void
dft_HardSphereA22_Tpetra_Operator<Scalar,MatrixType>::
apply
(const MV& X, MV& Y, Teuchos::ETransp mode, Scalar alpha, Scalar beta) const
{
TEUCHOS_TEST_FOR_EXCEPT(Y.getNumVectors()!=X.getNumVectors());
#ifdef KDEBUG
TEUCHOS_TEST_FOR_EXCEPT(!X.getMap()->isSameAs(*getDomainMap()));
TEUCHOS_TEST_FOR_EXCEPT(!Y.getMap()->isSameAs(*getRangeMap()));
#endif
Y.elementWiseMultiply(STS::one(), *densityOnDensityMatrix_, X, STS::zero());
}
void
dft_HardSphereA22_Tpetra_Operator<Scalar,MatrixType>::
applyInverse
(const MV& X, MV& Y) const
{
// Our algorithm is:
// Y = D \ X
TEUCHOS_TEST_FOR_EXCEPT(Y.getNumVectors()!=X.getNumVectors());
#ifdef KDEBUG
TEUCHOS_TEST_FOR_EXCEPT(!X.getMap()->isSameAs(*getDomainMap()));
TEUCHOS_TEST_FOR_EXCEPT(!Y.getMap()->isSameAs(*getRangeMap()));
#endif
Y.elementWiseMultiply(STS::one(), *densityOnDensityInverse_, X, STS::zero());
}
void
dft_PolyA11_Tpetra_Operator<Scalar,MatrixType>::
applyInverse
(const MV& X, MV& Y) const
{
TEUCHOS_TEST_FOR_EXCEPT(Y.getNumVectors()!=X.getNumVectors());
#ifdef KDEBUG
TEUCHOS_TEST_FOR_EXCEPT(!X.getMap()->isSameAs(*getDomainMap()));
TEUCHOS_TEST_FOR_EXCEPT(!Y.getMap()->isSameAs(*getRangeMap()));
printf("\n\n\n\ndft_PolyA11_Tpetra_Operator::applyInverse()\n\n\n\n");
#endif
Scalar ONE = STS::one();
Scalar ZERO = STS::zero();
size_t NumVectors = Y.getNumVectors();
size_t numMyElements = ownedMap_->getNodeNumElements();
RCP<MV > Ytmp = rcp(new MV(ownedMap_,NumVectors));
Y=X; // We can safely do this
RCP<MV > curY = Y.offsetViewNonConst(ownedMap_, 0);
RCP<VEC> diagVec = invDiagonal_->offsetViewNonConst(ownedMap_, 0)->getVectorNonConst(0);
curY->elementWiseMultiply(ONE, *diagVec, *curY, ZERO); // Scale Y by the first block diagonal
// Loop over block 1 through numBlocks (indexing 0 to numBlocks-1)
for (LocalOrdinal i=OTLO::zero(); i< numBlocks_-1; i++)
{
// Update views of Y and diagonal blocks
curY = Y.offsetViewNonConst(ownedMap_, (i+1)*numMyElements);
diagVec = invDiagonal_->offsetViewNonConst(ownedMap_, (i+1)*numMyElements)->getVectorNonConst(0);
matrixOperator_[i]->apply(Y, *Ytmp); // Multiply block lower triangular block
curY->update(-ONE, *Ytmp, ONE); // curY = curX - Ytmp (Note that curX is in curY from initial copy Y = X)
curY->elementWiseMultiply(ONE, *diagVec, *curY, ZERO); // Scale Y by the first block diagonal
}
} //end applyInverse
/// \brief Finish internode TSQR initialization.
///
/// \param mv [in] A valid Tpetra::MultiVector instance whose
/// communicator wrapper we will use to prepare TSQR.
///
/// \note It's OK to call this method more than once; it is idempotent.
void
prepareDistTsqr (const MV& mv)
{
using Teuchos::RCP;
using Teuchos::rcp_implicit_cast;
typedef TSQR::TeuchosMessenger<scalar_type> mess_type;
typedef TSQR::MessengerBase<scalar_type> base_mess_type;
RCP<const Teuchos::Comm<int> > comm = mv.getMap()->getComm();
RCP<mess_type> mess (new mess_type (comm));
RCP<base_mess_type> messBase = rcp_implicit_cast<base_mess_type> (mess);
distTsqr_->init (messBase);
}
void
dft_PolyA11_Tpetra_Operator<Scalar,MatrixType>::
apply
(const MV& X, MV& Y, Teuchos::ETransp mode, Scalar alpha, Scalar beta) const
{
TEUCHOS_TEST_FOR_EXCEPT(Y.getNumVectors()!=X.getNumVectors());
#ifdef KDEBUG
TEUCHOS_TEST_FOR_EXCEPT(!X.getMap()->isSameAs(*getDomainMap()));
TEUCHOS_TEST_FOR_EXCEPT(!Y.getMap()->isSameAs(*getRangeMap()));
#endif
size_t numMyElements = ownedMap_->getNodeNumElements();
RCP<MV > curY = Y.offsetViewNonConst(ownedMap_, 0);
for (LocalOrdinal i=OTLO::zero(); i< numBlocks_-1; i++) {
curY = Y.offsetViewNonConst(ownedMap_, (i+1)*numMyElements);
matrixOperator_[i]->apply(X, *curY); // This gives a result that is off-diagonal-matrix*X
}
Y.elementWiseMultiply(STS::one(),*diagonal_, X, STS::one()); // Add diagonal contribution
} //end Apply
/// \brief Extract A's underlying KokkosClassic::MultiVector instance.
