void Hiptmair<MatrixType>::initialize ()
{
using Teuchos::ParameterList;
using Teuchos::RCP;
using Teuchos::rcp;
TEUCHOS_TEST_FOR_EXCEPTION(
A_.is_null (), std::runtime_error, "Ifpack2::Hiptmair::initialize: "
"The input matrix A is null. Please call setMatrix() with a nonnull "
"input matrix before calling this method.");
// clear any previous allocation
IsInitialized_ = false;
IsComputed_ = false;
Teuchos::Time timer ("initialize");
{ // The body of code to time
Teuchos::TimeMonitor timeMon (timer);
Details::OneLevelFactory<row_matrix_type> factory;
ifpack2_prec1_=factory.create(precType1_,A_);
ifpack2_prec1_->initialize();
ifpack2_prec1_->setParameters(precList1_);
ifpack2_prec2_=factory.create(precType2_,PtAP_);
ifpack2_prec2_->initialize();
ifpack2_prec2_->setParameters(precList2_);
}
IsInitialized_ = true;
++NumInitialize_;
InitializeTime_ += timer.totalElapsedTime ();
}
void Iterative_Inverse_Operator::operator () (const Epetra_MultiVector &b, Epetra_MultiVector &x)
{
// Initialize the solution to zero
x.PutScalar( 0.0 );
// Reset the solver, problem, and status test for next solve (HKT)
pProb->setProblem( Teuchos::rcp(&x, false), Teuchos::rcp(&b, false) );
timer.start();
Belos::ReturnType ret = pBelos->solve();
timer.stop();
int pid = pComm->MyPID();
if (pid == 0 && print) {
if (ret == Belos::Converged)
{
std::cout << std::endl << "pid[" << pid << "] Block GMRES converged" << std::endl;
std::cout << "Solution time: " << timer.totalElapsedTime() << std::endl;
}
else
std::cout << std::endl << "pid[" << pid << "] Block GMRES did not converge" << std::endl;
}
}
void Hiptmair<MatrixType>::compute ()
{
TEUCHOS_TEST_FOR_EXCEPTION(
A_.is_null (), std::runtime_error, "Ifpack2::Hiptmair::compute: "
"The input matrix A is null. Please call setMatrix() with a nonnull "
"input matrix before calling this method.");
// Don't time the initialize(); that gets timed separately.
if (! isInitialized ()) {
initialize ();
}
Teuchos::Time timer ("compute");
{ // The body of code to time
Teuchos::TimeMonitor timeMon (timer);
ifpack2_prec1_->compute();
ifpack2_prec2_->compute();
}
IsComputed_ = true;
++NumCompute_;
ComputeTime_ += timer.totalElapsedTime ();
}
void ReportTimeAndMemory(Teuchos::Time const &timer, Teuchos::Comm<int> const &Comm)
{
double maxTime=0,minTime=0,avgTime=0;
double localTime = timer.totalElapsedTime();
int ntimers=1, root=0;
#ifdef HAVE_MPI
MPI_Reduce(&localTime,&maxTime,ntimers,MPI_DOUBLE,MPI_MAX,root,MPI_COMM_WORLD);
MPI_Reduce(&localTime,&minTime,ntimers,MPI_DOUBLE,MPI_MIN,root,MPI_COMM_WORLD);
MPI_Reduce(&localTime,&avgTime,ntimers,MPI_DOUBLE,MPI_SUM,root,MPI_COMM_WORLD);
#else
maxTime = localTime;
minTime = localTime;
avgTime = localTime;
#endif
avgTime /= Comm.getSize();
//std::cout << "(" << Comm.getRank() << ") " << localTime << std::endl;
if (Comm.getRank()==0) {
std::cout << "&&&" << timer.name()
<< " max=" << maxTime << " min=" << minTime << " avg=" << avgTime << std::endl;
std::cout << "&&&" << timer.name() << " " << MemUtils::PrintMemoryUsage() << std::endl;
}
