/usr/include/trilinos/TbbTsqr_FactorTask.hpp is in libtrilinos-tpetra-dev 12.12.1-5.
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// Kokkos: Node API and Parallel Node Kernels
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#ifndef __TSQR_TBB_FactorTask_hpp
#define __TSQR_TBB_FactorTask_hpp
#include <tbb/task.h>
#include <TbbTsqr_Partitioner.hpp>
#include <Tsqr_SequentialTsqr.hpp>
#include <Teuchos_Assert.hpp>
#include <algorithm>
////////////////////////////////////////////////////////////////////////////////
////////////////////////////////////////////////////////////////////////////////
namespace TSQR {
namespace TBB {
/// \class FactorTask
/// \brief TBB task for recursive TSQR factorization phase.
///
template<class LocalOrdinal, class Scalar, class TimerType>
class FactorTask : public tbb::task {
public:
typedef MatView<LocalOrdinal, Scalar> mat_view_type;
typedef ConstMatView<LocalOrdinal, Scalar> const_mat_view_type;
typedef std::pair<mat_view_type, mat_view_type> split_t;
typedef std::pair<const_mat_view_type, const_mat_view_type> const_split_t;
/// \typedef SeqOutput
/// Result of SequentialTsqr for each thread.
typedef typename SequentialTsqr<LocalOrdinal, Scalar>::FactorOutput SeqOutput;
/// \typedef ParOutput
///
/// Array of ncores "local tau arrays" from parallel TSQR.
/// (Local Q factors are stored in place.)
typedef std::vector<std::vector<Scalar> > ParOutput;
/// \typedef FactorOutput
/// Result of SequentialTsqr for the data on each thread,
/// and the result of combining the threads' data.
typedef typename std::pair<std::vector<SeqOutput>, ParOutput> FactorOutput;
/// \brief Constructor.
///
/// \note The timing references are only modified by one thread
/// at a time; recursive calls use distinct references and
/// combine the results.
FactorTask (const size_t P_first__,
const size_t P_last__,
mat_view_type A,
mat_view_type* const A_top_ptr,
std::vector<SeqOutput>& seq_outputs,
ParOutput& par_output,
const SequentialTsqr<LocalOrdinal, Scalar>& seq,
double& my_seq_timing,
double& min_seq_timing,
double& max_seq_timing,
const bool contiguous_cache_blocks) :
P_first_ (P_first__),
P_last_ (P_last__),
A_ (A),
A_top_ptr_ (A_top_ptr),
seq_outputs_ (seq_outputs),
par_output_ (par_output),
seq_ (seq),
contiguous_cache_blocks_ (contiguous_cache_blocks),
my_seq_timing_ (my_seq_timing),
min_seq_timing_ (min_seq_timing),
max_seq_timing_ (max_seq_timing)
{}
tbb::task* execute ()
{
if (P_first_ > P_last_ || A_.empty())
return NULL;
else if (P_first_ == P_last_)
{
execute_base_case ();
return NULL;
}
else
{
// Recurse on two intervals: [P_first, P_mid] and [P_mid+1, P_last]
const size_t P_mid = (P_first_ + P_last_) / 2;
split_t A_split =
partitioner_.split (A_, P_first_, P_mid, P_last_,
contiguous_cache_blocks_);
// The partitioner may decide that the current block A_
// has too few rows to be worth splitting. In that case,
// A_split.second (the bottom block) will be empty. We
// can deal with this by treating it as the base case.
if (A_split.second.empty() || A_split.second.nrows() == 0)
{
execute_base_case ();
return NULL;
}
double top_timing;
double top_min_timing = 0.0;
double top_max_timing = 0.0;
double bot_timing;
double bot_min_timing = 0.0;
double bot_max_timing = 0.0;
FactorTask& topTask = *new( allocate_child() )
FactorTask (P_first_, P_mid, A_split.first, A_top_ptr_,
seq_outputs_, par_output_, seq_,
top_timing, top_min_timing, top_max_timing,
contiguous_cache_blocks_);
// After the task finishes, A_bot will be set to the topmost
// partition of A_split.second. This will let us combine
// the two subproblems (using factor_pair()) after their
// tasks complete.