///
/// TSQR represents the local (to each MPI process) part of a
/// multivector as a KokkosClassic::MultiVector (KMV), which gives a
/// nonconstant view of the original multivector's data. This
/// class method tells TSQR how to get the KMV from the input
/// multivector. The KMV is not a persistent view of the data;
/// its scope is contained within the scope of the multivector.
///
/// \warning TSQR does not currently support multivectors with
/// nonconstant stride. If A has nonconstant stride, this
/// method will throw an exception.
static KokkosClassic::MultiVector<scalar_type, node_type>
getNonConstView (MV& A)
{
// FIXME (mfh 25 Oct 2010) We should be able to run TSQR even if
// storage of A uses nonconstant stride internally. We would
// have to copy and pack into a matrix with constant stride, and
// then unpack on exit. For now we choose just to raise an
// exception.
TEUCHOS_TEST_FOR_EXCEPTION(! A.isConstantStride(), std::invalid_argument,
"TSQR does not currently support Tpetra::MultiVector "
"inputs that do not have constant stride.");
typedef typename Teuchos::ArrayRCP<mp_scalar_type>::size_type size_type;
typedef typename MV::dual_view_type view_type;
typedef typename view_type::t_dev::array_type flat_array_type;
// Create new Kokkos::MultiVector reinterpreting the data as a longer
// array of the base scalar type
// Create new ArrayRCP holding data
view_type pce_mv = A.getDualView();
flat_array_type flat_mv = pce_mv.d_view;
const size_t num_rows = flat_mv.dimension_0();
const size_t num_cols = flat_mv.dimension_1();
const size_t size = num_rows * num_cols;
ArrayRCP<scalar_type> vals =
Teuchos::arcp(flat_mv.ptr_on_device(), size_type(0), size, false);
// Create new MultiVector
// Owing to the above comment, we don't need to worry about
// non-constant stride
size_t strides[2];
flat_mv.stride(strides);
const size_t stride = strides[0];
KokkosClassic::MultiVector<scalar_type, node_type> mv(A.getMap()->getNode());
mv.initializeValues(num_rows, num_cols, vals, stride);
return mv;
}
/// \brief Solve AX=B for X with Chebyshev iteration with left
/// diagonal scaling, imitating ML's implementation.
///
/// \pre A must be real-valued and symmetric positive definite.
/// \pre numIters >= 0
/// \pre eigRatio >= 1
/// \pre 0 < lambdaMax
/// \pre All entries of D_inv are positive.
///
/// \param A [in] The matrix A in the linear system to solve.
/// \param B [in] Right-hand side(s) in the linear system to solve.
/// \param X [in] Initial guess(es) for the linear system to solve.
/// \param numIters [in] Number of Chebyshev iterations.
/// \param lambdaMax [in] Estimate of max eigenvalue of D_inv*A.
/// \param lambdaMin [in] Estimate of min eigenvalue of D_inv*A. We
/// only use this to determine if A is the identity matrix.
/// \param eigRatio [in] Estimate of max / min eigenvalue ratio of
/// D_inv*A. We use this along with lambdaMax to compute the
/// Chebyshev coefficients. This need not be the same as
/// lambdaMax/lambdaMin.
/// \param D_inv [in] Vector of diagonal entries of A. It must have
/// the same distribution as b.
void
mlApplyImpl (const MAT& A,
const MV& B,
MV& X,
const int numIters,
const ST lambdaMax,
const ST lambdaMin,
const ST eigRatio,
const V& D_inv)
{
const ST zero = Teuchos::as<ST> (0);
const ST one = Teuchos::as<ST> (1);
const ST two = Teuchos::as<ST> (2);
MV pAux (B.getMap (), B.getNumVectors ()); // Result of A*X
MV dk (B.getMap (), B.getNumVectors ()); // Solution update
MV R (B.getMap (), B.getNumVectors ()); // Not in original ML; need for B - pAux
ST beta = Teuchos::as<ST> (1.1) * lambdaMax;
ST alpha = lambdaMax / eigRatio;
ST delta = (beta - alpha) / two;
ST theta = (beta + alpha) / two;
ST s1 = theta / delta;
ST rhok = one / s1;
// Diagonal: ML replaces entries containing 0 with 1. We
// shouldn't have any entries like that in typical test problems,
// so it's OK not to do that here.