} //ReportTimeAndMemory
//==========================================================================
void PrecPrecomputed::initialize() {
Teuchos::Time timer ("ILUT::initialize");
{
Teuchos::TimeMonitor timeMon (timer);
// clear any previous allocation
IsInitialized_ = false;
IsComputed_ = false;
L_ = Teuchos::null;
U_ = Teuchos::null;
//mapVec_ = rcp(new Tpetra::Map<Ordinal, Ordinal, Node>(A_->getGlobalNumRows(), 0, A_->getComm(), Tpetra::GloballyDistributed, Kokkos::DefaultNode::getDefaultNode()));
//Y_ = rcp(new MV(mapVec_, 1));
Y_ = newCorrespondingVector(A_);
// check only in serial if the matrix is square
TEUCHOS_TEST_FOR_EXCEPTION(
getComm ()->getSize () == 1 && A_->getNodeNumRows() != A_->getNodeNumCols(),
std::runtime_error, "Ifpack2::ILUT::initialize: In serial or one-process "
"mode, the input matrix must be square.");
}
IsInitialized_ = true;
++NumInitialize_;
InitializeTime_ += timer.totalElapsedTime ();
}
void Hiptmair<MatrixType>::
apply (const Tpetra::MultiVector<typename MatrixType::scalar_type,
typename MatrixType::local_ordinal_type,
typename MatrixType::global_ordinal_type,
typename MatrixType::node_type>& X,
Tpetra::MultiVector<typename MatrixType::scalar_type,
typename MatrixType::local_ordinal_type,
typename MatrixType::global_ordinal_type,
typename MatrixType::node_type>& Y,
Teuchos::ETransp mode,
typename MatrixType::scalar_type alpha,
typename MatrixType::scalar_type beta) const
{
using Teuchos::RCP;
using Teuchos::rcp;
using Teuchos::rcpFromRef;
typedef Tpetra::MultiVector<scalar_type, local_ordinal_type,
global_ordinal_type, node_type> MV;
TEUCHOS_TEST_FOR_EXCEPTION(
! isComputed (), std::runtime_error,
"Ifpack2::Hiptmair::apply: You must call compute() before you may call apply().");
TEUCHOS_TEST_FOR_EXCEPTION(
X.getNumVectors () != Y.getNumVectors (), std::invalid_argument,
"Ifpack2::Hiptmair::apply: The MultiVector inputs X and Y do not have the "
"same number of columns. X.getNumVectors() = " << X.getNumVectors ()
<< " != Y.getNumVectors() = " << Y.getNumVectors () << ".");
// Catch unimplemented cases: alpha != 1, beta != 0, mode != NO_TRANS.
TEUCHOS_TEST_FOR_EXCEPTION(
alpha != STS::one (), std::logic_error,
"Ifpack2::Hiptmair::apply: alpha != 1 has not been implemented.");
TEUCHOS_TEST_FOR_EXCEPTION(
beta != STS::zero (), std::logic_error,
"Ifpack2::Hiptmair::apply: zero != 0 has not been implemented.");
TEUCHOS_TEST_FOR_EXCEPTION(
mode != Teuchos::NO_TRANS, std::logic_error,
"Ifpack2::Hiptmair::apply: mode != Teuchos::NO_TRANS has not been implemented.");
Teuchos::Time timer ("apply");
{ // The body of code to time
Teuchos::TimeMonitor timeMon (timer);
// If X and Y are pointing to the same memory location,
// we need to create an auxiliary vector, Xcopy
RCP<const MV> Xcopy;
{
auto X_lcl_host = X.template getLocalView<Kokkos::HostSpace> ();
auto Y_lcl_host = Y.template getLocalView<Kokkos::HostSpace> ();
if (X_lcl_host.ptr_on_device () == Y_lcl_host.ptr_on_device ()) {
Xcopy = rcp (new MV (X, Teuchos::Copy));
} else {
Xcopy = rcpFromRef (X);
}
}
RCP<MV> Ycopy = rcpFromRef (Y);
