Coverage for pySDC/implementations/sweeper_classes/generic_implicit_MPI.py: 90%
105 statements
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1from mpi4py import MPI
3from pySDC.implementations.sweeper_classes.generic_implicit import generic_implicit
4from pySDC.core.sweeper import Sweeper, ParameterError
5import logging
8class SweeperMPI(Sweeper):
9 """
10 MPI based sweeper where each rank administers one collocation node. Adapt sweepers to MPI by use of multiple inheritance.
11 See for example the `generic_implicit_MPI` sweeper, which has a class definition:
13 ```
14 class generic_implicit_MPI(SweeperMPI, generic_implicit):
15 ```
17 this means in inherits both from `SweeperMPI` and `generic_implicit`. The hierarchy works such that functions are first
18 called from `SweeperMPI` and then from `generic_implicit`. For instance, in the `__init__` function, the `SweeperMPI`
19 class adds a communicator and nothing else. The `generic_implicit` implicit class adds a preconditioner and so on.
20 It's a bit confusing because `self.params` is overwritten in the second call to the `__init__` of the core `sweeper`
21 class, but the `SweeperMPI` class adds parameters to the `params` dictionary, which will again be added in
22 `generic_implicit`.
23 """
25 def __init__(self, params):
26 self.logger = logging.getLogger('sweeper')
28 if 'comm' not in params.keys():
29 params['comm'] = MPI.COMM_WORLD
30 self.logger.debug('Using MPI.COMM_WORLD for the communicator because none was supplied in the params.')
31 super().__init__(params)
33 if self.params.comm.size != self.coll.num_nodes:
34 raise NotImplementedError(
35 f'The communicator in the {type(self).__name__} sweeper needs to have one rank for each node as of now! That means we need {self.coll.num_nodes} nodes, but got {self.params.comm.size} processes.'
36 )
38 @property
39 def comm(self):
40 return self.params.comm
42 @property
43 def rank(self):
44 return self.comm.rank
46 def compute_end_point(self):
47 """
48 Compute u at the right point of the interval
50 The value uend computed here is a full evaluation of the Picard formulation unless do_full_update==False
52 Returns:
53 None
54 """
56 L = self.level
57 P = L.prob
58 L.uend = P.dtype_u(P.init, val=0.0)
60 # check if Mth node is equal to right point and do_coll_update is false, perform a simple copy
61 if self.coll.right_is_node and not self.params.do_coll_update:
62 # a copy is sufficient
63 root = self.comm.Get_size() - 1
64 if self.comm.rank == root:
65 L.uend[:] = L.u[-1]
66 self.comm.Bcast(L.uend, root=root)
67 else:
68 raise NotImplementedError('require last node to be identical with right interval boundary')
70 return None
72 def compute_residual(self, stage=None):
73 """
74 Computation of the residual using the collocation matrix Q
76 Args:
77 stage (str): The current stage of the step the level belongs to
78 """
80 L = self.level
82 # Check if we want to skip the residual computation to gain performance
83 # Keep in mind that skipping any residual computation is likely to give incorrect outputs of the residual!
84 if stage in self.params.skip_residual_computation:
85 L.status.residual = 0.0 if L.status.residual is None else L.status.residual
86 return None
88 # compute the residual for each node
90 # build QF(u)
91 res = self.integrate(last_only=L.params.residual_type[:4] == 'last')
92 res += L.u[0] - L.u[self.rank + 1]
93 # add tau if associated
94 if L.tau[self.rank] is not None:
95 res += L.tau[self.rank]
96 # use abs function from data type here
97 res_norm = abs(res)
99 # find maximal residual over the nodes
100 if L.params.residual_type == 'full_abs':
101 L.status.residual = self.comm.allreduce(res_norm, op=MPI.MAX)
102 elif L.params.residual_type == 'last_abs':
103 L.status.residual = self.comm.bcast(res_norm, root=self.comm.size - 1)
104 elif L.params.residual_type == 'full_rel':
105 L.status.residual = self.comm.allreduce(res_norm / abs(L.u[0]), op=MPI.MAX)
106 elif L.params.residual_type == 'last_rel':
107 L.status.residual = self.comm.bcast(res_norm / abs(L.u[0]), root=self.comm.size - 1)
108 else:
109 raise NotImplementedError(f'residual type \"{L.params.residual_type}\" not implemented!')
