Homec4science
Diffusion akantu (features/contact-unification) Browse

rAKA/src/solver/solver_petsc.cc
e852b7d54eaefeatures/contact…

Last Change
Raw File

solver_petsc.cc
View Options

// /**
// * @file solver_petsc.cc
// *
// * @author Nicolas Richart <nicolas.richart@epfl.ch>
// # @author Alejandro M. Aragón <alejandro.aragon@epfl.ch>
// *
// * @date Mon Dec 13 10:48:06 2010
// *
// * @brief Solver class implementation for the petsc solver
// *
// * @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/>.
// *
// */
// /* -------------------------------------------------------------------------- */
// #ifdef AKANTU_USE_MPI
// #include "static_communicator_mpi.hh"
// #endif
// #include "solver_petsc.hh"
// #include "dof_synchronizer.hh"
// __BEGIN_AKANTU__
// void finalize_petsc() {
// static bool finalized = false;
// if (!finalized) {
// cout<<"*** INFO *** PETSc is finalizing..."<<endl;
// // finalize PETSc
// PetscErrorCode ierr = PetscFinalize();CHKERRCONTINUE(ierr);
// finalized = true;
// }
// }
// SolverPETSc::sparse_vector_type
// SolverPETSc::operator()(const SolverPETSc::sparse_matrix_type& AA,
// const SolverPETSc::sparse_vector_type& bb) {
// #ifdef CPPUTILS_VERBOSE
// // parallel output stream
// Output_stream out;
// // timer
// cpputils::ctimer timer;
// out<<"Inside PETSc solver: "<<timer<<endl;
// #endif
// #ifdef CPPUTILS_VERBOSE
// out<<" Inside operator()(const sparse_matrix_type&, const sparse_vector_type&)... "<<timer<<endl;
// #endif
// assert(AA.rows() == bb.size());
// // KSP ksp; //!< linear solver context
// Vec b; /* RHS */
// PC pc; /* preconditioner context */
// PetscErrorCode ierr;
// PetscInt nlocal;
// PetscInt n = bb.size();
// VecScatter ctx;
// /*
// Create vectors. Note that we form 1 vector from scratch and
// then duplicate as needed. For this simple case let PETSc decide how
// many elements of the vector are stored on each processor. The second
// argument to VecSetSizes() below causes PETSc to decide.
// */
// ierr = VecCreate(PETSC_COMM_WORLD,&b);CHKERRCONTINUE(ierr);
// ierr = VecSetSizes(b,PETSC_DECIDE,n);CHKERRCONTINUE(ierr);
// ierr = VecSetFromOptions(b);CHKERRCONTINUE(ierr);
// if (!allocated_) {
// ierr = VecDuplicate(b,&x_);CHKERRCONTINUE(ierr);
// } else
// VecZeroEntries(x_);
// #ifdef CPPUTILS_VERBOSE
// out<<" Vectors created... "<<timer<<endl;
// #endif
// /* Set hight-hand-side vector */
// for (sparse_vector_type::const_hash_iterator it = bb.map_.begin(); it != bb.map_.end(); ++it) {
// int row = it->first;
// ierr = VecSetValues(b, 1, &row, &it->second, ADD_VALUES);
// }
// #ifdef CPPUTILS_VERBOSE
// out<<" Right hand side set... "<<timer<<endl;
// #endif
// /*
// Assemble vector, using the 2-step process:
// VecAssemblyBegin(), VecAssemblyEnd()
// Computations can be done while messages are in transition
// by placing code between these two statements.
