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// ************************************************************************
//
// Kokkos: Node API and Parallel Node Kernels
// 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.
//
// Redistribution and use in source and binary forms, with or without
// modification, are permitted provided that the following conditions are
// met:
//
// 1. Redistributions of source code must retain the above copyright
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// 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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// EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
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// Questions? Contact Michael A. Heroux (maherou@sandia.gov)
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// ************************************************************************
//@HEADER
/// \file Tsqr_KokkosNodeTsqr.hpp
/// \brief Parallel intranode TSQR implemented using the Kokkos Node API.
///
#ifndef __TSQR_KokkosNodeTsqr_hpp
#define __TSQR_KokkosNodeTsqr_hpp
#include <Tsqr_CacheBlocker.hpp>
#include <Tsqr_Combine.hpp>
#include <Tsqr_NodeTsqr.hpp>
#include <Teuchos_ParameterListAcceptorDefaultBase.hpp>
#include <Teuchos_ScalarTraits.hpp>
//#define KNR_DEBUG 1
#ifdef KNR_DEBUG
# include <iostream>
#endif // KNR_DEBUG
namespace TSQR {
namespace details {
/// \brief Half-exclusive range of my partition's cache block indices.
///
/// \c FactorFirstPass (used by the factor() method of \c
/// KokkosNodeTsqr) breaks up the matrix into contiguous
/// partitions of row blocks. The index argument of Kokkos'
/// parallel_for is the (zero-based) partition index. This
/// function returns the half-exclusive range of the cache block
/// indices belonging to the partition partitionIndex.
///
/// \param numRows [in] Number of rows in the matrix.
/// \param numCols [in] Number of columns in the matrix.
/// \param partitionIndex [in] Zero-based index of the partition.
/// This is specifically an int and not a LocalOrdinal, because
/// partition indices are arguments to Kokkos Node API methods
/// parallel_for and parallel_reduce. Cache block indices are
/// of LocalOrdinal type and should not be mixed with partition
/// indices, even though in most cases LocalOrdinal == int.
/// \param numPartitions [in] Total number of partitions; a
/// positive integer.
/// \param strategy [in] The cache blocking strategy to use.
///
/// \return (start cache block index, end cache block index).
/// This is a half-exclusive range: it does not include the end
/// point. Thus, if the two indices are equal, the range is
/// empty.
template<class LocalOrdinal, class Scalar>
std::pair<LocalOrdinal, LocalOrdinal>
cacheBlockIndexRange (const LocalOrdinal numRows,
const LocalOrdinal numCols,
const int partitionIndex,
const int numPartitions,
const CacheBlockingStrategy<LocalOrdinal, Scalar>& strategy)
{
#ifdef KNR_DEBUG
using std::cerr;
using std::endl;
// cerr << "cacheBlockIndexRange(numRows=" << numRows
// << ", numCols=" << numCols
// << ", partitionIndex=" << partitionIndex
// << ", numPartitions=" << numPartitions
// << ", strategy)" << endl;
#endif // KNR_DEBUG
// The input index is a zero-based index of the current
// partition (not the "current cache block" -- a partition
// contains zero or more cache blocks). If the input index is
// out of range, then return, since there is nothing to do.
//
// The nice thing about partitioning over cache blocks is that
// the cache blocking strategy guarantees that exactly one of
// the following is true:
//
// 1. The partition is empty (contains zero cache blocks)
// 2. All cache blocks in the partition are valid (none
// contains more columns than rows)
// Return an empty partition (an empty cache block range) if
// the partition index is out of range.
if (partitionIndex >= numPartitions)
return std::make_pair (LocalOrdinal(0), LocalOrdinal(0));
const LocalOrdinal numRowsCacheBlock =
strategy.cache_block_num_rows (numCols);
const LocalOrdinal numCacheBlocks =
strategy.num_cache_blocks (numRows, numCols, numRowsCacheBlock);
#ifdef KNR_DEBUG
// cerr << "numRowsCacheBlock=" << numRowsCacheBlock
// << ", numCacheBlocks=" << numCacheBlocks
// << endl;
#endif // KNR_DEBUG
// Figure out how many cache blocks my partition contains. If
// the number of partitions doesn't evenly divide the number
// of cache blocks, we spread out the remainder among the
// first few threads.
const LocalOrdinal quotient = numCacheBlocks / numPartitions;
const LocalOrdinal remainder = numCacheBlocks - quotient * numPartitions;
const LocalOrdinal myNumCacheBlocks =
(partitionIndex < remainder) ? (quotient + 1) : quotient;
#ifdef KNR_DEBUG
// cerr << "Partition " << partitionIndex << ": quotient=" << quotient
// << ", remainder=" << remainder << ", myNumCacheBlocks="
// << myNumCacheBlocks << endl;
#endif // KNR_DEBUG
// If there are no cache blocks, there is nothing to factor.
// Return an empty cache block range to indicate this.
if (myNumCacheBlocks == 0)
return std::make_pair (LocalOrdinal(0), LocalOrdinal(0));
// Index of my first cache block (inclusive).
const LocalOrdinal myFirstCacheBlockIndex =
(partitionIndex < remainder) ?
partitionIndex * (quotient+1) :
remainder * (quotient+1) + (partitionIndex - remainder) * quotient;
// Index of my last cache block (exclusive).
const LocalOrdinal myLastCacheBlockIndex =
(partitionIndex+1 < remainder) ?
(partitionIndex+1) * (quotient+1) :
remainder * (quotient+1) + (partitionIndex+1 - remainder) * quotient;
// Sanity check.
if (myLastCacheBlockIndex <= myFirstCacheBlockIndex)
{
std::ostringstream os;
os << "Partition " << (partitionIndex+1) << " of "
<< numPartitions << ": My range of cache block indices ["
<< myFirstCacheBlockIndex << ", " << myLastCacheBlockIndex
<< ") is empty.";
throw std::logic_error(os.str());
}
return std::make_pair (myFirstCacheBlockIndex, myLastCacheBlockIndex);
}
/// \class FactorFirstPass
/// \brief First pass of KokkosNodeTsqr's factorization.
/// \author Mark Hoemmen
template<class LocalOrdinal, class Scalar>
class FactorFirstPass {
public:
typedef MatView<LocalOrdinal, Scalar> mat_view_type;
private:
mat_view_type A_;
// While tauArrays_ is shared among tasks (i.e., partitions),
// there are no race conditions among entries, since each
// partition writes its own entry. Ditto for topBlocks_.
std::vector<std::vector<Scalar> >& tauArrays_;
std::vector<mat_view_type>& topBlocks_;
CacheBlockingStrategy<LocalOrdinal, Scalar> strategy_;
int numPartitions_;
bool contiguousCacheBlocks_;
std::vector<Scalar>
factorFirstCacheBlock (Combine<LocalOrdinal, Scalar>& combine,
const mat_view_type& A_top,
std::vector<Scalar>& work) const
{
std::vector<Scalar> tau (A_top.ncols());
// We should only call this if A_top.ncols() > 0 and therefore
// work.size() > 0, but we've already checked for that, so we
// don't have to check again.
combine.factor_first (A_top.nrows(), A_top.ncols(), A_top.get(),
A_top.lda(), &tau[0], &work[0]);
return tau;
}
std::vector<Scalar>
factorCacheBlock (Combine<LocalOrdinal, Scalar>& combine,
const mat_view_type& A_top,
const mat_view_type& A_cur,
std::vector<Scalar>& work) const
{
std::vector<Scalar> tau (A_top.ncols());
// We should only call this if A_top.ncols() > 0 and therefore
// tau.size() > 0 and work.size() > 0, but we've already
// checked for that, so we don't have to check again.
combine.factor_inner (A_cur.nrows(), A_top.ncols(),
A_top.get(), A_top.lda(),
A_cur.get(), A_cur.lda(),
&tau[0], &work[0]);
return tau;
}
/// \brief Factor the given cache block range using sequential TSQR.
///
/// \param cbIndices [in] Half-exclusive range of cache block indices.
/// \param partitionIndex [in] Zero-based index of my partition.
///
/// \return A view of the top block of the cache block range.
mat_view_type
factor (const std::pair<LocalOrdinal, LocalOrdinal> cbIndices,
const int partitionIndex) const
{
#ifdef KNR_DEBUG
using std::cerr;
using std::endl;
#endif // KNR_DEBUG
typedef CacheBlockRange<mat_view_type> range_type;
// Workspace is created here, because it must not be shared
// among threads.
std::vector<Scalar> work (A_.ncols());
// Range of cache blocks to factor.
range_type cbRange (A_, strategy_,
cbIndices.first,
cbIndices.second,
contiguousCacheBlocks_);
// Iterator in the forward direction over the range of cache
// blocks to factor.
typedef typename CacheBlockRange<mat_view_type>::iterator range_iter_type;
range_iter_type cbIter = cbRange.begin();
// Remember the top (first) block.
mat_view_type A_top = *cbIter;
if (A_top.empty ()) {
return A_top;
}
TEUCHOS_TEST_FOR_EXCEPTION(cbIndices.first >= cbIndices.second,
std::logic_error,
"FactorFirstPass::factor: A_top is not empty, but "
"the cache block index range " << cbIndices.first
<< "," << cbIndices.second << " is empty. Please "
"report this bug to the Kokkos developers.");
// Current cache block index.
