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// ***********************************************************************
//
// Tpetra: Templated Linear Algebra Services Package
// Copyright (2008) Sandia Corporation
//
// Under the terms of Contract DE-AC04-94AL85000 with Sandia Corporation,
// the U.S. Government retains certain rights in this software.
//
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// modification, are permitted provided that the following conditions are
// met:
//
// 1. Redistributions of source code must retain the above copyright
// notice, this list of conditions and the following disclaimer.
//
// 2. Redistributions in binary form must reproduce the above copyright
// notice, this list of conditions and the following disclaimer in the
// documentation and/or other materials provided with the distribution.
//
// 3. Neither the name of the Corporation nor the names of the
// contributors may be used to endorse or promote products derived from
// this software without specific prior written permission.
//
// THIS SOFTWARE IS PROVIDED BY SANDIA CORPORATION "AS IS" AND ANY
// EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
// IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
// PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL SANDIA CORPORATION OR THE
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// Questions? Contact Michael A. Heroux (maherou@sandia.gov)
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// @HEADER
#ifndef __Epetra_TsqrAdaptor_hpp
#define __Epetra_TsqrAdaptor_hpp
///
/// \file Epetra_TsqrAdaptor.hpp
/// \brief Epetra_MultiVector to TSQR adaptor
///
/// \note (mfh 27 Oct 2010) This file is in Tpetra (rather than
/// Epetra, where it would seem to belong) as a temporary fix.
/// Otherwise, Epetra would need an optional package dependency on
/// Teuchos and Kokkos, which would break third-party code linking to
/// the Epetra library. Third-party code should use FIND_PACKAGE on
/// Trilinos to get the correct list of libraries against which to
/// link, but we make this easy temporary fix now so they have time to
/// fix their build systems later.
///
#include <Tpetra_ConfigDefs.hpp>
#if defined(HAVE_TPETRA_EPETRA) && defined(HAVE_TPETRA_TSQR)
#include <Kokkos_DefaultNode.hpp> // Include minimal Kokkos Node types
#include <Tsqr_NodeTsqrFactory.hpp> // create intranode TSQR object
#include <Tsqr.hpp> // full (internode + intranode) TSQR
#include <Tsqr_DistTsqr.hpp> // internode TSQR
#include <Epetra_Comm.h>
// Subclass of TSQR::MessengerBase, implemented using Teuchos
// communicator template helper functions
#include <Epetra_TsqrMessenger.hpp>
#include <Epetra_MultiVector.h>
#include <Teuchos_ParameterListAcceptorDefaultBase.hpp>
#include <stdexcept>
namespace Epetra {
/// \class TsqrAdaptor
/// \brief Adaptor from Epetra_MultiVector to TSQR.
/// \author Mark Hoemmen
///
/// TSQR (Tall Skinny QR factorization) is an orthogonalization
/// kernel that is as accurate as Householder QR, yet requires only
/// \f$2 \log P\f$ messages between $P$ MPI processes, independently
/// of the number of columns in the multivector.
///
/// TSQR works independently of the particular multivector
/// implementation, and interfaces to the latter via an adaptor
/// class. This class is the adaptor class for \c
/// Epetra_MultiVector. It templates on the MultiVector (MV) type
/// so that it can pick up that class' typedefs. In particular,
/// TSQR chooses its intranode implementation based on the Kokkos
/// Node type of the multivector.
///
/// \note Epetra objects live in the global namespace. TSQR
/// requires support for namespaces, so it's acceptable for us to
/// create an "Epetra" namespace to contain this adaptor.
///
/// \warning The current implementation of this adaptor requires
/// that all Epetra_MultiVector inputs use the same communicator
/// object (that is, the same Epetra_Comm) and map.
class TsqrAdaptor : public Teuchos::ParameterListAcceptorDefaultBase {
public:
typedef Epetra_MultiVector MV;
/// \typedef magnitude_type
///
/// Epetra_MultiVector's "Scalar" type is double; it is not a
/// templated object. TSQR supports Tpetra as well, in which the
/// "Scalar" type is a template parameter. In fact, TSQR supports
/// complex arithmetic (see the magnitude_type typedef).
typedef double scalar_type;
/// \typedef ordinal_type
///
/// In Tpetra terms, this would be the "LocalOrdinal" type. TSQR
/// does not depend on the "GlobalOrdinal" type. Epetra does not
/// distinguish between the LocalOrdinal and GlobalOrdinal types:
/// both are int.
typedef int ordinal_type;
/// \typedef node_type
///
/// TSQR depends on a Kokkos Node type. We just use the default
/// Node type here.
typedef Tpetra::Details::DefaultTypes::node_type node_type;
/// \typedef dense_matrix_type
///
/// How we pass around small dense matrices that are either local
/// to each MPI process, or globally replicated.