// */
// ierr = VecAssemblyBegin(b);CHKERRCONTINUE(ierr);
// ierr = VecAssemblyEnd(b);CHKERRCONTINUE(ierr);
// #ifdef CPPUTILS_VERBOSE
// out<<" Right-hand-side vector assembled... "<<timer<<endl;
// ierr = VecView(b,PETSC_VIEWER_STDOUT_WORLD);CHKERRCONTINUE(ierr);
// Vec b_all;
// ierr = VecScatterCreateToAll(b, &ctx, &b_all);CHKERRCONTINUE(ierr);
// ierr = VecScatterBegin(ctx,b,b_all,INSERT_VALUES,SCATTER_FORWARD);CHKERRCONTINUE(ierr);
// ierr = VecScatterEnd(ctx,b,b_all,INSERT_VALUES,SCATTER_FORWARD);CHKERRCONTINUE(ierr);
// value_type nrm;
// VecNorm(b_all,NORM_2,&nrm);
// out<<" Norm of right hand side... "<<nrm<<endl;
// #endif
// /* Identify the starting and ending mesh points on each
// processor for the interior part of the mesh. We let PETSc decide
// above. */
// // PetscInt rstart,rend;
// // ierr = VecGetOwnershipRange(x_,&rstart,&rend);CHKERRCONTINUE(ierr);
// ierr = VecGetLocalSize(x_,&nlocal);CHKERRCONTINUE(ierr);
// /*
// Create matrix. When using MatCreate(), the matrix format can
// be specified at runtime.
// Performance tuning note: For problems of substantial size,
// preallocation of matrix memory is crucial for attaining good
// performance. See the matrix chapter of the users manual for details.
// We pass in nlocal as the "local" size of the matrix to force it
// to have the same parallel layout as the vector created above.
// */
// if (!allocated_) {
// ierr = MatCreate(PETSC_COMM_WORLD,&A_);CHKERRCONTINUE(ierr);
// ierr = MatSetSizes(A_,nlocal,nlocal,n,n);CHKERRCONTINUE(ierr);
// ierr = MatSetFromOptions(A_);CHKERRCONTINUE(ierr);
// ierr = MatSetUp(A_);CHKERRCONTINUE(ierr);
// } else {
// // zero-out matrix
// MatZeroEntries(A_);
// }
// /*
// Assemble matrix.
// The linear system is distributed across the processors by
// chunks of contiguous rows, which correspond to contiguous
// sections of the mesh on which the problem is discretized.
// For matrix assembly, each processor contributes entries for
// the part that it owns locally.
// */
// #ifdef CPPUTILS_VERBOSE
// out<<" Zeroed-out sparse matrix entries... "<<timer<<endl;
// #endif
// for (sparse_matrix_type::const_hash_iterator it = AA.map_.begin(); it != AA.map_.end(); ++it) {
// // get subscripts
// std::pair<size_t,size_t> subs = AA.unhash(it->first);
// PetscInt row = subs.first;
// PetscInt col = subs.second;
// ierr = MatSetValues(A_, 1, &row, 1, &col, &it->second, ADD_VALUES);CHKERRCONTINUE(ierr);
// }
// #ifdef CPPUTILS_VERBOSE
// out<<" Filled sparse matrix... "<<timer<<endl;
// #endif
// /* Assemble the matrix */
// ierr = MatAssemblyBegin(A_,MAT_FINAL_ASSEMBLY);CHKERRCONTINUE(ierr);
// ierr = MatAssemblyEnd(A_,MAT_FINAL_ASSEMBLY);CHKERRCONTINUE(ierr);
// if (!allocated_) {
// // set after the first MatAssemblyEnd
// // ierr = MatSetOption(A_, MAT_NEW_NONZERO_LOCATIONS, PETSC_FALSE);CHKERRCONTINUE(ierr);
// ierr = MatSetOption(A_, MAT_NEW_NONZERO_ALLOCATION_ERR, PETSC_FALSE);CHKERRCONTINUE(ierr);
// }
// #ifdef CPPUTILS_VERBOSE
// out<<" Sparse matrix assembled... "<<timer<<endl;
// // view matrix
// MatView(A_, PETSC_VIEWER_STDOUT_WORLD);
// MatNorm(A_,NORM_FROBENIUS,&nrm);
// out<<" Norm of stiffness matrix... "<<nrm<<endl;
// #endif
// /*
// Set operators. Here the matrix that defines the linear system
// also serves as the preconditioning matrix.