LocalOrdinal curTauIdx = cbIndices.first;
// Factor the first cache block.
Combine<LocalOrdinal, Scalar> combine;
tauArrays_[curTauIdx++] = factorFirstCacheBlock (combine, A_top, work);
// Move past the first cache block.
++cbIter;
// Number of cache block(s) we have factored thus far.
LocalOrdinal count = 1;
// Factor the remaining cache block(s).
range_iter_type cbEnd = cbRange.end();
while (cbIter != cbEnd) {
mat_view_type A_cur = *cbIter;
// Iteration over cache blocks of a partition should
// always result in nonempty cache blocks.
TEUCHOS_TEST_FOR_EXCEPTION(
A_cur.empty (), std::logic_error, "FactorFirstPass::factor: "
"The current cache block (the " << count << "-th to factor in the "
"range [" << cbIndices.first << "," << cbIndices.second << ") of "
"cache block indices) in partition " << (partitionIndex+1) << " "
"(out of " << numPartitions_ << " partitions) is empty. "
"Please report this bug to the Kokkos developers.");
TEUCHOS_TEST_FOR_EXCEPTION(static_cast<size_t>(curTauIdx) >= tauArrays_.size(),
std::logic_error,
"FactorFirstPass::factor: curTauIdx (= "
<< curTauIdx << ") >= tauArrays_.size() (= "
<< tauArrays_.size() << "). Please report this "
"bug to the Kokkos developers.");
tauArrays_[curTauIdx++] =
factorCacheBlock (combine, A_top, A_cur, work);
++count;
++cbIter;
}
#ifdef KNR_DEBUG
cerr << "Factored " << count << " cache blocks" << endl;
#endif // KNR_DEBUG
return A_top;
}
public:
/// \brief Constructor
///
/// \param A [in/out] On input: View of the matrix to factor.
/// On output: (Part of) the implicitly stored Q factor.
/// (The other part is tauArrays.)
/// \param tauArrays [out] Where to write the "TAU" arrays
/// (implicit factorization results) for each cache block.
/// (TAU is what LAPACK's QR factorization routines call this
/// array; see the LAPACK documentation for an explanation.)
/// Indexed by the cache block index; one TAU array per cache
/// block.
/// \param strategy [in] Cache blocking strategy to use.
/// \param numPartitions [in] Number of partitions (positive
/// integer), and therefore the maximum parallelism available
/// to the algorithm. Oversubscribing processors is OK, but
/// should not be done to excess. This is an int, and not a
/// LocalOrdinal, because it is the argument to Kokkos'
/// parallel_for.
/// \param contiguousCacheBlocks [in] Whether the cache blocks
/// of A are stored contiguously.
FactorFirstPass (const mat_view_type& A,
std::vector<std::vector<Scalar> >& tauArrays,
std::vector<mat_view_type>& topBlocks,
const CacheBlockingStrategy<LocalOrdinal, Scalar>& strategy,
const int numPartitions,
const bool contiguousCacheBlocks = false) :
A_ (A),
tauArrays_ (tauArrays),
topBlocks_ (topBlocks),
strategy_ (strategy),
numPartitions_ (numPartitions),
contiguousCacheBlocks_ (contiguousCacheBlocks)
{
TEUCHOS_TEST_FOR_EXCEPTION(A_.empty(), std::logic_error,
"TSQR::FactorFirstPass constructor: A is empty. "
"Please report this bug to the Kokkos developers.");
TEUCHOS_TEST_FOR_EXCEPTION(numPartitions < 1, std::logic_error,
"TSQR::FactorFirstPass constructor: numPartitions "
"must be positive, but numPartitions = "
<< numPartitions << ". Please report this bug to "
"the Kokkos developers.");
}
/// \brief First pass of intranode TSQR factorization.
///
/// Invoked by Kokkos' parallel_for template method. This
/// routine parallelizes over contiguous partitions of the
/// matrix. Each partition in turn contains cache blocks.
/// Partitions do not break up cache blocks. (This ensures that
/// the cache blocking scheme is the same as that used by
/// SequentialTsqr, as long as the cache blocking strategies are
/// the same. However, the implicit Q factor is not compatible
/// with that of SequentialTsqr.)
///
/// This method also saves a view of the top block of the
/// partition in the topBlocks_ array. This is useful for the
/// next factorization pass.
///
/// \param partitionIndex [in] Zero-based index of the
/// partition. If greater than or equal to the number of
/// partitions, this routine does nothing.
///
/// \warning This routine almost certainly won't work in CUDA.
/// If it does, it won't be efficient. If you are interested
/// in a GPU TSQR routine, please contact the author (Mark
/// Hoemmen <mhoemme@sandia.gov>) of this code to discuss the
/// possibilities. For this reason, we have not added the
/// KERNEL_PREFIX method prefix.
///
/// \note Unlike typical Kokkos work-data pairs (WDPs) passed
/// into parallel_for, this one is not declared inline. This
/// method is heavyweight enough that an inline declaration is
/// unlikely to improve performance.
void execute (const int partitionIndex) const
{
#ifdef KNR_DEBUG
using std::cerr;
using std::endl;
// cerr << "FactorFirstPass::execute (" << partitionIndex << ")" << endl;
#endif // KNR_DEBUG
if (partitionIndex < 0 || partitionIndex >= numPartitions_ || A_.empty ()) {
return;
}
else {
const std::pair<LocalOrdinal, LocalOrdinal> cbIndices =
cacheBlockIndexRange (A_.nrows(), A_.ncols(), partitionIndex,
numPartitions_, strategy_);
#ifdef KNR_DEBUG
cerr << "Partition " << partitionIndex
<< ": Factoring cache block indices ["
<< cbIndices.first << ", " << cbIndices.second << ")"
<< endl;
#endif // KNR_DEBUG
// It's legitimate, though suboptimal, for some partitions
// not to get any work to do (in this case, not to get any
// cache blocks to factor).
if (cbIndices.second <= cbIndices.first) {
return;
} else {
topBlocks_[partitionIndex] = factor (cbIndices, partitionIndex);
}
}
}
};
/// \class ApplyFirstPass
/// \brief "First" pass of applying KokkosNodeTsqr's implicit Q factor.
/// \author Mark Hoemmen
///
/// We call this ApplyFirstPass as a reminder that this algorithm
/// has the same form as FactorFirstPass and uses the results of
/// the latter, even though ApplyFirstPass is really the last pass
/// of applying the implicit Q factor.
template<class LocalOrdinal, class Scalar>
class ApplyFirstPass {
public:
typedef ConstMatView<LocalOrdinal, Scalar> const_mat_view_type;
typedef MatView<LocalOrdinal, Scalar> mat_view_type;
private:
ApplyType applyType_;
const_mat_view_type Q_;
const std::vector<std::vector<Scalar> >& tauArrays_;
const std::vector<mat_view_type>& topBlocks_;
mat_view_type C_;
CacheBlockingStrategy<LocalOrdinal, Scalar> strategy_;
int numPartitions_;
bool explicitQ_, contiguousCacheBlocks_;
void
applyFirstCacheBlock (Combine<LocalOrdinal, Scalar>& combine,
const ApplyType& applyType,
const const_mat_view_type& Q_top,
const std::vector<Scalar>& tau,
const mat_view_type& C_top,
std::vector<Scalar>& work) const
{
TEUCHOS_TEST_FOR_EXCEPTION(tau.size() < static_cast<size_t> (Q_top.ncols()),
std::logic_error,
"ApplyFirstPass::applyFirstCacheBlock: tau.size() "
"(= " << tau.size() << ") < number of columns "
<< Q_top.ncols() << " in the Q factor. Please "
"report this bug to the Kokkos developers.");
// If we get this far, it's fair to assume that we have
// checked whether tau and work have nonzero lengths.
combine.apply_first (applyType, C_top.nrows(), C_top.ncols(),
Q_top.ncols(), Q_top.get(), Q_top.lda(),
&tau[0], C_top.get(), C_top.lda(), &work[0]);
}
void
applyCacheBlock (Combine<LocalOrdinal, Scalar>& combine,
const ApplyType& applyType,
const const_mat_view_type& Q_cur,
const std::vector<Scalar>& tau,
const mat_view_type& C_top,
const mat_view_type& C_cur,
std::vector<Scalar>& work) const
{
TEUCHOS_TEST_FOR_EXCEPTION(tau.size() < static_cast<size_t> (Q_cur.ncols()),
std::logic_error,
"ApplyFirstPass::applyCacheBlock: tau.size() "
"(= " << tau.size() << ") < number of columns "
<< Q_cur.ncols() << " in the Q factor. Please "
"report this bug to the Kokkos developers.");
// If we get this far, it's fair to assume that we have
// checked whether tau and work have nonzero lengths.
combine.apply_inner (applyType, C_cur.nrows(), C_cur.ncols(),
Q_cur.ncols(), Q_cur.get(), Q_cur.lda(),
&tau[0],
C_top.get(), C_top.lda(),
C_cur.get(), C_cur.lda(),
&work[0]);
}
/// \fn apply
/// \brief Apply the sequential part of the implicit Q factor to C.
///
/// \param applyType [in] Whether we are applying Q, Q^T, or Q^H.
/// \param cbIndices [in] Half-exclusive range of cache block
/// indices.
