Ifpack2_BlockComputeResidualAndSolve_def.hpp

// @HEADER
// *****************************************************************************
//       Ifpack2: Templated Object-Oriented Algebraic Preconditioner Package
//
// Copyright 2009 NTESS and the Ifpack2 contributors.
// SPDX-License-Identifier: BSD-3-Clause
// *****************************************************************************
// @HEADER
 
#ifndef IFPACK2_BLOCKCOMPUTERES_AND_SOLVE_DEF_HPP
#define IFPACK2_BLOCKCOMPUTERES_AND_SOLVE_DEF_HPP
 
#include "Ifpack2_BlockComputeResidualAndSolve_decl.hpp"
 
namespace Ifpack2::BlockHelperDetails {
 
template <typename MatrixType, int B>
struct ComputeResidualAndSolve_SinglePass_Impl {
  using impl_type        = BlockHelperDetails::ImplType<MatrixType>;
  using node_device_type = typename impl_type::node_device_type;
  using execution_space  = typename impl_type::execution_space;
  using memory_space     = typename impl_type::memory_space;
 
  using local_ordinal_type = typename impl_type::local_ordinal_type;
  using size_type          = typename impl_type::size_type;
  using impl_scalar_type   = typename impl_type::impl_scalar_type;
  using magnitude_type     = typename impl_type::magnitude_type;
  using local_ordinal_type_1d_view =
      typename impl_type::local_ordinal_type_1d_view;
  using size_type_1d_view = typename impl_type::size_type_1d_view;
  using tpetra_block_access_view_type =
      typename impl_type::tpetra_block_access_view_type;  // block crs (layout
                                                          // right)
  using impl_scalar_type_1d_view = typename impl_type::impl_scalar_type_1d_view;
  using impl_scalar_type_2d_view_tpetra =
      typename impl_type::impl_scalar_type_2d_view_tpetra;  // block multivector
                                                            // (layout left)
  using btdm_scalar_type_3d_view = typename impl_type::btdm_scalar_type_3d_view;
  using btdm_scalar_type_4d_view = typename impl_type::btdm_scalar_type_4d_view;
  using i64_3d_view              = typename impl_type::i64_3d_view;
 
  using member_type = typename Kokkos::TeamPolicy<execution_space>::member_type;
 
  // enum for max blocksize and vector length
  enum : int { max_blocksize = 32 };
 
 private:
  ConstUnmanaged<impl_scalar_type_2d_view_tpetra> b;
  ConstUnmanaged<impl_scalar_type_2d_view_tpetra> x;  // x_owned
  ConstUnmanaged<impl_scalar_type_2d_view_tpetra> x_remote;
  Unmanaged<impl_scalar_type_2d_view_tpetra> y;
 
  // AmD information
  const ConstUnmanaged<impl_scalar_type_1d_view> tpetra_values;
 
  // blocksize
  const local_ordinal_type blocksize_requested;
 
  // block offsets
  const ConstUnmanaged<i64_3d_view> A_x_offsets;
  const ConstUnmanaged<i64_3d_view> A_x_offsets_remote;
 
  // diagonal block inverses
  const ConstUnmanaged<btdm_scalar_type_3d_view> d_inv;
 
  // squared update norms
  const Unmanaged<impl_scalar_type_1d_view> W;
 
  impl_scalar_type damping_factor;
 
 public:
  ComputeResidualAndSolve_SinglePass_Impl(const AmD<MatrixType>& amd,
                                          const btdm_scalar_type_3d_view& d_inv_,
                                          const impl_scalar_type_1d_view& W_,
                                          const local_ordinal_type& blocksize_requested_,
                                          const impl_scalar_type& damping_factor_)
    : tpetra_values(amd.tpetra_values)
    , blocksize_requested(blocksize_requested_)
    , A_x_offsets(amd.A_x_offsets)
    , A_x_offsets_remote(amd.A_x_offsets_remote)
    , d_inv(d_inv_)
    , W(W_)
    , damping_factor(damping_factor_) {}
 
  KOKKOS_INLINE_FUNCTION
  void operator()(const member_type& member) const {
    const local_ordinal_type blocksize      = (B == 0 ? blocksize_requested : B);
    const local_ordinal_type rowidx         = member.league_rank();
    const local_ordinal_type row            = rowidx * blocksize;
    const local_ordinal_type num_vectors    = b.extent(1);
    const local_ordinal_type num_local_rows = d_inv.extent(0);
 
    const impl_scalar_type* xx;
    auto A_block_cst = ConstUnmanaged<tpetra_block_access_view_type>(
        tpetra_values.data(), blocksize, blocksize);
 
