DistributedDirectSolverBase.h

// -*- tab-width: 2; indent-tabs-mode: nil; coding: utf-8-with-signature -*-
//-----------------------------------------------------------------------------
// Copyright 2000-2026 CEA (www.cea.fr) IFPEN (www.ifpenergiesnouvelles.com)
// See the top-level COPYRIGHT file for details.
// SPDX-License-Identifier: Apache-2.0
//-----------------------------------------------------------------------------
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/* DistributedDirectSolverBase.h                               (C) 2000-2026 */
/*                                                                           */
/* Base class for distributed direct solver.                                 */
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#ifndef ARCCORE_ALINA_MPI_DISTRIBUTEDDIRECTSOLVERBASE_H
#define ARCCORE_ALINA_MPI_DISTRIBUTEDDIRECTSOLVERBASE_H
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/*
 * This file is based on the work on AMGCL library (version march 2026)
 * which can be found at https://github.com/ddemidov/amgcl.
 *
 * Copyright (c) 2012-2022 Denis Demidov <dennis.demidov@gmail.com>
 * SPDX-License-Identifier: MIT
 */
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#include "arccore/alina/MessagePassingUtils.h"
#include "arccore/alina/DistributedMatrix.h"
 
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namespace Arcane::Alina
{
 
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template <class value_type, class Solver>
class DistributedDirectSolverBase
{
 public:
 
  typedef typename math::scalar_of<value_type>::type scalar_type;
  typedef typename math::rhs_of<value_type>::type rhs_type;
  typedef CSRMatrix<value_type> build_matrix;
 
  DistributedDirectSolverBase() {}
 
  void init(mpi_communicator comm, const build_matrix& Astrip)
  {
    this->comm = comm;
    n = Astrip.nbRow();
 
    std::vector<int> domain = comm.exclusive_sum(n);
    std::vector<int> active;
    active.reserve(comm.size);
 
    // Find out how many ranks are active (own non-zero matrix rows):
    int active_rank = 0;
    for (int i = 0; i < comm.size; ++i) {
      if (domain[i + 1] - domain[i] > 0) {
        if (comm.rank == i)
          active_rank = active.size();
        active.push_back(i);
      }
    }
 
    // Consolidate the matrix on a fewer processes.
    int nmasters = std::min<int>(active.size(), solver().comm_size(domain.back()));
    int slaves_per_master = (active.size() + nmasters - 1) / nmasters;
    int group_beg = (active_rank / slaves_per_master) * slaves_per_master;
 
    group_master = active[group_beg];
 
    // Communicator for masters (used to solve the coarse problem):
    MPI_Comm_split(comm,
                   comm.rank == group_master ? 0 : MPI_UNDEFINED,
                   comm.rank, &masters_comm);
 
    if (!n)
      return; // I am not active
 
    // Shift from row pointers to row widths:
    std::vector<ptrdiff_t> widths(n);
    for (ptrdiff_t i = 0; i < n; ++i)
      widths[i] = Astrip.ptr[i + 1] - Astrip.ptr[i];
 
    if (comm.rank == group_master) {
      int group_end = std::min<int>(group_beg + slaves_per_master, active.size());
      group_beg += 1;
      int group_size = group_end - group_beg;
 
      UniqueArray<MessagePassing::Request> cnt_req(group_size);
      UniqueArray<MessagePassing::Request> col_req(group_size);
      UniqueArray<MessagePassing::Request> val_req(group_size);
 
      solve_req.resize(group_size);
      slaves.reserve(group_size);
      counts.reserve(group_size);
 
      // Count rows in local chunk of the consolidated matrix,
      // see who is reporting to us.
      int nloc = n;
      for (int j = group_beg; j < group_end; ++j) {
        int i = active[j];
 
        int m = domain[i + 1] - domain[i];
        nloc += m;
        counts.push_back(m);
        slaves.push_back(i);
      }
 
      // Get matrix chunks from my slaves.
      build_matrix A;
      A.set_size(nloc, domain.back(), false);
      A.ptr[0] = 0;
 
      cons_f.resize(A.nbRow());
      cons_x.resize(A.nbRow());
 
      int shift = n + 1;
      std::copy(widths.begin(), widths.end(), &A.ptr[1]);
 
      for (int j = 0; j < group_size; ++j) {
        int i = slaves[j];
 
        cnt_req[j] = comm.doIReceive(&A.ptr[shift], counts[j], i, cnt_tag);
 
        shift += counts[j];
      }
 
      comm.waitAll(cnt_req);
 
