Line data Source code
1 :
2 : //* This file is part of the MOOSE framework
3 : //* https://mooseframework.inl.gov
4 : //*
5 : //* All rights reserved, see COPYRIGHT for full restrictions
6 : //* https://github.com/idaholab/moose/blob/master/COPYRIGHT
7 : //*
8 : //* Licensed under LGPL 2.1, please see LICENSE for details
9 : //* https://www.gnu.org/licenses/lgpl-2.1.html
10 :
11 : #include "MooseError.h"
12 : #include "POD.h"
13 :
14 : using namespace libMesh;
15 :
16 : namespace StochasticTools
17 : {
18 :
19 : #if PETSC_VERSION_LESS_THAN(3, 14, 0)
20 :
21 : POD::POD(const ParallelSolutionStorage * const, const std::string &, const Parallel::Communicator &)
22 : {
23 : mooseError("PETSc-3.14.0 or higher is required for using StochasticTools::POD.");
24 : }
25 :
26 : #else
27 :
28 170 : POD::POD(const ParallelSolutionStorage * const parallel_storage,
29 : const std::string & extra_slepc_options,
30 170 : const Parallel::Communicator & comm)
31 170 : : _parallel_storage(parallel_storage),
32 170 : _extra_slepc_options(extra_slepc_options),
33 170 : _communicator(comm)
34 : {
35 170 : }
36 :
37 : #endif
38 :
39 : void
40 130 : POD::computePOD(const VariableName & vname,
41 : std::vector<DenseVector<Real>> & left_basis_functions,
42 : std::vector<DenseVector<Real>> & right_basis_functions,
43 : std::vector<Real> & singular_values,
44 : const dof_id_type num_modes,
45 : const Real energy) const
46 : {
47 :
48 : #if !PETSC_VERSION_LESS_THAN(3, 14, 0)
49 :
50 : // Define the petsc matrix which needs and SVD, we will populate it using the snapshots
51 : Mat mat;
52 :
53 : // We make sure every rank knows how many global and local samples we have and how long the
54 : // snapshots are. At this point we assume that the snapshots are the same size so we don't
55 : // need to map them to a reference domain.
56 : dof_id_type local_rows = 0;
57 130 : dof_id_type snapshot_size = 0;
58 130 : dof_id_type global_rows = 0;
59 130 : if (_parallel_storage->getStorage().size())
60 : {
61 570 : for (const auto & row : _parallel_storage->getStorage(vname))
62 : {
63 440 : local_rows += row.second.size();
64 440 : if (row.second.size())
65 440 : snapshot_size = row.second[0].size();
66 : }
67 130 : global_rows = local_rows;
68 : }
69 :
70 130 : _communicator.sum(global_rows);
71 130 : _communicator.max(snapshot_size);
72 :
73 : // Generally snapshot matrices are dense.
74 130 : LibmeshPetscCallA(
75 : _communicator.get(),
76 : MatCreateDense(
77 : _communicator.get(), local_rows, PETSC_DECIDE, global_rows, snapshot_size, NULL, &mat));
78 :
79 : // Check where the local rows begin in the matrix, we use these to convert from local to
80 : // global indices
81 130 : dof_id_type local_beg = 0;
82 130 : dof_id_type local_end = 0;
83 130 : LibmeshPetscCallA(
84 : _communicator.get(),
85 : MatGetOwnershipRange(mat, numeric_petsc_cast(&local_beg), numeric_petsc_cast(&local_end)));
86 :
87 : unsigned int counter = 0;
88 130 : if (local_rows)
89 560 : for (const auto & row : _parallel_storage->getStorage(vname))
90 : {
91 : // Adds each snap individually. For problems with multiple snaps per run.
92 880 : for (const auto & snap : row.second)
93 : {
94 440 : std::vector<PetscInt> rows(snapshot_size, (counter++) + local_beg);
95 :
96 : // Fill the column indices with 0,1,...,snapshot_size-1
97 440 : std::vector<PetscInt> columns(snapshot_size);
98 : std::iota(std::begin(columns), std::end(columns), 0);
99 :
100 : // Set the rows in the "sparse" matrix
101 440 : LibmeshPetscCallA(_communicator.get(),
102 : MatSetValues(mat,
103 : 1,
104 : rows.data(),
105 : snapshot_size,
106 : columns.data(),
107 : snap.get_values().data(),
108 : INSERT_VALUES));
109 : }
110 : }
111 :
112 : // Assemble the matrix
113 130 : LibmeshPetscCallA(_communicator.get(), MatAssemblyBegin(mat, MAT_FINAL_ASSEMBLY));
114 130 : LibmeshPetscCallA(_communicator.get(), MatAssemblyEnd(mat, MAT_FINAL_ASSEMBLY));
115 :
116 : SVD svd;
117 130 : LibmeshPetscCallA(_communicator.get(), SVDCreate(_communicator.get(), &svd));
118 : // Now we set the operators for our SVD objects
119 130 : LibmeshPetscCallA(_communicator.get(), SVDSetOperators(svd, mat, NULL));
120 :
121 : // Set the parallel operation mode to "DISTRIBUTED", default is "REDUNDANT"
122 : DS ds;
123 130 : LibmeshPetscCallA(_communicator.get(), SVDGetDS(svd, &ds));
124 130 : LibmeshPetscCallA(_communicator.get(), DSSetParallel(ds, DS_PARALLEL_DISTRIBUTED));
125 :
126 : // We want the Lanczos method, might give the choice to the user
127 : // at some point
128 130 : LibmeshPetscCallA(_communicator.get(), SVDSetType(svd, SVDTRLANCZOS));
129 :
130 : // Default is the transpose is explicitly created. This method is less efficient
131 : // computationally but better for storage
132 130 : LibmeshPetscCallA(_communicator.get(), SVDSetImplicitTranspose(svd, PETSC_TRUE));
133 :
134 130 : LibmeshPetscCallA(_communicator.get(),
135 : PetscOptionsInsertString(NULL, _extra_slepc_options.c_str()));
136 :
137 : // Set the subspace size for the Lanczos method, we take twice as many
138 : // basis vectors as the requested number of POD modes. This guarantees in most of the case the
139 : // convergence of the singular triplets.