    // Get shared allocation for a local copy of x, residual, and A
    impl_scalar_type* local_residual = reinterpret_cast<impl_scalar_type*>(
        member.team_scratch(0).get_shmem(blocksize * sizeof(impl_scalar_type)));
    impl_scalar_type* local_Dinv_residual = reinterpret_cast<impl_scalar_type*>(
        member.team_scratch(0).get_shmem(blocksize * sizeof(impl_scalar_type)));
    impl_scalar_type* local_x =
        reinterpret_cast<impl_scalar_type*>(member.thread_scratch(0).get_shmem(
            blocksize * sizeof(impl_scalar_type)));
 
    magnitude_type norm = 0;
    for (local_ordinal_type col = 0; col < num_vectors; ++col) {
      if (col) member.team_barrier();
      // y -= Rx
      // Initialize accumulation arrays
      Kokkos::parallel_for(Kokkos::TeamVectorRange(member, blocksize),
                           [&](const local_ordinal_type& i) {
                             local_Dinv_residual[i] = 0;
                             local_residual[i]      = b(row + i, col);
                           });
      member.team_barrier();
 
      int numEntries = A_x_offsets.extent(2);
 
      Kokkos::parallel_for(
          Kokkos::TeamThreadRange(member, 0, numEntries), [&](const int k) {
            int64_t A_offset = A_x_offsets(rowidx, 0, k);
            int64_t x_offset = A_x_offsets(rowidx, 1, k);
            if (A_offset != KokkosKernels::ArithTraits<int64_t>::min()) {
              A_block_cst.assign_data(tpetra_values.data() + A_offset);
              // Pull x into local memory
              int64_t remote_cutoff = blocksize * num_local_rows;
              if (x_offset >= remote_cutoff)
                xx = &x_remote(x_offset - remote_cutoff, col);
              else
                xx = &x(x_offset, col);
 
              Kokkos::parallel_for(
                  Kokkos::ThreadVectorRange(member, blocksize),
                  [&](const local_ordinal_type& i) { local_x[i] = xx[i]; });
 
              // matvec on block: local_residual -= A_block_cst * local_x
              Kokkos::parallel_for(Kokkos::ThreadVectorRange(member, blocksize),
                                   [&](const int k0) {
                                     impl_scalar_type val = 0;
                                     for (int k1 = 0; k1 < blocksize; k1++)
                                       val += A_block_cst(k0, k1) * local_x[k1];
                                     Kokkos::atomic_add(local_residual + k0, -val);
                                   });
            }
          });
      member.team_barrier();
      // Compute local_Dinv_residual = D^-1 * local_residual
      Kokkos::parallel_for(
          Kokkos::TeamThreadRange(member, blocksize),
          [&](const local_ordinal_type& k0) {
            Kokkos::parallel_reduce(
                Kokkos::ThreadVectorRange(member, blocksize),
                [&](const local_ordinal_type& k1, impl_scalar_type& update) {
                  update += d_inv(rowidx, k0, k1) * local_residual[k1];
                },
                local_Dinv_residual[k0]);
          });
      member.team_barrier();
      // local_Dinv_residual is fully computed. Now compute the
      // squared y update norm and update y (using damping factor).
      magnitude_type colNorm;
      Kokkos::parallel_reduce(
          Kokkos::TeamVectorRange(member, blocksize),
          [&](const local_ordinal_type& k, magnitude_type& update) {
            // Compute the change in y (assuming damping_factor == 1) for this
            // entry.
            impl_scalar_type old_y    = x(row + k, col);
            impl_scalar_type y_update = local_Dinv_residual[k] - old_y;
            if constexpr (KokkosKernels::ArithTraits<impl_scalar_type>::is_complex) {
              magnitude_type ydiff =
                  KokkosKernels::ArithTraits<impl_scalar_type>::abs(y_update);
              update += ydiff * ydiff;
            } else {
              update += y_update * y_update;
            }
            y(row + k, col) = old_y + damping_factor * y_update;
          },
          colNorm);
      norm += colNorm;
    }
    Kokkos::single(Kokkos::PerTeam(member), [&]() { W(rowidx) = norm; });
  }
 
  void run(const ConstUnmanaged<impl_scalar_type_2d_view_tpetra>& b_,
           const ConstUnmanaged<impl_scalar_type_2d_view_tpetra>& x_,
           const ConstUnmanaged<impl_scalar_type_2d_view_tpetra>& x_remote_,
           const Unmanaged<impl_scalar_type_2d_view_tpetra>& y_) {
    IFPACK2_BLOCKHELPER_PROFILER_REGION_BEGIN;
    IFPACK2_BLOCKHELPER_TIMER_WITH_FENCE(
        "BlockTriDi::ComputeResidualAndSolve::RunSinglePass",
        ComputeResidualAndSolve0, execution_space);
 
