem > currentSize()) ? highMem : currentSize();
 
  // Clean memory
  delete[] work1;
  delete[] work2;
  if (vectWeight)
    delete vectWeight;
 
  // Sort the eigenpairs
  timePostProce -= MyWatch.WallTime();
  if ((info == 0) && (knownEV > 0)) {
    mySort.sortScalars_Vectors(knownEV, lambda, Q.Values(), Q.MyLength());
  }
  timePostProce += MyWatch.WallTime();
 
  return (info == 0) ? knownEV : info;
 
}
 
 
void BlockDACG::accuracyCheck(const Epetra_MultiVector *X, const Epetra_MultiVector *MX,
                       const Epetra_MultiVector *R, const Epetra_MultiVector *Q,
                       const Epetra_MultiVector *P) const {
 
  cout.precision(2);
  cout.setf(ios::scientific, ios::floatfield);
  double tmp;
 
  int myPid = MyComm.MyPID();
 
  if (X) {
    if (M) {
      if (MX) {
        tmp = myVerify.errorEquality(X, MX, M);
        if (myPid == 0)
          cout << " >> Difference between MX and M*X = " << tmp << endl;
      }
      tmp = myVerify.errorOrthonormality(X, M);
      if (myPid == 0)
        cout << " >> Error in X^T M X - I = " << tmp << endl;
    }
    else {
      tmp = myVerify.errorOrthonormality(X, 0);
      if (myPid == 0)
        cout << " >> Error in X^T X - I = " << tmp << endl;
    }
  }
 
  if ((R) && (X)) {
    tmp = myVerify.errorOrthogonality(X, R);
    if (myPid == 0)
      cout << " >> Orthogonality X^T R up to " << tmp << endl;
  }
 
  if (Q == 0)
    return;
 
  if (M) {
    tmp = myVerify.errorOrthonormality(Q, M);
    if (myPid == 0)
      cout << " >> Error in Q^T M Q - I = " << tmp << endl;
    if (X) {
      tmp = myVerify.errorOrthogonality(Q, X, M);
      if (myPid == 0)
        cout << " >> Orthogonality Q^T M X up to " << tmp << endl;
    }
    if (P) {
      tmp = myVerify.errorOrthogonality(Q, P, M);
      if (myPid == 0)
        cout << " >> Orthogonality Q^T M P up to " << tmp << endl;
    }
  }
  else {
    tmp = myVerify.errorOrthonormality(Q, 0);
    if (myPid == 0)
      cout << " >> Error in Q^T Q - I = " << tmp << endl;
    if (X) {
      tmp = myVerify.errorOrthogonality(Q, X, 0);
      if (myPid == 0)
        cout << " >> Orthogonality Q^T X up to " << tmp << endl;
    }
    if (P) {
      tmp = myVerify.errorOrthogonality(Q, P, 0);
      if (myPid == 0)
        cout << " >> Orthogonality Q^T P up to " << tmp << endl;
    }
  }
 
}
 
 
void BlockDACG::initializeCounters() {
 
  historyCount = 0;
  if (resHistory) {
    delete[] resHistory;
    resHistory = 0;
  }
 
  memRequested = 0.0;
  highMem = 0.0;
 
  massOp = 0;
  numRestart = 0;
  outerIter = 0;
  precOp = 0;
  residual = 0;
  stifOp = 0;
 
  timeLocalProj = 0.0;
  timeLocalSolve = 0.0;
  timeLocalUpdate = 0.0;
  timeMassOp = 0.0;
  timeNorm = 0.0;
  timeOrtho = 0.0;
  timeOuterLoop = 0.0;
  timePostProce = 0.0;
  timePrecOp = 0.0;
  timeResidual = 0.0;
  timeRestart = 0.0;
  timeSearchP = 0.0;
  timeStifOp = 0.0;
 
 
}
 
 
void BlockDACG::algorithmInfo() const {
 
  int myPid = MyComm.MyPID();
  
  if (myPid ==0) {
    cout << " Algorithm: DACG (block version) with Cholesky-based local eigensolver\n";
    cout << " Block Size: " << blockSize << endl;
  }
 
}
 
 
void BlockDACG::historyInfo() const {
 
  if (resHistory) {
    int j;
    cout << " Block Size: " << blockSize << endl;
    cout << endl;
    cout << " Residuals\n";
    cout << endl;
    cout.precision(4);
    cout.setf(ios::scientific, ios::floatfield);
    for (j = 0; j < historyCount; ++j) {
      int ii;
      for (ii = 0; ii < blockSize; ++ii)
        cout << resHistory[blockSize*j + ii] << endl;
    }
    cout << endl;
  }
 
}
 
 
void BlockDACG::memoryInfo() const {
 
  int myPid = MyComm.MyPID();
 
  double maxHighMem = 0.0;
  double tmp = highMem;
  MyComm.MaxAll(&tmp, &maxHighMem, 1);
 
  double maxMemRequested = 0.0;
  tmp = memRequested;
  MyComm.MaxAll(&tmp, &maxMemRequested, 1);
 
  if (myPid == 0) {
    cout.precision(2);
    cout.setf(ios::fixed, ios::floatfield);
    cout << " Memory requested per processor by the eigensolver   = (EST) ";
    cout.width(6);
    cout << maxMemRequested << " MB " << endl;
    cout << endl;
    cout << " High water mark in eigensolver                      = (EST) ";
    cout.width(6);
    cout << maxHighMem << " MB " << endl;
    cout << endl;
  }
 