      A.set_nonzeros(A.scan_row_sizes());
 
      std::copy(Astrip.col.data(), Astrip.col.data() + Astrip.nbNonZero(), A.col.data());
      std::copy(Astrip.val.data(), Astrip.val.data() + Astrip.nbNonZero(), A.val.data());
 
      shift = Astrip.nbNonZero();
      for (int j = 0, d0 = domain[comm.rank]; j < group_size; ++j) {
        int i = slaves[j];
 
        int nnz = A.ptr[domain[i + 1] - d0] - A.ptr[domain[i] - d0];
 
        col_req[j] = comm.doIReceive(A.col + shift, nnz, i, col_tag);
        val_req[j] = comm.doIReceive(A.val + shift, nnz, i, val_tag);
 
        shift += nnz;
      }
 
      comm.waitAll(col_req);
      comm.waitAll(val_req);
 
      solver().init(mpi_communicator(masters_comm), A);
    }
    else {
      comm.doSend(widths.data(), n, group_master, cnt_tag);
      comm.doSend(Astrip.col.data(), Astrip.nbNonZero(), group_master, col_tag);
      comm.doSend(Astrip.val.data(), Astrip.nbNonZero(), group_master, val_tag);
    }
 
    host_v.resize(n);
  }
 
  template <class B>
  void init(mpi_communicator comm, const DistributedMatrix<B>& A)
  {
    const build_matrix& A_loc = *A.local();
    const build_matrix& A_rem = *A.remote();
 
    build_matrix a;
 
    a.set_size(A.loc_rows(), A.glob_cols(), false);
    a.set_nonzeros(A_loc.nbNonZero() + A_rem.nbNonZero());
    a.ptr[0] = 0;
 
    for (size_t i = 0, head = 0; i < A_loc.nbRow(); ++i) {
      ptrdiff_t shift = A.loc_col_shift();
 
      for (ptrdiff_t j = A_loc.ptr[i], e = A_loc.ptr[i + 1]; j < e; ++j) {
        a.col[head] = A_loc.col[j] + shift;
        a.val[head] = A_loc.val[j];
        ++head;
      }
 
      for (ptrdiff_t j = A_rem.ptr[i], e = A_rem.ptr[i + 1]; j < e; ++j) {
        a.col[head] = A_rem.col[j];
        a.val[head] = A_rem.val[j];
        ++head;
      }
 
      a.ptr[i + 1] = head;
    }
 
    init(comm, a);
  }
 
  virtual ~DistributedDirectSolverBase()
  {
    if (masters_comm != MPI_COMM_NULL)
      MPI_Comm_free(&masters_comm);
  }
 
  Solver& solver()
  {
    return *static_cast<Solver*>(this);
  }
 
  const Solver& solver() const
  {
    return *static_cast<const Solver*>(this);
  }
 
  template <class VecF, class VecX>
  void operator()(const VecF& f, VecX& x) const
  {
    if (!n)
      return;
 
    backend::copy(f, host_v);
 
    if (comm.rank == group_master) {
      std::copy(host_v.begin(), host_v.end(), cons_f.begin());
 
      int shift = n, j = 0;
      for (int i : slaves) {
        solve_req[j] = comm.doIReceive(&cons_f[shift], counts[j], i, rhs_tag);
        shift += counts[j++];
      }
 
      comm.waitAll(solve_req);
 
      solver().solve(cons_f, cons_x);
 
      std::copy(cons_x.begin(), cons_x.begin() + n, host_v.begin());
      shift = n;
      j = 0;
 
      for (int i : slaves) {
        solve_req[j] = comm.doISend(&cons_x[shift], counts[j], i, sol_tag);
        shift += counts[j++];
      }
 
      comm.waitAll(solve_req);
    }
    else {
      comm.doSend(host_v.data(), n, group_master, rhs_tag);
      comm.doReceive(host_v.data(), n, group_master, sol_tag);
    }
 
    backend::copy(host_v, x);
  }
 
 private:
 
  static const int cnt_tag = 5001;
  static const int col_tag = 5002;
  static const int val_tag = 5003;
  static const int rhs_tag = 5004;
  static const int sol_tag = 5005;
 
  mpi_communicator comm;
  int n;
  int group_master;
  MPI_Comm masters_comm;
  std::vector<int> slaves;
  std::vector<int> counts;
  mutable std::vector<rhs_type> cons_f, cons_x, host_v;
  mutable UniqueArray<MessagePassing::Request> solve_req;
};
 
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} // namespace Arcane::Alina
 
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#endif