    y        = y_;
    b        = b_;
    x        = x_;
    x_remote = x_remote_;
 
    const local_ordinal_type blocksize = blocksize_requested;
    const local_ordinal_type nrows     = d_inv.extent(0);
 
    const local_ordinal_type team_size   = 8;
    const local_ordinal_type vector_size = 8;
    // team: local_residual, local_Dinv_residual
    const size_t shmem_team_size = 2 * blocksize * sizeof(impl_scalar_type);
    // thread: local_x
    const size_t shmem_thread_size = blocksize * sizeof(impl_scalar_type);
    Kokkos::TeamPolicy<execution_space> policy(nrows, team_size, vector_size);
    policy.set_scratch_size(0, Kokkos::PerTeam(shmem_team_size),
                            Kokkos::PerThread(shmem_thread_size));
    Kokkos::parallel_for("ComputeResidualAndSolve::TeamPolicy::SinglePass",
                         policy, *this);
 
    IFPACK2_BLOCKHELPER_PROFILER_REGION_END;
    IFPACK2_BLOCKHELPER_TIMER_FENCE(execution_space)
  }
};
 
template <typename MatrixType, int B>
struct ComputeResidualAndSolve_2Pass_Impl {
  using impl_type        = BlockHelperDetails::ImplType<MatrixType>;
  using node_device_type = typename impl_type::node_device_type;
  using execution_space  = typename impl_type::execution_space;
  using memory_space     = typename impl_type::memory_space;
 
  using local_ordinal_type = typename impl_type::local_ordinal_type;
  using size_type          = typename impl_type::size_type;
  using impl_scalar_type   = typename impl_type::impl_scalar_type;
  using magnitude_type     = typename impl_type::magnitude_type;
  using local_ordinal_type_1d_view =
      typename impl_type::local_ordinal_type_1d_view;
  using size_type_1d_view = typename impl_type::size_type_1d_view;
  using tpetra_block_access_view_type =
      typename impl_type::tpetra_block_access_view_type;  // block crs (layout
                                                          // right)
  using impl_scalar_type_1d_view = typename impl_type::impl_scalar_type_1d_view;
  using impl_scalar_type_2d_view_tpetra =
      typename impl_type::impl_scalar_type_2d_view_tpetra;  // block multivector
                                                            // (layout left)
  using btdm_scalar_type_3d_view = typename impl_type::btdm_scalar_type_3d_view;
  using btdm_scalar_type_4d_view = typename impl_type::btdm_scalar_type_4d_view;
  using i64_3d_view              = typename impl_type::i64_3d_view;
 
  using member_type = typename Kokkos::TeamPolicy<execution_space>::member_type;
 
  // enum for max blocksize and vector length
  enum : int { max_blocksize = 32 };
 
  // Tag for computing residual with owned columns only (pass 1)
  struct OwnedTag {};
 
  // Tag for finishing the residual with nonowned columns, and solving/norming
  // (pass 2)
  struct NonownedTag {};
 
 private:
  ConstUnmanaged<impl_scalar_type_2d_view_tpetra> b;
  ConstUnmanaged<impl_scalar_type_2d_view_tpetra> x;  // x_owned
  ConstUnmanaged<impl_scalar_type_2d_view_tpetra> x_remote;
  Unmanaged<impl_scalar_type_2d_view_tpetra> y;
 
  // AmD information
  const ConstUnmanaged<impl_scalar_type_1d_view> tpetra_values;
 
  // blocksize
  const local_ordinal_type blocksize_requested;
 
  // block offsets
  const ConstUnmanaged<i64_3d_view> A_x_offsets;
  const ConstUnmanaged<i64_3d_view> A_x_offsets_remote;
 
  // diagonal block inverses
  const ConstUnmanaged<btdm_scalar_type_3d_view> d_inv;
 
  // squared update norms
  const Unmanaged<impl_scalar_type_1d_view> W;
 
  impl_scalar_type damping_factor;
 
 public:
  ComputeResidualAndSolve_2Pass_Impl(
      const AmD<MatrixType>& amd,
      const btdm_scalar_type_3d_view& d_inv_,
      const impl_scalar_type_1d_view& W_,
      const local_ordinal_type& blocksize_requested_,
      const impl_scalar_type& damping_factor_)
    : tpetra_values(amd.tpetra_values)
    , blocksize_requested(blocksize_requested_)
    , A_x_offsets(amd.A_x_offsets)
    , A_x_offsets_remote(amd.A_x_offsets_remote)
    , d_inv(d_inv_)
    , W(W_)
    , damping_factor(damping_factor_) {}
 