// */
// // ierr = KSPSetOperators(ksp,A_,A_,DIFFERENT_NONZERO_PATTERN);CHKERRCONTINUE(ierr);
// ierr = KSPSetOperators(ksp_,A_,A_,SAME_NONZERO_PATTERN);CHKERRCONTINUE(ierr);
// //
// // /*
// // Set runtime options, e.g.,
// // -ksp_type <type> -pc_type <type> -ksp_monitor -ksp_rtol <rtol>
// // These options will override those specified above as long as
// // KSPSetFromOptions() is called _after_ any other customization
// // routines.
// // */
// // ierr = KSPSetFromOptions(ksp);CHKERRCONTINUE(ierr);
// #ifdef CPPUTILS_VERBOSE
// out<<" Solving system... "<<timer<<endl;
// #endif
// /* - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
// Solve the linear system
// - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - */
// /*
// Solve linear system
// */
// ierr = KSPSolve(ksp_,b,x_);CHKERRCONTINUE(ierr);
// #ifdef CPPUTILS_VERBOSE
// /*
// View solver info; we could instead use the option -ksp_view to
// print this info to the screen at the conclusion of KSPSolve().
// */
// ierr = KSPView(ksp_,PETSC_VIEWER_STDOUT_WORLD);CHKERRCONTINUE(ierr);
// int iter;
// KSPGetIterationNumber(ksp_, &iter);
// out<<" System solved in "<<iter<<" iterations... "<<timer<<endl;
// KSPConvergedReason reason;
// ierr = KSPGetConvergedReason(ksp_,&reason);CHKERRCONTINUE(ierr);
// if (reason < 0)
// out<<"*** WARNING *** PETSc solver diverged with flag ";
// else
// out<<"*** INFO *** PETSc solver converged with flag ";
// if (reason == KSP_CONVERGED_RTOL)
// out<<"KSP_CONVERGED_RTOL"<<endl;
// else if (reason == KSP_CONVERGED_ATOL)
// out<<"KSP_CONVERGED_ATOL"<<endl;
// else if (reason == KSP_CONVERGED_ITS)
// out<<"KSP_CONVERGED_ITS"<<endl;
// else if (reason == KSP_CONVERGED_CG_NEG_CURVE)
// out<<"KSP_CONVERGED_CG_NEG_CURVE"<<endl;
// else if (reason == KSP_CONVERGED_CG_CONSTRAINED)
// out<<"KSP_CONVERGED_CG_CONSTRAINED"<<endl;
// else if (reason == KSP_CONVERGED_STEP_LENGTH)
// out<<"KSP_CONVERGED_STEP_LENGTH"<<endl;
// else if (reason == KSP_CONVERGED_HAPPY_BREAKDOWN)
// out<<"KSP_CONVERGED_HAPPY_BREAKDOWN"<<endl;
// else if (reason == KSP_DIVERGED_NULL)
// out<<"KSP_DIVERGED_NULL"<<endl;
// else if (reason == KSP_DIVERGED_ITS)
// out<<"KSP_DIVERGED_ITS"<<endl;
// else if (reason == KSP_DIVERGED_DTOL)
// out<<"KSP_DIVERGED_DTOL"<<endl;
// else if (reason == KSP_DIVERGED_BREAKDOWN)
// out<<"KSP_DIVERGED_BREAKDOWN"<<endl;
// else if (reason == KSP_DIVERGED_BREAKDOWN_BICG)
// out<<"KSP_DIVERGED_BREAKDOWN_BICG"<<endl;
// else if (reason == KSP_DIVERGED_NONSYMMETRIC)
// out<<"KSP_DIVERGED_NONSYMMETRIC"<<endl;
// else if (reason == KSP_DIVERGED_INDEFINITE_PC)
// out<<"KSP_DIVERGED_INDEFINITE_PC"<<endl;
// else if (reason == KSP_DIVERGED_NAN)
// out<<"KSP_DIVERGED_NAN"<<endl;
// else if (reason == KSP_DIVERGED_INDEFINITE_MAT)
// out<<"KSP_DIVERGED_INDEFINITE_MAT"<<endl;
// else if (reason == KSP_CONVERGED_ITERATING)
// out<<"KSP_CONVERGED_ITERATING"<<endl;
// PetscReal rnorm;
// ierr = KSPGetResidualNorm(ksp_,&rnorm);CHKERRCONTINUE(ierr);