///
/// \note TSQR lives in the Kokkos package, which requires the
/// Teuchos package, so it's acceptable for us to require
/// Teuchos components.
typedef Teuchos::SerialDenseMatrix<ordinal_type, scalar_type> dense_matrix_type;
/// \typedef magnitude_type
///
/// Epetra_MultiVector's "Scalar" type is real. TSQR supports
/// complex arithmetic as well, in which magnitude_type would
/// differ from scalar_type.
typedef double magnitude_type;
private:
typedef TSQR::MatView<ordinal_type, scalar_type> matview_type;
typedef TSQR::NodeTsqrFactory<node_type, scalar_type, ordinal_type> node_tsqr_factory_type;
// Don't need a "typename" here, because there are no template
// parameters involved in the type definition.
typedef node_tsqr_factory_type::node_tsqr_type node_tsqr_type;
typedef TSQR::DistTsqr<ordinal_type, scalar_type> dist_tsqr_type;
typedef TSQR::Tsqr<ordinal_type, scalar_type, node_tsqr_type> tsqr_type;
public:
/// \brief Constructor (that accepts a parameter list).
///
/// \param plist [in/out] List of parameters for configuring TSQR.
/// The specific parameter keys that are read depend on the TSQR
/// implementation. For details, call \c getValidParameters()
/// and examine the documentation embedded therein.
TsqrAdaptor (const Teuchos::RCP<Teuchos::ParameterList>& plist) :
nodeTsqr_ (new node_tsqr_type),
distTsqr_ (new dist_tsqr_type),
tsqr_ (new tsqr_type (nodeTsqr_, distTsqr_)),
ready_ (false)
{
setParameterList (plist);
}
//! Constructor (that uses default parameters).
TsqrAdaptor () :
nodeTsqr_ (new node_tsqr_type),
distTsqr_ (new dist_tsqr_type),
tsqr_ (new tsqr_type (nodeTsqr_, distTsqr_)),
ready_ (false)
{
setParameterList (Teuchos::null);
}
Teuchos::RCP<const Teuchos::ParameterList>
getValidParameters () const
{
using Teuchos::RCP;
using Teuchos::rcp;
using Teuchos::ParameterList;
using Teuchos::parameterList;
if (defaultParams_.is_null()) {
RCP<ParameterList> params = parameterList ("TSQR implementation");
params->set ("NodeTsqr", *(nodeTsqr_->getValidParameters ()));
params->set ("DistTsqr", *(distTsqr_->getValidParameters ()));
defaultParams_ = params;
}
return defaultParams_;
}
void
setParameterList (const Teuchos::RCP<Teuchos::ParameterList>& plist)
{
using Teuchos::ParameterList;
using Teuchos::parameterList;
using Teuchos::RCP;
using Teuchos::sublist;
RCP<ParameterList> params = plist.is_null() ?
parameterList (*getValidParameters ()) : plist;
nodeTsqr_->setParameterList (sublist (params, "NodeTsqr"));
distTsqr_->setParameterList (sublist (params, "DistTsqr"));
this->setMyParamList (params);
}
/// \brief Compute QR factorization [Q,R] = qr(A,0).
///
/// \param A [in/out] On input: the multivector to factor.
/// Overwritten with garbage on output.
///
/// \param Q [out] On output: the (explicitly stored) Q factor in
/// the QR factorization of the (input) multivector A.
///
/// \param R [out] On output: the R factor in the QR factorization
/// of the (input) multivector A.
///
/// \param forceNonnegativeDiagonal [in] If true, then (if
/// necessary) do extra work (modifying both the Q and R
/// factors) in order to force the R factor to have a
/// nonnegative diagonal.
///
/// \warning Currently, this method only works if A and Q have the
/// same communicator and row distribution ("map," in Petra
/// terms) as those of the multivector given to this TsqrAdaptor
/// instance's constructor. Otherwise, the result of this
/// method is undefined.
void
factorExplicit (MV& A,
MV& Q,
dense_matrix_type& R,
const bool forceNonnegativeDiagonal=false)
{
typedef KokkosClassic::MultiVector<scalar_type, node_type> KMV;
prepareTsqr (Q); // Finish initializing TSQR.
KMV A_view = getNonConstView (A);
KMV Q_view = getNonConstView (Q);
tsqr_->factorExplicit (A_view, Q_view, R, false,
forceNonnegativeDiagonal);
}
/// \brief Rank-revealing decomposition
///
/// Using the R factor and explicit Q factor from
/// factorExplicit(), compute the singular value decomposition
/// (SVD) of R (\f$R = U \Sigma V^*\f$). If R is full rank (with
/// respect to the given relative tolerance tol), don't change Q
/// or R. Otherwise, compute \f$Q := Q \cdot U\f$ and \f$R :=
/// \Sigma V^*\f$ in place (the latter may be no longer upper
/// triangular).
///
/// \param Q [in/out] On input: explicit Q factor computed by
/// factorExplicit(). (Must be an orthogonal resp. unitary
/// matrix.) On output: If R is of full numerical rank with
/// respect to the tolerance tol, Q is unmodified. Otherwise, Q
/// is updated so that the first rank columns of Q are a basis
/// for the column space of A (the original matrix whose QR
/// factorization was computed by factorExplicit()). The
/// remaining columns of Q are a basis for the null space of A.
