/usr/include/trilinos/TbbTsqr_TbbRecursiveTsqr.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_TbbRecursiveTsqr_hpp
#define __TSQR_TbbRecursiveTsqr_hpp
#include <Tsqr_ApplyType.hpp>
#include <Tsqr_CacheBlocker.hpp>
#include <Tsqr_SequentialTsqr.hpp>
#include <TbbTsqr_Partitioner.hpp>
#include <stdexcept>
#include <string>
#include <utility> // std::pair
#include <vector>
////////////////////////////////////////////////////////////////////////////////
////////////////////////////////////////////////////////////////////////////////
namespace TSQR {
namespace TBB {
/// \class TbbRecursiveTsqr
/// \brief Non-parallel "functioning stub" implementation of \c TbbTsqr.
///
template< class LocalOrdinal, class Scalar >
class TbbRecursiveTsqr {
public:
/// \brief Constructor.
///
/// \param num_cores [in] Maximum parallelism to use (i.e.,
/// maximum number of partitions into which to divide the
/// matrix to factor).
///
/// \param cache_size_hint [in] Approximate cache size in bytes
/// per CPU core. A hint, not a command. If zero, set to a
/// reasonable default.
TbbRecursiveTsqr (const size_t num_cores = 1,
const size_t cache_size_hint = 0);
/// Number of cores to use to solve the problem (i.e., number of
/// subproblems into which to divide the main problem, to solve
/// it in parallel).
size_t ncores() const { return ncores_; }
//! Cache size hint (in bytes) used for the factorization.
size_t cache_size_hint() const { return seq_.cache_size_hint(); }
//! Results of SequentialTsqr for each core.
typedef typename SequentialTsqr<LocalOrdinal, Scalar>::FactorOutput SeqOutput;
/// \typedef ParOutput
/// \brief 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
/// \brief Return type of factor().
///
/// factor() returns a pair: the results of SequentialTsqr for
/// data on each core, and the results of combining the data on
/// the cores.
typedef typename std::pair<std::vector<SeqOutput>, ParOutput> FactorOutput;
/// Copy the nrows by ncols matrix A_in (with leading dimension
/// lda_in >= nrows) into A_out, such that cache blocks are
/// arranged contiguously in memory.
void
cache_block (const LocalOrdinal nrows,
const LocalOrdinal ncols,
Scalar A_out[],
const Scalar A_in[],
const LocalOrdinal lda_in) const;
/// Copy the nrows by ncols matrix A_in, whose cache blocks are
/// arranged contiguously in memory, into A_out (with leading
/// dimension lda_out >= nrows), which is in standard
/// column-major order.
void
un_cache_block (const LocalOrdinal nrows,
const LocalOrdinal ncols,
Scalar A_out[],
const LocalOrdinal lda_out,
const Scalar A_in[]) const;
/// Compute the QR factorization of the nrows by ncols matrix A
/// (with leading dimension lda >= nrows), returning a
/// representation of the Q factor (which includes data stored
/// in-place in A), and overwriting R (an ncols by ncols matrix
/// in column-major order with leading dimension ldr >= ncols)
/// with the R factor.
FactorOutput
factor (const LocalOrdinal nrows,
const LocalOrdinal ncols,
Scalar A[],
const LocalOrdinal lda,
Scalar R[],
const LocalOrdinal ldr,
const bool contiguous_cache_blocks) const;
/// Apply the Q factor computed by factor() (which see) to the
/// nrows by ncols_C matrix C, with leading dimension ldc >=
/// nrows.
void
apply (const std::string& op,
const LocalOrdinal nrows,
const LocalOrdinal ncols_C,
Scalar C[],
const LocalOrdinal ldc,
const LocalOrdinal ncols_Q,
const Scalar Q[],
const LocalOrdinal ldq,
const FactorOutput& factor_output,
const bool contiguous_cache_blocks) const;
/// Compute the explicit representation of the Q factor computed
/// by factor().
