DistributedRuntimeSDD.cc

// -*- 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
//-----------------------------------------------------------------------------
/*---------------------------------------------------------------------------*/
/*---------------------------------------------------------------------------*/
/*
 * Ce fichier est basé sur le travail sur la bibliothèque AMGCL (version mars 2026)
 * qui peut être trouvée à https://github.com/ddemidov/amgcl.
 *
 * Copyright (c) 2012-2022 Denis Demidov <dennis.demidov@gmail.com>
 * SPDX-License-Identifier: MIT
 */
/*---------------------------------------------------------------------------*/
/*---------------------------------------------------------------------------*/
 
#include <iostream>
#include <iomanip>
#include <fstream>
#include <vector>
#include <numeric>
#include <cmath>
#include <stdexcept>
 
#if defined(SOLVER_BACKEND_CUDA)
// Cela ne semble pas défini avec CUDA
namespace boost::math
{
class rounding_error
{};
} // namespace boost::math
#endif
 
#include "DomainPartition.h"
 
#include "MBA.h"
 
#include <memory>
 
#include <boost/multi_array.hpp>
#if defined(SOLVER_BACKEND_CUDA)
#include "arccore/alina/CudaBackend.h"
#include "arccore/alina/relaxation_cusparse_ilu0.h"
typedef Arcane::Alina::backend::cuda<double> Backend;
#else
#ifndef SOLVER_BACKEND_BUILTIN
#define SOLVER_BACKEND_BUILTIN
#endif
#include "arccore/alina/BuiltinBackend.h"
typedef Arcane::Alina::BuiltinBackend<double> Backend;
#endif
 
#include "arccore/alina/PreconditionedSolver.h"
#include "arccore/alina/AMG.h"
#include "arccore/alina/CoarseningRuntime.h"
#include "arccore/alina/RelaxationRuntime.h"
#include "arccore/alina/PreconditionerRuntime.h"
#include "arccore/alina/DistributedDirectSolverRuntime.h"
#include "arccore/alina/DistributedSolverRuntime.h"
#include "arccore/alina/DistributedSubDomainDeflation.h"
#include "arccore/alina/Adapters.h"
#include "arccore/alina/Profiler.h"
 
#include "arccore/common/internal/ProgramOptions.h"
 
#include "AlinaSamplesCommon.h"
 
using namespace Arcane;
 
struct partitioned_deflation
{
  unsigned nparts;
  std::vector<unsigned> domain;
 
  partitioned_deflation(boost::array<ptrdiff_t, 2> LO,
                        boost::array<ptrdiff_t, 2> HI,
                        unsigned nparts)
  : nparts(nparts)
  {
    DomainPartition<2> part(LO, HI, nparts);
 
    ptrdiff_t nx = HI[0] - LO[0] + 1;
    ptrdiff_t ny = HI[1] - LO[1] + 1;
 
    domain.resize(nx * ny);
    for (unsigned p = 0; p < nparts; ++p) {
      boost::array<ptrdiff_t, 2> lo = part.domain(p).min_corner();
      boost::array<ptrdiff_t, 2> hi = part.domain(p).max_corner();
 
      for (int j = lo[1]; j <= hi[1]; ++j) {
        for (int i = lo[0]; i <= hi[0]; ++i) {
          domain[(j - LO[1]) * nx + (i - LO[0])] = p;
        }
      }
    }
  }
 
  size_t dim() const { return nparts; }
 
  double operator()(ptrdiff_t i, unsigned j) const
  {
    return domain[i] == j;
  }
};
 
struct linear_deflation
{
  std::vector<double> x;
  std::vector<double> y;
 
  linear_deflation(ptrdiff_t chunk,
                   boost::array<ptrdiff_t, 2> lo,
                   boost::array<ptrdiff_t, 2> hi)
  {
    double hx = 1.0 / (hi[0] - lo[0]);
    double hy = 1.0 / (hi[1] - lo[1]);
 
    ptrdiff_t nx = hi[0] - lo[0] + 1;
    ptrdiff_t ny = hi[1] - lo[1] + 1;
 
    x.reserve(chunk);
    y.reserve(chunk);
 