/// \param partitionIndex [in] The argument to \c execute(); the
/// index of the partition which instance of ApplyFirstPass
/// is currently processing.
void
apply (const ApplyType& applyType,
const std::pair<LocalOrdinal, LocalOrdinal> cbIndices,
const int partitionIndex) const
{
#ifdef KNR_DEBUG
using std::cerr;
using std::endl;
#endif // KNR_DEBUG
typedef CacheBlockRange<const_mat_view_type> const_range_type;
typedef CacheBlockRange<mat_view_type> range_type;
if (cbIndices.first >= cbIndices.second) {
return; // My range of cache blocks is empty; nothing to do
}
// Q_range: Range of cache blocks in the Q factor.
// C_range: Range of cache blocks in the matrix C.
const_range_type Q_range (Q_, strategy_,
cbIndices.first, cbIndices.second,
contiguousCacheBlocks_);
range_type C_range (C_, strategy_,
cbIndices.first, cbIndices.second,
contiguousCacheBlocks_);
TEUCHOS_TEST_FOR_EXCEPTION(Q_range.empty(), std::logic_error,
"Q_range is empty, but the range of cache block "
"indices [" << cbIndices.first << ", "
<< cbIndices.second << ") is not empty. Please "
"report this bug to the Kokkos developers.");
TEUCHOS_TEST_FOR_EXCEPTION(C_range.empty(), std::logic_error,
"C_range is empty, but the range of cache block "
"indices [" << cbIndices.first << ", "
<< cbIndices.second << ") is not empty. Please "
"report this bug to the Kokkos developers.");
// Task-local workspace array of length C_.ncols(). Workspace
// must be per task, else there will be race conditions as
// different tasks attempt to write to and read from the same
// workspace simultaneously.
std::vector<Scalar> work (C_.ncols());
Combine<LocalOrdinal, Scalar> combine;
if (applyType.transposed ()) {
typename const_range_type::iterator Q_rangeIter = Q_range.begin();
typename range_type::iterator C_rangeIter = C_range.begin();
TEUCHOS_TEST_FOR_EXCEPTION(Q_rangeIter == Q_range.end(), std::logic_error,
"The Q cache block range claims to be nonempty, "
"but the iterator range is empty. Please report"
" this bug to the Kokkos developers.");
TEUCHOS_TEST_FOR_EXCEPTION(C_rangeIter == C_range.end(), std::logic_error,
"The C cache block range claims to be nonempty, "
"but the iterator range is empty. Please report"
" this bug to the Kokkos developers.");
// Q_top: Topmost cache block in the cache block range of Q.
// C_top: Topmost cache block in the cache block range of C.
const_mat_view_type Q_top = *Q_rangeIter;
mat_view_type C_top = *C_rangeIter;
if (explicitQ_) {
C_top.fill (Teuchos::ScalarTraits<Scalar>::zero ());
if (partitionIndex == 0) {
for (LocalOrdinal j = 0; j < C_top.ncols(); ++j) {
C_top(j,j) = Teuchos::ScalarTraits<Scalar>::one ();
}
}
}
LocalOrdinal curTauIndex = cbIndices.first;
// Apply the first block.
applyFirstCacheBlock (combine, applyType, Q_top,
tauArrays_[curTauIndex++], C_top, work);
// Apply the rest of the blocks, if any.
++Q_rangeIter;
++C_rangeIter;
while (Q_rangeIter != Q_range.end ()) {
TEUCHOS_TEST_FOR_EXCEPTION(C_rangeIter == C_range.end(),
std::logic_error,
"When applying Q^T or Q^H to C: The Q cache "
"block iterator is not yet at the end, but "
"the C cache block iterator is. Please "
"report this bug to the Kokkos developers.");
const_mat_view_type Q_cur = *Q_rangeIter;
mat_view_type C_cur = *C_rangeIter;
++Q_rangeIter;
++C_rangeIter;
if (explicitQ_)
C_cur.fill (Teuchos::ScalarTraits<Scalar>::zero());
applyCacheBlock (combine, applyType, Q_cur,
tauArrays_[curTauIndex++],
C_top, C_cur, work);
}
}
else {
// Q_top: Topmost cache block in the cache block range of Q.
// C_top: Topmost cache block in the cache block range of C.
const_mat_view_type Q_top = *(Q_range.begin());
mat_view_type C_top = *(C_range.begin());
if (explicitQ_) {
// We've already filled the top ncols x ncols block of
// C_top with data (that's the result of applying the
// internode part of the Q factor via DistTsqr). However,
// we still need to fill the rest of C_top (everything but
// the top ncols rows of C_top) with zeros.
mat_view_type C_top_rest (C_top.nrows() - C_top.ncols(), C_top.ncols(),
C_top.get() + C_top.ncols(), C_top.lda());
C_top_rest.fill (Teuchos::ScalarTraits<Scalar>::zero());
}
LocalOrdinal curTauIndex = cbIndices.second-1;
// When applying Q (rather than Q^T or Q^H), we apply the
// cache blocks in reverse order.
typename const_range_type::iterator Q_rangeIter = Q_range.rbegin();
typename range_type::iterator C_rangeIter = C_range.rbegin();
TEUCHOS_TEST_FOR_EXCEPTION(Q_rangeIter == Q_range.rend(), std::logic_error,
"The Q cache block range claims to be nonempty, "
"but the iterator range is empty. Please report"
" this bug to the Kokkos developers.");
TEUCHOS_TEST_FOR_EXCEPTION(C_rangeIter == C_range.rend(), std::logic_error,
"The C cache block range claims to be nonempty, "
"but the iterator range is empty. Please report"
" this bug to the Kokkos developers.");
// Equality of cache block range iterators only tests the
// cache block index, not reverse-ness. This means we can
// compare a reverse-direction iterator (Q_rangeIter) with
// a forward-direction iterator (Q_range.begin()).
//
// We do this because we need to handle the topmost block
// of Q_range separately (applyFirstCacheBlock(), rather
// than applyCacheBlock()).
while (Q_rangeIter != Q_range.begin ()) {
const_mat_view_type Q_cur = *Q_rangeIter;
mat_view_type C_cur = *C_rangeIter;
if (explicitQ_) {
C_cur.fill (Teuchos::ScalarTraits<Scalar>::zero());
}
#ifdef KNR_DEBUG
cerr << "tauArrays_[curTauIndex=" << curTauIndex << "].size() = "
<< tauArrays_[curTauIndex].size() << endl;
#endif // KNR_DEBUG
TEUCHOS_TEST_FOR_EXCEPTION(curTauIndex < cbIndices.first, std::logic_error,
"curTauIndex=" << curTauIndex << " out of valid "
"range [" << cbIndices.first << ","
<< cbIndices.second << "). Please report this "
"bug to the Kokkos developers.");
applyCacheBlock (combine, applyType, Q_cur,
tauArrays_[curTauIndex--],
C_top, C_cur, work);
++Q_rangeIter;
++C_rangeIter;
}
TEUCHOS_TEST_FOR_EXCEPTION(curTauIndex < cbIndices.first, std::logic_error,
"curTauIndex=" << curTauIndex << " out of valid "
"range [" << cbIndices.first << ","
<< cbIndices.second << "). Please report this "
"bug to the Kokkos developers.");
#ifdef KNR_DEBUG
cerr << "tauArrays_[curTauIndex=" << curTauIndex << "].size() = "
<< tauArrays_[curTauIndex].size() << endl;
#endif // KNR_DEBUG
// Apply the first block.
applyFirstCacheBlock (combine, applyType, Q_top,
tauArrays_[curTauIndex--], C_top, work);
}
}
public:
/// \brief Constructor
///
/// \param applyType [in] Whether we are applying Q, Q^T, or Q^H.
/// \param A [in/out] On input: View of the matrix to factor.
/// On output: (Part of) the implicitly stored Q factor.
/// (The other part is tauArrays.)
/// \param tauArrays [in] Where to write the "TAU" arrays
/// (implicit factorization results) for each cache block.
/// (TAU is what LAPACK's QR factorization routines call this
/// array; see the LAPACK documentation for an explanation.)
/// Indexed by the cache block index; one TAU array per cache
/// block.
/// \param strategy [in] Cache blocking strategy to use.
/// \param numPartitions [in] Number of partitions (positive
/// integer), and therefore the maximum parallelism available
/// to the algorithm. Oversubscribing processors is OK, but
/// should not be done to excess. This is an int, and not a
/// LocalOrdinal, because it is the argument to Kokkos'
/// parallel_for.
/// \param contiguousCacheBlocks [in] Whether the cache blocks
/// of A are stored contiguously.
ApplyFirstPass (const ApplyType& applyType,
const const_mat_view_type& Q,
const std::vector<std::vector<Scalar> >& tauArrays,
const std::vector<mat_view_type>& topBlocks,
const mat_view_type& C,
const CacheBlockingStrategy<LocalOrdinal, Scalar>& strategy,
const int numPartitions,
const bool explicitQ = false,
const bool contiguousCacheBlocks = false) :
applyType_ (applyType),
Q_ (Q),
tauArrays_ (tauArrays),
topBlocks_ (topBlocks),
C_ (C),
strategy_ (strategy),
numPartitions_ (numPartitions),
explicitQ_ (explicitQ),
contiguousCacheBlocks_ (contiguousCacheBlocks)
{}
/// \brief First pass of applying intranode TSQR's implicit Q factor.