void
explicit_Q (const LocalOrdinal nrows,
const LocalOrdinal ncols_Q_in,
const Scalar Q_in[],
const LocalOrdinal ldq_in,
const LocalOrdinal ncols_Q_out,
Scalar Q_out[],
const LocalOrdinal ldq_out,
const FactorOutput& factor_output,
const bool contiguous_cache_blocks) const;
private:
size_t ncores_;
TSQR::SequentialTsqr<LocalOrdinal, Scalar> seq_;
Partitioner<LocalOrdinal, Scalar> partitioner_;
typedef MatView<LocalOrdinal, Scalar> mat_view_type;
typedef ConstMatView<LocalOrdinal, Scalar> const_mat_view_type;
typedef std::pair<const_mat_view_type, const_mat_view_type> const_split_t;
typedef std::pair<mat_view_type, mat_view_type> split_t;
typedef std::pair<const_mat_view_type, mat_view_type> top_blocks_t;
typedef std::vector<top_blocks_t> array_top_blocks_t;
void
explicit_Q_helper (const size_t P_first,
const size_t P_last,
mat_view_type& Q_out,
const bool contiguous_cache_blocks) const;
/// \brief Return a nonconst view of the topmost block.
///
/// This is helpful for combining the R factors and extracting
/// the final R factor result.
mat_view_type
factor_helper (const size_t P_first,
const size_t P_last,
const size_t depth,
mat_view_type A,
std::vector<SeqOutput>& seq_outputs,
ParOutput& par_outputs,
Scalar R[],
const LocalOrdinal ldr,
const bool contiguous_cache_blocks) const;
bool
apply_helper_empty (const size_t P_first,
const size_t P_last,
const_mat_view_type &Q,
mat_view_type& C) const;
/// \brief Build array of ncores() blocks, one for each partition.
///
/// Each block is the topmost block in that partition. This is
/// useful for apply_helper.
void
build_partition_array (const size_t P_first,
const size_t P_last,
array_top_blocks_t& top_blocks,
const_mat_view_type& Q,
mat_view_type& C,
const bool contiguous_cache_blocks) const;
/// Apply Q (not Q^T or Q^H, which is why we don't ask for "op")
/// to C.
void
apply_helper (const size_t P_first,
const size_t P_last,
const_mat_view_type Q,
mat_view_type C,
array_top_blocks_t& top_blocks,
const FactorOutput& factor_output,
const bool contiguous_cache_blocks) const;
/// Apply Q^T or Q^H to C.
///
/// \return Views of the topmost partitions of Q resp. C.
std::pair<const_mat_view_type, mat_view_type>
apply_transpose_helper (const std::string& op,
const size_t P_first,
const size_t P_last,
const_mat_view_type Q,
mat_view_type C,
const FactorOutput& factor_output,
const bool contiguous_cache_blocks) const;
void
factor_pair (const size_t P_top,
const size_t P_bot,
mat_view_type& A_top,
mat_view_type& A_bot,
std::vector< std::vector< Scalar > >& par_outputs,
const bool contiguous_cache_blocks) const;
void
apply_pair (const std::string& trans,
const size_t P_top,
const size_t P_bot,
const_mat_view_type& Q_bot,
const std::vector< std::vector< Scalar > >& tau_arrays,
mat_view_type& C_top,
mat_view_type& C_bot,
const bool contiguous_cache_blocks) const;
void
cache_block_helper (mat_view_type& A_out,
const_mat_view_type& A_in,
const size_t P_first,
const size_t P_last) const;
void
un_cache_block_helper (mat_view_type& A_out,
const const_mat_view_type& A_in,
const size_t P_first,
const size_t P_last) const;
}; // class TbbRecursiveTsqr
} // namespace TBB
} // namespace TSQR
#include <TSQR/TBB/TbbRecursiveTsqr_Def.hpp>
#endif // __TSQR_TbbRecursiveTsqr_hpp
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