  KOKKOS_INLINE_FUNCTION
  void operator()(const OwnedTag, const member_type& member) const {
    const local_ordinal_type blocksize   = (B == 0 ? blocksize_requested : B);
    const local_ordinal_type rowidx      = member.league_rank();
    const local_ordinal_type row         = rowidx * blocksize;
    const local_ordinal_type num_vectors = b.extent(1);
 
    auto A_block_cst = ConstUnmanaged<tpetra_block_access_view_type>(
        tpetra_values.data(), blocksize, blocksize);
 
    // Get shared allocation for a local copy of x, Ax, and A
    impl_scalar_type* local_residual = reinterpret_cast<impl_scalar_type*>(
        member.team_scratch(0).get_shmem(blocksize * sizeof(impl_scalar_type)));
    impl_scalar_type* local_x =
        reinterpret_cast<impl_scalar_type*>(member.thread_scratch(0).get_shmem(
            blocksize * sizeof(impl_scalar_type)));
 
    for (local_ordinal_type col = 0; col < num_vectors; ++col) {
      if (col) member.team_barrier();
      // y -= Rx
      // Initialize accumulation arrays
      Kokkos::parallel_for(
          Kokkos::TeamVectorRange(member, blocksize),
          [&](const local_ordinal_type& i) { local_residual[i] = b(row + i, col); });
      member.team_barrier();
 
      int numEntries = A_x_offsets.extent(2);
 
      Kokkos::parallel_for(
          Kokkos::TeamThreadRange(member, 0, numEntries), [&](const int k) {
            int64_t A_offset = A_x_offsets(rowidx, 0, k);
            int64_t x_offset = A_x_offsets(rowidx, 1, k);
            if (A_offset != KokkosKernels::ArithTraits<int64_t>::min()) {
              A_block_cst.assign_data(tpetra_values.data() + A_offset);
              // Pull x into local memory
              Kokkos::parallel_for(Kokkos::ThreadVectorRange(member, blocksize),
                                   [&](const local_ordinal_type& i) {
                                     local_x[i] = x(x_offset + i, col);
                                   });
 
              // MatVec op Ax += A*x
              Kokkos::parallel_for(Kokkos::ThreadVectorRange(member, blocksize),
                                   [&](const local_ordinal_type& k0) {
                                     impl_scalar_type val = 0;
                                     for (int k1 = 0; k1 < blocksize; k1++)
                                       val += A_block_cst(k0, k1) * local_x[k1];
                                     Kokkos::atomic_add(local_residual + k0, -val);
                                   });
            }
          });
      member.team_barrier();
      // Write back the partial residual to y
      if (member.team_rank() == 0) {
        Kokkos::parallel_for(Kokkos::ThreadVectorRange(member, blocksize),
                             [&](const local_ordinal_type& k) {
                               y(row + k, col) = local_residual[k];
                             });
      }
    }
  }
 
  KOKKOS_INLINE_FUNCTION
  void operator()(const NonownedTag, const member_type& member) const {
    const local_ordinal_type blocksize   = (B == 0 ? blocksize_requested : B);
    const local_ordinal_type rowidx      = member.league_rank();
    const local_ordinal_type row         = rowidx * blocksize;
    const local_ordinal_type num_vectors = y.extent(1);
 
    auto A_block_cst = ConstUnmanaged<tpetra_block_access_view_type>(
        tpetra_values.data(), blocksize, blocksize);
 
    // Get shared allocation for a local copy of x, Ax, and A
    impl_scalar_type* local_residual = reinterpret_cast<impl_scalar_type*>(
        member.team_scratch(0).get_shmem(blocksize * sizeof(impl_scalar_type)));
    impl_scalar_type* local_Dinv_residual = reinterpret_cast<impl_scalar_type*>(
        member.team_scratch(0).get_shmem(blocksize * sizeof(impl_scalar_type)));
    impl_scalar_type* local_x =
        reinterpret_cast<impl_scalar_type*>(member.thread_scratch(0).get_shmem(
            blocksize * sizeof(impl_scalar_type)));
 
    magnitude_type norm = 0;
    for (local_ordinal_type col = 0; col < num_vectors; ++col) {
      if (col) member.team_barrier();
      // y -= Rx
      // Initialize accumulation arrays.
      Kokkos::parallel_for(Kokkos::TeamVectorRange(member, blocksize),
                           [&](const local_ordinal_type& i) {
                             local_Dinv_residual[i] = 0;
                             local_residual[i]      = y(row + i, col);
                           });
      member.team_barrier();
 
      int numEntries = A_x_offsets_remote.extent(2);
 
