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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 Tue Aug 18 16:31:23 2015
*
* @brief Implementation of the AIJ sparse matrix
*
* @section LICENSE
*
* Copyright (©) 2010-2011 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 "dof_manager_default.hh"
/* -------------------------------------------------------------------------- */
#include <fstream>
/* -------------------------------------------------------------------------- */
__BEGIN_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"),
profile_release(0), value_release(0) {}
/* -------------------------------------------------------------------------- */
SparseMatrixAIJ::SparseMatrixAIJ(const SparseMatrixAIJ & matrix, const ID & id)
: SparseMatrix(matrix, id), dof_manager(matrix.dof_manager),
irn(matrix.irn, true, id + ":irn"), jcn(matrix.jcn, true, id + ":jcn"),
a(matrix.a, true, id + ":a"), profile_release(0), value_release(0) {}
/* -------------------------------------------------------------------------- */
SparseMatrixAIJ::~SparseMatrixAIJ() {}
/* -------------------------------------------------------------------------- */
void SparseMatrixAIJ::applyBoundary(Real block_val) {
AKANTU_DEBUG_IN();
const Array<bool> & blocked_dofs = this->dof_manager.getGlobalBlockedDOFs();
Array<Int>::const_scalar_iterator irn_val = irn.begin();
Array<Int>::const_scalar_iterator jcn_val = jcn.begin();
Array<Real>::scalar_iterator a_val = a.begin();
for (UInt i = 0; i < nb_non_zero; ++i) {
UInt ni = this->dof_manager.globalToLocalEquationNumber(*irn_val - 1);
UInt nj = this->dof_manager.globalToLocalEquationNumber(*jcn_val - 1);
if (blocked_dofs(ni) || blocked_dofs(nj)) {
if (*irn_val != *jcn_val) {
*a_val = 0;
} else {
if (this->dof_manager.isLocalOrMasterDOF(ni)) {
*a_val = block_val;
} else {
*a_val = 0;
}
}
}
++irn_val;
++jcn_val;
++a_val;
}
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());
outfile << "%%MatrixMarket matrix coordinate pattern";
if (this->matrix_type == _symmetric)
outfile << " symmetric";
else
outfile << " general";
outfile << std::endl;
UInt m = this->size;
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();
std::ofstream outfile;
outfile.precision(std::numeric_limits<Real>::digits10);
outfile.open(filename.c_str());
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;
for (UInt i = 0; i < this->nb_non_zero; ++i) {
outfile << this->irn(i) << " " << this->jcn(i) << " " << this->a(i)
<< std::endl;
}
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;
Array<Int>::const_scalar_iterator i_it = this->irn.storage();
Array<Int>::const_scalar_iterator j_it = this->jcn.storage();
Array<Real>::const_scalar_iterator a_it = this->a.storage();
Array<Real>::const_scalar_iterator x_it = x.storage();
Array<Real>::scalar_iterator y_it = y.storage();
for (UInt k = 0; k < this->nb_non_zero; ++k, ++i_it, ++j_it, ++a_it) {
Int i = *i_it - 1;
Int j = *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 (dof_synchronizer)
// dof_synchronizer->reduceSynchronize<AddOperation>(vect);
AKANTU_DEBUG_OUT();
}
/* -------------------------------------------------------------------------- */
void SparseMatrixAIJ::copyContent(const SparseMatrix & matrix) {
AKANTU_DEBUG_IN();
const SparseMatrixAIJ & 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 {
Array<Int>::const_scalar_iterator i_it = this->irn.begin();
Array<Int>::const_scalar_iterator j_it = this->jcn.begin();
Array<Real>::const_scalar_iterator a_it = this->a.begin();
for (UInt n = 0; n < this->nb_non_zero; ++n, ++a_it, ++i_it, ++j_it) {
const Int & i = *i_it;
const Int & j = *j_it;
const Real & A_ij = *a_it;
B.addToMatrix(i - 1, j - 1, alpha * A_ij);
}
}
/* -------------------------------------------------------------------------- */
void SparseMatrixAIJ::addMeTo(SparseMatrix & B, Real alpha) const {
if(SparseMatrixAIJ * 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++;
}
__END_AKANTU__
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