if (ZeroStartingSolution_) {
Ycopy->putScalar (STS::zero ());
}
// apply Hiptmair Smoothing
applyHiptmairSmoother (*Xcopy, *Ycopy);
}
++NumApply_;
ApplyTime_ += timer.totalElapsedTime ();
}
void PrecPrecomputed::applyTempl(
const Tpetra::MultiVector<DomainScalar, Ordinal, Ordinal, Node>& X,
Tpetra::MultiVector<RangeScalar, Ordinal, Ordinal, Node>& Z,
Teuchos::ETransp mode,
RangeScalar alpha,
RangeScalar beta) const
{
using Teuchos::RCP;
using Teuchos::rcp;
typedef Tpetra::MultiVector<DomainScalar, Ordinal, Ordinal, Node> MV;
Teuchos::Time timer ("ILUT::apply");
{ // Timer scope for timing apply()
Teuchos::TimeMonitor timeMon (timer, true);
TEUCHOS_TEST_FOR_EXCEPTION(
! isComputed (), std::runtime_error,
"Ifpack2::ILUT::apply: You must call compute() to compute the incomplete "
"factorization, before calling apply().");
TEUCHOS_TEST_FOR_EXCEPTION(
X.getNumVectors() != Z.getNumVectors(), std::runtime_error,
"Ifpack2::ILUT::apply: X and Y must have the same number of columns. "
"X has " << X.getNumVectors () << " columns, but Y has "
<< Z.getNumVectors () << " columns.");
TEUCHOS_TEST_FOR_EXCEPTION(
beta != STS::zero (), std::logic_error,
"Ifpack2::ILUT::apply: This method does not currently work when beta != 0.");
// If X and Y are pointing to the same memory location,
// we need to create an auxiliary vector, Xcopy
RCP<const MV> Xcopy;
if (X.getLocalMV ().getValues () == Z.getLocalMV ().getValues ()) {
Xcopy = rcp (new MV (X));
}
else {
Xcopy = rcpFromRef (X);
//ZACH always taken
}
//if (mode == Teuchos::NO_TRANS) { // Solve L U Y = X
/*
L_->template localSolve<RangeScalar,DomainScalar> (*Xcopy, Y, Teuchos::NO_TRANS);
U_->template localSolve<RangeScalar,RangeScalar> (Y, Y, Teuchos::NO_TRANS);
*/
//dump(X, "##X");
//dump(*Xcopy, "##Xcopy");
//ZACH the problem is here: This actually needs to be a global triangular solve
L_->template localSolve<RangeScalar,DomainScalar> (*Xcopy, *Y_, Teuchos::NO_TRANS);
//dump(*Y_, "##Y");
U_->template localSolve<RangeScalar,RangeScalar> (*Y_, Z, Teuchos::NO_TRANS);
//U_->template localSolve<RangeScalar,RangeScalar> (*Xcopy, Z, Teuchos::NO_TRANS);
//dump(Z, "##Z");
//U_->template localSolve<RangeScalar,DomainScalar> (*Xcopy, Y, Teuchos::NO_TRANS);
//U_->template localSolve<RangeScalar,RangeScalar> (Y, Y, Teuchos::TRANS);
//}
//else { // Solve U^* L^* Y = X
/*
U_->template localSolve<RangeScalar,DomainScalar> (*Xcopy, Y, mode);
L_->template localSolve<RangeScalar,RangeScalar> (Y, Y, mode);
*/
//}
if (alpha != STS::one ()) {
Z.scale (alpha);
}
}
++NumApply_;
ApplyTime_ += timer.totalElapsedTime ();
}
//==========================================================================
void PrecPrecomputed::compute() {
using Teuchos::Array;
using Teuchos::ArrayRCP;
using Teuchos::ArrayView;
using Teuchos::as;
using Teuchos::rcp;
using Teuchos::reduceAll;
//--------------------------------------------------------------------------
// Ifpack2::ILUT is a translation of the Aztec ILUT implementation. The Aztec
// ILUT implementation was written by Ray Tuminaro.