    for (ptrdiff_t j = 0; j < ny; ++j) {
      for (ptrdiff_t i = 0; i < nx; ++i) {
        x.push_back(i * hx - 0.5);
        y.push_back(j * hy - 0.5);
      }
    }
  }
 
  size_t dim() const { return 3; }
 
  double operator()(ptrdiff_t i, unsigned j) const
  {
    switch (j) {
    default:
    case 0:
      return 1;
    case 1:
      return x[i];
    case 2:
      return y[i];
    }
  }
};
 
struct bilinear_deflation
{
  size_t nv, chunk;
  std::vector<double> v;
 
  bilinear_deflation(ptrdiff_t n,
                     ptrdiff_t chunk,
                     boost::array<ptrdiff_t, 2> lo,
                     boost::array<ptrdiff_t, 2> hi)
  : nv(0)
  , chunk(chunk)
  {
    // Voir quels voisins nous avons.
    int neib[2][2] = {
      { lo[0] > 0 || lo[1] > 0, hi[0] + 1 < n || lo[1] > 0 },
      { lo[0] > 0 || hi[1] + 1 < n, hi[0] + 1 < n || hi[1] + 1 < n }
    };
 
    for (int j = 0; j < 2; ++j)
      for (int i = 0; i < 2; ++i)
        if (neib[j][i])
          ++nv;
 
    if (nv == 0) {
      // Processus MPI unique ?
      nv = 1;
      v.resize(chunk, 1);
      return;
    }
 
    v.resize(chunk * nv, 0);
 
    double* dv = v.data();
 
    ptrdiff_t nx = hi[0] - lo[0] + 1;
    ptrdiff_t ny = hi[1] - lo[1] + 1;
 
    double hx = 1.0 / (nx - 1);
    double hy = 1.0 / (ny - 1);
 
    for (int j = 0; j < 2; ++j) {
      for (int i = 0; i < 2; ++i) {
        if (!neib[j][i])
          continue;
 
        boost::multi_array_ref<double, 2> V(dv, boost::extents[ny][nx]);
 
        for (ptrdiff_t jj = 0; jj < ny; ++jj) {
          double y = jj * hy;
          double b = std::abs((1 - j) - y);
          for (ptrdiff_t ii = 0; ii < nx; ++ii) {
            double x = ii * hx;
 
            double a = std::abs((1 - i) - x);
            V[jj][ii] = a * b;
          }
        }
 
        dv += chunk;
      }
    }
  }
 
  size_t dim() const { return nv; }
 
  double operator()(ptrdiff_t i, unsigned j) const
  {
    return v[j * chunk + i];
  }
};
 
#ifndef SOLVER_BACKEND_CUDA
struct mba_deflation
{
  size_t chunk, nv;
  std::vector<double> v;
 
  mba_deflation(ptrdiff_t n,
                ptrdiff_t chunk,
                boost::array<ptrdiff_t, 2> lo,
                boost::array<ptrdiff_t, 2> hi)
  : chunk(chunk)
  , nv(1)
  {
    // Voir quels voisins nous avons.
    int neib[2][2] = {
      { lo[0] > 0 || lo[1] > 0, hi[0] + 1 < n || lo[1] > 0 },
      { lo[0] > 0 || hi[1] + 1 < n, hi[0] + 1 < n || hi[1] + 1 < n }
    };
 
    for (int j = 0; j < 2; ++j)
      for (int i = 0; i < 2; ++i)
        if (neib[j][i])
          ++nv;
 
    v.resize(chunk * nv, 0);
 
    double* dv = v.data();
    std::fill(dv, dv + chunk, 1.0);
    dv += chunk;
 
    ptrdiff_t nx = hi[0] - lo[0] + 1;
    ptrdiff_t ny = hi[1] - lo[1] + 1;
 
    double hx = 1.0 / (nx - 1);
    double hy = 1.0 / (ny - 1);
 
    std::array<double, 2> cmin = { -0.01, -0.01 };
    std::array<double, 2> cmax = { 1.01, 1.01 };
    std::array<size_t, 2> grid = { 3, 3 };
 