// out<<"PETSc last residual norm computed: "<<rnorm<<endl;
// ierr = VecView(x_,PETSC_VIEWER_STDOUT_WORLD);CHKERRCONTINUE(ierr);
// VecNorm(x_,NORM_2,&nrm);
// out<<" Norm of solution vector... "<<nrm<<endl;
// #endif
// /* - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
// Check solution and clean up
// - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - */
// Vec x_all;
// ierr = VecScatterCreateToAll(x_, &ctx, &x_all);CHKERRCONTINUE(ierr);
// ierr = VecScatterBegin(ctx,x_,x_all,INSERT_VALUES,SCATTER_FORWARD);CHKERRCONTINUE(ierr);
// ierr = VecScatterEnd(ctx,x_,x_all,INSERT_VALUES,SCATTER_FORWARD);CHKERRCONTINUE(ierr);
// #ifdef CPPUTILS_VERBOSE
// out<<" Solution vector scattered... "<<timer<<endl;
// VecNorm(x_all,NORM_2,&nrm);
// out<<" Norm of scattered vector... "<<nrm<<endl;
// // ierr = VecView(x_all,PETSC_VIEWER_STDOUT_WORLD);CHKERRCONTINUE(ierr);
// #endif
// /* - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
// Get values from solution and store them in the object that will be
// returned
// - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - */
// sparse_vector_type xx(bb.size());
// /* Set solution vector */
// double zero = 0.;
// double val;
// for (sparse_vector_type::const_hash_iterator it = bb.map_.begin(); it != bb.map_.end(); ++it) {
// int row = it->first;
// ierr = VecGetValues(x_all, 1, &row, &val);
// if (val != zero)
// xx[row] = val;
// }
// #ifdef CPPUTILS_VERBOSE
// out<<" Solution vector copied... "<<timer<<endl;
// // out<<" Norm of copied solution vector... "<<norm(xx, Insert_t)<<endl;
// #endif
// /*
// Free work space. All PETSc objects should be destroyed when they
// are no longer needed.
// */
// ierr = VecDestroy(&b);CHKERRCONTINUE(ierr);
// // ierr = KSPDestroy(&ksp);CHKERRCONTINUE(ierr);
// // set allocated flag
// if (!allocated_) {
// allocated_ = true;
// }
// #ifdef CPPUTILS_VERBOSE
// out<<" Temporary data structures destroyed... "<<timer<<endl;
// #endif
// return xx;
// }
// SolverPETSc::sparse_vector_type SolverPETSc::operator()(const SolverPETSc::sparse_matrix_type& KKpf, const SolverPETSc::sparse_matrix_type& KKpp, const SolverPETSc::sparse_vector_type& UUp) {
// #ifdef CPPUTILS_VERBOSE
// // parallel output stream
// Output_stream out;
// // timer
// cpputils::ctimer timer;
// out<<"Inside SolverPETSc::operator()(const sparse_matrix&, const sparse_matrix&, const sparse_vector&). "<<timer<<endl;
// #endif
// Mat Kpf, Kpp;
// Vec Up, Pf, Pp;
// PetscErrorCode ierr = MatCreate(PETSC_COMM_WORLD,&Kpf);CHKERRCONTINUE(ierr);
// ierr = MatCreate(PETSC_COMM_WORLD,&Kpp);CHKERRCONTINUE(ierr);
// #ifdef CPPUTILS_VERBOSE
// out<<" Allocating memory... "<<timer<<endl;
// #endif
// ierr = MatSetFromOptions(Kpf);CHKERRCONTINUE(ierr);
// ierr = MatSetFromOptions(Kpp);CHKERRCONTINUE(ierr);
// ierr = MatSetSizes(Kpf,PETSC_DECIDE,PETSC_DECIDE, KKpf.rows(), KKpf.columns());CHKERRCONTINUE(ierr);
// ierr = MatSetSizes(Kpp,PETSC_DECIDE,PETSC_DECIDE, KKpp.rows(), KKpp.columns());CHKERRCONTINUE(ierr);
