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sparse_solver_mumps.cc
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			/**
			* @file sparse_solver_mumps.cc
			*
			* @author Nicolas Richart <nicolas.richart@epfl.ch>
			*
			* @date creation: Mon Dec 13 2010
			* @date last modification: Tue Feb 20 2018
			*
			* @brief implem of SparseSolverMumps class
			*
			*
			* Copyright (©) 2010-2018 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/>.
			*
			*
			* @subsection Ctrl_param Control parameters
			*
			* ICNTL(1),
			* ICNTL(2),
			* ICNTL(3) : output streams for error, diagnostics, and global messages
			*
			* ICNTL(4) : verbose level : 0 no message - 4 all messages
			*
			* ICNTL(5) : type of matrix, 0 assembled, 1 elementary
			*
			* ICNTL(6) : control the permutation and scaling(default 7) see mumps doc for
			* more information
			*
			* ICNTL(7) : determine the pivot order (default 7) see mumps doc for more
			* information
			*
			* ICNTL(8) : describe the scaling method used
			*
			* ICNTL(9) : 1 solve A x = b, 0 solve At x = b
			*
			* ICNTL(10) : number of iterative refinement when NRHS = 1
			*
			* ICNTL(11) : > 0 return statistics
			*
			* ICNTL(12) : only used for SYM = 2, ordering strategy
			*
			* ICNTL(13) :
			*
			* ICNTL(14) : percentage of increase of the estimated working space
			*
			* ICNTL(15-17) : not used
			*
			* ICNTL(18) : only used if ICNTL(5) = 0, 0 matrix centralized, 1 structure on
			* host and mumps give the mapping, 2 structure on host and distributed matrix
			* for facto, 3 distributed matrix
			*
			* ICNTL(19) : > 0, Shur complement returned
			*
			* ICNTL(20) : 0 rhs dense, 1 rhs sparse
			*
			* ICNTL(21) : 0 solution in rhs, 1 solution distributed in ISOL_loc and SOL_loc
			* allocated by user
			*
			* ICNTL(22) : 0 in-core, 1 out-of-core
			*
			* ICNTL(23) : maximum memory allocatable by mumps pre proc
			*
			* ICNTL(24) : controls the detection of "null pivot rows"
			*
			* ICNTL(25) :
			*
			* ICNTL(26) :
			*
			* ICNTL(27) :
			*
			* ICNTL(28) : 0 automatic choice, 1 sequential analysis, 2 parallel analysis
			*
			* ICNTL(29) : 0 automatic choice, 1 PT-Scotch, 2 ParMetis
			*/

			/* -------------------------------------------------------------------------- */
			#include "aka_common.hh"
			#include "dof_manager_default.hh"
			#include "dof_synchronizer.hh"
			#include "solver_vector_default.hh"
			#include "sparse_matrix_aij.hh"
			#if defined(AKANTU_USE_MPI)
			#include "mpi_communicator_data.hh"
			#endif
			#include "sparse_solver_mumps.hh"
			/* -------------------------------------------------------------------------- */

			/* -------------------------------------------------------------------------- */
			// static std::ostream & operator <<(std::ostream & stream, const DMUMPS_STRUC_C
			// & _this) {
			// stream << "DMUMPS Data [" << std::endl;
			// stream << " + job : " << _this.job << std::endl;
			// stream << " + par : " << _this.par << std::endl;
			// stream << " + sym : " << _this.sym << std::endl;
			// stream << " + comm_fortran : " << _this.comm_fortran << std::endl;
			// stream << " + nz : " << _this.nz << std::endl;
			// stream << " + irn : " << _this.irn << std::endl;
			// stream << " + jcn : " << _this.jcn << std::endl;
			// stream << " + nz_loc : " << _this.nz_loc << std::endl;
			// stream << " + irn_loc : " << _this.irn_loc << std::endl;
			// stream << " + jcn_loc : " << _this.jcn_loc << std::endl;
			// stream << "]";
			// return stream;
			// }

			namespace akantu {

			/* -------------------------------------------------------------------------- */
			SparseSolverMumps::SparseSolverMumps(DOFManagerDefault & dof_manager,
			const ID & matrix_id, const ID & id)
			: SparseSolver(dof_manager, matrix_id, id),
			dof_manager(dof_manager), master_rhs_solution(0, 1) {
			AKANTU_DEBUG_IN();

			this->prank = communicator.whoAmI();

			#ifdef AKANTU_USE_MPI
			this->parallel_method = _fully_distributed;
			#else // AKANTU_USE_MPI
			this->parallel_method = _not_parallel;
			#endif // AKANTU_USE_MPI

			AKANTU_DEBUG_OUT();
			}

			/* -------------------------------------------------------------------------- */
			SparseSolverMumps::~SparseSolverMumps() {
			AKANTU_DEBUG_IN();

			mumpsDataDestroy();