///
/// Invoked by Kokkos' parallel_for template method. This
/// routine parallelizes over contiguous partitions of the C
/// matrix. Each partition in turn contains cache blocks. We
/// take care not to break up the cache blocks among partitions;
/// this ensures that the cache blocking scheme is the same as
/// SequentialTsqr uses. (However, the implicit Q factor is not
/// compatible with that of SequentialTsqr.)
///
/// \param partitionIndex [in] Zero-based index of the partition
/// which this instance of ApplyFirstPass is currently
/// processing. If greater than or equal to the number of
/// partitions, this routine does nothing.
///
/// \warning This routine almost certainly won't work in CUDA.
/// If it does, it won't be efficient. If you are interested
/// in a GPU TSQR routine, please contact the author (Mark
/// Hoemmen <mhoemme@sandia.gov>) of this code to discuss the
/// possibilities.
///
/// \note Unlike typical Kokkos work-data pairs (WDPs) passed
/// into parallel_for, this one is not declared inline. This
/// method is heavyweight enough that an inline declaration is
/// unlikely to improve performance.
void execute (const int partitionIndex) const
{
if (partitionIndex < 0 || partitionIndex >= numPartitions_ ||
Q_.empty () || C_.empty ()) {
return;
}
// We use the same cache block indices for Q and for C.
std::pair<LocalOrdinal, LocalOrdinal> cbIndices =
cacheBlockIndexRange (Q_.nrows(), Q_.ncols(), partitionIndex,
numPartitions_, strategy_);
if (cbIndices.second <= cbIndices.first)
return;
{
std::pair<size_t, size_t> cbInds (static_cast<size_t> (cbIndices.first),
static_cast<size_t> (cbIndices.second));
TEUCHOS_TEST_FOR_EXCEPTION(
cbIndices.first < static_cast<LocalOrdinal>(0), std::logic_error,
"TSQR::ApplyFirstPass::execute: cacheBlockIndexRange(" <<
Q_.nrows () << ", " << Q_.ncols() << ", " << partitionIndex << ", "
<< numPartitions_ << ", strategy) returned a cache block range " <<
cbIndices.first << "," << cbIndices.second << " with negative start"
"ing index. Please report this bug to the Kokkos developers.");
TEUCHOS_TEST_FOR_EXCEPTION(
cbInds.second > tauArrays_.size (), std::logic_error,
"TSQR::ApplyFirstPass::execute: cacheBlockIndexRange(" <<
Q_.nrows () << ", " << Q_.ncols() << ", " << partitionIndex << ", "
<< numPartitions_ << ", strategy) returned a cache block range "
<< cbIndices.first << "," << cbIndices.second << " with starting "
"index larger than the number of tau arrays " << tauArrays_.size ()
<< ". Please report this bug to the Kokkos developers.");
}
apply (applyType_, cbIndices, partitionIndex);
}
};
/// \class CacheBlockWDP
/// \brief Kokkos work-data pair (WDP) for KokkosNodeTsqr's (un_)cache_block() methods.
/// \author Mark Hoemmen
template<class LocalOrdinal, class Scalar>
class CacheBlockWDP {
private:
typedef ConstMatView<LocalOrdinal, Scalar> const_mat_view_type;
typedef MatView<LocalOrdinal, Scalar> mat_view_type;
typedef CacheBlockRange<const_mat_view_type> const_range_type;
typedef CacheBlockRange<mat_view_type> range_type;
const_mat_view_type A_in_;
mat_view_type A_out_;
CacheBlockingStrategy<LocalOrdinal, Scalar> strategy_;
int numPartitions_;
bool unblock_;
/// \brief Copy one range of cache blocks into another.
///
/// \param cbInputRange [in] Range of input cache blocks.
/// \param cbOutputRange [out] Range of output cache blocks.
void copyRange (const_range_type& cbInputRange, range_type& cbOutputRange) const
{
typedef typename const_range_type::iterator input_iter_type;
typedef typename range_type::iterator output_iter_type;
input_iter_type inputIter = cbInputRange.begin();
output_iter_type outputIter = cbOutputRange.begin();
input_iter_type inputEnd = cbInputRange.end();
// TODO (mfh 29 Jun 2012) In a debug build, check in the loop
// below whether outputIter == cbOutputRange.end(). If so,
// throw std::logic_error. Don't declare outputEnd unless
// we're in a debug build, because otherwise the compiler may
// report warnings (gcc 4.5 doesn't; gcc 4.6 does).
// output_iter_type outputEnd = cbOutputRange.end();
while (inputIter != inputEnd) {
const_mat_view_type A_in_cur = *inputIter;
mat_view_type A_out_cur = *outputIter;
deep_copy (A_out_cur, A_in_cur);
++inputIter;
++outputIter;
}
}
public:
/// \brief Constructor
///
/// \param A_in [in] The matrix to (un-)cache-block.
/// \param A_out [in/out] Result of (un-)cache-blocking the
/// matrix A_in.
/// \param strategy [in] Cache blocking strategy.
/// \param numPartitions [in] Number of partitions; maximum
/// available parallelism.
/// \param unblock [in] If false, cache-block A_in (a matrix in
/// column-major order) into A_out. If true, un-cache-block
/// A_in into A_out (a matrix in column-major order).
CacheBlockWDP (const const_mat_view_type A_in,
const mat_view_type A_out,
const CacheBlockingStrategy<LocalOrdinal, Scalar>& strategy,
const int numPartitions,
const bool unblock) :
A_in_ (A_in),
A_out_ (A_out),
strategy_ (strategy),
numPartitions_ (numPartitions),
unblock_ (unblock)
{
TEUCHOS_TEST_FOR_EXCEPTION(A_in_.nrows() != A_out_.nrows() ||
A_in_.ncols() != A_out_.ncols(),
std::invalid_argument,
"A_in and A_out do not have the same dimensions: "
"A_in is " << A_in_.nrows() << " by "
<< A_in_.ncols() << ", but A_out is "
<< A_out_.nrows() << " by "
<< A_out_.ncols() << ".");
TEUCHOS_TEST_FOR_EXCEPTION(numPartitions_ < 1,
std::invalid_argument,
"The number of partitions " << numPartitions_
<< " is not a positive integer.");
}
/// \brief Method called by Kokkos' parallel_for.
///
/// \param partitionIndex [in] Zero-based index of the partition
/// of the matrix. We parallelize over partitions.
/// Partitions respect cache blocks.
void execute (const int partitionIndex) const
{
if (partitionIndex < 0 || partitionIndex >= numPartitions_ ||
A_in_.empty()) {
return;
}
else {
typedef std::pair<LocalOrdinal, LocalOrdinal> index_range_type;
const index_range_type cbIndices =
cacheBlockIndexRange (A_in_.nrows (), A_in_.ncols (),
partitionIndex, numPartitions_, strategy_);
// It's perfectly legal for a partitioning to assign zero
// cache block indices to a particular partition. In that
// case, this task has nothing to do.
if (cbIndices.first >= cbIndices.second) {
return;
}
else {
// If unblock_ is false, then A_in_ is in column-major
// order, and we want to cache-block it into A_out_. If
// unblock_ is true, then A_in_ is cache-blocked, and we
// want to un-cache-block it into A_out_ (a matrix in
// column-major order).
const_range_type inputRange (A_in_, strategy_, cbIndices.first,
cbIndices.second, unblock_);
range_type outputRange (A_out_, strategy_, cbIndices.first,
cbIndices.second, ! unblock_);
copyRange (inputRange, outputRange);
}
}
}
};
/// \class MultWDP
/// \brief Kokkos work-data pair (WDP) for \c KokkosNodeTsqr::Q_times_B().
/// \author Mark Hoemmen
template<class LocalOrdinal, class Scalar>
class MultWDP {
private:
typedef ConstMatView<LocalOrdinal, Scalar> const_mat_view_type;
typedef MatView<LocalOrdinal, Scalar> mat_view_type;
typedef CacheBlockRange<mat_view_type> range_type;
mat_view_type Q_;
const_mat_view_type B_;
CacheBlockingStrategy<LocalOrdinal, Scalar> strategy_;
int numPartitions_;
bool contiguousCacheBlocks_;
void
multBlock (Teuchos::BLAS<LocalOrdinal, Scalar>& blas,
const mat_view_type& Q_cur,
Matrix<LocalOrdinal, Scalar>& Q_temp) const
{
using Teuchos::NO_TRANS;
const LocalOrdinal numCols = Q_cur.ncols ();
// GEMM doesn't like aliased arguments, so we use a copy. We
// only copy the current cache block, rather than all of Q;
// this saves memory.
Q_temp.reshape (Q_cur.nrows (), numCols);
deep_copy (Q_temp, Q_cur);
// Q_cur := Q_temp * B.
blas.GEMM (NO_TRANS, NO_TRANS, Q_cur.nrows(), numCols, numCols,
Teuchos::ScalarTraits<Scalar>::one(),
Q_temp.get(), Q_temp.lda(), B_.get(), B_.lda(),
Scalar(0), Q_cur.get(), Q_cur.lda());
}
/// \brief Multiply (in place) each cache block in the range by B_.