// // get number of non-zeros in both diagonal and non-diagonal portions of the matrix
// std::pair<size_t,size_t> Kpf_nz = KKpf.non_zero_off_diagonal();
// std::pair<size_t,size_t> Kpp_nz = KKpp.non_zero_off_diagonal();
// ierr = MatMPIAIJSetPreallocation(Kpf, Kpf_nz.first, PETSC_NULL, Kpf_nz.second, PETSC_NULL); CHKERRCONTINUE(ierr);
// ierr = MatMPIAIJSetPreallocation(Kpp, Kpp_nz.first, PETSC_NULL, Kpp_nz.second, PETSC_NULL); CHKERRCONTINUE(ierr);
// ierr = MatSeqAIJSetPreallocation(Kpf, Kpf_nz.first, PETSC_NULL); CHKERRCONTINUE(ierr);
// ierr = MatSeqAIJSetPreallocation(Kpp, Kpp_nz.first, PETSC_NULL); CHKERRCONTINUE(ierr);
// for (sparse_matrix_type::const_hash_iterator it = KKpf.map_.begin(); it != KKpf.map_.end(); ++it) {
// // get subscripts
// std::pair<size_t,size_t> subs = KKpf.unhash(it->first);
// int row = subs.first;
// int col = subs.second;
// ierr = MatSetValues(Kpf, 1, &row, 1, &col, &it->second, ADD_VALUES);CHKERRCONTINUE(ierr);
// }
// for (sparse_matrix_type::const_hash_iterator it = KKpp.map_.begin(); it != KKpp.map_.end(); ++it) {
// // get subscripts
// std::pair<size_t,size_t> subs = KKpp.unhash(it->first);
// int row = subs.first;
// int col = subs.second;
// ierr = MatSetValues(Kpf, 1, &row, 1, &col, &it->second, ADD_VALUES);CHKERRCONTINUE(ierr);
// }
// #ifdef CPPUTILS_VERBOSE
// out<<" Filled sparse matrices... "<<timer<<endl;
// #endif
// /*
// Assemble matrix, using the 2-step process:
// MatAssemblyBegin(), MatAssemblyEnd()
// Computations can be done while messages are in transition
// by placing code between these two statements.
// */
// ierr = MatAssemblyBegin(Kpf,MAT_FINAL_ASSEMBLY);CHKERRCONTINUE(ierr);
// ierr = MatAssemblyBegin(Kpp,MAT_FINAL_ASSEMBLY);CHKERRCONTINUE(ierr);
// ierr = MatAssemblyEnd(Kpf,MAT_FINAL_ASSEMBLY);CHKERRCONTINUE(ierr);
// ierr = MatAssemblyEnd(Kpp,MAT_FINAL_ASSEMBLY);CHKERRCONTINUE(ierr);
// #ifdef CPPUTILS_VERBOSE
// out<<" Sparse matrices assembled... "<<timer<<endl;
// #endif
// ierr = VecCreate(PETSC_COMM_WORLD,&Up);CHKERRCONTINUE(ierr);
// ierr = VecSetSizes(Up,PETSC_DECIDE, UUp.size());CHKERRCONTINUE(ierr);
// ierr = VecSetFromOptions(Up);CHKERRCONTINUE(ierr);
// ierr = VecCreate(PETSC_COMM_WORLD,&Pf);CHKERRCONTINUE(ierr);
// ierr = VecSetSizes(Pf,PETSC_DECIDE, KKpf.rows());CHKERRCONTINUE(ierr);
// ierr = VecSetFromOptions(Pf);CHKERRCONTINUE(ierr);
// ierr = VecDuplicate(Pf,&Pp);CHKERRCONTINUE(ierr);
// #ifdef CPPUTILS_VERBOSE
// out<<" Vectors created... "<<timer<<endl;
// #endif
// /* Set hight-hand-side vector */
// for (sparse_vector_type::const_hash_iterator it = UUp.map_.begin(); it != UUp.map_.end(); ++it) {
// int row = it->first;
// ierr = VecSetValues(Up, 1, &row, &it->second, ADD_VALUES);
// }
// /*
// Assemble vector, using the 2-step process:
// VecAssemblyBegin(), VecAssemblyEnd()
// Computations can be done while messages are in transition
// by placing code between these two statements.
