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sparse_matrix_aij.cc
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Wed, Dec 4, 18:47

sparse_matrix_aij.cc
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/**
* Copyright (©) 2015-2023 EPFL (Ecole Polytechnique Fédérale de Lausanne)
* Laboratory (LSMS - Laboratoire de Simulation en Mécanique des Solides)
*
* This file is part of Akantu
*
* Akantu is free software: you can redistribute it and/or modify it under the
* terms of the GNU Lesser General Public License as published by the Free
* Software Foundation, either version 3 of the License, or (at your option) any
* later version.
*
* Akantu is distributed in the hope that it will be useful, but WITHOUT ANY
* WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR
* A PARTICULAR PURPOSE. See the GNU Lesser General Public License for more
* details.
*
* You should have received a copy of the GNU Lesser General Public License
* along with Akantu. If not, see <http://www.gnu.org/licenses/>.
*/
/* -------------------------------------------------------------------------- */
#include "sparse_matrix_aij.hh"
#include "aka_iterators.hh"
#include "aka_math.hh"
#include "dof_manager_default.hh"
#include "dof_synchronizer.hh"
#include "solver_vector_default.hh"
#include "terms_to_assemble.hh"
/* -------------------------------------------------------------------------- */
#include <fstream>
/* -------------------------------------------------------------------------- */
namespace akantu {
/* -------------------------------------------------------------------------- */
SparseMatrixAIJ::SparseMatrixAIJ(DOFManagerDefault & dof_manager,
const MatrixType & matrix_type, const ID & id)
: SparseMatrix(dof_manager, matrix_type, id), dof_manager(dof_manager),
irn(0, 1, id + ":irn"), jcn(0, 1, id + ":jcn"), a(0, 1, id + ":a") {}
/* -------------------------------------------------------------------------- */
SparseMatrixAIJ::SparseMatrixAIJ(const SparseMatrixAIJ & matrix, const ID & id)
: SparseMatrix(matrix, id), dof_manager(matrix.dof_manager),
irn(matrix.irn, id + ":irn"), jcn(matrix.jcn, id + ":jcn"),
a(matrix.a, id + ":a") {}
/* -------------------------------------------------------------------------- */
SparseMatrixAIJ::~SparseMatrixAIJ() = default;
/* -------------------------------------------------------------------------- */
void SparseMatrixAIJ::applyBoundary(Real block_val) {
AKANTU_DEBUG_IN();
const auto & blocked_dofs = this->dof_manager.getGlobalBlockedDOFs();
auto begin = blocked_dofs.begin();
auto end = blocked_dofs.end();
auto is_blocked = [&](auto && i) -> bool {
auto il = this->dof_manager.globalToLocalEquationNumber(i);
return std::binary_search(begin, end, il);
};
for (auto && ij_a : zip(irn, jcn, a)) {
auto ni = std::get<0>(ij_a) - 1;
auto nj = std::get<1>(ij_a) - 1;
if (is_blocked(ni) or is_blocked(nj)) {
std::get<2>(ij_a) =
std::get<0>(ij_a) != std::get<1>(ij_a) ? 0.
: this->dof_manager.isLocalOrMasterDOF(
this->dof_manager.globalToLocalEquationNumber(ni))
? block_val
: 0.;
}
}
this->value_release++;
AKANTU_DEBUG_OUT();
}
/* -------------------------------------------------------------------------- */
void SparseMatrixAIJ::saveProfile(const std::string & filename) const {
AKANTU_DEBUG_IN();
std::ofstream outfile;
outfile.open(filename.c_str());
auto m = this->size_;
auto & comm = dof_manager.getCommunicator();
// write header
if (comm.whoAmI() == 0) {
outfile << "%%MatrixMarket matrix coordinate pattern";
if (this->matrix_type == _symmetric) {
outfile << " symmetric";
} else {
outfile << " general";
}
outfile << std::endl;
outfile << m << " " << m << " " << this->nb_non_zero << std::endl;
}
for (auto p : arange(comm.getNbProc())) {
// write content
if (comm.whoAmI() == p) {
for (auto && data : zip(this->irn, this->jcn)) {
outfile << std::get<0>(data) << " " << std::get<1>(data) << " 1"
<< std::endl;
}
}
comm.barrier();
}
outfile.close();
AKANTU_DEBUG_OUT();
}
/* -------------------------------------------------------------------------- */
void SparseMatrixAIJ::saveMatrix(const std::string & filename) const {
AKANTU_DEBUG_IN();
auto & comm = dof_manager.getCommunicator();
// open and set the properties of the stream
std::ofstream outfile;
if (0 == comm.whoAmI()) {
outfile.open(filename.c_str());