    std::array<std::array<double, 2>, 4> coo;
    std::array<double, 4> val;
 
    for (int j = 0, idx = 0; j < 2; ++j) {
      for (int i = 0; i < 2; ++i, ++idx) {
        coo[idx][0] = i;
        coo[idx][1] = j;
      }
    }
 
    for (int j = 0, idx = 0; j < 2; ++j) {
      for (int i = 0; i < 2; ++i, ++idx) {
        if (!neib[j][i])
          continue;
 
        std::fill(val.begin(), val.end(), 0.0);
        val[idx] = 1.0;
 
        mba::MBA<2> interp(cmin, cmax, grid, coo, val, 8, 1e-8, 0.5, zero);
 
        boost::multi_array_ref<double, 2> V(dv, boost::extents[ny][nx]);
 
        for (int jj = 0; jj < ny; ++jj)
          for (int ii = 0; ii < nx; ++ii) {
            std::array<double, 2> p = { ii * hx, jj * hy };
            V[jj][ii] = interp(p);
          }
 
        dv += chunk;
      }
    }
  }
 
  size_t dim() const { return nv; }
 
  double operator()(ptrdiff_t i, unsigned j) const
  {
    return v[j * chunk + i];
  }
 
  static double zero(const std::array<double, 2>&)
  {
    return 0;
  }
};
#endif
 
struct harmonic_deflation
{
  size_t nv, chunk;
  std::vector<double> v;
 
  harmonic_deflation(ptrdiff_t n,
                     ptrdiff_t chunk,
                     boost::array<ptrdiff_t, 2> lo,
                     boost::array<ptrdiff_t, 2> hi)
  : nv(0)
  , chunk(chunk)
  {
    Alina::AlinaContext context;
 
    // Voir quels voisins nous avons.
    int neib[2][2] = {
      { lo[0] > 0 || lo[1] > 0, hi[0] + 1 < n || lo[1] > 0 },
      { lo[0] > 0 || hi[1] + 1 < n, hi[0] + 1 < n || hi[1] + 1 < n }
    };
 
    for (int j = 0; j < 2; ++j)
      for (int i = 0; i < 2; ++i)
        if (neib[j][i])
          ++nv;
 
    if (nv == 0) {
      // Processus MPI unique ?
      nv = 1;
      v.resize(chunk, 1);
      return;
    }
 
    v.resize(chunk * nv, 0);
    double* dv = v.data();
 
    ptrdiff_t nx = hi[0] - lo[0] + 1;
    ptrdiff_t ny = hi[1] - lo[1] + 1;
 
    std::vector<Int32> ptr;
    std::vector<Int32> col;
    std::vector<double> val;
    std::vector<double> rhs(chunk, 0.0);
 
    ptr.reserve(chunk + 1);
    col.reserve(chunk * 5);
    val.reserve(chunk * 5);
 
    ptr.push_back(0);
 
    for (int j = 0, k = 0; j < ny; ++j) {
      for (int i = 0; i < nx; ++i, ++k) {
        if (
        (i == 0 && j == 0) ||
        (i == 0 && j == ny - 1) ||
        (i == nx - 1 && j == 0) ||
        (i == nx - 1 && j == ny - 1)) {
          col.push_back(k);
          val.push_back(1);
        }
        else {
          col.push_back(k);
          val.push_back(1.0);
 
          if (j == 0) {
            col.push_back(k + nx);
            val.push_back(-0.5);
          }
          else if (j == ny - 1) {
            col.push_back(k - nx);
            val.push_back(-0.5);
          }
          else {
            col.push_back(k - nx);
            val.push_back(-0.25);
 
            col.push_back(k + nx);
            val.push_back(-0.25);
          }
 
          if (i == 0) {
            col.push_back(k + 1);
            val.push_back(-0.5);
          }
          else if (i == nx - 1) {
            col.push_back(k - 1);
            val.push_back(-0.5);
          }
          else {
            col.push_back(k - 1);
            val.push_back(-0.25);
 