// */
// ierr = VecAssemblyBegin(Up);CHKERRCONTINUE(ierr);
// ierr = VecAssemblyEnd(Up);CHKERRCONTINUE(ierr);
// // add Kpf*Uf
// ierr = MatMult(Kpf, x_, Pf);
// // add Kpp*Up
// ierr = MatMultAdd(Kpp, Up, Pf, Pp);
// #ifdef CPPUTILS_VERBOSE
// out<<" Matrices multiplied... "<<timer<<endl;
// #endif
// VecScatter ctx;
// Vec Pp_all;
// ierr = VecScatterCreateToAll(Pp, &ctx, &Pp_all);CHKERRCONTINUE(ierr);
// ierr = VecScatterBegin(ctx,Pp,Pp_all,INSERT_VALUES,SCATTER_FORWARD);CHKERRCONTINUE(ierr);
// ierr = VecScatterEnd(ctx,Pp,Pp_all,INSERT_VALUES,SCATTER_FORWARD);CHKERRCONTINUE(ierr);
// #ifdef CPPUTILS_VERBOSE
// out<<" Vector scattered... "<<timer<<endl;
// #endif
// /* - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
// Get values from solution and store them in the object that will be
// returned
// - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - */
// sparse_vector_type pp(KKpf.rows());
// // get reaction vector
// for (int i=0; i<KKpf.rows(); ++i) {
// PetscScalar v;
// ierr = VecGetValues(Pp_all, 1, &i, &v);
// if (v != PetscScalar())
// pp[i] = v;
// }
// #ifdef CPPUTILS_VERBOSE
// out<<" Vector copied... "<<timer<<endl;
// #endif
// ierr = MatDestroy(&Kpf);CHKERRCONTINUE(ierr);
// ierr = MatDestroy(&Kpp);CHKERRCONTINUE(ierr);
// ierr = VecDestroy(&Up);CHKERRCONTINUE(ierr);
// ierr = VecDestroy(&Pf);CHKERRCONTINUE(ierr);
// ierr = VecDestroy(&Pp);CHKERRCONTINUE(ierr);
// ierr = VecDestroy(&Pp_all);CHKERRCONTINUE(ierr);
// #ifdef CPPUTILS_VERBOSE
// out<<" Temporary data structures destroyed... "<<timer<<endl;
// #endif
// return pp;
// }
// SolverPETSc::sparse_vector_type SolverPETSc::operator()(const SolverPETSc::sparse_vector_type& aa, const SolverPETSc::sparse_vector_type& bb) {
// assert(aa.size() == bb.size());
// #ifdef CPPUTILS_VERBOSE
// // parallel output stream
// Output_stream out;
// // timer
// cpputils::ctimer timer;
// out<<"Inside SolverPETSc::operator()(const sparse_vector&, const sparse_vector&). "<<timer<<endl;
// #endif
// Vec r;
// PetscErrorCode ierr = VecCreate(PETSC_COMM_WORLD,&r);CHKERRCONTINUE(ierr);
// ierr = VecSetSizes(r,PETSC_DECIDE, aa.size());CHKERRCONTINUE(ierr);
// ierr = VecSetFromOptions(r);CHKERRCONTINUE(ierr);
// #ifdef CPPUTILS_VERBOSE
// out<<" Vectors created... "<<timer<<endl;
// #endif
// // set values
// for (sparse_vector_type::const_hash_iterator it = aa.map_.begin(); it != aa.map_.end(); ++it) {
// int row = it->first;
// ierr = VecSetValues(r, 1, &row, &it->second, ADD_VALUES);
// }
// for (sparse_vector_type::const_hash_iterator it = bb.map_.begin(); it != bb.map_.end(); ++it) {
// int row = it->first;
// ierr = VecSetValues(r, 1, &row, &it->second, ADD_VALUES);
// }
// /*
// Assemble vector, using the 2-step process:
// VecAssemblyBegin(), VecAssemblyEnd()
// Computations can be done while messages are in transition
// by placing code between these two statements.
