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rAKA akantu
solver_mumps.cc
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/**
* @file solver_mumps.cc
* @author Nicolas Richart <nicolas.richart@epfl.ch>
* @date Wed Nov 17 17:32:27 2010
*
* @brief implem of SolverMumps class
*
* @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/>.
*
* @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
*/
/* -------------------------------------------------------------------------- */
#ifdef AKANTU_USE_MPI
#include "static_communicator_mpi.hh"
#endif
#include "solver_mumps.hh"
#include "dof_synchronizer.hh"
__BEGIN_AKANTU__
/* -------------------------------------------------------------------------- */
SolverMumps::SolverMumps(SparseMatrix & matrix,
const ID & id,
const MemoryID & memory_id) :
Solver(matrix, id, memory_id), rhs_is_local(true) {
AKANTU_DEBUG_IN();
#ifdef AKANTU_USE_MPI
parallel_method = SolverMumpsOptions::_fully_distributed;
#else //AKANTU_USE_MPI
parallel_method = SolverMumpsOptions::_master_slave_distributed;
#endif //AKANTU_USE_MPI
// UInt nb_degree_of_freedom = matrix.getNbDegreOfFreedom();
// std::stringstream sstr; sstr << id << ":sparse_matrix";
// matrix = new SparseMatrix(mesh, sparse_matrix_type, nb_degree_of_freedom, sstr_mat.str(), memory_id);
AKANTU_DEBUG_OUT();
}
/* -------------------------------------------------------------------------- */
SolverMumps::~SolverMumps() {
AKANTU_DEBUG_IN();
mumps_data.job = _smj_destroy; // destroy
dmumps_c(&mumps_data);
AKANTU_DEBUG_OUT();
}
/* -------------------------------------------------------------------------- */
void SolverMumps::initMumpsData(SolverMumpsOptions::ParallelMethod parallel_method) {
switch(parallel_method) {
case SolverMumpsOptions::_fully_distributed:
icntl(18) = 3; //fully distributed
icntl(28) = 0; //automatic choice
mumps_data.nz_loc = matrix->getNbNonZero();
mumps_data.irn_loc = matrix->getIRN().values;
mumps_data.jcn_loc = matrix->getJCN().values;
break;
case SolverMumpsOptions::_master_slave_distributed:
if(prank == 0) {
mumps_data.nz = matrix->getNbNonZero();
mumps_data.irn = matrix->getIRN().values;
mumps_data.jcn = matrix->getJCN().values;
} else {
mumps_data.nz = 0;
mumps_data.irn = NULL;
mumps_data.jcn = NULL;
icntl(18) = 0; //centralized
icntl(28) = 0; //sequential analysis
}
break;
}
}
/* -------------------------------------------------------------------------- */
void SolverMumps::initialize(SolverOptions & options) {
AKANTU_DEBUG_IN();
mumps_data.par = 1;
if(SolverMumpsOptions * opt = dynamic_cast<SolverMumpsOptions *>(&options)) {
if(opt->parallel_method == SolverMumpsOptions::_master_slave_distributed) {
mumps_data.par = 0;
}
}
mumps_data.sym = 2 * (matrix->getSparseMatrixType() == _symmetric);
communicator = StaticCommunicator::getStaticCommunicator();
prank = communicator->whoAmI();
#ifdef AKANTU_USE_MPI
mumps_data.comm_fortran = MPI_Comm_c2f(dynamic_cast<const StaticCommunicatorMPI &>(communicator->getRealStaticCommunicator()).getMPICommunicator());
#endif
if(AKANTU_DEBUG_TEST(dblTrace)) {
icntl(1) = 2;
icntl(2) = 2;
icntl(3) = 2;
icntl(4) = 4;
}
mumps_data.job = _smj_initialize; //initialize
dmumps_c(&mumps_data);
/* ------------------------------------------------------------------------ */
UInt size = matrix->getSize();
if(prank == 0) {
std::stringstream sstr_rhs; sstr_rhs << id << ":rhs";
rhs = &(alloc<Real>(sstr_rhs.str(), size, 1, REAL_INIT_VALUE));
} else {
rhs = NULL;
}
/// No outputs
icntl(1) = 0;
icntl(2) = 0;
icntl(3) = 0;
icntl(4) = 0;
mumps_data.nz_alloc = 0;
if (AKANTU_DEBUG_TEST(dblDump)) icntl(4) = 4;
mumps_data.n = size;
if(AKANTU_DEBUG_TEST(dblDump)) {
strcpy(mumps_data.write_problem, "mumps_matrix.mtx");
}
/* ------------------------------------------------------------------------ */
// Default Scaling
icntl(8) = 77;
icntl(5) = 0; // Assembled matrix
try{
SolverMumpsOptions & opt = dynamic_cast<SolverMumpsOptions & >(options);
parallel_method = opt.parallel_method;
} catch (std::bad_cast & bce) { }
initMumpsData(parallel_method);
mumps_data.job = _smj_analyze; //analyze
dmumps_c(&mumps_data);
AKANTU_DEBUG_OUT();
}
/* -------------------------------------------------------------------------- */
void SolverMumps::setRHS(Vector<Real> & rhs) {
if(prank == 0) {
matrix->getDOFSynchronizer().gather(rhs, 0, this->rhs);
} else {
matrix->getDOFSynchronizer().gather(rhs, 0);
}
}
/* -------------------------------------------------------------------------- */
void SolverMumps::solve() {
AKANTU_DEBUG_IN();
if(parallel_method == SolverMumpsOptions::_fully_distributed)
mumps_data.a_loc = matrix->getA().values;
else
if(prank == 0) {
mumps_data.a = matrix->getA().values;
}
if(prank == 0) {
mumps_data.rhs = rhs->values;
}
/// Default centralized dense second member
icntl(20) = 0;
icntl(21) = 0;
mumps_data.job = _smj_factorize_solve; //solve
dmumps_c(&mumps_data);
AKANTU_DEBUG_ASSERT(info(1) != -10, "Singular matrix");
AKANTU_DEBUG_ASSERT(info(1) == 0,
"Error in mumps during solve process, check mumps user guide INFO(1) ="
<< info(1));
AKANTU_DEBUG_OUT();
}
/* -------------------------------------------------------------------------- */
void SolverMumps::solve(Vector<Real> & solution) {
AKANTU_DEBUG_IN();
solve();
if(prank == 0) {
matrix->getDOFSynchronizer().scatter(solution, 0, this->rhs);
} else {
matrix->getDOFSynchronizer().scatter(solution, 0);
}
AKANTU_DEBUG_OUT();
}
__END_AKANTU__
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