            col.push_back(k + 1);
            val.push_back(-0.25);
          }
        }
 
        ptr.push_back(col.size());
      }
    }
 
    Alina::PreconditionedSolver<Alina::AMG<Alina::BuiltinBackend<double>,
                                  Alina::SmoothedAggregationCoarserning,
                                  Alina::GaussSeidelRelaxation>,
                       Alina::GMRESSolver<
                       Alina::BuiltinBackend<double>>>
    solve(context, Alina::adapter::zero_copy(chunk, ptr.data(), col.data(), val.data()));
 
    for (int j = 0; j < 2; ++j) {
      for (int i = 0; i < 2; ++i) {
        if (!neib[j][i])
          continue;
 
        ptrdiff_t idx = i * (nx - 1) + j * (ny - 1) * nx;
        rhs[idx] = 1.0;
 
        SmallSpan<double> x(dv, chunk);
        solve(rhs, x);
 
        rhs[idx] = 0.0;
 
        dv += chunk;
      }
    }
  }
 
  size_t dim() const { return nv; }
 
  double operator()(ptrdiff_t i, unsigned j) const
  {
    return v[j * chunk + i];
  }
};
 
struct renumbering
{
  const DomainPartition<2>& part;
  const std::vector<ptrdiff_t>& dom;
 
  renumbering(const DomainPartition<2>& p,
              const std::vector<ptrdiff_t>& d)
  : part(p)
  , dom(d)
  {}
 
  ptrdiff_t operator()(ptrdiff_t i, ptrdiff_t j) const
  {
    boost::array<ptrdiff_t, 2> p = { { i, j } };
    std::pair<int, ptrdiff_t> v = part.index(p);
    return dom[v.first] + v.second;
  }
};
 
int main2(const Alina::SampleMainContext& ctx, int argc, char* argv[])
{
  auto& prof = Alina::Profiler::globalProfiler();
 
  Alina::AlinaCommunicator world(ctx.messagePassingMng());
  if (world.rank == 0)
    std::cout << "World size: " << world.size << std::endl;
  Alina::AlinaContext context;
 
  // Lire la configuration à partir de la ligne de commande
  ptrdiff_t n = 1024;
  std::string deflation_type = "bilinear";
 
  auto coarsening = Alina::eCoarserningType::smoothed_aggregation;
  auto relaxation = Alina::eRelaxationType::spai0;
  auto iterative_solver = Alina::eSolverType::bicgstabl;
  auto direct_solver = Alina::eDistributedDirectSolverType::skyline_lu;
 
  bool just_relax = false;
  bool symm_dirichlet = true;
  std::string problem = "laplace2d";
  std::string parameter_file;
  std::string out_file;
 
  namespace po = Arcane::ProgramOptions;
  po::options_description desc("Options");
 
  desc.add_options()("help,h", "show help")(
  "problem",
  po::value<std::string>(&problem)->default_value(problem),
  "laplace2d, recirc2d")(
  "symbc",
  po::value<bool>(&symm_dirichlet)->default_value(symm_dirichlet),
  "Utiliser des conditions de Dirichlet symétriques dans laplace2d")(
  "size,n",
  po::value<ptrdiff_t>(&n)->default_value(n),
  "taille du domaine")(
  "coarsening,c",
  po::value<Alina::eCoarserningType>(&coarsening)->default_value(coarsening),
  "ruge_stuben, aggregation, smoothed_aggregation, smoothed_aggr_emin")(
  "relaxation,r",
  po::value<Alina::eRelaxationType>(&relaxation)->default_value(relaxation),
  "gauss_seidel, ilu0, iluk, ilut, damped_jacobi, spai0, spai1, chebyshev")(
  "iter_solver,i",
  po::value<Alina::eSolverType>(&iterative_solver)->default_value(iterative_solver),
  "cg, bicgstab, bicgstabl, gmres")(
  "dir_solver,d",
  po::value<Alina::eDistributedDirectSolverType>(&direct_solver)->default_value(direct_solver),
  "skyline_lu"
#ifdef ARCCORE_ALINA_HAVE_PASTIX
  ", pastix"
#endif
  )(
  "deflation,v",
  po::value<std::string>(&deflation_type)->default_value(deflation_type),
  "constant, partitioned, linear, bilinear, mba, harmonic")(
  "subparts",
  po::value<int>()->default_value(16),
  "nombre de partitions pour la déflation partitionnée")(
  "params,P",
  po::value<std::string>(&parameter_file),
  "fichier de paramètres au format json")(
  "prm,p",
  po::value<std::vector<std::string>>()->multitoken(),
  "Paramètres spécifiés sous forme de paires nom=valeur. "
  "Peut être fourni plusieurs fois. Exemples :\n"
  "  -p solver.tol=1e-3\n"
  "  -p precond.coarse_enough=300")(
  "just-relax,0",
  po::bool_switch(&just_relax),
  "Ne pas créer la hiérarchie AMG, utiliser la relaxation comme préconditionneur")(
  "out,o",
  po::value<std::string>(&out_file),
  "fichier de sortie");
 