///
/// \param R [in/out] On input: ncols by ncols upper triangular
/// matrix with leading dimension ldr >= ncols. On output: if
/// input is full rank, R is unchanged on output. Otherwise, if
/// \f$R = U \Sigma V^*\f$ is the SVD of R, on output R is
/// overwritten with $\Sigma \cdot V^*$. This is also an ncols by
/// ncols matrix, but may not necessarily be upper triangular.
///
/// \param tol [in] Relative tolerance for computing the numerical
/// rank of the matrix R.
///
/// \return Rank \f$r\f$ of R: \f$ 0 \leq r \leq ncols\f$.
int
revealRank (MV& Q,
dense_matrix_type& R,
const magnitude_type& tol)
{
typedef KokkosClassic::MultiVector<scalar_type, node_type> KMV;
prepareTsqr (Q); // Finish initializing TSQR.
// FIXME (mfh 25 Oct 2010) Check Epetra_Comm object in Q to make
// sure it is the same communicator as the one we are using in
// our dist_tsqr_type implementation.
KMV Q_view = getNonConstView (Q);
return tsqr_->revealRank (Q_view, R, tol, false);
}
private:
//! The intranode TSQR implementation instance.
Teuchos::RCP<node_tsqr_type> nodeTsqr_;
//! The internode TSQR implementation instance.
Teuchos::RCP<dist_tsqr_type> distTsqr_;
//! The (full) TSQR implementation instance.
Teuchos::RCP<tsqr_type> tsqr_;
//! Default parameter list. Initialized by \c getValidParameters().
mutable Teuchos::RCP<const Teuchos::ParameterList> defaultParams_;
//! Whether TSQR has been fully initialized.
bool ready_;
/// \brief Finish TSQR initialization.
///
/// The intranode and internode TSQR implementations both have a
/// two-stage initialization procedure: first, setting parameters
/// (which may happen at construction), and second, getting
/// information they need from the multivector input in order to
/// finish initialization. For intranode TSQR, this includes the
/// Kokkos Node instance; for internode TSQR, this includes the
/// communicator. The second stage of initialization happens in
/// this class' computational routines; all of those routines
/// accept one or more multivector inputs, which this method can
/// use for finishing initialization. Thus, users of this class
/// never need to see the two-stage initialization.
///
/// \param mv [in] Multivector object, used only to access the
/// underlying communicator object (in this case, Epetra_Comm).
/// All multivector objects used with this Adaptor instance must
/// have the same map and communicator.
void
prepareTsqr (const MV& mv)
{
if (! ready_) {
prepareDistTsqr (mv);
prepareNodeTsqr (mv);
ready_ = true;
}
}
/// \brief Finish intranode TSQR initialization.
///
/// \note It's OK to call this method more than once; it is idempotent.
void
prepareNodeTsqr (const MV& mv)
{
(void) mv; // Epetra objects don't have a Kokkos Node.
// Create Node with empty ParameterList.
Teuchos::ParameterList plist;
Teuchos::RCP<node_type> node (new node_type (plist));
node_tsqr_factory_type::prepareNodeTsqr (nodeTsqr_, node);
}
/// \brief Finish internode TSQR initialization.
///
/// \param mv [in] A multivector, from which to extract the
/// Epetra_Comm communicator wrapper to use to initialize 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;
using TSQR::Epetra::makeTsqrMessenger;
typedef TSQR::MessengerBase<scalar_type> base_mess_type;
// If mv falls out of scope, its Epetra_Comm may become invalid.
// Thus, we clone the input Epetra_Comm, so that the messenger
// owns the object.
RCP<const Epetra_Comm> comm = rcp (mv.Comm().Clone());
RCP<base_mess_type> messBase = makeTsqrMessenger<scalar_type> (comm);
distTsqr_->init (messBase);
}
/// \brief Return a nonconstant view of the input MultiVector.
///
/// 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. This method will raise an exception
/// if A has nonconstant stride.
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.ConstantStride(), std::invalid_argument,
"TSQR does not currently support Epetra_MultiVector "
"inputs that do not have constant stride.");
const int numRows = A.MyLength();
const int numCols = A.NumVectors();
const int stride = A.Stride();
// A_ptr does _not_ own the data. TSQR only operates within the
// scope of the multivector objects on which it operates, so it
// doesn't need ownership of the data.
Teuchos::ArrayRCP<double> A_ptr (A.Values(), 0, numRows*stride, false);
typedef KokkosClassic::MultiVector<scalar_type, node_type> KMV;
// KMV objects want a Kokkos Node instance. Epetra objects
// don't have a Kokkos Node, so we make a temporary node just
// for the KMV.
//
// Create Node with empty ParameterList.
Teuchos::ParameterList plist;
Teuchos::RCP<node_type> node (new node_type (plist));
KMV A_kmv (node);
A_kmv.initializeValues (numRows, numCols, A_ptr, stride);
return A_kmv;
}
};
} // namespace Epetra
#endif // defined(HAVE_TPETRA_EPETRA) && defined(HAVE_TPETRA_TSQR)
#endif // __Epetra_TsqrAdaptor_hpp
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