}
 
 
void BlockDACG::operationInfo() const {
 
  int myPid = MyComm.MyPID();
  
  if (myPid == 0) {
    cout << " --- Operations ---\n\n";
    cout << " Total number of mass matrix multiplications      = ";
    cout.width(9);
    cout << massOp + modalTool.getNumProj_MassMult() + modalTool.getNumNorm_MassMult() << endl;
    cout << "       Mass multiplications in eigensolver        = ";
    cout.width(9);
    cout << massOp << endl;
    cout << "       Mass multiplications for orthogonalization = ";
    cout.width(9);
    cout << modalTool.getNumProj_MassMult() << endl;
    cout << "       Mass multiplications for normalization     = ";
    cout.width(9);
    cout << modalTool.getNumNorm_MassMult() << endl;
    cout << " Total number of stiffness matrix operations      = ";
    cout.width(9);
    cout << stifOp << endl;
    cout << " Total number of preconditioner applications      = ";
    cout.width(9);
    cout << precOp << endl;
    cout << " Total number of computed eigen-residuals         = ";
    cout.width(9);
    cout << residual << endl;
    cout << "\n";
    cout << " Total number of outer iterations                 = ";
    cout.width(9);
    cout << outerIter << endl;
    cout << "       Number of restarts                         = ";
    cout.width(9);
    cout << numRestart << endl;
    cout << "\n";
  }
 
}
 
 
void BlockDACG::timeInfo() const {
 
  int myPid = MyComm.MyPID();
  
  if (myPid == 0) {
    cout << " --- Timings ---\n\n";
    cout.setf(ios::fixed, ios::floatfield);
    cout.precision(2);
    cout << " Total time for outer loop                       = ";
    cout.width(9);
    cout << timeOuterLoop << " s" << endl;
    cout << "       Time for mass matrix operations           = ";
    cout.width(9);
    cout << timeMassOp << " s     ";
    cout.width(5);
    cout << 100*timeMassOp/timeOuterLoop << " %\n";
    cout << "       Time for stiffness matrix operations      = ";
    cout.width(9);
    cout << timeStifOp << " s     ";
    cout.width(5);
    cout << 100*timeStifOp/timeOuterLoop << " %\n";
    cout << "       Time for preconditioner applications      = ";
    cout.width(9);
    cout << timePrecOp << " s     ";
    cout.width(5);
    cout << 100*timePrecOp/timeOuterLoop << " %\n";
    cout << "       Time for defining search directions       = ";
    cout.width(9);
    cout << timeSearchP << " s     ";
    cout.width(5);
    cout << 100*timeSearchP/timeOuterLoop << " %\n";
    cout << "       Time for orthogonalizations               = ";
    cout.width(9);
    cout << timeOrtho << " s     ";
    cout.width(5);
    cout << 100*timeOrtho/timeOuterLoop << " %\n";
    cout << "            Projection step          : ";
    cout.width(9);
    cout << modalTool.getTimeProj() << " s\n";
    cout << "                 Q^T mult.  :: ";
    cout.width(9);
    cout << modalTool.getTimeProj_QtMult() << " s\n";
    cout << "                 Q mult.    :: ";
    cout.width(9);
    cout << modalTool.getTimeProj_QMult() << " s\n";
    cout << "                 Mass mult. :: ";
    cout.width(9);
    cout << modalTool.getTimeProj_MassMult() << " s\n";
    cout << "            Normalization step       : ";
    cout.width(9);
    cout << modalTool.getTimeNorm() << " s\n";
    cout << "                 Mass mult. :: ";
    cout.width(9);
    cout << modalTool.getTimeNorm_MassMult() << " s\n";
    cout << "       Time for local projection                 = ";
    cout.width(9);
    cout << timeLocalProj << " s     ";
    cout.width(5);
    cout << 100*timeLocalProj/timeOuterLoop << " %\n";
    cout << "       Time for local eigensolve                 = ";
    cout.width(9);
    cout << timeLocalSolve << " s     ";
    cout.width(5);
    cout << 100*timeLocalSolve/timeOuterLoop << " %\n";
    cout << "       Time for local update                     = ";
    cout.width(9);
    cout << timeLocalUpdate << " s     ";
    cout.width(5);
    cout << 100*timeLocalUpdate/timeOuterLoop << " %\n";
    cout << "       Time for residual computations            = ";
    cout.width(9);
    cout << timeResidual << " s     ";
    cout.width(5);
    cout << 100*timeResidual/timeOuterLoop << " %\n";
    cout << "       Time for residuals norm computations      = ";
    cout.width(9);
    cout << timeNorm << " s     ";
    cout.width(5);
    cout << 100*timeNorm/timeOuterLoop << " %\n";
    cout << "       Time for restarting space definition      = ";
    cout.width(9);
    cout << timeRestart << " s     ";
    cout.width(5);
    cout << 100*timeRestart/timeOuterLoop << " %\n";
    cout << "\n";
    cout << " Total time for post-processing                  = ";
    cout.width(9);
    cout << timePostProce << " s\n";
    cout << endl;
  } // if (myPid == 0)
 
}