			AKANTU_DEBUG_OUT();
			}

			/* -------------------------------------------------------------------------- */
			void SparseSolverMumps::mumpsDataDestroy() {
			#ifdef AKANTU_USE_MPI
			int finalized = 0;
			MPI_Finalized(&finalized);
			if (finalized != 0) { // Da fuck !?
			return;
			}
			#endif

			if (this->is_initialized) {
			this->mumps_data.job = _smj_destroy; // destroy
			dmumps_c(&this->mumps_data);
			this->is_initialized = false;
			}
			}

			/* -------------------------------------------------------------------------- */
			void SparseSolverMumps::destroyInternalData() { mumpsDataDestroy(); }

			/* -------------------------------------------------------------------------- */
			void SparseSolverMumps::checkInitialized() {
			if (this->is_initialized) {
			return;
			}

			this->initialize();
			}

			/* -------------------------------------------------------------------------- */
			void SparseSolverMumps::setOutputLevel() {
			// Output setup
			icntl(1) = 0; // error output
			icntl(2) = 0; // diagnostics output
			icntl(3) = 0; // information
			icntl(4) = 0;

			#if !defined(AKANTU_NDEBUG)
			DebugLevel dbg_lvl = debug::debugger.getDebugLevel();

			if (AKANTU_DEBUG_TEST(dblDump)) {
			strcpy(this->mumps_data.write_problem, "mumps_matrix.mtx");
			}

			// clang-format off
			icntl(1) = (dbg_lvl >= dblWarning) ? 6 : 0;
			icntl(3) = (dbg_lvl >= dblInfo) ? 6 : 0;
			icntl(2) = (dbg_lvl >= dblTrace) ? 6 : 0;

			icntl(4) =
			dbg_lvl >= dblDump ? 4 :
			dbg_lvl >= dblTrace ? 3 :
			dbg_lvl >= dblInfo ? 2 :
			dbg_lvl >= dblWarning ? 1 :
			0;

			// clang-format on
			#endif
			}

			/* -------------------------------------------------------------------------- */
			void SparseSolverMumps::initMumpsData() {
			auto & A = dof_manager.getMatrix(matrix_id);

			// Default Scaling
			icntl(8) = 77;

			// Assembled matrix
			icntl(5) = 0;

			/// Default centralized dense second member
			icntl(20) = 0;
			icntl(21) = 0;

			// automatic choice for analysis
			icntl(28) = 0;

			UInt size = A.size();

			if (prank == 0) {
			this->master_rhs_solution.resize(size);
			}

			this->mumps_data.nz_alloc = 0;
			this->mumps_data.n = size;

			switch (this->parallel_method) {
			case _fully_distributed:
			icntl(18) = 3; // fully distributed

			this->mumps_data.nz_loc = A.getNbNonZero();
			this->mumps_data.irn_loc = A.getIRN().storage();
			this->mumps_data.jcn_loc = A.getJCN().storage();

			break;
			case _not_parallel:
			case _master_slave_distributed:
			icntl(18) = 0; // centralized

			if (prank == 0) {
			this->mumps_data.nz = A.getNbNonZero();
			this->mumps_data.irn = A.getIRN().storage();
			this->mumps_data.jcn = A.getJCN().storage();
			} else {
			this->mumps_data.nz = 0;
			this->mumps_data.irn = nullptr;
			this->mumps_data.jcn = nullptr;
			}
			break;
			default:
			AKANTU_ERROR("This case should not happen!!");
			}
			}

			/* -------------------------------------------------------------------------- */
			void SparseSolverMumps::initialize() {
			AKANTU_DEBUG_IN();

			this->mumps_data.par = 1; // The host is part of computations

			switch (this->parallel_method) {
			case _not_parallel:
			break;
			case _master_slave_distributed:
			this->mumps_data.par = 0; // The host is not part of the computations
			/* FALLTHRU */
			/* [[fallthrough]]; un-comment when compiler will get it */
			case _fully_distributed:
			#ifdef AKANTU_USE_MPI
			const auto & mpi_data =
			aka::as_type<MPICommunicatorData>(communicator.getCommunicatorData());
			MPI_Comm mpi_comm = mpi_data.getMPICommunicator();
			this->mumps_data.comm_fortran = MPI_Comm_c2f(mpi_comm);
			#else
			AKANTU_ERROR(
			"You cannot use parallel method to solve without activating MPI");
			#endif
			break;
			}

			const auto & A = dof_manager.getMatrix(matrix_id);

			this->mumps_data.sym = 2 * static_cast<int>(A.getMatrixType() == _symmetric);
			this->prank = communicator.whoAmI();

			this->setOutputLevel();

			this->mumps_data.job = _smj_initialize; // initialize
			dmumps_c(&this->mumps_data);

			this->setOutputLevel();

			this->is_initialized = true;