//
// This isn't an exact translation of the Aztec ILUT algorithm, for the
// following reasons:
// 1. Minor differences result from the fact that Aztec factors a MSR format
// matrix in place, while the code below factors an input CrsMatrix which
// remains untouched and stores the resulting factors in separate L and U
// CrsMatrix objects.
// Also, the Aztec code begins by shifting the matrix pointers back
// by one, and the pointer contents back by one, and then using 1-based
// Fortran-style indexing in the algorithm. This Ifpack2 code uses C-style
// 0-based indexing throughout.
// 2. Aztec stores the inverse of the diagonal of U. This Ifpack2 code
// stores the non-inverted diagonal in U.
// The triangular solves (in Ifpack2::ILUT::apply()) are performed by
// calling the Tpetra::CrsMatrix::solve method on the L and U objects, and
// this requires U to contain the non-inverted diagonal.
//
// ABW.
//--------------------------------------------------------------------------
// Don't count initialization in the compute() time.
if (! isInitialized ()) {
initialize ();
}
Teuchos::Time timer ("ILUT::compute");
{ // Timer scope for timing compute()
Teuchos::TimeMonitor timeMon (timer, true);
/*
const scalar_type zero = STS::zero ();
const scalar_type one = STS::one ();
const local_ordinal_type myNumRows = A_->getNodeNumRows ();
L_ = rcp (new MatrixType (A_->getRowMap (), A_->getColMap (), 0));
U_ = rcp (new MatrixType (A_->getRowMap (), A_->getColMap (), 0));
// CGB: note, this caching approach may not be necessary anymore
// We will store ArrayView objects that are views of the rows of U, so that
// we don't have to repeatedly retrieve the view for each row. These will
// be populated row by row as the factorization proceeds.
Array<ArrayView<const local_ordinal_type> > Uindices (myNumRows);
Array<ArrayView<const scalar_type> > Ucoefs (myNumRows);
// If this macro is defined, files containing the L and U factors
// will be written. DON'T CHECK IN THE CODE WITH THIS MACRO ENABLED!!!
// #define IFPACK2_WRITE_FACTORS
#ifdef IFPACK2_WRITE_FACTORS
std::ofstream ofsL("L.tif.mtx", std::ios::out);
std::ofstream ofsU("U.tif.mtx", std::ios::out);
#endif
// mfh 28 Nov 2012: The code here uses double for fill calculations.
// This really has nothing to do with magnitude_type. The point is
// to avoid rounding and overflow for integer calculations. That
// should work just fine for reasonably-sized integer values, so I'm
// leaving it. However, the fill level parameter is a
// magnitude_type, so I do need to cast to double. Typical values
// of the fill level are small, so this should not cause overflow.
// Calculate how much fill will be allowed in addition to the space that
// corresponds to the input matrix entries.
double local_nnz = static_cast<double>(A_->getNodeNumEntries());
double fill;
{
const double fillLevel = as<double> (getLevelOfFill ());
fill = ((fillLevel - 1) * local_nnz) / (2 * myNumRows);
}
// std::ceil gives the smallest integer larger than the argument.