  po::variables_map vm;
  po::store(po::parse_command_line(argc, argv, desc), vm);
  po::notify(vm);
 
  if (vm.count("help")) {
    std::cout << desc << std::endl;
    return 0;
  }
 
  Alina::PropertyTree prm;
  if (vm.count("params"))
    prm.read_json(parameter_file);
 
  if (vm.count("prm")) {
    for (const std::string& v : vm["prm"].as<std::vector<std::string>>()) {
      prm.putKeyValue(v);
    }
  }
 
  prm.put("isolver.type", iterative_solver);
  prm.put("dsolver.type", direct_solver);
 
  const ptrdiff_t n2 = n * n;
  const double hinv = (n - 1);
  const double h2i = (n - 1) * (n - 1);
  const double h = 1 / hinv;
 
  boost::array<ptrdiff_t, 2> lo = { { 0, 0 } };
  boost::array<ptrdiff_t, 2> hi = { { n - 1, n - 1 } };
 
  prof.tic("partition");
  DomainPartition<2> part(lo, hi, world.size);
  ptrdiff_t chunk = part.size(world.rank);
 
  std::vector<ptrdiff_t> domain(world.size + 1);
  ConstArrayView<ptrdiff_t> send_buf(1, &chunk);
  ArrayView<ptrdiff_t> receive_buf(world.size, &domain[1]);
  mpAllGather(world.m_message_passing_mng.get(), send_buf, receive_buf);
  std::partial_sum(domain.begin(), domain.end(), domain.begin());
 
  lo = part.domain(world.rank).min_corner();
  hi = part.domain(world.rank).max_corner();
  prof.toc("partition");
 
  renumbering renum(part, domain);
 
  prof.tic("deflation");
  std::function<double(ptrdiff_t, unsigned)> dv;
  Int32 ndv = 1;
 
  if (deflation_type == "constant") {
    dv = Alina::constant_deflation(1);
  }
  else if (deflation_type == "partitioned") {
    ndv = vm["subparts"].as<int>();
    dv = partitioned_deflation(lo, hi, ndv);
  }
  else if (deflation_type == "linear") {
    ndv = 3;
    dv = linear_deflation(chunk, lo, hi);
  }
  else if (deflation_type == "bilinear") {
    bilinear_deflation bld(n, chunk, lo, hi);
    ndv = bld.dim();
    dv = bld;
#ifndef SOLVER_BACKEND_CUDA
  }
  else if (deflation_type == "mba") {
    mba_deflation mba(n, chunk, lo, hi);
    ndv = mba.dim();
    dv = mba;
#endif
  }
  else if (deflation_type == "harmonic") {
    harmonic_deflation hd(n, chunk, lo, hi);
    ndv = hd.dim();
    dv = hd;
  }
  else {
    throw std::runtime_error("Unsupported deflation type");
  }
 
  prm.put("num_def_vec", ndv);
  prm.put("def_vec", &dv);
  prof.toc("deflation");
 
  prof.tic("assemble");
  std::vector<ptrdiff_t> ptr;
  std::vector<ptrdiff_t> col;
  std::vector<double> val;
  std::vector<double> rhs;
 
  ptr.reserve(chunk + 1);
  col.reserve(chunk * 5);
  val.reserve(chunk * 5);
  rhs.reserve(chunk);
 
  ptr.push_back(0);
 