// */
// ierr = VecAssemblyBegin(r);CHKERRCONTINUE(ierr);
// ierr = VecAssemblyEnd(r);CHKERRCONTINUE(ierr);
// sparse_vector_type rr(aa.size());
// for (sparse_vector_type::const_hash_iterator it = aa.map_.begin(); it != aa.map_.end(); ++it) {
// int row = it->first;
// ierr = VecGetValues(r, 1, &row, &rr[row]);
// }
// for (sparse_vector_type::const_hash_iterator it = bb.map_.begin(); it != bb.map_.end(); ++it) {
// int row = it->first;
// ierr = VecGetValues(r, 1, &row, &rr[row]);
// }
// #ifdef CPPUTILS_VERBOSE
// out<<" Vector copied... "<<timer<<endl;
// #endif
// ierr = VecDestroy(&r);CHKERRCONTINUE(ierr);
// #ifdef CPPUTILS_VERBOSE
// out<<" Temporary data structures destroyed... "<<timer<<endl;
// #endif
// return rr;
// }
// SolverPETSc::value_type SolverPETSc::norm(const SolverPETSc::sparse_matrix_type& aa, Element_insertion_type flag) {
// #ifdef CPPUTILS_VERBOSE
// // parallel output stream
// Output_stream out;
// // timer
// cpputils::ctimer timer;
// out<<"Inside SolverPETSc::norm(const sparse_matrix&). "<<timer<<endl;
// #endif
// Mat r;
// PetscErrorCode ierr = MatCreate(PETSC_COMM_WORLD,&r);CHKERRCONTINUE(ierr);
// ierr = MatSetSizes(r,PETSC_DECIDE,PETSC_DECIDE, aa.rows(), aa.columns());CHKERRCONTINUE(ierr);
// ierr = MatSetFromOptions(r);CHKERRCONTINUE(ierr);
// #ifdef CPPUTILS_VERBOSE
// out<<" Matrix created... "<<timer<<endl;
// #endif
// // set values
// for (sparse_vector_type::const_hash_iterator it = aa.map_.begin(); it != aa.map_.end(); ++it) {
// // get subscripts
// std::pair<size_t,size_t> subs = aa.unhash(it->first);
// int row = subs.first;
// int col = subs.second;
// if (flag == Add_t)
// ierr = MatSetValues(r, 1, &row, 1, &col, &it->second, ADD_VALUES);
// else if (flag == Insert_t)
// ierr = MatSetValues(r, 1, &row, 1, &col, &it->second, INSERT_VALUES);
// CHKERRCONTINUE(ierr);
// }
// #ifdef CPPUTILS_VERBOSE
// out<<" Matrix filled..."<<timer<<endl;
// #endif
// /*
// Assemble vector, using the 2-step process:
// VecAssemblyBegin(), VecAssemblyEnd()
// Computations can be done while messages are in transition
// by placing code between these two statements.
// */
// ierr = MatAssemblyBegin(r,MAT_FINAL_ASSEMBLY);CHKERRCONTINUE(ierr);
// ierr = MatAssemblyEnd(r,MAT_FINAL_ASSEMBLY);CHKERRCONTINUE(ierr);
// value_type nrm;
// MatNorm(r,NORM_FROBENIUS,&nrm);
// #ifdef CPPUTILS_VERBOSE
// out<<" Norm computed... "<<timer<<endl;
// #endif
// ierr = MatDestroy(&r);CHKERRCONTINUE(ierr);
// #ifdef CPPUTILS_VERBOSE
// out<<" Temporary data structures destroyed... "<<timer<<endl;
// #endif
// return nrm;
// }
// SolverPETSc::value_type SolverPETSc::norm(const SolverPETSc::sparse_vector_type& aa, Element_insertion_type flag) {
// #ifdef CPPUTILS_VERBOSE
// // parallel output stream
// Output_stream out;
// // timer
// cpputils::ctimer timer;
// out<<"Inside SolverPETSc::norm(const sparse_vector&). "<<timer<<endl;
// #endif
// Vec r;
// PetscErrorCode ierr = VecCreate(PETSC_COMM_WORLD,&r);CHKERRCONTINUE(ierr);
// ierr = VecSetSizes(r,PETSC_DECIDE, aa.size());CHKERRCONTINUE(ierr);
// ierr = VecSetFromOptions(r);CHKERRCONTINUE(ierr);
// #ifdef CPPUTILS_VERBOSE
// out<<" Vector created... "<<timer<<endl;
// #endif
// // set values
// for (sparse_vector_type::const_hash_iterator it = aa.map_.begin(); it != aa.map_.end(); ++it) {
// int row = it->first;
// if (flag == Add_t)
// ierr = VecSetValues(r, 1, &row, &it->second, ADD_VALUES);
// else if (flag == Insert_t)
// ierr = VecSetValues(r, 1, &row, &it->second, INSERT_VALUES);
// CHKERRCONTINUE(ierr);
// }
// #ifdef CPPUTILS_VERBOSE
// out<<" Vector filled..."<<timer<<endl;
// #endif
// /*
// Assemble vector, using the 2-step process:
// VecAssemblyBegin(), VecAssemblyEnd()
// Computations can be done while messages are in transition
// by placing code between these two statements.