///
/// \param cbRange [in/out] Range of cache blocks.
void multRange (range_type& cbRange) const
{
typedef typename range_type::iterator iter_type;
iter_type iter = cbRange.begin();
iter_type end = cbRange.end();
// Temporary storage for the BLAS' matrix-matrix multiply
// routine (which forbids aliasing of any input argument and
// the output argument).
Matrix<LocalOrdinal, Scalar> Q_temp;
Teuchos::BLAS<LocalOrdinal, Scalar> blas;
while (iter != end) {
mat_view_type Q_cur = *iter;
multBlock (blas, Q_cur, Q_temp);
++iter;
}
}
public:
/// \brief Constructor
///
/// \param Q [in/out] Matrix to multiply in place by B.
/// \param B [in] \f$Q := Q * B\f$.
/// \param strategy [in] Cache-blocking strategy.
/// \param numPartitions [in] Number of partitions of the matrix
/// Q; maximum available parallelism.
/// \param contiguousCacheBlocks [in] Whether the cache blocks
/// of Q are stored contiguously.
MultWDP (const mat_view_type Q,
const const_mat_view_type B,
const CacheBlockingStrategy<LocalOrdinal, Scalar>& strategy,
const int numPartitions,
const bool contiguousCacheBlocks) :
Q_ (Q),
B_ (B),
strategy_ (strategy),
numPartitions_ (numPartitions),
contiguousCacheBlocks_ (contiguousCacheBlocks)
{}
/// \brief Method called by Kokkos' parallel_for.
///
/// \param partitionIndex [in] Zero-based index of the partition
/// of the matrix. We parallelize over partitions.
/// Partitions respect cache blocks.
void execute (const int partitionIndex) const
{
if (partitionIndex < 0 || partitionIndex >= numPartitions_ ||
Q_.empty ()) {
return;
}
else {
typedef std::pair<LocalOrdinal, LocalOrdinal> index_range_type;
const index_range_type cbIndices =
cacheBlockIndexRange (Q_.nrows (), Q_.ncols (), partitionIndex,
numPartitions_, strategy_);
if (cbIndices.first >= cbIndices.second) {
return;
}
else {
range_type range (Q_, strategy_, cbIndices.first,
cbIndices.second, contiguousCacheBlocks_);
multRange (range);
}
}
}
};
/// \class FillWDP
/// \brief Kokkos work-data pair (WDP) for \c KokkosNodeTsqr::fill_with_zeros().
/// \author Mark Hoemmen
template<class LocalOrdinal, class Scalar>
class FillWDP {
private:
typedef MatView<LocalOrdinal, Scalar> mat_view_type;
typedef CacheBlockRange<mat_view_type> range_type;
mat_view_type A_;
CacheBlockingStrategy<LocalOrdinal, Scalar> strategy_;
const Scalar value_;
int numPartitions_;
bool contiguousCacheBlocks_;
//! Fill (in place) each cache block in the range with value.
void fillRange (range_type& cbRange, const Scalar value) const
{
typedef typename range_type::iterator iter_type;
iter_type iter = cbRange.begin();
iter_type end = cbRange.end();
while (iter != end) {
mat_view_type A_cur = *iter;
A_cur.fill (value);
++iter;
}
}
public:
/// \brief Constructor
///
/// \param A [in/out] Matrix to fill with the value.
/// \param strategy [in] Cache-blocking strategy.
/// \param value [in] The value with which to fill A.
/// \param numPartitions [in] Number of partitions of
/// the matrix A; maximum available parallelism.
/// \param contiguousCacheBlocks [in] Whether the cache
/// blocks of A are stored contiguously.
FillWDP (const mat_view_type A,
const CacheBlockingStrategy<LocalOrdinal, Scalar>& strategy,
const Scalar value,
const int numPartitions,
const bool contiguousCacheBlocks) :
A_ (A),
strategy_ (strategy),
value_ (value),
numPartitions_ (numPartitions),
contiguousCacheBlocks_ (contiguousCacheBlocks)
{}
/// \brief Method called by Kokkos' parallel_for.
///
/// \param partitionIndex [in] Zero-based index of the partition
/// of the matrix. We parallelize over partitions.
/// Partitions respect cache blocks.
void execute (const int partitionIndex) const
{
if (partitionIndex < 0 || partitionIndex >= numPartitions_ ||
A_.empty ()) {
return;
}
else {
typedef std::pair<LocalOrdinal, LocalOrdinal> index_range_type;
const index_range_type cbIndices =
cacheBlockIndexRange (A_.nrows(), A_.ncols(), partitionIndex,
numPartitions_, strategy_);
if (cbIndices.first >= cbIndices.second) {
return;
}
else {
range_type range (A_, strategy_, cbIndices.first,
cbIndices.second, contiguousCacheBlocks_);
fillRange (range, value_);
}
}
}
};
} // namespace details
/// \class KokkosNodeTsqrFactorOutput
/// \brief Part of KokkosNodeTsqr's implicit Q representation.
/// \author Mark Hoemmen
///
/// The \c KokkoNodeTsqr::factor() method represents the Q factor of
/// the matrix A implicitly. Part of that representation is in the
/// A matrix on output, and the other part is returned as an object
/// of this type. The apply() and explicit_Q() methods need both
/// parts of the implicit Q representation in order to do their
/// work.
template<class LocalOrdinal, class Scalar>
struct KokkosNodeTsqrFactorOutput {
typedef MatView<LocalOrdinal, Scalar> mat_view_type;
/// \brief Constructor
///
/// \param theNumCacheBlocks [in] Total number of cache blocks
/// (over all partitions).
/// \param theNumPartitions [in] Number of partitions. This is
/// an int because partition indices are ints, and the latter
/// are ints because they end up as range arguments to Kokkos'
/// parallel_for.
KokkosNodeTsqrFactorOutput (const size_t theNumCacheBlocks,
const int theNumPartitions) :
firstPassTauArrays (theNumCacheBlocks)
{
// Protect the cast to size_t from a negative number of
// partitions.
TEUCHOS_TEST_FOR_EXCEPTION(theNumPartitions < 1, std::invalid_argument,
"TSQR::KokkosNodeTsqrFactorOutput: Invalid number of "
"partitions " << theNumPartitions << "; number of "
"partitions must be a positive integer.");
// If there's only one partition, we don't even need a second
// pass (it's just sequential TSQR), and we don't need a TAU
// array for the top partition.
secondPassTauArrays.resize (static_cast<size_t> (theNumPartitions-1));
topBlocks.resize (static_cast<size_t> (theNumPartitions));
}
//! Total number of cache blocks in the matrix (over all partitions).
int numCacheBlocks() const { return firstPassTauArrays.size(); }
//! Number of partitions of the matrix; max available parallelism.
int numPartitions() const { return topBlocks.size(); }
//! TAU arrays from the first pass; one per cache block.
std::vector<std::vector<Scalar> > firstPassTauArrays;
/// \brief TAU arrays from the second pass.
///
/// There is one TAU array per partition, except for the topmost
/// partition.
///
/// For now, KokkosNodeTsqr::factor() uses only two passes over
/// the matrix. firstPassTauArrays contains the result of the
/// pass over cache blocks, and secondPassTauArrays contains the
/// result of combining the upper triangular R factors from the
/// first pass. Later, we may add more passes, in which case we
/// will likely combine firstPassTauArrays and secondPassTauArrays
/// into a single std::vector (variable number of passes) or
/// Teuchos::Tuple (fixed number of passes).
std::vector<std::vector<Scalar> > secondPassTauArrays;
/// \brief Views of the topmost cache blocks in each partition.
///
/// One entry for each partition.
std::vector<mat_view_type> topBlocks;
};
/// \class KokkosNodeTsqr
/// \brief Intranode TSQR parallelized using the Kokkos Node API.
/// \author Mark Hoemmen
///
/// \tparam LocalOrdinal The type of indices in the (node-local)
/// matrix.
///
/// \tparam Scalar The type of entries in the (node-local) matrix.
///
/// \tparam NodeType The Kokkos Node type. This currently must be a
/// CPU node; this algorithm is not (yet) appropriate for GPUs.
///
/// This implementation of the intranode part of TSQR factors the
/// matrix in two passes. The first pass parallelizes over
/// partitions, doing Sequential TSQR over each partition. The
/// second pass combines the R factors from the partitions, and is
/// not currently parallel. Thus, the overall algorithm is similar
/// to that of \c TbbTsqr, except that:
/// - TbbTsqr partitions differently; KokkosNodeTsqr's partitions
/// use the same layout of cache blocks as SequentialTsqr, whereas
/// TbbTsqr uses a different layout.
/// - TbbTsqr reduces the R factors in parallel; it only needs one
/// "pass."
template<class LocalOrdinal, class Scalar, class NodeType>
class KokkosNodeTsqr :
public NodeTsqr<LocalOrdinal, Scalar, KokkosNodeTsqrFactorOutput<LocalOrdinal, Scalar> >,
public Teuchos::ParameterListAcceptorDefaultBase
{
public:
typedef LocalOrdinal local_ordinal_type;
typedef Scalar scalar_type;
typedef NodeType node_type;
typedef ConstMatView<LocalOrdinal, Scalar> const_mat_view_type;
typedef MatView<LocalOrdinal, Scalar> mat_view_type;
/// \typedef FactorOutput
/// \brief Part of the implicit Q representation returned by factor().
typedef typename NodeTsqr<LocalOrdinal, Scalar, KokkosNodeTsqrFactorOutput<LocalOrdinal, Scalar> >::factor_output_type FactorOutput;
/// \brief Constructor (with user-specified parameters).