      Kokkos::parallel_for(
          Kokkos::TeamThreadRange(member, 0, numEntries), [&](const int k) {
            int64_t A_offset = A_x_offsets_remote(rowidx, 0, k);
            int64_t x_offset = A_x_offsets_remote(rowidx, 1, k);
            if (A_offset != KokkosKernels::ArithTraits<int64_t>::min()) {
              A_block_cst.assign_data(tpetra_values.data() + A_offset);
              // Pull x into local memory
              Kokkos::parallel_for(Kokkos::ThreadVectorRange(member, blocksize),
                                   [&](const local_ordinal_type& i) {
                                     local_x[i] = x_remote(x_offset + i, col);
                                   });
 
              // matvec on block: local_residual -= A_block_cst * local_x
              Kokkos::parallel_for(Kokkos::ThreadVectorRange(member, blocksize),
                                   [&](const int k0) {
                                     impl_scalar_type val = 0;
                                     for (int k1 = 0; k1 < blocksize; k1++)
                                       val += A_block_cst(k0, k1) * local_x[k1];
                                     Kokkos::atomic_add(local_residual + k0, -val);
                                   });
            }
          });
      member.team_barrier();
      // Compute local_Dinv_residual = D^-1 * local_residual
      Kokkos::parallel_for(
          Kokkos::TeamThreadRange(member, blocksize),
          [&](const local_ordinal_type& k0) {
            Kokkos::parallel_reduce(
                Kokkos::ThreadVectorRange(member, blocksize),
                [&](const local_ordinal_type& k1, impl_scalar_type& update) {
                  update += d_inv(rowidx, k0, k1) * local_residual[k1];
                },
                local_Dinv_residual[k0]);
          });
      member.team_barrier();
      // local_Dinv_residual is fully computed. Now compute the
      // squared y update norm and update y (using damping factor).
      magnitude_type colNorm;
      Kokkos::parallel_reduce(
          Kokkos::TeamVectorRange(member, blocksize),
          [&](const local_ordinal_type& k, magnitude_type& update) {
            // Compute the change in y (assuming damping_factor == 1) for this
            // entry.
            impl_scalar_type old_y    = x(row + k, col);
            impl_scalar_type y_update = local_Dinv_residual[k] - old_y;
            if constexpr (KokkosKernels::ArithTraits<impl_scalar_type>::is_complex) {
              magnitude_type ydiff =
                  KokkosKernels::ArithTraits<impl_scalar_type>::abs(y_update);
              update += ydiff * ydiff;
            } else {
              update += y_update * y_update;
            }
            y(row + k, col) = old_y + damping_factor * y_update;
          },
          colNorm);
      norm += colNorm;
    }
    Kokkos::single(Kokkos::PerTeam(member), [&]() { W(rowidx) = norm; });
  }
 
  // Launch pass 1 of the 2-pass version.
  // This computes just the owned part of residual and writes that back to y.
  void run_pass1(const ConstUnmanaged<impl_scalar_type_2d_view_tpetra>& b_,
                 const ConstUnmanaged<impl_scalar_type_2d_view_tpetra>& x_,
                 const Unmanaged<impl_scalar_type_2d_view_tpetra>& y_) {
    IFPACK2_BLOCKHELPER_PROFILER_REGION_BEGIN;
    IFPACK2_BLOCKHELPER_TIMER_WITH_FENCE(
        "BlockTriDi::ComputeResidualAndSolve::RunPass1",
        ComputeResidualAndSolve0, execution_space);
 
    b = b_;
    x = x_;
    y = y_;
 
    const local_ordinal_type blocksize = blocksize_requested;
    const local_ordinal_type nrows     = d_inv.extent(0);
 
    const local_ordinal_type team_size   = 8;
    const local_ordinal_type vector_size = 8;
    const size_t shmem_team_size         = blocksize * sizeof(impl_scalar_type);
    const size_t shmem_thread_size       = blocksize * sizeof(impl_scalar_type);
    Kokkos::TeamPolicy<execution_space, OwnedTag> policy(nrows, team_size,
                                                         vector_size);
    policy.set_scratch_size(0, Kokkos::PerTeam(shmem_team_size),
                            Kokkos::PerThread(shmem_thread_size));
    Kokkos::parallel_for("ComputeResidualAndSolve::TeamPolicy::Pass1", policy,
                         *this);
    IFPACK2_BLOCKHELPER_PROFILER_REGION_END;
    IFPACK2_BLOCKHELPER_TIMER_FENCE(execution_space)
  }
 