// */
// ierr = VecAssemblyBegin(r);CHKERRCONTINUE(ierr);
// ierr = VecAssemblyEnd(r);CHKERRCONTINUE(ierr);
// value_type nrm;
// VecNorm(r,NORM_2,&nrm);
// #ifdef CPPUTILS_VERBOSE
// out<<" Norm computed... "<<timer<<endl;
// #endif
// ierr = VecDestroy(&r);CHKERRCONTINUE(ierr);
// #ifdef CPPUTILS_VERBOSE
// out<<" Temporary data structures destroyed... "<<timer<<endl;
// #endif
// return nrm;
// }
// //
// ///* -------------------------------------------------------------------------- */
// //SolverMumps::SolverMumps(SparseMatrix & matrix,
// // const ID & id,
// // const MemoryID & memory_id) :
// //Solver(matrix, id, memory_id), is_mumps_data_initialized(false), 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
// //
// // CommunicatorEventHandler & comm_event_handler = *this;
// //
// // communicator.registerEventHandler(comm_event_handler);
// //
// // AKANTU_DEBUG_OUT();
// //}
// //
// ///* -------------------------------------------------------------------------- */
// //SolverMumps::~SolverMumps() {
// // AKANTU_DEBUG_IN();
// //
// // AKANTU_DEBUG_OUT();
// //}
// //
// ///* -------------------------------------------------------------------------- */
// //void SolverMumps::destroyMumpsData() {
// // AKANTU_DEBUG_IN();
// //
// // if(is_mumps_data_initialized) {
// // mumps_data.job = _smj_destroy; // destroy
// // dmumps_c(&mumps_data);
// // is_mumps_data_initialized = false;
// // }
// //
// // AKANTU_DEBUG_OUT();
// //}
// //
// ///* -------------------------------------------------------------------------- */
// //void SolverMumps::onCommunicatorFinalize(const StaticCommunicator & comm) {
// // AKANTU_DEBUG_IN();
// //
// // try{
// // const StaticCommunicatorMPI & comm_mpi =
// // dynamic_cast<const StaticCommunicatorMPI &>(comm.getRealStaticCommunicator());
// // if(mumps_data.comm_fortran == MPI_Comm_c2f(comm_mpi.getMPICommunicator()))
// // destroyMumpsData();
// // } catch(...) {}
// //
// // 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);
// // 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);
// // is_mumps_data_initialized = true;
// //
// // /* ------------------------------------------------------------------------ */
// // 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
// //
// // SolverMumpsOptions * opt = dynamic_cast<SolverMumpsOptions *>(&options);
// // if(opt)
// // parallel_method = opt->parallel_method;
// //
// // initMumpsData(parallel_method);
// //
// // mumps_data.job = _smj_analyze; //analyze
// // dmumps_c(&mumps_data);
// //
// // AKANTU_DEBUG_OUT();
// //}
// //
// ///* -------------------------------------------------------------------------- */
// //void SolverMumps::setRHS(Array<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(Array<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__
c4science · Help