///
/// \param node [in] Kokkos Node instance. If you don't have this
/// yet, you can set it to null and call \c setNode() later once
/// you have the Node instance. (This is the typical case for
/// lazy initialization of a Belos or Anasazi (Mat)OrthoManager
/// subclass, where you need a vector before you can get a Node
/// instance.)
///
/// \param params [in/out] List of parameters. Missing parameters
/// will be filled in with default values.
KokkosNodeTsqr (const Teuchos::RCP<node_type>& node,
const Teuchos::RCP<Teuchos::ParameterList>& params) :
node_ (node)
{
setParameterList (params);
}
/// \brief Constructor (with user-specified parameters but no node).
///
/// This version of the constructor sets the Kokkos Node instance
/// to null. You must call \c setNode() with a valid Kokkos Node
/// instance before you can invoke any methods that perform
/// computations.
///
/// \param params [in/out] List of parameters. Missing parameters
/// will be filled in with default values.
KokkosNodeTsqr (const Teuchos::RCP<Teuchos::ParameterList>& params) :
node_ (Teuchos::null)
{
setParameterList (params);
}
/// \brief Constructor (sets default parameters).
///
/// \param node [in] Kokkos Node instance. If you don't have this
/// yet, you can set it to null and call \c setNode() later once
/// you have the Node instance.
KokkosNodeTsqr (const Teuchos::RCP<node_type>& node) :
node_ (node)
{
setParameterList (Teuchos::null);
}
/// \brief Default constructor (sets default parameters).
///
/// This version of the constructor sets the Kokkos Node instance
/// to null. You must call \c setNode() with a valid Kokkos Node
/// instance before you can invoke any methods that perform
/// computations.
KokkosNodeTsqr () : node_ (Teuchos::null)
{
setParameterList (Teuchos::null);
}
/// \brief Set the Kokkos Node instance.
///
/// You can't compute anything until you set the Kokkos Node
/// instance.
///
/// \note The whole reason for allowing initialization of the
/// Kokkos Node instance after construction is so that this
/// class can implement \c Teuchos::ParameterListAcceptor.
/// ParameterListAcceptor's getValidParameters() is an instance
/// method, not a class method, so the object has to be
/// instantiated before getValidParameters() can be called. \c
/// NodeTsqrFactory in turn needs to call getValidParameters()
/// so that callers can get a default parameter list before
/// instantiating the NodeTsqr subclass instance. However,
/// NodeTsqrFactory doesn't have the Kokkos Node instance until
/// TSQR gets a multivector to factor.
void setNode (const Teuchos::RCP<node_type>& node) {
node_ = node;
}
/// \brief Whether this object is ready to perform computations.
///
/// It is <i>not</i> ready if the Kokkos Node instance has not yet
/// been set.
bool ready() const {
return ! getNode().is_null();
}
/// \brief One-line description of this object.
///
/// This implements Teuchos::Describable::description().
std::string description () const {
using Teuchos::TypeNameTraits;
std::ostringstream os;
os << "KokkosNodeTsqr<LocalOrdinal="
<< TypeNameTraits<LocalOrdinal>::name()
<< ", Scalar="
<< TypeNameTraits<Scalar>::name()
<< ", NodeType="
<< TypeNameTraits<node_type>::name()
<< ">: \"Cache Size Hint\"=" << strategy_.cache_size_hint()
<< ", \"Size of Scalar\"=" << strategy_.size_of_scalar()
<< ", \"Num Tasks\"=" << numPartitions_;
return os.str();
}
/// \brief Validate and read in parameters.
///
/// \param paramList [in/out] On input: non-null parameter list
/// containing zero or more of the parameters in \c
/// getValidParameters(). On output: missing parameters (i.e.,
/// parameters in \c getValidParameters() but not in the input
/// list) are filled in with default values.
void
setParameterList (const Teuchos::RCP<Teuchos::ParameterList>& paramList)
{
using Teuchos::ParameterList;
using Teuchos::parameterList;
using Teuchos::RCP;
using Teuchos::rcp;
RCP<ParameterList> plist;
if (paramList.is_null()) {
plist = rcp (new ParameterList (*getValidParameters ()));
} else {
plist = paramList;
plist->validateParametersAndSetDefaults (*getValidParameters ());
}
// Get values of parameters. We do this "transactionally" so
// that (except for validation and filling in defaults above)
// this method has the strong exception guarantee (it either
// returns, or throws an exception with no externally visible
// side effects).
size_t cacheSizeHint, sizeOfScalar;
int numPartitions;
try {
cacheSizeHint = plist->get<size_t> ("Cache Size Hint");
sizeOfScalar = plist->get<size_t> ("Size of Scalar");
numPartitions = plist->get<int> ("Num Tasks");
} catch (Teuchos::Exceptions::InvalidParameter& e) {
std::ostringstream os;
os << "Failed to read default parameters after setting defaults. Pleas"
"e report this bug to the Kokkos developers. Original exception mess"
"age: " << e.what();
throw std::logic_error (os.str());
}
numPartitions_ = numPartitions;
// Recreate the cache blocking strategy.
typedef CacheBlockingStrategy<LocalOrdinal, Scalar> strategy_type;
strategy_ = strategy_type (cacheSizeHint, sizeOfScalar);
// Save the input parameter list.
setMyParamList (plist);
}
/// \brief Default valid parameter list.
///
/// The returned list contains all parameters accepted by \c
/// KokkosNodeTsqr, with their default values and documentation.
Teuchos::RCP<const Teuchos::ParameterList>
getValidParameters() const
{
using Teuchos::ParameterList;
using Teuchos::parameterList;
using Teuchos::RCP;
if (defaultParams_.is_null()) {
RCP<ParameterList> params = parameterList ("Intranode TSQR");
params->set ("Cache Size Hint",
static_cast<size_t>(0),
std::string("Cache size in bytes; a hint for TSQR. Set to t"
"he size of the largest private cache per CPU co"
"re, or the fraction of shared cache per core. "
"If zero, we pick a reasonable default."));
params->set ("Size of Scalar",
sizeof(Scalar),
std::string ("Size in bytes of the Scalar type. In most "
"cases, the default sizeof(Scalar) is fine. "
"Set a non-default value only when Scalar's "
"data is dynamically allocated (such as for a "
"type with precision variable at run time)."));
// The number of partitions is an int rather than a
// LocalOrdinal, to ensure that it is always stored with the
// same type, despite the type of LocalOrdinal. Besides, Kokkos
// wants an int anyway.
params->set ("Num Tasks",
defaultNumPartitions (),
std::string ("Number of partitions; the maximum available pa"
"rallelelism in intranode TSQR. Slight oversub"
"scription is OK; undersubscription may have a "
"performance cost."));
defaultParams_ = params;
}
return defaultParams_;
}
FactorOutput
factor (const LocalOrdinal numRows,
const LocalOrdinal numCols,
Scalar A[],
const LocalOrdinal lda,
Scalar R[],
const LocalOrdinal ldr,
const bool contiguousCacheBlocks) const
{
mat_view_type A_view (numRows, numCols, A, lda);
mat_view_type R_view (numCols, numCols, R, ldr);
return factorImpl (A_view, R_view, contiguousCacheBlocks);
}
void
apply (const ApplyType& applyType,
const LocalOrdinal nrows,
const LocalOrdinal ncols_Q,
const Scalar Q[],
const LocalOrdinal ldq,
const FactorOutput& factorOutput,
const LocalOrdinal ncols_C,
Scalar C[],
const LocalOrdinal ldc,
const bool contiguousCacheBlocks) const
{
const_mat_view_type Q_view (nrows, ncols_Q, Q, ldq);
mat_view_type C_view (nrows, ncols_C, C, ldc);
applyImpl (applyType, Q_view, factorOutput, C_view,
false, contiguousCacheBlocks);
}
void
explicit_Q (const LocalOrdinal nrows,
const LocalOrdinal ncols_Q,
const Scalar Q[],
const LocalOrdinal ldq,
const FactorOutput& factorOutput,
const LocalOrdinal ncols_C,
Scalar C[],
const LocalOrdinal ldc,
const bool contiguousCacheBlocks) const
{
const_mat_view_type Q_view (nrows, ncols_Q, Q, ldq);
mat_view_type C_view (nrows, ncols_C, C, ldc);
applyImpl (ApplyType::NoTranspose, Q_view, factorOutput,
C_view, true, contiguousCacheBlocks);
}
bool QR_produces_R_factor_with_nonnegative_diagonal () const {
return combine_.QR_produces_R_factor_with_nonnegative_diagonal ();
}
size_t cache_size_hint() const {
return strategy_.cache_size_hint();
}
void
fill_with_zeros (const LocalOrdinal nrows,
const LocalOrdinal ncols,
Scalar A[],
const LocalOrdinal lda,
const bool contiguousCacheBlocks) const
{
Teuchos::RCP<node_type> node = getNode ();
TEUCHOS_TEST_FOR_EXCEPTION(node.is_null(), std::runtime_error,
"The Kokkos Node instance has not yet been set. "
"KokkosNodeTsqr needs a Kokkos Node instance in order "
"to perform computations.");
mat_view_type A_view (nrows, ncols, A, lda);
typedef details::FillWDP<LocalOrdinal, Scalar> fill_wdp_type;
typedef Teuchos::ScalarTraits<Scalar> STS;
fill_wdp_type filler (A_view, strategy_, STS::zero(),
numPartitions_, contiguousCacheBlocks);
node->parallel_for (0, numPartitions_, filler);
}
void
cache_block (const LocalOrdinal nrows,
const LocalOrdinal ncols,
Scalar A_out[],
const Scalar A_in[],
const LocalOrdinal lda_in) const
{
Teuchos::RCP<node_type> node = getNode ();
TEUCHOS_TEST_FOR_EXCEPTION(node.is_null(), std::runtime_error,
"The Kokkos Node instance has not yet been set. "
"KokkosNodeTsqr needs a Kokkos Node instance in order "
"to perform computations.");
const_mat_view_type A_in_view (nrows, ncols, A_in, lda_in);
// The leading dimension of A_out doesn't matter here, since its
// cache blocks are to be stored contiguously. We set it
// arbitrarily to a sensible value.