// The (scaled) matrix is the identity: set X = D_inv * B. (If A
// is the identity, then certainly D_inv is too. D_inv comes from
// A, so if D_inv * A is the identity, then we still need to apply
// the "preconditioner" D_inv to B as well, to get X.)
if (lambdaMin == one && lambdaMin == lambdaMax) {
solve (X, D_inv, B);
return;
}
// The next bit of code is a direct translation of code from ML's
// ML_Cheby function, in the "normal point scaling" section, which
// is in lines 7365-7392 of ml_smoother.c.
if (! zeroStartingSolution_) {
// dk = (1/theta) * D_inv * (B - (A*X))
A.apply (X, pAux); // pAux = A * X
R = B;
R.update (-one, pAux, one); // R = B - pAux
dk.elementWiseMultiply (one/theta, D_inv, R, zero); // dk = (1/theta)*D_inv*R
X.update (one, dk, one); // X = X + dk
} else {
dk.elementWiseMultiply (one/theta, D_inv, B, zero); // dk = (1/theta)*D_inv*B
X = dk;
}
ST rhokp1, dtemp1, dtemp2;
for (int k = 0; k < numIters-1; ++k) {
A.apply (X, pAux);
rhokp1 = one / (two*s1 - rhok);
dtemp1 = rhokp1*rhok;
dtemp2 = two*rhokp1/delta;
rhok = rhokp1;
R = B;
R.update (-one, pAux, one); // R = B - pAux
// dk = dtemp1 * dk + dtemp2 * D_inv * (B - pAux)
dk.elementWiseMultiply (dtemp2, D_inv, B, dtemp1);
X.update (one, dk, one); // X = X + dk
}
}
/// \brief Finish intranode TSQR initialization.
///
/// \note It's OK to call this method more than once; it is idempotent.
void
prepareNodeTsqr (const MV& mv)
{
node_tsqr_factory_type::prepareNodeTsqr (nodeTsqr_, mv.getMap()->getNode());
}
void
dft_PolyA22_Tpetra_Operator<Scalar,MatrixType>::
apply
(const MV& X, MV& Y, Teuchos::ETransp mode, Scalar alpha, Scalar beta) const
{
TEUCHOS_TEST_FOR_EXCEPT(Y.getNumVectors()!=X.getNumVectors());
#ifdef KDEBUG
TEUCHOS_TEST_FOR_EXCEPT(!X.getMap()->isSameAs(*getDomainMap()));
TEUCHOS_TEST_FOR_EXCEPT(!Y.getMap()->isSameAs(*getRangeMap()));
#endif
Scalar ONE = STS::one();
Scalar ZERO = STS::zero();
if (F_location_ == 1)
{
//F in NE
size_t numCmsElements = cmsMap_->getNodeNumElements();
// Y1 is a view of the first numCms elements of Y
RCP<MV> Y1 = Y.offsetViewNonConst(cmsMap_, 0);
// Y2 is a view of the last numDensity elements of Y
RCP<MV> Y2 = Y.offsetViewNonConst(densityMap_, numCmsElements);
// X1 is a view of the first numCms elements of X
RCP<const MV> X1 = X.offsetView(cmsMap_, 0);
// X2 is a view of the last numDensity elements of X
RCP<const MV> X2 = X.offsetView(densityMap_, numCmsElements);
// First block row
cmsOnDensityMatrixOp_->apply(*X2, *Y1);
cmsOnCmsMatrixOp_->apply(*X1, *tmpCmsVec_);
Y1->update(ONE, *tmpCmsVec_, ONE);
// Second block row
if (hasDensityOnCms_) {
densityOnCmsMatrixOp_->apply(*X1, *Y2);
Y2->elementWiseMultiply(ONE, *densityOnDensityMatrix_, *X2, ONE);
} else {
Y2->elementWiseMultiply(ONE, *densityOnDensityMatrix_, *X2, ZERO);
}
}
else
{
//F in SW
size_t numDensityElements = densityMap_->getNodeNumElements();
// Y1 is a view of the first numDensity elements of Y
RCP<MV> Y1 = Y.offsetViewNonConst(densityMap_, 0);
// Y2 is a view of the last numCms elements of Y
RCP<MV> Y2 = Y.offsetViewNonConst(cmsMap_, numDensityElements);
// X1 is a view of the first numDensity elements of X
RCP<const MV> X1 = X.offsetView(densityMap_, 0);
// X2 is a view of the last numCms elements of X
RCP<const MV> X2 = X.offsetView(cmsMap_, numDensityElements);
// First block row
if (hasDensityOnCms_) {
densityOnCmsMatrixOp_->apply(*X2, *Y1);
Y1->elementWiseMultiply(ONE, *densityOnDensityMatrix_, *X1, ONE);
} else {
Y1->elementWiseMultiply(ONE, *densityOnDensityMatrix_, *X1, ZERO);
}
// Second block row
cmsOnDensityMatrixOp_->apply(*X1, *Y2);
cmsOnCmsMatrixOp_->apply(*X2, *tmpCmsVec_);
Y2->update(ONE, *tmpCmsVec_, ONE);
}
} //end Apply
void
dft_PolyA22_Tpetra_Operator<Scalar,MatrixType>::
applyInverse
(const MV& X, MV& Y) const
{
// The true A22 block is of the form:
// | Dcc F |
// | Ddc Ddd |
// where
// Dcc is Cms on Cms (diagonal),
// F is Cms on Density (fairly dense)
// Ddc is Density on Cms (diagonal with small coefficient values),
// Ddd is Density on Density (diagonal).
