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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();

			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)) {
			UInt ni = std::get<0>(ij_a) - 1;
			UInt nj = std::get<1>(ij_a) - 1;

			if (is_blocked(ni) or is_blocked(nj)) {
			// clang-format off
			std::get<2>(ij_a) =
			std::get<0>(ij_a) != std::get<1>(ij_a) ? 0.
			: this->dof_manager.isLocalOrMasterDOF(ni) ? block_val
			: 0.;
			// clang-format on
			}
			}

			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());

			UInt 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 (UInt i = 0; i < this->nb_non_zero; ++i) {
			outfile << this->irn.storage()[i] << " " << this->jcn.storage()[i]
			<< " 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 (UInt i = 0; i < this->nb_non_zero; ++i) {
			outfile << this->irn(i) << " " << this->jcn(i) << " " << this->a(i)
			<< 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();

			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 = 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.storage(), mat.getA().storage(), nb_non_zero * sizeof(Real));

			this->value_release++;

			AKANTU_DEBUG_OUT();
			}

			/* -------------------------------------------------------------------------- */
			void SparseMatrixAIJ::copyProfile(const SparseMatrix & other) {
			auto & A = aka::as_type<SparseMatrixAIJ>(other);

			SparseMatrix::clearProfile();

			this->irn.copy(A.irn);
			this->jcn.copy(A.jcn);

			this->irn_jcn_k.clear();

			UInt i, j, 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 {
			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 (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::clear() {
			a.set(0.);

			this->value_release++;
			}

			} // namespace akantu

rAKA/src/solver/sparse_matrix_aij.cc8a8cbaa53817bugfixes/41-group-not…

sparse_matrix_aij.ccView Options

rAKA/src/solver/sparse_matrix_aij.cc
8a8cbaa53817bugfixes/41-group-not…

sparse_matrix_aij.cc
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