// this may give a slightly different result than Aztec's fill value in
// some cases.
double fill_ceil=std::ceil(fill);
// Similarly to Aztec, we will allow the same amount of fill for each
// row, half in L and half in U.
size_type fillL = static_cast<size_type>(fill_ceil);
size_type fillU = static_cast<size_type>(fill_ceil);
Array<scalar_type> InvDiagU (myNumRows, zero);
Array<local_ordinal_type> tmp_idx;
Array<scalar_type> tmpv;
enum { UNUSED, ORIG, FILL };
local_ordinal_type max_col = myNumRows;
Array<int> pattern(max_col, UNUSED);
Array<scalar_type> cur_row(max_col, zero);
Array<magnitude_type> unorm(max_col);
magnitude_type rownorm;
Array<local_ordinal_type> L_cols_heap;
Array<local_ordinal_type> U_cols;
Array<local_ordinal_type> L_vals_heap;
Array<local_ordinal_type> U_vals_heap;
// A comparison object which will be used to create 'heaps' of indices
// that are ordered according to the corresponding values in the
// 'cur_row' array.
greater_indirect<scalar_type,local_ordinal_type> vals_comp(cur_row);
// =================== //
// start factorization //
// =================== //
ArrayRCP<local_ordinal_type> ColIndicesARCP;
ArrayRCP<scalar_type> ColValuesARCP;
if (! A_->supportsRowViews ()) {
const size_t maxnz = A_->getNodeMaxNumRowEntries ();
ColIndicesARCP.resize (maxnz);
ColValuesARCP.resize (maxnz);
}
for (local_ordinal_type row_i = 0 ; row_i < myNumRows ; ++row_i) {
ArrayView<const local_ordinal_type> ColIndicesA;
ArrayView<const scalar_type> ColValuesA;
size_t RowNnz;
if (A_->supportsRowViews ()) {
A_->getLocalRowView (row_i, ColIndicesA, ColValuesA);
RowNnz = ColIndicesA.size ();
}
else {
A_->getLocalRowCopy (row_i, ColIndicesARCP (), ColValuesARCP (), RowNnz);
ColIndicesA = ColIndicesARCP (0, RowNnz);
ColValuesA = ColValuesARCP (0, RowNnz);
}
// Always include the diagonal in the U factor. The value should get
// set in the next loop below.
U_cols.push_back(row_i);
cur_row[row_i] = zero;
pattern[row_i] = ORIG;
size_type L_cols_heaplen = 0;
rownorm = STM::zero ();
for (size_t i = 0; i < RowNnz; ++i) {
if (ColIndicesA[i] < myNumRows) {
if (ColIndicesA[i] < row_i) {
add_to_heap(ColIndicesA[i], L_cols_heap, L_cols_heaplen);
}
else if (ColIndicesA[i] > row_i) {
U_cols.push_back(ColIndicesA[i]);
}
cur_row[ColIndicesA[i]] = ColValuesA[i];
pattern[ColIndicesA[i]] = ORIG;
rownorm += scalar_mag(ColValuesA[i]);
}
}
// Alter the diagonal according to the absolute-threshold and
// relative-threshold values. If not set, those values default
// to zero and one respectively.
const magnitude_type rthresh = getRelativeThreshold();
const scalar_type& v = cur_row[row_i];
cur_row[row_i] = as<scalar_type> (getAbsoluteThreshold() * IFPACK2_SGN(v)) + rthresh*v;
size_type orig_U_len = U_cols.size();
RowNnz = L_cols_heap.size() + orig_U_len;
rownorm = getDropTolerance() * rownorm/RowNnz;
// The following while loop corresponds to the 'L30' goto's in Aztec.