mat_view_type A_out_view (nrows, ncols, A_out, nrows);
typedef details::CacheBlockWDP<LocalOrdinal, Scalar> cb_wdp_type;
cb_wdp_type cacheBlocker (A_in_view, A_out_view, strategy_,
numPartitions_, false);
node->parallel_for (0, numPartitions_, cacheBlocker);
}
void
un_cache_block (const LocalOrdinal nrows,
const LocalOrdinal ncols,
Scalar A_out[],
const LocalOrdinal lda_out,
const Scalar A_in[]) const
{
Teuchos::RCP<node_type> node = getNode ();
TEUCHOS_TEST_FOR_EXCEPTION(node.is_null(), std::runtime_error,
"The Kokkos Node instance has not yet been set. "
"KokkosNodeTsqr needs a Kokkos Node instance in order "
"to perform computations.");
// The leading dimension of A_in doesn't matter here, since its
// cache blocks are contiguously stored. We set it arbitrarily
// to a sensible value.
const_mat_view_type A_in_view (nrows, ncols, A_in, nrows);
mat_view_type A_out_view (nrows, ncols, A_out, lda_out);
typedef details::CacheBlockWDP<LocalOrdinal, Scalar> cb_wdp_type;
cb_wdp_type cacheBlocker (A_in_view, A_out_view, strategy_,
numPartitions_, true);
node->parallel_for (0, numPartitions_, cacheBlocker);
}
void
Q_times_B (const LocalOrdinal nrows,
const LocalOrdinal ncols,
Scalar Q[],
const LocalOrdinal ldq,
const Scalar B[],
const LocalOrdinal ldb,
const bool contiguousCacheBlocks) const
{
Teuchos::RCP<node_type> node = getNode ();
TEUCHOS_TEST_FOR_EXCEPTION(node.is_null(), std::runtime_error,
"The Kokkos Node instance has not yet been set. "
"KokkosNodeTsqr needs a Kokkos Node instance in order "
"to perform computations.");
mat_view_type Q_view (nrows, ncols, Q, ldq);
const_mat_view_type B_view (ncols, ncols, B, ldb);
typedef details::MultWDP<LocalOrdinal, Scalar> mult_wdp_type;
mult_wdp_type mult (Q_view, B_view, strategy_, numPartitions_,
contiguousCacheBlocks);
node->parallel_for (0, numPartitions_, mult);
}
private:
//! Get the Kokkos Node instance (may be null if it was not set).
Teuchos::RCP<node_type> getNode () const {
return node_;
}
//! Implementation of fundamental TSQR kernels.
Combine<LocalOrdinal, Scalar> combine_;
//! Workspace for Combine operations.
mutable std::vector<Scalar> work_;
//! Pointer to the Kokkos Node object.
Teuchos::RCP<node_type> node_;
//! Cache blocking strategy.
CacheBlockingStrategy<LocalOrdinal, Scalar> strategy_;
/// \brief Number of partitions; max available parallelism.
///
/// The number of partitions is an int rather than a LocalOrdinal,
/// to ensure that it is always stored in the ParameterList with
/// the same type, despite the type of LocalOrdinal. Besides,
/// Kokkos wants an int anyway.
int numPartitions_;
//! Default parameter list (set by \c getValidParameters()).
mutable Teuchos::RCP<const Teuchos::ParameterList> defaultParams_;
/// \brief Default number of partitions.
///
/// This method may in the future try to "learn" the optimal
/// number of partitions. For now, it's a constant. Later, we
/// may even try to "learn" the best value, perhaps even at
/// runtime. As a result, this method may not necessarily return
/// the same value each time it is called.
///
/// \warning We may change this method to take an RCP to a const
/// Kokkos node_type instance, if the Kokkos Node API later
/// supports queries for available computational resources
/// (e.g., number of CPU cores per node).
int
defaultNumPartitions () const
{
// Currently the Kokkos Node API does not give us access to the
// amount of available parallelism, so we return a constant.
// Mild oversubscription is OK.
return 16;
}
FactorOutput
factorImpl (mat_view_type A,
mat_view_type R,
const bool contiguousCacheBlocks) const
{
if (A.empty()) {
TEUCHOS_TEST_FOR_EXCEPTION(! R.empty(), std::logic_error,
"KokkosNodeTsqr::factorImpl: A is empty, but R "
"is not. Please report this bug to the Kokkos "
"developers.");
return FactorOutput (0, 0);
}
Teuchos::RCP<node_type> node = getNode ();
TEUCHOS_TEST_FOR_EXCEPTION(node.is_null(), std::runtime_error,
"The Kokkos Node instance has not yet been set. "
"KokkosNodeTsqr needs a Kokkos Node instance in order "
"to perform computations.");
const LocalOrdinal numRowsPerCacheBlock =
strategy_.cache_block_num_rows (A.ncols());
const LocalOrdinal numCacheBlocks =
strategy_.num_cache_blocks (A.nrows(), A.ncols(), numRowsPerCacheBlock);
//
// Compute the first factorization pass (over partitions).
//
FactorOutput result (numCacheBlocks, numPartitions_);
typedef details::FactorFirstPass<LocalOrdinal, Scalar> first_pass_type;
first_pass_type firstPass (A, result.firstPassTauArrays,
result.topBlocks, strategy_,
numPartitions_, contiguousCacheBlocks);
// parallel_for wants an exclusive range.
node->parallel_for (0, numPartitions_, firstPass);
// Each partition collected a view of its top block, where that
// partition's R factor is stored. The second pass reduces
// those R factors. We do this on one thread to avoid the
// overhead of parallelizing it. If the typical use case is
// oversubscription, you should parallelize this step with
// multiple passes. Note that we can't use parallel_reduce,
// because the tree topology matters.
factorSecondPass (result.topBlocks, result.secondPassTauArrays,
numPartitions_);
// The "topmost top block" contains the resulting R factor.
const mat_view_type& R_top = result.topBlocks[0];
TEUCHOS_TEST_FOR_EXCEPTION(R_top.empty(), std::logic_error,
"After factorSecondPass: result.topBlocks[0] is an "
"empty view. Please report this bug to the Kokkos "
"developers.");
mat_view_type R_top_square (R_top.ncols(), R_top.ncols(),
R_top.get(), R_top.lda());
R.fill (Teuchos::ScalarTraits<Scalar>::zero());
// Only copy the upper triangle of R_top into R.
copy_upper_triangle (R.ncols(), R.ncols(), R.get(), R.lda(),
R_top.get(), R_top.lda());
return result;
}
void
applyImpl (const ApplyType& applyType,
const const_mat_view_type& Q,
const FactorOutput& factorOutput,
const mat_view_type& C,
const bool explicitQ,
const bool contiguousCacheBlocks) const
{
using details::cacheBlockIndexRange;
typedef details::ApplyFirstPass<LocalOrdinal, Scalar> first_pass_type;
Teuchos::RCP<node_type> node = getNode ();
TEUCHOS_TEST_FOR_EXCEPTION(node.is_null(), std::runtime_error,
"The Kokkos Node instance has not yet been set. "
"KokkosNodeTsqr needs a Kokkos Node instance in order "
"to perform computations.");
TEUCHOS_TEST_FOR_EXCEPTION(numPartitions_ != factorOutput.numPartitions(),
std::invalid_argument,
"applyImpl: KokkosNodeTsqr's number of partitions "
<< numPartitions_ << " does not match the given "
"factorOutput's number of partitions "
<< factorOutput.numPartitions() << ". This likely "
"means that the given factorOutput object comes from "
"a different instance of KokkosNodeTsqr. Please "
"report this bug to the Kokkos developers.");
const int numParts = numPartitions_;
first_pass_type firstPass (applyType, Q, factorOutput.firstPassTauArrays,
factorOutput.topBlocks, C, strategy_,
numParts, explicitQ, contiguousCacheBlocks);
// Get a view of each partition's top block of the C matrix.
std::vector<mat_view_type> topBlocksOfC (numParts);
{
typedef std::pair<LocalOrdinal, LocalOrdinal> index_range_type;
typedef CacheBlocker<LocalOrdinal, Scalar> blocker_type;
blocker_type C_blocker (C.nrows(), C.ncols(), strategy_);
// For each partition, collect its top block of C.