mat_view_type A_bot;
FactorTask& botTask = *new( allocate_child() )
FactorTask (P_mid+1, P_last_, A_split.second, &A_bot,
seq_outputs_, par_output_, seq_,
bot_timing, bot_min_timing, bot_max_timing,
contiguous_cache_blocks_);
set_ref_count (3); // 3 children (2 + 1 for the wait)
spawn (topTask);
spawn_and_wait_for_all (botTask);
// Combine the two results
factor_pair (P_first_, P_mid+1, *A_top_ptr_, A_bot);
top_min_timing = (top_min_timing == 0.0) ? top_timing : top_min_timing;
top_max_timing = (top_max_timing == 0.0) ? top_timing : top_max_timing;
bot_min_timing = (bot_min_timing == 0.0) ? bot_timing : bot_min_timing;
bot_max_timing = (bot_max_timing == 0.0) ? bot_timing : bot_max_timing;
min_seq_timing_ = std::min (top_min_timing, bot_min_timing);
max_seq_timing_ = std::min (top_max_timing, bot_max_timing);
return NULL;
}
}
private:
const size_t P_first_, P_last_;
mat_view_type A_;
mat_view_type* const A_top_ptr_;
std::vector<SeqOutput>& seq_outputs_;
ParOutput& par_output_;
SequentialTsqr<LocalOrdinal, Scalar> seq_;
TSQR::Combine<LocalOrdinal, Scalar> combine_;
Partitioner<LocalOrdinal, Scalar> partitioner_;
const bool contiguous_cache_blocks_;
double& my_seq_timing_;
double& min_seq_timing_;
double& max_seq_timing_;
void
factor_pair (const size_t P_top,
const size_t P_bot,
mat_view_type& A_top, // different than A_top_
mat_view_type& A_bot)
{
const char thePrefix[] = "TSQR::TBB::Factor::factor_pair: ";
TEUCHOS_TEST_FOR_EXCEPTION(P_top == P_bot, std::logic_error,
thePrefix << "Should never get here! P_top == P_bot (= "
<< P_top << "), that is, the indices of the thread "
"partitions are the same.");
// We only read and write the upper ncols x ncols triangle of
// each block.
TEUCHOS_TEST_FOR_EXCEPTION(A_top.ncols() != A_bot.ncols(), std::logic_error,
thePrefix << "The top cache block A_top is "
<< A_top.nrows() << " x " << A_top.ncols()
<< ", and the bottom cache block A_bot is "
<< A_bot.nrows() << " x " << A_bot.ncols()
<< "; this means we can't factor [A_top; A_bot].");
const LocalOrdinal ncols = A_top.ncols();
std::vector<Scalar>& tau = par_output_[P_bot];
std::vector<Scalar> work (ncols);
combine_.factor_pair (ncols, A_top.get(), A_top.lda(),
A_bot.get(), A_bot.lda(), &tau[0], &work[0]);
}
void
execute_base_case ()
{
TimerType timer("");
timer.start();
seq_outputs_[P_first_] =
seq_.factor (A_.nrows(), A_.ncols(), A_.get(),
A_.lda(), contiguous_cache_blocks_);
// Assign the topmost cache block of the current partition to
// *A_top_ptr_. Every base case invocation does this, so that
// we can combine subproblems. The root task also does this,
// but for a different reason: so that we can extract the R
// factor, once we're done with the factorization.
*A_top_ptr_ = seq_.top_block (A_, contiguous_cache_blocks_);
my_seq_timing_ = timer.stop();
}
};
} // namespace TBB
} // namespace TSQR
#endif // __TSQR_TBB_FactorTask_hpp
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