sparse_solver_mumps.cc
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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
	*
	* @section LICENSE
	*
	* 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/>.
	*
	* @section DESCRIPTION
	*
	* @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 "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,
	const MemoryID & memory_id)
	: SparseSolver(dof_manager, matrix_id, id, memory_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() {
	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 = dynamic_cast<const 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 * (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(this->dof_manager.getGlobalResidual());

	this->solveInternal();

	this->dof_manager.setGlobalSolution(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)) {
	std::stringstream sstr;
	sstr << prank << ".mtx";
	A.saveMatrix("solver_mumps" + sstr.str());
	}

	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]);
	}
	}

	} // akantu

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