size_type L_vals_heaplen = 0;
while (L_cols_heaplen > 0) {
local_ordinal_type row_k = L_cols_heap.front();
scalar_type multiplier = cur_row[row_k] * InvDiagU[row_k];
cur_row[row_k] = multiplier;
magnitude_type mag_mult = scalar_mag(multiplier);
if (mag_mult*unorm[row_k] < rownorm) {
pattern[row_k] = UNUSED;
rm_heap_root(L_cols_heap, L_cols_heaplen);
continue;
}
if (pattern[row_k] != ORIG) {
if (L_vals_heaplen < fillL) {
add_to_heap(row_k, L_vals_heap, L_vals_heaplen, vals_comp);
}
else if (L_vals_heaplen==0 ||
mag_mult < scalar_mag(cur_row[L_vals_heap.front()])) {
pattern[row_k] = UNUSED;
rm_heap_root(L_cols_heap, L_cols_heaplen);
continue;
}
else {
pattern[L_vals_heap.front()] = UNUSED;
rm_heap_root(L_vals_heap, L_vals_heaplen, vals_comp);
add_to_heap(row_k, L_vals_heap, L_vals_heaplen, vals_comp);
}
}
*/
/* Reduce current row */
/*
ArrayView<const local_ordinal_type>& ColIndicesU = Uindices[row_k];
ArrayView<const scalar_type>& ColValuesU = Ucoefs[row_k];
size_type ColNnzU = ColIndicesU.size();
for(size_type j=0; j<ColNnzU; ++j) {
if (ColIndicesU[j] > row_k) {
scalar_type tmp = multiplier * ColValuesU[j];
local_ordinal_type col_j = ColIndicesU[j];
if (pattern[col_j] != UNUSED) {
cur_row[col_j] -= tmp;
}
else if (scalar_mag(tmp) > rownorm) {
cur_row[col_j] = -tmp;
pattern[col_j] = FILL;
if (col_j > row_i) {
U_cols.push_back(col_j);
}
else {
add_to_heap(col_j, L_cols_heap, L_cols_heaplen);
}
}
}
}
rm_heap_root(L_cols_heap, L_cols_heaplen);
}//end of while(L_cols_heaplen) loop
// Put indices and values for L into arrays and then into the L_ matrix.
// first, the original entries from the L section of A:
for (size_type i = 0; i < ColIndicesA.size (); ++i) {
if (ColIndicesA[i] < row_i) {
tmp_idx.push_back(ColIndicesA[i]);
tmpv.push_back(cur_row[ColIndicesA[i]]);
pattern[ColIndicesA[i]] = UNUSED;
}
}
// next, the L entries resulting from fill:
for (size_type j = 0; j < L_vals_heaplen; ++j) {
tmp_idx.push_back(L_vals_heap[j]);
tmpv.push_back(cur_row[L_vals_heap[j]]);
pattern[L_vals_heap[j]] = UNUSED;
}
// L has a one on the diagonal, but we don't explicitly store it.
// If we don't store it, then the Tpetra/Kokkos kernel which performs
// the triangular solve can assume a unit diagonal, take a short-cut
// and perform faster.
L_->insertLocalValues (row_i, tmp_idx (), tmpv ());
#ifdef IFPACK2_WRITE_FACTORS
for (size_type ii = 0; ii < tmp_idx.size (); ++ii) {
ofsL << row_i << " " << tmp_idx[ii] << " " << tmpv[ii] << std::endl;
}
#endif
tmp_idx.clear();
tmpv.clear();
// Pick out the diagonal element, store its reciprocal.
if (cur_row[row_i] == zero) {
std::cerr << "Ifpack2::ILUT::Compute: zero pivot encountered! Replacing with rownorm and continuing...(You may need to set the parameter 'fact: absolute threshold'.)" << std::endl;
cur_row[row_i] = rownorm;
}
InvDiagU[row_i] = one / cur_row[row_i];
// Non-inverted diagonal is stored for U:
tmp_idx.push_back(row_i);
tmpv.push_back(cur_row[row_i]);
unorm[row_i] = scalar_mag(cur_row[row_i]);
pattern[row_i] = UNUSED;
// Now put indices and values for U into arrays and then into the U_ matrix.
