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rAKA/src/solver/sparse_matrix_aij.cc
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sparse_matrix_aij.cc
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/**
* @file sparse_matrix_aij.cc
*
* @author Nicolas Richart <nicolas.richart@epfl.ch>
*
* @date creation: Fri Aug 21 2015
* @date last modification: Mon Dec 04 2017
*
* @brief Implementation of the AIJ sparse matrix
*
* @section LICENSE
*
* Copyright (©) 2015-2018 EPFL (Ecole Polytechnique Fédérale de Lausanne)
* Laboratory (LSMS - Laboratoire de Simulation en Mécanique des Solides)
*
* 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 "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();
// clang-format off
const auto & blocked_dofs = this->dof_manager.getGlobalBlockedDOFs();
for (auto && ij_a : zip(irn, jcn, a)) {
UInt ni = this->dof_manager.globalToLocalEquationNumber(std::get<0>(ij_a) - 1);
UInt nj = this->dof_manager.globalToLocalEquationNumber(std::get<1>(ij_a) - 1);
if (blocked_dofs(ni) || blocked_dofs(nj)) {
std::get<2>(ij_a) =
std::get<0>(ij_a) != std::get<1>(ij_a) ? 0.
: this->dof_manager.isLocalOrMasterDOF(ni) ? block_val
: 0.;
}
}
this->value_release++;
// clang-format on
AKANTU_DEBUG_OUT();
}
/* -------------------------------------------------------------------------- */
void SparseMatrixAIJ::saveProfile(const std::string & filename) const {
AKANTU_DEBUG_IN();
std::ofstream outfile;
outfile.open(filename.c_str());
UInt m = this->size_;
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 (UInt i = 0; i < this->nb_non_zero; ++i) {
outfile << this->irn.storage()[i] << " " << this->jcn.storage()[i] << " 1"
<< std::endl;
}
outfile.close();
AKANTU_DEBUG_OUT();
}
/* -------------------------------------------------------------------------- */
void SparseMatrixAIJ::saveMatrix(const std::string & filename) const {
AKANTU_DEBUG_IN();
// open and set the properties of the stream
std::ofstream outfile;
outfile.open(filename.c_str());
outfile.precision(std::numeric_limits<Real>::digits10);
// write header
outfile << "%%MatrixMarket matrix coordinate real";
if (this->matrix_type == _symmetric)
outfile << " symmetric";
else
outfile << " general";
outfile << std::endl;
outfile << this->size_ << " " << this->size_ << " " << this->nb_non_zero
<< std::endl;
// write content
for (UInt i = 0; i < this->nb_non_zero; ++i) {
outfile << this->irn(i) << " " << this->jcn(i) << " " << this->a(i)
<< std::endl;
}
// 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();
y *= beta;
auto i_it = this->irn.begin();
auto j_it = this->jcn.begin();
auto a_it = this->a.begin();
auto a_end = this->a.end();
auto x_it = x.begin_reinterpret(x.size() * x.getNbComponent());
auto y_it = y.begin_reinterpret(x.size() * x.getNbComponent());
for (; a_it != a_end; ++i_it, ++j_it, ++a_it) {
Int i = this->dof_manager.globalToLocalEquationNumber(*i_it - 1);
Int j = this->dof_manager.globalToLocalEquationNumber(*j_it - 1);
const Real & A = *a_it;
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().reduceSynchronize<AddOperation>(y);
AKANTU_DEBUG_OUT();
}
/* -------------------------------------------------------------------------- */
void SparseMatrixAIJ::matVecMul(const SolverVector & _x, SolverVector & _y,
Real alpha, Real beta) const {
AKANTU_DEBUG_IN();
auto && x = dynamic_cast<const SolverVectorArray &>(_x).getVector();
auto && y = dynamic_cast<SolverVectorArray &>(_y).getVector();
this->matVecMul(x, y, alpha, beta);
}
/* -------------------------------------------------------------------------- */
void SparseMatrixAIJ::copyContent(const SparseMatrix & matrix) {
AKANTU_DEBUG_IN();
const auto & mat = dynamic_cast<const SparseMatrixAIJ &>(matrix);
AKANTU_DEBUG_ASSERT(nb_non_zero == mat.getNbNonZero(),
"The to matrix don't have the same profiles");
memcpy(a.storage(), mat.getA().storage(), nb_non_zero * sizeof(Real));
this->value_release++;
AKANTU_DEBUG_OUT();
}
/* -------------------------------------------------------------------------- */
template <class MatrixType>
void SparseMatrixAIJ::addMeToTemplated(MatrixType & B, Real alpha) const {
UInt i, 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 (auto * B_aij = dynamic_cast<SparseMatrixAIJ *>(&B)) {
this->addMeToTemplated<SparseMatrixAIJ>(*B_aij, alpha);
} else {
// this->addMeToTemplated<SparseMatrix>(*this, alpha);
}
}
/* -------------------------------------------------------------------------- */
void SparseMatrixAIJ::mul(Real alpha) {
this->a *= alpha;
this->value_release++;
}
/* -------------------------------------------------------------------------- */
void SparseMatrixAIJ::clear() {
a.set(0.);
this->value_release++;
}
} // namespace akantu
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