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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 <mpi.h>
#include "static_communicator_mpi.hh"
#endif
#include "solver_mumps.hh"
__BEGIN_AKANTU__
/* -------------------------------------------------------------------------- */
SolverMumps::SolverMumps(SparseMatrix & matrix,
const SolverID & id,
const MemoryID & memory_id) :
Solver(matrix, id, memory_id) {
AKANTU_DEBUG_IN();
UInt size = matrix.getSize();
UInt nb_degre_of_freedom = matrix.getNbDegreOfFreedom();
// std::stringstream sstr; sstr << id << ":sparse_matrix";
// matrix = new SparseMatrix(mesh, sparse_matrix_type, nb_degre_of_freedom, sstr_mat.str(), memory_id);
std::stringstream sstr_rhs; sstr_rhs << id << ":rhs";
mumps_data.sym = 2 * (matrix.getSparseMatrixType() == _symmetric);
communicator = StaticCommunicator::getStaticCommunicator();
#ifdef AKANTU_USE_MPI
mumps_data.par = 1;
mumps_data.comm_fortran = MPI_Comm_c2f(dynamic_cast<const StaticCommunicatorMPI *>(communicator)->getMPICommunicator());
#else
mumps_data.par = 0;
#endif
if(communicator->whoAmI() == 0) {
rhs = &(alloc<Real>(sstr_rhs.str(), size * nb_degre_of_freedom, 1, REAL_INIT_VALUE));
} else {
rhs = NULL;
}
icntl(1) = 2;
icntl(2) = 2;
icntl(3) = 2;
icntl(4) = 1;
if (debug::getDebugLevel() >= 10)
icntl(4) = 4;
else if (debug::getDebugLevel() >= 5)
icntl(4) = 3;
else if (debug::getDebugLevel() >= 3)
icntl(4) = 2;
mumps_data.job = _smj_initialize; //initialize
dmumps_c(&mumps_data);
mumps_data.n = size * nb_degre_of_freedom;
strcpy(mumps_data.write_problem, "mumps_matrix.mtx");
AKANTU_DEBUG_OUT();
}
/* -------------------------------------------------------------------------- */
SolverMumps::~SolverMumps() {
AKANTU_DEBUG_IN();
delete matrix;
mumps_data.job = _smj_destroy; // destroy
dmumps_c(&mumps_data);
AKANTU_DEBUG_OUT();
}
/* -------------------------------------------------------------------------- */
void SolverMumps::initialize() {
AKANTU_DEBUG_IN();
// matrix->buildProfile();
icntl(5) = 0; // Assembled matrix
#ifdef AKANTU_USE_MPI
icntl(18) = 3; //fully distributed
icntl(28) = 0; //parallel analysis
mumps_data.nz_loc = matrix->getNbNonZero();
mumps_data.irn_loc = matrix->getIRN().values;
mumps_data.jcn_loc = matrix->getJCN().values;
// #ifdef AKANTU_USE_PTSCOTCH
// mumps_data.nz_loc = matrix->getNbNonZero();
// mumps_data.irn_loc = matrix->getIRN().values;
// mumps_data.jcn_loc = matrix->getJCN().values;
// icntl(18) = 3; //fully distributed
// icntl(28) = 2; //parallel analysis
// #else // AKANTU_USE_PTSCOTCH
// icntl(18) = 2; // distributed, sequential analysis
// icntl(28) = 1; //sequential analysis
// if (communicator->whoAmI() == 0) {
// Int * nb_non_zero_loc = new Int[communicator->getNbProc()];
// nb_non_zero_loc[0] = matrix->getNbNonZero();
// communicator->gather(nb_non_zero_loc, 1, 0);
// UInt nb_non_zero = 0;
// for (Int i = 0; i < communicator->getNbProc(); ++i) {
// nb_non_zero += nb_non_zero_loc[i];
// }
// Int * irn = new Int[nb_non_zero];
// Int * jcn = new Int[nb_non_zero];
// memcpy(irn, matrix->getIRN().values, *nb_non_zero_loc * sizeof(int));
// memcpy(jcn, matrix->getJCN().values, *nb_non_zero_loc * sizeof(int));
// communicator->gatherv(irn, nb_non_zero_loc, 0);
// communicator->gatherv(jcn, nb_non_zero_loc, 0);
// mumps_data.nz = nb_non_zero;
// mumps_data.irn = irn;
// mumps_data.jcn = jcn;
// } else {
// Int nb_non_zero_loc = matrix->getNbNonZero();
// communicator->gather(&nb_non_zero_loc, 1, 0);
// communicator->gatherv(matrix->getIRN().values, &nb_non_zero_loc, 0);
// communicator->gatherv(matrix->getJCN().values, &nb_non_zero_loc, 0);
// }
// #endif // AKANTU_USE_PTSCOTCH
#else //AKANTU_USE_MPI
mumps_data.nz = matrix->getNbNonZero();
mumps_data.irn = matrix->getIRN().values;
mumps_data.jcn = matrix->getJCN().values;
icntl(18) = 0; //centralized
icntl(28) = 0; //sequential analysis
#endif //AKANTU_USE_MPI
mumps_data.job = _smj_analyze; //analyse
dmumps_c(&mumps_data);
// #ifdef AKANTU_USE_MPI
// #ifdef AKANTU_USE_PTSCOTCH
// delete [] mumps_data.irn;
// delete [] mumps_data.jcn;
// #endif
// #endif
AKANTU_DEBUG_OUT();
}
void SolverMumps::setRHS(Vector<Real> & rhs) {
AKANTU_DEBUG_ASSERT(rhs.getSize() == this->rhs->getSize(),
"Size of rhs and this->rhs do not match.");
memcpy(this->rhs->values, rhs.values, rhs.getSize() * sizeof(Real));
}
/* -------------------------------------------------------------------------- */
void SolverMumps::solve() {
AKANTU_DEBUG_IN();
#ifdef AKANTU_USE_MPI
mumps_data.a_loc = matrix->getA().values;
#else
mumps_data.a = matrix->getA().values;
#endif
if(communicator->whoAmI() == 0) {
mumps_data.rhs = rhs->values;
}
icntl(20) = 0;
icntl(21) = 0;
mumps_data.job = _smj_factorize_solve; //solve
dmumps_c(&mumps_data);
AKANTU_DEBUG_OUT();
}
/* -------------------------------------------------------------------------- */
void SolverMumps::solve(Vector<Real> & solution) {
AKANTU_DEBUG_IN();
solve();
/// @todo spread the rhs vector form host to slaves
if(communicator->whoAmI() == 0) {
memcpy(solution.values, rhs->values, rhs->getSize() * sizeof(Real));
}
AKANTU_DEBUG_OUT();
}
__END_AKANTU__
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