// The first entry in U_cols is the diagonal, which we just handled, so we'll
// start our loop at j=1.
size_type U_vals_heaplen = 0;
for(size_type j=1; j<U_cols.size(); ++j) {
local_ordinal_type col = U_cols[j];
if (pattern[col] != ORIG) {
if (U_vals_heaplen < fillU) {
add_to_heap(col, U_vals_heap, U_vals_heaplen, vals_comp);
}
else if (U_vals_heaplen!=0 && scalar_mag(cur_row[col]) >
scalar_mag(cur_row[U_vals_heap.front()])) {
rm_heap_root(U_vals_heap, U_vals_heaplen, vals_comp);
add_to_heap(col, U_vals_heap, U_vals_heaplen, vals_comp);
}
}
else {
tmp_idx.push_back(col);
tmpv.push_back(cur_row[col]);
unorm[row_i] += scalar_mag(cur_row[col]);
}
pattern[col] = UNUSED;
}
for(size_type j=0; j<U_vals_heaplen; ++j) {
tmp_idx.push_back(U_vals_heap[j]);
tmpv.push_back(cur_row[U_vals_heap[j]]);
unorm[row_i] += scalar_mag(cur_row[U_vals_heap[j]]);
}
unorm[row_i] /= (orig_U_len + U_vals_heaplen);
U_->insertLocalValues(row_i, tmp_idx(), tmpv() );
#ifdef IFPACK2_WRITE_FACTORS
for(int ii=0; ii<tmp_idx.size(); ++ii) {
ofsU <<row_i<< " " <<tmp_idx[ii]<< " " <<tmpv[ii]<< std::endl;
}
#endif
tmp_idx.clear();
tmpv.clear();
U_->getLocalRowView(row_i, Uindices[row_i], Ucoefs[row_i] );
L_cols_heap.clear();
U_cols.clear();
L_vals_heap.clear();
U_vals_heap.clear();
} // end of for(row_i) loop
// FIXME (mfh 03 Apr 2013) Do we need to supply a domain and range Map?
L_->fillComplete();
U_->fillComplete();
*/
U_ = Tpetra::MatrixMarket::Reader<OP>::readSparseFile(precFileName_, getComm(), Kokkos::DefaultNode::getDefaultNode());
Tpetra::RowMatrixTransposer<Scalar, Ordinal, Ordinal, Node, Kokkos::DefaultKernels<Scalar, Ordinal, Node>::SparseOps> trans(U_.getConst());
L_ = trans.createTranspose();
//std::cout << "###" << U_->isUpperTriangular() << std::endl;
}
ComputeTime_ += timer.totalElapsedTime ();
IsComputed_ = true;
++NumCompute_;
}
void Krylov<MatrixType>::
apply (const Tpetra::MultiVector<typename MatrixType::scalar_type,
typename MatrixType::local_ordinal_type,
typename MatrixType::global_ordinal_type,
typename MatrixType::node_type>& X,
Tpetra::MultiVector<typename MatrixType::scalar_type,
typename MatrixType::local_ordinal_type,
typename MatrixType::global_ordinal_type,
typename MatrixType::node_type>& Y,
Teuchos::ETransp mode,
typename MatrixType::scalar_type alpha,
typename MatrixType::scalar_type beta) const
{
using Teuchos::RCP;
using Teuchos::rcp;
using Teuchos::rcpFromRef;
typedef Tpetra::MultiVector<scalar_type, local_ordinal_type,
global_ordinal_type, node_type> MV;
TEUCHOS_TEST_FOR_EXCEPTION(
! isComputed (), std::runtime_error,
"Ifpack2::Krylov::apply: You must call compute() before you may call apply().");
TEUCHOS_TEST_FOR_EXCEPTION(
X.getNumVectors () != Y.getNumVectors (), std::invalid_argument,
"Ifpack2::Krylov::apply: The MultiVector inputs X and Y do not have the "
"same number of columns. X.getNumVectors() = " << X.getNumVectors ()
<< " != Y.getNumVectors() = " << Y.getNumVectors () << ".");
// Catch unimplemented cases: alpha != 1, beta != 0, mode != NO_TRANS.