for (int partIdx = 0; partIdx < numParts; ++partIdx) {
const index_range_type cbIndices =
cacheBlockIndexRange (C.nrows(), C.ncols(), partIdx,
numParts, strategy_);
if (cbIndices.first >= cbIndices.second) {
topBlocksOfC[partIdx] = mat_view_type (0, 0, NULL, 0);
} else {
topBlocksOfC[partIdx] =
C_blocker.get_cache_block (C, cbIndices.first,
contiguousCacheBlocks);
}
}
}
if (applyType.transposed()) {
// parallel_for wants an exclusive range.
node->parallel_for (0, numPartitions_, firstPass);
applySecondPass (applyType, factorOutput, topBlocksOfC,
strategy_, explicitQ);
} else {
applySecondPass (applyType, factorOutput, topBlocksOfC,
strategy_, explicitQ);
// parallel_for wants an exclusive range.
node->parallel_for (0, numPartitions_, firstPass);
}
}
std::vector<Scalar>
factorPair (const mat_view_type& R_top,
const mat_view_type& R_bot) const
{
TEUCHOS_TEST_FOR_EXCEPTION(R_top.empty(), std::logic_error,
"R_top is empty!");
TEUCHOS_TEST_FOR_EXCEPTION(R_bot.empty(), std::logic_error,
"R_bot is empty!");
TEUCHOS_TEST_FOR_EXCEPTION(work_.size() == 0, std::logic_error,
"Workspace array work_ has length zero.");
TEUCHOS_TEST_FOR_EXCEPTION(work_.size() < static_cast<size_t> (R_top.ncols()),
std::logic_error,
"Workspace array work_ has length = "
<< work_.size() << " < R_top.ncols() = "
<< R_top.ncols() << ".");
std::vector<Scalar> tau (R_top.ncols());
// Our convention for such helper methods is for the immediate
// parent to allocate workspace (the work_ array in this case).
//
// The statement below only works if R_top and R_bot have a
// nonzero (and the same) number of columns, but we have already
// checked that above.
combine_.factor_pair (R_top.ncols(), R_top.get(), R_top.lda(),
R_bot.get(), R_bot.lda(), &tau[0], &work_[0]);
return tau;
}
void
factorSecondPass (std::vector<mat_view_type >& topBlocks,
std::vector<std::vector<Scalar> >& tauArrays,
const int numPartitions) const
{
if (numPartitions <= 1)
return; // Done!
TEUCHOS_TEST_FOR_EXCEPTION (topBlocks.size() < static_cast<size_t>(numPartitions),
std::logic_error,
"KokkosNodeTsqr::factorSecondPass: topBlocks.size() "
"(= " << topBlocks.size() << ") < numPartitions (= "
<< numPartitions << "). Please report this bug to "
"the Kokkos developers.");
TEUCHOS_TEST_FOR_EXCEPTION (tauArrays.size() < static_cast<size_t>(numPartitions-1),
std::logic_error,
"KokkosNodeTsqr::factorSecondPass: topBlocks.size() "
"(= " << topBlocks.size() << ") < numPartitions-1 (= "
<< (numPartitions-1) << "). Please report this bug "
"to the Kokkos developers.");
// The top partition (partition index zero) should always be
// nonempty if we get this far, so its top block should also be
// nonempty.
TEUCHOS_TEST_FOR_EXCEPTION(topBlocks[0].empty(), std::logic_error,
"KokkosNodeTsqr::factorSecondPass: topBlocks[0] is "
"empty. Please report this bug to the Kokkos "
"developers.");
// However, other partitions besides the top one might be empty,
// in which case their top blocks will be empty. We skip over
// the empty partitions in the loop below.
work_.resize (static_cast<size_t> (topBlocks[0].ncols()));
for (int partIdx = 1; partIdx < numPartitions; ++partIdx)
if (! topBlocks[partIdx].empty())
tauArrays[partIdx-1] = factorPair (topBlocks[0], topBlocks[partIdx]);
}
void
applyPair (const ApplyType& applyType,
const mat_view_type& R_bot,
const std::vector<Scalar>& tau,
const mat_view_type& C_top,
const mat_view_type& C_bot) const
{
// Our convention for such helper methods is for the immediate
// parent to allocate workspace (the work_ array in this case).
//
// The statement below only works if C_top, R_bot, and C_bot
// have a nonzero (and the same) number of columns, but we have
// already checked that above.
combine_.apply_pair (applyType, C_top.ncols(), R_bot.ncols(),
R_bot.get(), R_bot.lda(), &tau[0],
C_top.get(), C_top.lda(),
C_bot.get(), C_bot.lda(), &work_[0]);
}
void
applySecondPass (const ApplyType& applyType,
const FactorOutput& factorOutput,
std::vector<mat_view_type >& topBlocksOfC,
const CacheBlockingStrategy<LocalOrdinal, Scalar>& strategy,
const bool explicitQ) const
{
const int numParts = factorOutput.numPartitions();
if (numParts <= 1)
return; // Done!
TEUCHOS_TEST_FOR_EXCEPTION(topBlocksOfC.size() != static_cast<size_t>(numParts),
std::logic_error,
"KokkosNodeTsqr:applySecondPass: topBlocksOfC.size() ("
"= " << topBlocksOfC.size() << ") != number of partiti"
"ons (= " << numParts << "). Please report this bug t"
"o the Kokkos developers.");
TEUCHOS_TEST_FOR_EXCEPTION(factorOutput.secondPassTauArrays.size() !=
static_cast<size_t>(numParts-1),
std::logic_error,
"KokkosNodeTsqr:applySecondPass: factorOutput"
".secondPassTauArrays.size() (= "
<< factorOutput.secondPassTauArrays.size()
<< ") != number of partitions minus 1 (= "
<< (numParts-1) << "). Please report this bug"
" to the Kokkos developers.");
const LocalOrdinal numCols = topBlocksOfC[0].ncols();
work_.resize (static_cast<size_t> (numCols));
// Top blocks of C are the whole cache blocks. We only want to
// affect the top ncols x ncols part of each of those blocks in
// this method.
mat_view_type C_top_square (numCols, numCols, topBlocksOfC[0].get(),
topBlocksOfC[0].lda());
if (applyType.transposed()) {
// Don't include the topmost (index 0) partition in the
// iteration; that corresponds to C_top_square.
for (int partIdx = 1; partIdx < numParts; ++partIdx) {
// It's legitimate for some partitions not to have any
// cache blocks. In that case, their top block will be
// empty, and we can skip over them.
const mat_view_type& C_cur = topBlocksOfC[partIdx];
if (! C_cur.empty()) {
mat_view_type C_cur_square (numCols, numCols, C_cur.get (),
C_cur.lda ());
// If explicitQ: We've already done the first pass and
// filled the top blocks of C.
applyPair (applyType, factorOutput.topBlocks[partIdx],
factorOutput.secondPassTauArrays[partIdx-1],
C_top_square, C_cur_square);
}
}
} else {
// In non-transposed mode, when computing the first
// C.ncols() columns of the explicit Q factor, intranode
// TSQR would run after internode TSQR (i.e., DistTsqr)
// (even if only running on a single node in non-MPI mode).
// Therefore, internode TSQR is responsible for filling the
// top block of this node's part of the C matrix.
//
// Don't include the topmost partition in the iteration;
// that corresponds to C_top_square.
for (int partIdx = numParts - 1; partIdx > 0; --partIdx) {
// It's legitimate for some partitions not to have any
// cache blocks. In that case, their top block will be
// empty, and we can skip over them.
const mat_view_type& C_cur = topBlocksOfC[partIdx];
if (! C_cur.empty()) {
mat_view_type C_cur_square (numCols, numCols,
C_cur.get (), C_cur.lda ());
// The "first" pass (actually the last, only named
// "first" by analogy with factorFirstPass()) will
// fill the rest of these top blocks. For now, we
// just fill the top n x n part of the top blocks
// with zeros.
if (explicitQ) {
C_cur_square.fill (Teuchos::ScalarTraits<Scalar>::zero());
}
applyPair (applyType, factorOutput.topBlocks[partIdx],
factorOutput.secondPassTauArrays[partIdx-1],
C_top_square, C_cur_square);
}
}
}
}
protected:
/// \brief Return the topmost cache block of the matrix C.
///
/// NodeTsqr's top_block() method must be implemented using its
/// subclasses' const_top_block() method. This is because
/// top_block() is a template method, and template methods cannot
/// be virtual.
///
/// \param C [in] View of a matrix, with at least as many rows as
/// columns.
/// \param contiguous_cache_blocks [in] Whether the cache blocks
/// of C are stored contiguously.
///
/// \return View of the topmost cache block of the matrix C.
const_mat_view_type
const_top_block (const const_mat_view_type& C,
const bool contiguous_cache_blocks) const
{
typedef CacheBlocker<LocalOrdinal, Scalar> blocker_type;
blocker_type blocker (C.nrows(), C.ncols(), strategy_);
// C_top_block is a view of the topmost cache block of C.
// C_top_block should have >= ncols rows, otherwise either cache
// blocking is broken or the input matrix C itself had fewer
// rows than columns.
const_mat_view_type C_top = blocker.top_block (C, contiguous_cache_blocks);
return C_top;
}
};
} // namespace TSQR
#endif // __TSQR_KokkosNodeTsqr_hpp
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