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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 <fstream>
/* -------------------------------------------------------------------------- */
__BEGIN_AKANTU__
/* -------------------------------------------------------------------------- */
SparseMatrixAIJ::SparseMatrixAIJ(DOFManager & dof_manager,
const MatrixType & matrix_type, const ID & id,
const MemoryID & memory_id)
: SparseMatrix(dof_manager, matrix_type, id, memory_id),
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,
const MemoryID & memory_id)
: SparseMatrix(matrix, id, memory_id), 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::buildProfile(const Mesh & mesh,
// const DOFSynchronizer & dof_synchronizer,
// UInt nb_degree_of_freedom) {
// AKANTU_DEBUG_IN();
// // if(irn_jcn_to_k) delete irn_jcn_to_k;
// // irn_jcn_to_k = new std::map<std::pair<UInt, UInt>, UInt>;
// clearProfile();
// this->dof_synchronizer = &const_cast<DOFSynchronizer &>(dof_synchronizer);
// coordinate_list_map::iterator irn_jcn_k_it;
// Int * eq_nb_val = dof_synchronizer.getGlobalDOFEquationNumbers().storage();
// Mesh::type_iterator it =
// mesh.firstType(mesh.getSpatialDimension(), _not_ghost,
// _ek_not_defined);
// Mesh::type_iterator end =
// mesh.lastType(mesh.getSpatialDimension(), _not_ghost, _ek_not_defined);
// for (; it != end; ++it) {
// UInt nb_element = mesh.getNbElement(*it);
// UInt nb_nodes_per_element = Mesh::getNbNodesPerElement(*it);
// UInt size_mat = nb_nodes_per_element * nb_degree_of_freedom;
// UInt * conn_val = mesh.getConnectivity(*it, _not_ghost).storage();
// Int * local_eq_nb_val =
// new Int[nb_degree_of_freedom * nb_nodes_per_element];
// for (UInt e = 0; e < nb_element; ++e) {
// Int * tmp_local_eq_nb_val = local_eq_nb_val;
// for (UInt i = 0; i < nb_nodes_per_element; ++i) {
// UInt n = conn_val[i];
// for (UInt d = 0; d < nb_degree_of_freedom; ++d) {
// *tmp_local_eq_nb_val++ = eq_nb_val[n * nb_degree_of_freedom + d];
// }
// // memcpy(tmp_local_eq_nb_val, eq_nb_val + n * nb_degree_of_freedom,
// // nb_degree_of_freedom * sizeof(Int));
// // tmp_local_eq_nb_val += nb_degree_of_freedom;
// }
// for (UInt i = 0; i < size_mat; ++i) {
// UInt c_irn = local_eq_nb_val[i];
// if (c_irn < this->size) {
// UInt j_start = (sparse_matrix_type == _symmetric) ? i : 0;
// for (UInt j = j_start; j < size_mat; ++j) {
// UInt c_jcn = local_eq_nb_val[j];
// if (c_jcn < this->size) {
// KeyCOO irn_jcn = key(c_irn, c_jcn);
// irn_jcn_k_it = irn_jcn_k.find(irn_jcn);
// if (irn_jcn_k_it == irn_jcn_k.end()) {
// irn_jcn_k[irn_jcn] = nb_non_zero;
// irn.push_back(c_irn + 1);
// jcn.push_back(c_jcn + 1);
// nb_non_zero++;
// }
// }
// }
// }
// }
// conn_val += nb_nodes_per_element;
// }
// delete[] local_eq_nb_val;
// }
// /// for pbc @todo correct it for parallel
// if (StaticCommunicator::getStaticCommunicator().getNbProc() == 1) {
// for (UInt i = 0; i < size; ++i) {
// KeyCOO irn_jcn = key(i, i);
// irn_jcn_k_it = irn_jcn_k.find(irn_jcn);
// if (irn_jcn_k_it == irn_jcn_k.end()) {
// irn_jcn_k[irn_jcn] = nb_non_zero;
// irn.push_back(i + 1);
// jcn.push_back(i + 1);
// nb_non_zero++;
// }
// }
// }
// a.resize(nb_non_zero);
// AKANTU_DEBUG_OUT();
// }
// /* --------------------------------------------------------------------------
// */
// void SparseMatrixAIJ::applyBoundary(const Array<bool> & boundary,
// Real block_val) {
// AKANTU_DEBUG_IN();
// const DOFSynchronizer::GlobalEquationNumberMap & local_eq_num_to_global =
// dof_synchronizer->getGlobalEquationNumberToLocal();
// Int * irn_val = irn.storage();
// Int * jcn_val = jcn.storage();
// Real * a_val = a.storage();
// for (UInt i = 0; i < nb_non_zero; ++i) {
// /// @todo fix this hack, put here for the implementation of augmented
// /// lagrangian contact
// if (local_eq_num_to_global.find(*irn_val - 1) ==
// local_eq_num_to_global.end())
// continue;
// if (local_eq_num_to_global.find(*jcn_val - 1) ==
// local_eq_num_to_global.end())
// continue;
// UInt ni = local_eq_num_to_global.find(*irn_val - 1)->second;
// UInt nj = local_eq_num_to_global.find(*jcn_val - 1)->second;
// if (boundary.storage()[ni] || boundary.storage()[nj]) {
// if (*irn_val != *jcn_val) { *a_val = 0; } else {
// if (dof_synchronizer->getDOFTypes()(ni) >= 0) { *a_val = 0; } else {
// *a_val = block_val;
// }
// }
// }
// irn_val++;
// jcn_val++;
// a_val++;
// }
// 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) {
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) {
const Int & i = *i_it;
const Int & j = *j_it;
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();
}
/* -------------------------------------------------------------------------- */
void SparseMatrixAIJ::add(const SparseMatrix & B, Real alpha) {
Array<Real>::scalar_iterator a_it = this->a.storage();
Array<Int>::const_scalar_iterator i_it = this->irn.storage();
Array<Int>::const_scalar_iterator j_it = this->jcn.storage();
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;
Real & A_ij = *a_it;
A_ij += alpha * B(i - 1, j - 1);
}
this->value_release++;
}
/* -------------------------------------------------------------------------- */
void SparseMatrixAIJ::clear() {
memset(a.storage(), 0, nb_non_zero * sizeof(Real));
this->value_release++;
}
__END_AKANTU__
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