TEUCHOS_TEST_FOR_EXCEPTION(
alpha != STS::one (), std::logic_error,
"Ifpack2::Krylov::apply: alpha != 1 has not been implemented.");
TEUCHOS_TEST_FOR_EXCEPTION(
beta != STS::zero (), std::logic_error,
"Ifpack2::Krylov::apply: zero != 0 has not been implemented.");
TEUCHOS_TEST_FOR_EXCEPTION(
mode != Teuchos::NO_TRANS, std::logic_error,
"Ifpack2::Krylov::apply: mode != Teuchos::NO_TRANS has not been implemented.");
Teuchos::Time timer ("apply");
{ // The body of code to time
Teuchos::TimeMonitor timeMon (timer);
// If X and Y are pointing to the same memory location,
// we need to create an auxiliary vector, Xcopy
RCP<const MV> Xcopy;
if (X.getLocalMV ().getValues () == Y.getLocalMV ().getValues ()) {
Xcopy = rcp (new MV (X, Teuchos::Copy));
} else {
Xcopy = rcpFromRef (X);
}
RCP<MV> Ycopy = rcpFromRef (Y);
if (ZeroStartingSolution_) {
Ycopy->putScalar (STS::zero ());
}
// Set left and right hand sides for Belos
belosProblem_->setProblem (Ycopy, Xcopy);
belosSolver_->solve (); // solve the linear system
}
++NumApply_;
ApplyTime_ += timer.totalElapsedTime ();
}
void Krylov<MatrixType>::initialize ()
{
using Teuchos::ParameterList;
using Teuchos::RCP;
using Teuchos::rcp;
typedef Tpetra::MultiVector<scalar_type, local_ordinal_type,
global_ordinal_type, node_type> TMV;
typedef Tpetra::Operator<scalar_type, local_ordinal_type,
global_ordinal_type, node_type> TOP;
TEUCHOS_TEST_FOR_EXCEPTION(
A_.is_null (), std::runtime_error, "Ifpack2::Krylov::initialize: "
"The input matrix A is null. Please call setMatrix() with a nonnull "
"input matrix before calling this method.");
// clear any previous allocation
IsInitialized_ = false;
IsComputed_ = false;
Teuchos::Time timer ("initialize");
{ // The body of code to time
Teuchos::TimeMonitor timeMon (timer);
// Belos parameter list
RCP<ParameterList> belosList = rcp (new ParameterList ("GMRES"));
belosList->set ("Maximum Iterations", numIters_);
belosList->set ("Convergence Tolerance", resTol_);
// FIXME (17 Jan 2014) This whole "preconditioner type" thing is
// not how we want Krylov to initialize its inner preconditioner.
// Krylov should be initialized like AdditiveSchwarz: the Factory
// should create it, in order to avoid circular dependencies.
if (PreconditionerType_ == 0) {
// no preconditioner
}
else if (PreconditionerType_==1) {
ifpack2_prec_=rcp (new Relaxation<row_matrix_type> (A_));
}
else if (PreconditionerType_==2) {
ifpack2_prec_=rcp (new ILUT<row_matrix_type> (A_));
}
else if (PreconditionerType_==3) {
ifpack2_prec_ = rcp (new RILUK<row_matrix_type> (A_));
}
else if (PreconditionerType_==4) {
ifpack2_prec_ = rcp (new Chebyshev<row_matrix_type> (A_));
}
if (PreconditionerType_>0) {
ifpack2_prec_->initialize();
ifpack2_prec_->setParameters(precParams_);
}
belosProblem_ = rcp (new Belos::LinearProblem<belos_scalar_type,TMV,TOP> ());
belosProblem_->setOperator (A_);
if (iterationType_ == "GMRES") {
belosSolver_ =
rcp (new Belos::BlockGmresSolMgr<belos_scalar_type,TMV,TOP> (belosProblem_, belosList));
}
else {
belosSolver_ =
rcp (new Belos::BlockCGSolMgr<belos_scalar_type,TMV,TOP> (belosProblem_, belosList));
}
}
IsInitialized_ = true;
++NumInitialize_;
InitializeTime_ += timer.totalElapsedTime ();
}