} else {
outfile.open(filename.c_str(), std::ios_base::app);
}
outfile.precision(std::numeric_limits<Real>::digits10);
// write header
decltype(nb_non_zero) nnz = this->nb_non_zero;
comm.allReduce(nnz);
if (comm.whoAmI() == 0) {
outfile << "%%MatrixMarket matrix coordinate real";
if (this->matrix_type == _symmetric) {
outfile << " symmetric";
} else {
outfile << " general";
}
outfile << std::endl;
outfile << this->size_ << " " << this->size_ << " " << nnz << std::endl;
}
for (auto p : arange(comm.getNbProc())) {
// write content
if (comm.whoAmI() == p) {
for (auto && data : zip(this->irn, this->jcn, this->a)) {
outfile << std::get<0>(data) << " " << std::get<1>(data) << " "
<< std::get<2>(data) << std::endl;
}
}
comm.barrier();
}
// time to end
outfile.close();
AKANTU_DEBUG_OUT();
}
/* -------------------------------------------------------------------------- */
void SparseMatrixAIJ::matVecMul(const Array<Real> & x, Array<Real> & y,
Real alpha, Real beta) const {
AKANTU_DEBUG_IN();
Array<Real> tmp(y);
tmp.zero();
y *= beta;
auto x_it = make_view(x).begin();
auto y_it = make_view(y).begin();
for (auto && data : zip(this->irn, this->jcn, this->a)) {
auto i =
this->dof_manager.globalToLocalEquationNumber(std::get<0>(data) - 1);
auto j =
this->dof_manager.globalToLocalEquationNumber(std::get<1>(data) - 1);
const auto & A = std::get<2>(data);
y_it[i] += alpha * A * x_it[j];
if ((this->matrix_type == _symmetric) && (i != j)) {
y_it[j] += alpha * A * x_it[i];
}
}
if (this->dof_manager.hasSynchronizer()) {
this->dof_manager.getSynchronizer().reduceSynchronizeArray<AddOperation>(
tmp);
}
y += tmp;
AKANTU_DEBUG_OUT();
}
/* -------------------------------------------------------------------------- */
void SparseMatrixAIJ::matVecMul(const SolverVector & _x, SolverVector & _y,
Real alpha, Real beta) const {
AKANTU_DEBUG_IN();
auto && x = aka::as_type<SolverVectorArray>(_x).getVector();
auto && y = aka::as_type<SolverVectorArray>(_y).getVector();
this->matVecMul(x, y, alpha, beta);
}
/* -------------------------------------------------------------------------- */
void SparseMatrixAIJ::copyContent(const SparseMatrix & matrix) {
AKANTU_DEBUG_IN();
const auto & mat = aka::as_type<SparseMatrixAIJ>(matrix);
AKANTU_DEBUG_ASSERT(nb_non_zero == mat.getNbNonZero(),
"The to matrix don't have the same profiles");
memcpy(a.data(), mat.getA().data(), nb_non_zero * sizeof(Real));
this->value_release++;
AKANTU_DEBUG_OUT();
}
/* -------------------------------------------------------------------------- */
void SparseMatrixAIJ::copyProfile(const SparseMatrix & other) {
const auto & A = aka::as_type<SparseMatrixAIJ>(other);
SparseMatrix::clearProfile();
this->irn.copy(A.irn);
this->jcn.copy(A.jcn);
this->irn_jcn_k.clear();
Idx i;
Idx j;
Idx k;
for (auto && data : enumerate(irn, jcn)) {
std::tie(k, i, j) = data;
this->irn_jcn_k[this->key(i - 1, j - 1)] = k;
}
this->nb_non_zero = this->irn.size();
this->a.resize(this->nb_non_zero);
this->a.set(0.);
this->size_ = A.size_;
this->profile_release = A.profile_release;
this->value_release++;
}
/* -------------------------------------------------------------------------- */
template <class MatrixType>
void SparseMatrixAIJ::addMeToTemplated(MatrixType & B, Real alpha) const {
Idx i;
Idx j;
Real A_ij;
for (auto && tuple : zip(irn, jcn, a)) {
std::tie(i, j, A_ij) = tuple;
B.add(i - 1, j - 1, alpha * A_ij);
}
}
/* -------------------------------------------------------------------------- */
void SparseMatrixAIJ::addMeTo(SparseMatrix & B, Real alpha) const {
if (aka::is_of_type<SparseMatrixAIJ>(B)) {
this->addMeToTemplated<SparseMatrixAIJ>(aka::as_type<SparseMatrixAIJ>(B),
alpha);
} else {
// this->addMeToTemplated<SparseMatrix>(*this, alpha);
}
}
/* -------------------------------------------------------------------------- */
void SparseMatrixAIJ::mul(Real alpha) {
this->a *= alpha;
this->value_release++;
}
/* -------------------------------------------------------------------------- */
void SparseMatrixAIJ::set(Real val) {
a.set(val);
this->value_release++;
}
/* -------------------------------------------------------------------------- */
bool SparseMatrixAIJ::isFinite() const { return this->a.isFinite(); }
} // namespace akantu

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