/*
//@HEADER
// ************************************************************************
//
//                        Kokkos v. 3.0
//       Copyright (2020) National Technology & Engineering
//               Solutions of Sandia, LLC (NTESS).
//
// Under the terms of Contract DE-NA0003525 with NTESS,
// 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 NTESS "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 NTESS 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
// PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF
// LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING
// NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
// SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
//
// Questions? Contact Siva Rajamanickam (srajama@sandia.gov)
//
// ************************************************************************
//@HEADER
*/

#ifndef KOKKOS_SPMV_HPP_
#define KOKKOS_SPMV_HPP_

template <class AMatrix, class XVector, class YVector, int dobeta,
          bool conjugate>
struct SPMV_Functor {
  typedef typename AMatrix::execution_space execution_space;
  typedef typename AMatrix::non_const_ordinal_type ordinal_type;
  typedef typename AMatrix::non_const_value_type value_type;
  typedef typename Kokkos::TeamPolicy<execution_space> team_policy;
  typedef typename team_policy::member_type team_member;
  typedef Kokkos::Details::ArithTraits<value_type> ATV;

  const value_type alpha;
  AMatrix m_A;
  XVector m_x;
  const value_type beta;
  YVector m_y;

  const ordinal_type rows_per_team;

  SPMV_Functor(const value_type alpha_, const AMatrix m_A_, const XVector m_x_,
               const value_type beta_, const YVector m_y_,
               const int rows_per_team_)
      : alpha(alpha_),
        m_A(m_A_),
        m_x(m_x_),
        beta(beta_),
        m_y(m_y_),
        rows_per_team(rows_per_team_) {
    static_assert(static_cast<int>(XVector::rank) == 1,
                  "XVector must be a rank 1 View.");
    static_assert(static_cast<int>(YVector::rank) == 1,
                  "YVector must be a rank 1 View.");
  }

  KOKKOS_INLINE_FUNCTION void operator()(const team_member& dev) const {
    Kokkos::parallel_for(
        Kokkos::TeamThreadRange(dev, 0, rows_per_team),
        [&](const ordinal_type& loop) {
          const ordinal_type iRow =
              static_cast<ordinal_type>(dev.league_rank()) * rows_per_team +
              loop;
          if (iRow >= m_A.numRows()) {
            return;
          }
          const KokkosSparse::SparseRowViewConst<AMatrix> row =
              m_A.rowConst(iRow);
          const ordinal_type row_length = static_cast<ordinal_type>(row.length);
          value_type sum                = 0;

          Kokkos::parallel_reduce(
              Kokkos::ThreadVectorRange(dev, row_length),
              [&](const ordinal_type& iEntry, value_type& lsum) {
                const value_type val = conjugate ? ATV::conj(row.value(iEntry))
                                                 : row.value(iEntry);
                lsum += val * m_x(row.colidx(iEntry));
              },
              sum);

          Kokkos::single(Kokkos::PerThread(dev), [&]() {
            sum *= alpha;

            if (dobeta == 0) {
              m_y(iRow) = sum;
            } else {
              m_y(iRow) = beta * m_y(iRow) + sum;
            }
          });
        });
  }
};

template <class execution_space>
int launch_parameters(int numRows, int nnz, int rows_per_thread, int& team_size,
                      int& vector_length) {
  int rows_per_team;
  int nnz_per_row = nnz / numRows;
  if (nnz_per_row < 1) nnz_per_row = 1;

  if (vector_length < 1) {
    vector_length = 1;
    while (vector_length < 32 && vector_length * 6 < nnz_per_row)
      vector_length *= 2;
  }

  // Determine rows per thread
  if (rows_per_thread < 1) {
#ifdef KOKKOS_ENABLE_CUDA
    if (std::is_same<Kokkos::Cuda, execution_space>::value)
      rows_per_thread = 1;
    else
#endif
    {
      if (nnz_per_row < 20 && nnz > 5000000) {
        rows_per_thread = 256;
      } else
        rows_per_thread = 64;
    }
  }

#ifdef KOKKOS_ENABLE_CUDA
  if (team_size < 1) team_size = 256 / vector_length;
#endif

  rows_per_team = rows_per_thread * team_size;

  if (rows_per_team < 0) {
    int nnz_per_team = 4096;
    int conc         = execution_space::concurrency();
    while ((conc * nnz_per_team * 4 > nnz) && (nnz_per_team > 256))
      nnz_per_team /= 2;
    int tmp_nnz_per_row = nnz / numRows;
    rows_per_team = (nnz_per_team + tmp_nnz_per_row - 1) / tmp_nnz_per_row;
  }

  return rows_per_team;
}

template <typename AType, typename XType, typename YType, class ScheduleType>
void kokkos_matvec(AType A, XType x, YType y, int rows_per_thread,
                   int team_size, int vector_length) {
  typedef typename AType::execution_space execution_space;
  typedef typename AType::non_const_size_type size_type;
  typedef typename AType::non_const_ordinal_type lno_t;
  typedef typename AType::non_const_value_type scalar_t;
  typedef KokkosSparse::CrsMatrix<const scalar_t, lno_t, execution_space, void,
                                  size_type>
      matrix_type;

  int rows_per_team = launch_parameters<execution_space>(
      A.numRows(), A.nnz(), rows_per_thread, team_size, vector_length);

  double s_a = 1.0;
  double s_b = 0.0;
  SPMV_Functor<matrix_type, XType, YType, 0, false> func(s_a, A, x, s_b, y,
                                                         rows_per_team);

  int worksets = (y.extent(0) + rows_per_team - 1) / rows_per_team;

  Kokkos::TeamPolicy<Kokkos::Schedule<ScheduleType> > policy(1, 1);

  if (team_size > 0)
    policy = Kokkos::TeamPolicy<Kokkos::Schedule<ScheduleType> >(
        worksets, team_size, vector_length);
  else
    policy = Kokkos::TeamPolicy<Kokkos::Schedule<ScheduleType> >(
        worksets, Kokkos::AUTO, vector_length);

  Kokkos::parallel_for("KokkosSparse::PerfTest::SpMV", policy, func);
}

#endif /* KOKKOS_SPMV_HPP_ */
