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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.
//
// 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
// 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
// CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,
// EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
// PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR
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// SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
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// Questions? Contact Michael A. Heroux (maherou@sandia.gov)
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//@HEADER

#ifndef __TSQR_TBB_CacheBlockTask_hpp
#define __TSQR_TBB_CacheBlockTask_hpp

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

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

namespace TSQR {
  namespace TBB {

    /// \class CacheBlockTask
    /// \brief TBB task for recursive TSQR cache blocking phase.
    ///
    /// "Cache blocking" here means copying the input matrix, which is
    /// stored with noncontiguous cache blocks, to the output matrix,
    /// which is stored with contiguous cache blocks.
    ///
    template<class LocalOrdinal, class Scalar>
    class CacheBlockTask : 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;

      CacheBlockTask (const size_t P_first__,
                      const size_t P_last__,
                      mat_view_type& A_out,
                      const_mat_view_type& A_in,
                      const SequentialTsqr<LocalOrdinal, Scalar>& seq) :
        P_first_ (P_first__),
        P_last_ (P_last__),
        A_out_ (A_out),
        A_in_ (A_in),
        seq_ (seq)
      {}

      tbb::task* execute ()
      {
        using tbb::task;

        if (P_first_ > P_last_ || A_out_.empty() || A_in_.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 out_split =
              partitioner_.split (A_out_, P_first_, P_mid, P_last_, true);
            const_split_t in_split =
              partitioner_.split (A_in_, P_first_, P_mid, P_last_, false);

            // The partitioner may decide that the current blocks
            // A_out_ and A_in_ have too few rows to be worth
            // splitting.  (It should split both A_out_ and A_in_ in
            // the same way.)  In that case, out_split.second and
            // in_split.second (the bottom block) will be empty.  We
            // can deal with this by treating it as the base case.
            if (out_split.second.empty() || out_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
            CacheBlockTask& topTask = *new( c.allocate_child() )
              CacheBlockTask (P_first_, P_mid, out_split.first,
                              in_split.first, seq_);
            CacheBlockTask& botTask = *new( c.allocate_child() )
              CacheBlockTask (P_mid+1, P_last_, out_split.second,
                              in_split.second, seq_);
            // 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 A_out_;
      const_mat_view_type A_in_;
      SequentialTsqr<LocalOrdinal, Scalar> seq_;
      Partitioner<LocalOrdinal, Scalar> partitioner_;

      void
      execute_base_case ()
      {
        seq_.cache_block (A_out_.nrows(), A_out_.ncols(),
                          A_out_.get(), A_in_.get(), A_in_.lda());
      }
    };

  } // namespace TBB
} // namespace TSQR


#endif // __TSQR_TBB_CacheBlockTask_hpp