  // Launch pass 2 of the 2-pass version.
  // This finishes computing residual with x_remote,
  // and then applies Dinv and computes norm.
  void run_pass2(
      const ConstUnmanaged<impl_scalar_type_2d_view_tpetra>& x_,
      const ConstUnmanaged<impl_scalar_type_2d_view_tpetra>& x_remote_,
      const Unmanaged<impl_scalar_type_2d_view_tpetra>& y_) {
    IFPACK2_BLOCKHELPER_PROFILER_REGION_BEGIN;
    IFPACK2_BLOCKHELPER_TIMER_WITH_FENCE(
        "BlockTriDi::ComputeResidualAndSolve::RunPass2",
        ComputeResidualAndSolve0, execution_space);
 
    x        = x_;
    x_remote = x_remote_;
    y        = y_;
 
    const local_ordinal_type blocksize = blocksize_requested;
    const local_ordinal_type nrows     = d_inv.extent(0);
 
    const local_ordinal_type team_size   = 8;
    const local_ordinal_type vector_size = 8;
    const size_t shmem_team_size         = 2 * blocksize * sizeof(impl_scalar_type);
    const size_t shmem_thread_size       = blocksize * sizeof(impl_scalar_type);
    Kokkos::TeamPolicy<execution_space, NonownedTag> policy(nrows, team_size,
                                                            vector_size);
    policy.set_scratch_size(0, Kokkos::PerTeam(shmem_team_size),
                            Kokkos::PerThread(shmem_thread_size));
    Kokkos::parallel_for("ComputeResidualAndSolve::TeamPolicy::Pass2", policy,
                         *this);
    IFPACK2_BLOCKHELPER_PROFILER_REGION_END;
    IFPACK2_BLOCKHELPER_TIMER_FENCE(execution_space)
  }
};
 
template <typename MatrixType, int B>
struct ComputeResidualAndSolve_YZero_Impl {
  using impl_type        = BlockHelperDetails::ImplType<MatrixType>;
  using node_device_type = typename impl_type::node_device_type;
  using execution_space  = typename impl_type::execution_space;
  using memory_space     = typename impl_type::memory_space;
 
  using local_ordinal_type = typename impl_type::local_ordinal_type;
  using size_type          = typename impl_type::size_type;
  using impl_scalar_type   = typename impl_type::impl_scalar_type;
  using magnitude_type     = typename impl_type::magnitude_type;
  using local_ordinal_type_1d_view =
      typename impl_type::local_ordinal_type_1d_view;
  using size_type_1d_view = typename impl_type::size_type_1d_view;
  using tpetra_block_access_view_type =
      typename impl_type::tpetra_block_access_view_type;  // block crs (layout
                                                          // right)
  using impl_scalar_type_1d_view = typename impl_type::impl_scalar_type_1d_view;
  using impl_scalar_type_2d_view_tpetra =
      typename impl_type::impl_scalar_type_2d_view_tpetra;  // block multivector
                                                            // (layout left)
  using btdm_scalar_type_3d_view = typename impl_type::btdm_scalar_type_3d_view;
  using btdm_scalar_type_4d_view = typename impl_type::btdm_scalar_type_4d_view;
  using i64_3d_view              = typename impl_type::i64_3d_view;
 
  using member_type = typename Kokkos::TeamPolicy<execution_space>::member_type;
 
 private:
  ConstUnmanaged<impl_scalar_type_2d_view_tpetra> b;
  Unmanaged<impl_scalar_type_2d_view_tpetra> y;
 
  // AmD information
  const ConstUnmanaged<impl_scalar_type_1d_view> tpetra_values;
 
  // blocksize
  const local_ordinal_type blocksize_requested;
 
  // block offsets
  const ConstUnmanaged<i64_3d_view> A_x_offsets;
  const ConstUnmanaged<i64_3d_view> A_x_offsets_remote;
 
  // diagonal block inverses
  const ConstUnmanaged<btdm_scalar_type_3d_view> d_inv;
 
  // squared update norms
  const Unmanaged<impl_scalar_type_1d_view> W;
 
  impl_scalar_type damping_factor;
 
 public:
  ComputeResidualAndSolve_YZero_Impl(
      const AmD<MatrixType>& amd, const btdm_scalar_type_3d_view& d_inv_,
      const impl_scalar_type_1d_view& W_,
      const local_ordinal_type& blocksize_requested_,
      const impl_scalar_type& damping_factor_)
    : tpetra_values(amd.tpetra_values)
    , blocksize_requested(blocksize_requested_)
    , A_x_offsets(amd.A_x_offsets)
    , A_x_offsets_remote(amd.A_x_offsets_remote)
    , d_inv(d_inv_)
    , W(W_)
    , damping_factor(damping_factor_) {}
 