  if (problem == "recirc2d") {
    const double eps = 1e-5;
 
    for (ptrdiff_t j = lo[1]; j <= hi[1]; ++j) {
      double y = h * j;
      for (ptrdiff_t i = lo[0]; i <= hi[0]; ++i) {
        double x = h * i;
 
        if (i == 0 || j == 0 || i + 1 == n || j + 1 == n) {
          col.push_back(renum(i, j));
          val.push_back(1);
          rhs.push_back(
          sin(M_PI * x) + sin(M_PI * y) +
          sin(13 * M_PI * x) + sin(13 * M_PI * y));
        }
        else {
          double a = -sin(M_PI * x) * cos(M_PI * y) * hinv;
          double b = sin(M_PI * y) * cos(M_PI * x) * hinv;
 
          if (j > 0) {
            col.push_back(renum(i, j - 1));
            val.push_back(-eps * h2i - std::max(b, 0.0));
          }
 
          if (i > 0) {
            col.push_back(renum(i - 1, j));
            val.push_back(-eps * h2i - std::max(a, 0.0));
          }
 
          col.push_back(renum(i, j));
          val.push_back(4 * eps * h2i + fabs(a) + fabs(b));
 
          if (i + 1 < n) {
            col.push_back(renum(i + 1, j));
            val.push_back(-eps * h2i + std::min(a, 0.0));
          }
 
          if (j + 1 < n) {
            col.push_back(renum(i, j + 1));
            val.push_back(-eps * h2i + std::min(b, 0.0));
          }
 
          rhs.push_back(1.0);
        }
        ptr.push_back(col.size());
      }
    }
  }
  else {
    for (ptrdiff_t j = lo[1]; j <= hi[1]; ++j) {
      for (ptrdiff_t i = lo[0]; i <= hi[0]; ++i) {
        if (!symm_dirichlet && (i == 0 || j == 0 || i + 1 == n || j + 1 == n)) {
          col.push_back(renum(i, j));
          val.push_back(1);
          rhs.push_back(0);
        }
        else {
          if (j > 0) {
            col.push_back(renum(i, j - 1));
            val.push_back(-h2i);
          }
 
          if (i > 0) {
            col.push_back(renum(i - 1, j));
            val.push_back(-h2i);
          }
 
          col.push_back(renum(i, j));
          val.push_back(4 * h2i);
 
          if (i + 1 < n) {
            col.push_back(renum(i + 1, j));
            val.push_back(-h2i);
          }
 
          if (j + 1 < n) {
            col.push_back(renum(i, j + 1));
            val.push_back(-h2i);
          }
 
          rhs.push_back(1);
        }
        ptr.push_back(col.size());
      }
    }
  }
  prof.toc("assemble");
 
  Backend::params bprm;
 
#if defined(SOLVER_BACKEND_CUDA)
  cusparseCreate(&bprm.cusparse_handle);
#endif
 
  auto f = Backend::copy_vector(rhs, bprm);
  auto x = Backend::create_vector(chunk, bprm);
 
  Alina::backend::clear(*x);
 
  if (just_relax) {
    prm.put("local.class", "relaxation");
    prm.put("local.type", relaxation);
  }
  else {
    prm.put("local.coarsening.type", coarsening);
    prm.put("local.relax.type", relaxation);
  }
 
  prof.tic("setup");
  using SDD = Alina::DistributedSubDomainDeflation<Alina::PreconditionerRuntime<Backend>,
                                                   Alina::DistributedSolverRuntime<Backend>,
                                                   Alina::DistributedDirectSolverRuntime<Backend>>;
 
  SDD solve(context, world, std::tie(chunk, ptr, col, val), prm, bprm);
  prof.toc("setup");
 
  prof.tic("solve");
  Alina::SolverResult r = solve(*f, *x);
  prof.toc("solve");
 
  if (world.rank == 0) {
    std::cout << "Itérations : " << r.nbIteration() << std::endl
              << "Erreur :      " << r.residual() << std::endl
              << prof << std::endl;
  }
  return 0;
}
 
int main(int argc, char* argv[])
{
  return Arcane::Alina::SampleMainContext::execMain(main2, argc, argv);
}