//
// We will approximate A22 with:
// | Dcc F |
// | 0 Ddd |
// replacing Ddc with a zero matrix for the applyInverse method only.
// Our algorithm is then:
// Y2 = Ddd \ X2
// Y1 = Dcc \ (X1 - F*Y2)
// Or, if F is in the SW quadrant:
// The true A22 block is of the form:
// | Ddd Ddc |
// | F Dcc |
// where
// Ddd is Density on Density (diagonal),
// Ddc is Density on Cms (diagonal with small coefficient values),
// F is Cms on Density (fairly dense),
// Dcc is Cms on Cms (diagonal).
//
// We will approximate A22 with:
// | Ddd 0 |
// | F Dcc |
// replacing Ddc with a zero matrix for the applyInverse method only.
// Our algorithm is then:
// Y1 = Ddd \ X1
// Y2 = Dcc \ (X2 - F*Y1)
TEUCHOS_TEST_FOR_EXCEPT(Y.getNumVectors()!=X.getNumVectors());
#ifdef KDEBUG
TEUCHOS_TEST_FOR_EXCEPT(!X.getMap()->isSameAs(*getDomainMap()));
TEUCHOS_TEST_FOR_EXCEPT(!Y.getMap()->isSameAs(*getRangeMap()));
printf("\n\n\n\ndft_PolyA22_Tpetra_Operator::applyInverse()\n\n\n\n");
#endif
Scalar ONE = STS::one();
Scalar ZERO = STS::zero();
if (F_location_ == 1)
{
//F in NE
size_t numCmsElements = cmsMap_->getNodeNumElements();
// Y1 is a view of the first numCms elements of Y
RCP<MV > Y1 = Y.offsetViewNonConst(cmsMap_, 0);
// Y2 is a view of the last numDensity elements of Y
RCP<MV > Y2 = Y.offsetViewNonConst(densityMap_, numCmsElements);
// X1 is a view of the first numCms elements of X
RCP<const MV > X1 = X.offsetView(cmsMap_, 0);
// X2 is a view of the last numDensity elements of X
RCP<const MV > X2 = X.offsetView(densityMap_, numCmsElements);
// Second block row: Y2 = DD\X2
Y2->elementWiseMultiply(ONE, *densityOnDensityInverse_, *X2, ZERO);
// First block row: Y1 = CC \ (X1 - CD*Y2)
cmsOnDensityMatrixOp_->apply(*Y2, *tmpCmsVec_);
tmpCmsVec_->update(ONE, *X1, -ONE);
cmsOnCmsInverseOp_->apply(*tmpCmsVec_, *Y1);
}
else
{
//F in SW
size_t numDensityElements = densityMap_->getNodeNumElements();
// Y1 is a view of the first numDensity elements of Y
RCP<MV > Y1 = Y.offsetViewNonConst(densityMap_, 0);
// Y2 is a view of the last numCms elements of Y
RCP<MV > Y2 = Y.offsetViewNonConst(cmsMap_, numDensityElements);
// X1 is a view of the first numDensity elements of X
RCP<const MV > X1 = X.offsetView(densityMap_, 0);
// X2 is a view of the last numCms elements of X
RCP<const MV > X2 = X.offsetView(cmsMap_, numDensityElements);
// First block row: Y1 = DD\X1
Y1->elementWiseMultiply(ONE, *densityOnDensityInverse_, *X1, ZERO);
// Second block row: Y2 = CC \ (X2 - CD*Y1)
cmsOnDensityMatrixOp_->apply(*Y1, *tmpCmsVec_);
tmpCmsVec_->update(ONE, *X2, -ONE);
cmsOnCmsInverseOp_->apply(*tmpCmsVec_, *Y2);
}
} //end applyInverse