  KOKKOS_INLINE_FUNCTION
  void operator()(const member_type& member) const {
    const local_ordinal_type blocksize = (B == 0 ? blocksize_requested : B);
    const local_ordinal_type rowidx =
        member.league_rank() * member.team_size() + member.team_rank();
    const local_ordinal_type row         = rowidx * blocksize;
    const local_ordinal_type num_vectors = b.extent(1);
 
    // Get shared allocation for a local copy of x, Ax, and A
    impl_scalar_type* local_Dinv_residual =
        reinterpret_cast<impl_scalar_type*>(member.thread_scratch(0).get_shmem(
            blocksize * sizeof(impl_scalar_type)));
 
    if (rowidx >= (local_ordinal_type)d_inv.extent(0)) return;
 
    magnitude_type norm = 0;
    for (local_ordinal_type col = 0; col < num_vectors; ++col) {
      // Compute local_Dinv_residual = D^-1 * local_residual
      Kokkos::parallel_for(Kokkos::ThreadVectorRange(member, blocksize),
                           [&](const local_ordinal_type& k0) {
                             impl_scalar_type val = 0;
                             for (local_ordinal_type k1 = 0; k1 < blocksize;
                                  k1++) {
                               val += d_inv(rowidx, k0, k1) * b(row + k1, col);
                             }
                             local_Dinv_residual[k0] = val;
                           });
 
      magnitude_type colNorm;
      Kokkos::parallel_reduce(
          Kokkos::ThreadVectorRange(member, blocksize),
          [&](const local_ordinal_type& k, magnitude_type& update) {
            // Compute the change in y (assuming damping_factor == 1) for this
            // entry.
            impl_scalar_type y_update = local_Dinv_residual[k];
            if constexpr (KokkosKernels::ArithTraits<impl_scalar_type>::is_complex) {
              magnitude_type ydiff =
                  KokkosKernels::ArithTraits<impl_scalar_type>::abs(y_update);
              update += ydiff * ydiff;
            } else {
              update += y_update * y_update;
            }
            y(row + k, col) = damping_factor * y_update;
          },
          colNorm);
      norm += colNorm;
    }
    Kokkos::single(Kokkos::PerThread(member), [&]() { W(rowidx) = norm; });
  }
 
  // ComputeResidualAndSolve_SolveOnly::run does the solve for the first
  // iteration, when the initial guess for y is zero. This means the residual
  // vector is just b. The kernel applies the inverse diags to b to find y, and
  // also puts the partial squared update norms (1 per row) into W.
  void run(const ConstUnmanaged<impl_scalar_type_2d_view_tpetra>& b_,
           const Unmanaged<impl_scalar_type_2d_view_tpetra>& y_) {
    IFPACK2_BLOCKHELPER_PROFILER_REGION_BEGIN;
    IFPACK2_BLOCKHELPER_TIMER_WITH_FENCE(
        "BlockTriDi::ComputeResidualAndSolve::Run_Y_Zero",
        ComputeResidualAndSolve0, execution_space);
 
    this->y = y_;
    this->b = b_;
 
    const local_ordinal_type blocksize = blocksize_requested;
    const local_ordinal_type nrows     = d_inv.extent(0);
 
    const local_ordinal_type team_size   = 8;
    const local_ordinal_type vector_size = 8;
    const size_t shmem_thread_size       = blocksize * sizeof(impl_scalar_type);
    Kokkos::TeamPolicy<execution_space> policy(
        (nrows + team_size - 1) / team_size, team_size, vector_size);
    policy.set_scratch_size(0, Kokkos::PerThread(shmem_thread_size));
    Kokkos::parallel_for("ComputeResidualAndSolve::TeamPolicy::y_zero", policy, *this);
    IFPACK2_BLOCKHELPER_PROFILER_REGION_END;
    IFPACK2_BLOCKHELPER_TIMER_FENCE(execution_space)
  }
};
 
