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//@HEADER
// ************************************************************************
//
//          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.
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// modification, are permitted provided that the following conditions are
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// 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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#ifndef __TSQR_TBB_ExplicitQTask_hpp
#define __TSQR_TBB_ExplicitQTask_hpp

#include <tbb/task.h>
#include <TbbTsqr_Partitioner.hpp>
#include <Tsqr_SequentialTsqr.hpp>

////////////////////////////////////////////////////////////////////////////////
////////////////////////////////////////////////////////////////////////////////

namespace TSQR {
  namespace TBB {

    /// \class ExplicitQTask
    /// \brief TBB task for recursive TSQR "compute explicit Q" phase.
    ///
    template< class LocalOrdinal, class Scalar >
    class ExplicitQTask : public tbb::task {
    public:
      typedef MatView<LocalOrdinal, Scalar> mat_view_type;
      typedef ConstMatView<LocalOrdinal, Scalar> const_mat_view_type;

    private:
      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;

    public:
      /// \brief Constructor.
      ///
      ExplicitQTask (const size_t P_first__,
                     const size_t P_last__,
                     mat_view_type Q_out,
                     const SequentialTsqr<LocalOrdinal, Scalar>& seq,
                     const bool contiguous_cache_blocks) :
        P_first_ (P_first__), P_last_ (P_last__), Q_out_ (Q_out),
        seq_ (seq), contiguous_cache_blocks_ (contiguous_cache_blocks)
      {}

      tbb::task* execute ()
      {
        if (P_first_ > P_last_ || Q_out_.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 Q_split =
            partitioner_.split (Q_out_, P_first_, P_mid, P_last_,
                                contiguous_cache_blocks_);
          // The partitioner may decide that the current block Q_out
          // has too few rows to be worth splitting.  In that case,
          // Q_split.second (the bottom block) will be empty.  We
          // can deal with this by treating it as the base case.
          if (Q_split.second.empty() || Q_split.second.nrows() == 0) {
            execute_base_case ();
            return NULL;
          }

          // "c": continuation task
          tbb::empty_task& c =
            *new( allocate_continuation() ) tbb::empty_task;
          // Recurse on the split
          ExplicitQTask& topTask = *new( c.allocate_child() )
            ExplicitQTask (P_first_, P_mid, Q_split.first, seq_,
                           contiguous_cache_blocks_);
          ExplicitQTask& botTask = *new( c.allocate_child() )
            ExplicitQTask (P_mid+1, P_last_, Q_split.second, seq_,
                           contiguous_cache_blocks_);
          // Set reference count of parent (in this case, the
          // continuation task) to 2 (since 2 children -- no
          // additional task since no waiting).
          c.set_ref_count (2);
          c.spawn (botTask);
          return &topTask; // scheduler bypass optimization
        }
      }

    private:
      size_t P_first_, P_last_;
      mat_view_type Q_out_;
      SequentialTsqr<LocalOrdinal, Scalar> seq_;
      Partitioner<LocalOrdinal, Scalar> partitioner_;
      bool contiguous_cache_blocks_;

      void
      execute_base_case ()
      {
        // Fill my partition with zeros.
        seq_.fill_with_zeros (Q_out_.nrows(), Q_out_.ncols(), Q_out_.get(),
                              Q_out_.lda(), contiguous_cache_blocks_);
        // If our partition is the first (topmost), fill it with
        // the first Q_out.ncols() columns of the identity matrix.
        if (P_first_ == 0)
          {
            // Fetch the topmost cache block of my partition.  Its
            // leading dimension should be set correctly by
            // top_block().
            mat_view_type Q_out_top =
              seq_.top_block (Q_out_, contiguous_cache_blocks_);
            // Set the top block of Q_out to the first ncols
            // columns of the identity matrix.
            for (LocalOrdinal j = 0; j < Q_out_top.ncols(); ++j)
              Q_out_top(j,j) = Scalar(1);
          }
      }
    };

  } // namespace TBB
} // namespace TSQR


#endif // __TSQR_TBB_ExplicitQTask_hpp