111 # indicate that the residual has seen the new values
112 L.status.updated = False
114 return None
116 def predict(self):
117 """
118 Predictor to fill values at nodes before first sweep
120 Default prediction for the sweepers, only copies the values to all collocation nodes
121 and evaluates the RHS of the ODE there
122 """
124 L = self.level
125 P = L.prob
127 # evaluate RHS at left point
128 L.f[0] = P.eval_f(L.u[0], L.time)
130 m = self.rank
132 if self.params.initial_guess == 'spread':
133 # copy u[0] to all collocation nodes, evaluate RHS
134 L.u[m + 1] = P.dtype_u(L.u[0])
135 L.f[m + 1] = P.eval_f(L.u[m + 1], L.time + L.dt * self.coll.nodes[m])
136 elif self.params.initial_guess == 'copy':
137 # copy u[0] and RHS evaluation to all collocation nodes
138 L.u[m + 1] = P.dtype_u(L.u[0])
139 L.f[m + 1] = P.dtype_f(L.f[0])
140 elif self.params.initial_guess == 'zero':
141 # zeros solution for u and RHS
142 L.u[m + 1] = P.dtype_u(init=P.init, val=0.0)
143 L.f[m + 1] = P.dtype_f(init=P.init, val=0.0)
144 else:
145 raise ParameterError(f'initial_guess option {self.params.initial_guess} not implemented')
147 # indicate that this level is now ready for sweeps
148 L.status.unlocked = True
149 L.status.updated = True
151 def communicate_tau_correction_for_full_interval(self):
152 L = self.level
153 P = L.prob
154 if self.rank < self.comm.size - 1:
155 L.tau[-1] = P.u_init
156 self.comm.Bcast(L.tau[-1], root=self.comm.size - 1)
159class generic_implicit_MPI(SweeperMPI, generic_implicit):
160 """
161 Generic implicit sweeper parallelized across the nodes.
162 Please supply a communicator as `comm` to the parameters!
164 Attributes:
165 rank (int): MPI rank
166 """
168 def integrate(self, last_only=False):
169 """
170 Integrates the right-hand side
172 Args:
173 last_only (bool): Integrate only the last node for the residual or all of them
175 Returns:
176 list of dtype_u: containing the integral as values
177 """
178 L = self.level
179 P = L.prob
181 me = P.dtype_u(P.init, val=0.0)
182 for m in [self.coll.num_nodes - 1] if last_only else range(self.coll.num_nodes):
183 recvBuf = me if m == self.rank else None
184 self.comm.Reduce(
185 L.dt * self.coll.Qmat[m + 1, self.rank + 1] * L.f[self.rank + 1], recvBuf, root=m, op=MPI.SUM
186 )
188 return me
190 def update_nodes(self):
191 """
192 Update the u- and f-values at the collocation nodes -> corresponds to a single sweep over all nodes
194 Returns:
195 None
196 """
198 L = self.level
199 P = L.prob
201 # only if the level has been touched before
202 assert L.status.unlocked
204 # update the MIN-SR-FLEX preconditioner
205 self.updateVariableCoeffs(L.status.sweep)
207 # gather all terms which are known already (e.g. from the previous iteration)
208 # this corresponds to u0 + QF(u^k) - QdF(u^k) + tau
210 # get QF(u^k)
211 rhs = self.integrate()
213 rhs -= L.dt * self.QI[self.rank + 1, self.rank + 1] * L.f[self.rank + 1]
215 # add initial value
216 rhs += L.u[0]
217 # add tau if associated
218 if L.tau[self.rank] is not None:
219 rhs += L.tau[self.rank]
221 # build rhs, consisting of the known values from above and new values from previous nodes (at k+1)
223 # implicit solve with prefactor stemming from the diagonal of Qd
224 L.u[self.rank + 1][:] = P.solve_system(
225 rhs,
226 L.dt * self.QI[self.rank + 1, self.rank + 1],
227 L.u[self.rank + 1],
228 L.time + L.dt * self.coll.nodes[self.rank],
229 )
230 # update function values
231 L.f[self.rank + 1] = P.eval_f(L.u[self.rank + 1], L.time + L.dt * self.coll.nodes[self.rank])
233 # indicate presence of new values at this level
234 L.status.updated = True
236 return None
238 def compute_end_point(self):
239 """
240 Compute u at the right point of the interval
242 The value uend computed here is a full evaluation of the Picard formulation unless do_full_update==False
244 Returns:
245 None
246 """
248 L = self.level
249 P = L.prob
251 # check if Mth node is equal to right point and do_coll_update is false, perform a simple copy
252 if self.coll.right_is_node and not self.params.do_coll_update:
253 super().compute_end_point()
254 else:
255 L.uend = P.dtype_u(L.u[0])
256 self.comm.Allreduce(L.dt * self.coll.weights[self.rank] * L.f[self.rank + 1], L.uend, op=MPI.SUM)
257 L.uend += L.u[0]
259 # add up tau correction of the full interval (last entry)
260 if L.tau[self.rank] is not None:
261 self.communicate_tau_correction_for_full_interval()
262 L.uend += L.tau[-1]
263 return None