140 290 : LibmeshPetscCallA(_communicator.get(),
141 : SVDSetDimensions(svd,
142 : num_modes,
143 : std::min(2 * num_modes, global_rows),
144 : std::min(2 * num_modes, global_rows)));
145 :
146 : // Gives the user the ability to override any option set before the solve.
147 130 : LibmeshPetscCallA(_communicator.get(), SVDSetFromOptions(svd));
148 :
149 : // Compute the singular value triplets
150 130 : LibmeshPetscCallA(_communicator.get(), SVDSolve(svd));
151 :
152 : // Check how many singular triplets converged
153 : PetscInt nconv;
154 130 : LibmeshPetscCallA(_communicator.get(), SVDGetConverged(svd, &nconv));
155 :
156 : // We start extracting the basis functions and the singular values.
157 :
158 : // Find the local size needed for u
159 130 : dof_id_type local_snapsize = 0;
160 130 : LibmeshPetscCallA(_communicator.get(),
161 : MatGetLocalSize(mat, NULL, numeric_petsc_cast(&local_snapsize)));
162 :
163 130 : PetscVector<Real> u(_communicator);
164 130 : u.init(snapshot_size, local_snapsize, false, PARALLEL);
165 :
166 130 : PetscVector<Real> v(_communicator);
167 130 : v.init(global_rows, local_rows, false, PARALLEL);
168 :
169 : left_basis_functions.clear();
170 : right_basis_functions.clear();
171 : singular_values.clear();
172 :
173 130 : singular_values.resize(nconv);
174 : // Fetch the singular value triplet and immediately save the singular value
175 970 : for (PetscInt j = 0; j < nconv; ++j)
176 840 : LibmeshPetscCallA(_communicator.get(),
177 : SVDGetSingularTriplet(svd, j, &singular_values[j], NULL, NULL));
178 :
179 : // Determine how many modes we need
180 130 : unsigned int num_requested_modes = determineNumberOfModes(singular_values, num_modes, energy);
181 : // Only save the basis functions which are needed. We serialize the modes
182 : // on every processor so all of them have access to every mode.
183 130 : left_basis_functions.resize(num_requested_modes);
184 130 : right_basis_functions.resize(num_requested_modes);
185 370 : for (PetscInt j = 0; j < cast_int<PetscInt>(num_requested_modes); ++j)
186 : {
187 240 : LibmeshPetscCallA(_communicator.get(), SVDGetSingularTriplet(svd, j, NULL, v.vec(), u.vec()));
188 240 : u.localize(left_basis_functions[j].get_values());
189 240 : v.localize(right_basis_functions[j].get_values());
190 : }
191 130 : LibmeshPetscCallA(_communicator.get(), MatDestroy(&mat));
192 130 : LibmeshPetscCallA(_communicator.get(), SVDDestroy(&svd));
193 : #else
194 : // These variables would otherwise be unused
195 : libmesh_ignore(vname);
196 : libmesh_ignore(left_basis_functions);
197 : libmesh_ignore(right_basis_functions);
198 : libmesh_ignore(singular_values);
199 : libmesh_ignore(num_modes);
200 : libmesh_ignore(energy);
201 : #endif
202 130 : }
203 :
204 : dof_id_type
205 130 : POD::determineNumberOfModes(const std::vector<Real> & singular_values,
206 : const dof_id_type num_modes_compute,
207 : const Real energy) const
208 : {
209 : dof_id_type num_modes = 0;
210 : // We either use the number of modes defined by the user or the maximum number of converged
211 : // modes. We don't want to use modes which are unconverged.
212 : std::size_t num_requested_modes =
213 130 : std::min((std::size_t)num_modes_compute, singular_values.size());
214 : // Grab a cumulative sum of singular value squared
215 130 : std::vector<Real> ev_sum(singular_values.begin(), singular_values.begin() + num_requested_modes);
216 130 : std::partial_sum(ev_sum.cbegin(),
217 : ev_sum.cend(),
218 : ev_sum.begin(),
219 490 : [](Real sum, Real ev) { return sum + ev * ev; });
220 :
221 : // Find the first element that satisfies the threshold
222 : const Real threshold = energy;
223 240 : for (num_modes = 0; num_modes < ev_sum.size(); ++num_modes)
224 240 : if (ev_sum[num_modes] / ev_sum.back() > 1 - threshold)
225 : break;
226 :
227 130 : return num_modes + 1;
228 : }
229 : }
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