Source code for pymor.discretizations.mpi

# This file is part of the pyMOR project (
# Copyright 2013-2017 pyMOR developers and contributors. All rights reserved.
# License: BSD 2-Clause License (

from pymor.core.interfaces import ImmutableInterface
from pymor.discretizations.basic import DiscretizationBase
from pymor.operators.mpi import mpi_wrap_operator
from import mpi
from pymor.vectorarrays.mpi import MPIVectorSpace, _register_local_space

[docs]class MPIDiscretization(DiscretizationBase): """Wrapper class for MPI distributed |Discretizations|. Given a single-rank implementation of a |Discretization|, this wrapper class uses the event loop from :mod:`` to allow an MPI distributed usage of the |Discretization|. The underlying implementation needs to be MPI aware. In particular, the discretization's :meth:`~pymor.discretizations.interfaces.DiscretizationInterface.solve` method has to perform an MPI parallel solve of the discretization. Note that this class is not intended to be instantiated directly. Instead, you should use :func:`mpi_wrap_discretization`. Parameters ---------- obj_id :class:`` of the local |Discretization| on each rank. operators Dictionary of all |Operators| contained in the discretization, wrapped for use on rank 0. Use :func:`mpi_wrap_discretization` to automatically wrap all operators of a given MPI-aware |Discretization|. products See `operators`. pickle_local_spaces See :class:`~pymor.operators.mpi.MPIOperator`. space_type See :class:`~pymor.operators.mpi.MPIOperator`. """ def __init__(self, obj_id, operators, products=None, pickle_local_spaces=True, space_type=MPIVectorSpace): d = mpi.get_object(obj_id) visualizer = MPIVisualizer(obj_id) super().__init__(operators=operators, products=products, visualizer=visualizer, cache_region=None, self.obj_id = obj_id local_spaces =, obj_id, pickle_local_spaces) if all(ls == local_spaces[0] for ls in local_spaces): local_spaces = (local_spaces[0],) self.solution_space = space_type(local_spaces) self.build_parameter_type(d) self.parameter_space = d.parameter_space def _solve(self, mu=None): return self.solution_space.make_array(, self.obj_id, 'solve', mu=mu) ) def __del__(self):, self.obj_id)
def _MPIDiscretization_get_local_spaces(self, pickle_local_spaces): self = mpi.get_object(self) local_space = self.solution_space if not pickle_local_spaces: local_space = _register_local_space(local_space) local_spaces = mpi.comm.gather(local_space, root=0) if mpi.rank0: return tuple(local_spaces)
[docs]class MPIVisualizer(ImmutableInterface): def __init__(self, d_obj_id): self.d_obj_id = d_obj_id def visualize(self, U, d, **kwargs): if isinstance(U, tuple): U = tuple(u.obj_id for u in U) else: U = U.obj_id, self.d_obj_id, U, **kwargs)
def _MPIVisualizer_visualize(d, U, **kwargs): d = mpi.get_object(d) if isinstance(U, tuple): U = tuple(mpi.get_object(u) for u in U) else: U = mpi.get_object(U) d.visualize(U, **kwargs)
[docs]def mpi_wrap_discretization(local_discretizations, use_with=False, with_apply2=False, pickle_local_spaces=True, space_type=MPIVectorSpace): """Wrap MPI distributed local |Discretizations| to a global |Discretization| on rank 0. Given MPI distributed local |Discretizations| referred to by the :class:`` `local_discretizations`, return a new |Discretization| which manages these distributed discretizations from rank 0. This is done by first wrapping all |Operators| of the |Discretization| using :func:`~pymor.operators.mpi.mpi_wrap_operator`. Alternatively, `local_discretizations` can be a callable (with no arguments) which is then called on each rank to instantiate the local |Discretizations|. When `use_with` is `False`, an :class:`MPIDiscretization` is instantiated with the wrapped operators. A call to :meth:`~pymor.discretizations.interfaces.DiscretizationInterface.solve` will then use an MPI parallel call to the :meth:`~pymor.discretizations.interfaces.DiscretizationInterface.solve` methods of the wrapped local |Discretizations| to obtain the solution. This is usually what you want when the actual solve is performed by an implementation in the external solver. When `use_with` is `True`, :meth:`~pymor.core.interfaces.ImmutableInterface.with_` is called on the local |Discretization| on rank 0, to obtain a new |Discretization| with the wrapped MPI |Operators|. This is mainly useful when the local discretizations are generic |Discretizations| as in :mod:`pymor.discretizations.basic` and :meth:`~pymor.discretizations.interfaces.DiscretizationInterface.solve` is implemented directly in pyMOR via operations on the contained |Operators|. Parameters ---------- local_discretizations :class:`` of the local |Discretizations| on each rank or a callable generating the |Discretizations|. use_with See above. with_apply2 See :class:`~pymor.operators.mpi.MPIOperator`. pickle_local_spaces See :class:`~pymor.operators.mpi.MPIOperator`. space_type See :class:`~pymor.operators.mpi.MPIOperator`. """ if not isinstance(local_discretizations, mpi.ObjectId): local_discretizations =, local_discretizations) operators, products =, local_discretizations) operators = {k: mpi_wrap_operator(v, with_apply2=with_apply2, pickle_local_spaces=pickle_local_spaces, space_type=space_type) if v else None for k, v in operators.items()} products = {k: mpi_wrap_operator(v, with_apply2=with_apply2, pickle_local_spaces=pickle_local_spaces, space_type=space_type) if v else None for k, v in products.items()} if use_with: d = mpi.get_object(local_discretizations) visualizer = MPIVisualizer(local_discretizations) return d.with_(operators=operators, products=products, visualizer=visualizer, cache_region=None) else: return MPIDiscretization(local_discretizations, operators, products, pickle_local_spaces=pickle_local_spaces, space_type=space_type)
def _mpi_wrap_discretization_manage_operators(obj_id): d = mpi.get_object(obj_id) operators = {k: mpi.manage_object(v) if v else None for k, v in sorted(d.operators.items())} products = {k: mpi.manage_object(v) if v else None for k, v in sorted(d.products.items())} if d.products else {} if mpi.rank0: return operators, products