			AKANTU_DEBUG_OUT();
			}

			/* -------------------------------------------------------------------------- */
			void SparseSolverMumps::analysis() {
			AKANTU_DEBUG_IN();

			initMumpsData();

			this->mumps_data.job = _smj_analyze; // analyze
			dmumps_c(&this->mumps_data);

			AKANTU_DEBUG_OUT();
			}

			/* -------------------------------------------------------------------------- */
			void SparseSolverMumps::factorize() {
			AKANTU_DEBUG_IN();

			auto & A = dof_manager.getMatrix(matrix_id);

			if (parallel_method == _fully_distributed) {
			this->mumps_data.a_loc = A.getA().storage();
			} else {
			if (prank == 0) {
			this->mumps_data.a = A.getA().storage();
			}
			}

			this->mumps_data.job = _smj_factorize; // factorize
			dmumps_c(&this->mumps_data);

			this->printError();

			AKANTU_DEBUG_OUT();
			}

			/* -------------------------------------------------------------------------- */
			void SparseSolverMumps::solve(Array<Real> & x, const Array<Real> & b) {
			auto & synch = this->dof_manager.getSynchronizer();

			if (this->prank == 0) {
			this->master_rhs_solution.resize(this->dof_manager.getSystemSize());
			synch.gather(b, this->master_rhs_solution);
			} else {
			synch.gather(b);
			}

			this->solveInternal();

			if (this->prank == 0) {
			synch.scatter(x, this->master_rhs_solution);
			} else {
			synch.scatter(x);
			}
			}

			/* -------------------------------------------------------------------------- */
			void SparseSolverMumps::solve() {
			this->master_rhs_solution.copy(
			aka::as_type<SolverVectorDefault>(this->dof_manager.getResidual())
			.getGlobalVector());

			this->solveInternal();

			aka::as_type<SolverVectorDefault>(this->dof_manager.getSolution())
			.setGlobalVector(this->master_rhs_solution);

			this->dof_manager.splitSolutionPerDOFs();
			}

			/* -------------------------------------------------------------------------- */
			void SparseSolverMumps::solveInternal() {
			AKANTU_DEBUG_IN();

			this->checkInitialized();

			const auto & A = dof_manager.getMatrix(matrix_id);

			this->setOutputLevel();

			if (this->last_profile_release != A.getProfileRelease()) {
			this->analysis();
			this->last_profile_release = A.getProfileRelease();
			}

			if (AKANTU_DEBUG_TEST(dblDump)) {
			A.saveMatrix("solver_mumps" + std::to_string(prank) + ".mtx");
			}

			if (this->last_value_release != A.getValueRelease()) {
			this->factorize();
			this->last_value_release = A.getValueRelease();
			}

			if (prank == 0) {
			this->mumps_data.rhs = this->master_rhs_solution.storage();
			}

			this->mumps_data.job = _smj_solve; // solve
			dmumps_c(&this->mumps_data);

			this->printError();

			AKANTU_DEBUG_OUT();
			}

			/* -------------------------------------------------------------------------- */
			void SparseSolverMumps::printError() {
			Vector<Int> _info_v(2);
			_info_v[0] = info(1); // to get errors
			_info_v[1] = -info(1); // to get warnings
			dof_manager.getCommunicator().allReduce(_info_v, SynchronizerOperation::_min);
			_info_v[1] = -_info_v[1];

			if (_info_v[0] < 0) { // < 0 is an error
			switch (_info_v[0]) {
			case -10: {
			AKANTU_CUSTOM_EXCEPTION(
			debug::SingularMatrixException(dof_manager.getMatrix(matrix_id)));
			break;
			}
			case -9: {
			icntl(14) += 10;
			if (icntl(14) != 90) {
			// std::cout << "Dynamic memory increase of 10%" << std::endl;
			AKANTU_DEBUG_WARNING("MUMPS dynamic memory is insufficient it will be "
			"increased allowed to use 10% more");

			// change releases to force a recompute
			this->last_value_release--;
			this->last_profile_release--;

			this->solve();
			} else {
			AKANTU_ERROR("The MUMPS workarray is too small INFO(2)="
			<< info(2) << "No further increase possible");
			}
			break;
			}
			default:
			AKANTU_ERROR("Error in mumps during solve process, check mumps "
			"user guide INFO(1) = "
			<< _info_v[1]);
			}
			} else if (_info_v[1] > 0) {
			AKANTU_DEBUG_WARNING("Warning in mumps during solve process, check mumps "
			"user guide INFO(1) = "
			<< _info_v[1]);
			}
			}

			} // namespace akantu

rAKA/src/solver/sparse_solver_mumps.cc205db61909bcbugfixes/41-group-not…

sparse_solver_mumps.ccView Options

rAKA/src/solver/sparse_solver_mumps.cc
205db61909bcbugfixes/41-group-not…

sparse_solver_mumps.cc
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