// run_y_zero does the solve for the first
// iteration, when the initial guess for y is zero. This means the residual
// vector is just b. The kernel applies the inverse diags to b to find y, and
// also puts the partial squared update norms (1 per row) into W.
template <typename MatrixType>
void ComputeResidualAndSolve<MatrixType, BlockTriDiContainerDetails::ImplSimdTag>::run_y_zero(
    const Const<impl_scalar_type_2d_view_tpetra>& b_,
    const impl_scalar_type_2d_view_tpetra& y_) {
#define RUN_CASE(B)                                                                                                \
  {                                                                                                                \
    ComputeResidualAndSolve_YZero_Impl<MatrixType, B> functor(amd, d_inv, W, blocksize_requested, damping_factor); \
    functor.run(b_, y_);                                                                                           \
    break;                                                                                                         \
  }
 
  switch (blocksize_requested) {
    case 3: RUN_CASE(3);
    case 5: RUN_CASE(5);
    case 7: RUN_CASE(7);
    case 9: RUN_CASE(9);
    case 10: RUN_CASE(10);
    case 11: RUN_CASE(11);
    case 16: RUN_CASE(16);
    case 17: RUN_CASE(17);
    case 18: RUN_CASE(18);
    default: RUN_CASE(0);
  }
#undef RUN_CASE
}
 
template <typename MatrixType>
void ComputeResidualAndSolve<MatrixType, BlockTriDiContainerDetails::ImplSimdTag>::run_single_pass(
    const Const<impl_scalar_type_2d_view_tpetra>& b_,
    const impl_scalar_type_2d_view_tpetra& x_,
    const impl_scalar_type_2d_view_tpetra& x_remote_,
    const impl_scalar_type_2d_view_tpetra& y_) {
#define RUN_CASE(B)                                                                                                     \
  {                                                                                                                     \
    ComputeResidualAndSolve_SinglePass_Impl<MatrixType, B> functor(amd, d_inv, W, blocksize_requested, damping_factor); \
    functor.run(b_, x_, x_remote_, y_);                                                                                 \
    break;                                                                                                              \
  }
 
  switch (blocksize_requested) {
    case 3: RUN_CASE(3);
    case 5: RUN_CASE(5);
    case 7: RUN_CASE(7);
    case 9: RUN_CASE(9);
    case 10: RUN_CASE(10);
    case 11: RUN_CASE(11);
    case 16: RUN_CASE(16);
    case 17: RUN_CASE(17);
    case 18: RUN_CASE(18);
    default: RUN_CASE(0);
  }
#undef RUN_CASE
}
 
template <typename MatrixType>
void ComputeResidualAndSolve<MatrixType, BlockTriDiContainerDetails::ImplSimdTag>::run_pass1_of_2(
    const Const<impl_scalar_type_2d_view_tpetra>& b_,
    const impl_scalar_type_2d_view_tpetra& x_,
    const impl_scalar_type_2d_view_tpetra& y_) {
#define RUN_CASE(B)                                                                                                \
  {                                                                                                                \
    ComputeResidualAndSolve_2Pass_Impl<MatrixType, B> functor(amd, d_inv, W, blocksize_requested, damping_factor); \
    functor.run_pass1(b_, x_, y_);                                                                                 \
    break;                                                                                                         \
  }
 
  switch (blocksize_requested) {
    case 3: RUN_CASE(3);
    case 5: RUN_CASE(5);
    case 7: RUN_CASE(7);
    case 9: RUN_CASE(9);
    case 10: RUN_CASE(10);
    case 11: RUN_CASE(11);
    case 16: RUN_CASE(16);
    case 17: RUN_CASE(17);
    case 18: RUN_CASE(18);
    default: RUN_CASE(0);
  }
#undef RUN_CASE
}
 
template <typename MatrixType>
void ComputeResidualAndSolve<MatrixType, BlockTriDiContainerDetails::ImplSimdTag>::run_pass2_of_2(
    const impl_scalar_type_2d_view_tpetra& x_,
    const impl_scalar_type_2d_view_tpetra& x_remote_,
    const impl_scalar_type_2d_view_tpetra& y_) {
#define RUN_CASE(B)                                                                                                \
  {                                                                                                                \
    ComputeResidualAndSolve_2Pass_Impl<MatrixType, B> functor(amd, d_inv, W, blocksize_requested, damping_factor); \
    functor.run_pass2(x_, x_remote_, y_);                                                                          \
    break;                                                                                                         \
  }
 
  switch (blocksize_requested) {
    case 3: RUN_CASE(3);
    case 5: RUN_CASE(5);
    case 7: RUN_CASE(7);
    case 9: RUN_CASE(9);
    case 10: RUN_CASE(10);
    case 11: RUN_CASE(11);
    case 16: RUN_CASE(16);
    case 17: RUN_CASE(17);
    case 18: RUN_CASE(18);
    default: RUN_CASE(0);
  }
#undef RUN_CASE
}
 
}  // namespace Ifpack2::BlockHelperDetails
 
#define IFPACK2_BLOCKCOMPUTERESIDUALANDSOLVE_INSTANT(S, LO, GO, N) \
  template class Ifpack2::BlockHelperDetails::ComputeResidualAndSolve<Tpetra::RowMatrix<S, LO, GO, N> >;
 
#endif