Source code for pymor.parallel.interface
# This file is part of the pyMOR project (http://www.pymor.org).
# Copyright 2013-2020 pyMOR developers and contributors. All rights reserved.
# License: BSD 2-Clause License (http://opensource.org/licenses/BSD-2-Clause)
from pymor.core.base import BasicObject, abstractmethod
[docs]class WorkerPool(BasicObject):
"""Interface for parallel worker pools.
|WorkerPools| allow to easily parallelize algorithms which involve
no or little communication between the workers at runtime. The interface
methods give the user simple means to distribute data to
workers (:meth:`~WorkerPool.push`, :meth:`~WorkerPool.scatter_array`,
:meth:`~WorkerPool.scatter_list`) and execute functions on
the distributed data in parallel (:meth:`~WorkerPool.apply`),
collecting the return values from each function call. A
single worker can be instructed to execute a function using the
:meth:`WorkerPool.apply_only` method. Finally, a parallelized
:meth:`~WorkerPool.map` function is available, which
automatically scatters the data among the workers.
All operations are performed synchronously.
"""
[docs] @abstractmethod
def __len__(self):
"""The number of workers in the pool."""
pass
[docs] @abstractmethod
def push(self, obj):
"""Push a copy of `obj` to all workers of the pool.
A |RemoteObject| is returned as a handle to the pushed object.
This object can be used as a keyword argument to :meth:`~WorkerPool.apply`,
:meth:`~WorkerPool.apply_only`, :meth:`~WorkerPool.map`
and will then be transparently mapped to the respective copy
of the pushed object on the worker.
|Immutable| objects will be pushed only once. If the same |immutable| object
is pushed a second time, the returned |RemoteObject| will refer to the
already transferred copy. It is therefore safe to use `push` to ensure
that a given |immutable| object is available on the worker. No unnecessary
copies will be created.
Parameters
----------
obj
The object to push to all workers.
Returns
-------
A |RemoteObject| referring to the pushed data.
"""
pass
[docs] @abstractmethod
def scatter_array(self, U, copy=True):
"""Distribute |VectorArray| evenly among the workers.
On each worker a |VectorArray| is created holding an (up to rounding) equal
amount of vectors of `U`. The returned |RemoteObject| therefore refers
to different data on each of the workers.
Parameters
----------
U
The |VectorArray| to distribute.
copy
If `False`, `U` will be emptied during distribution of the vectors.
Returns
-------
A |RemoteObject| referring to the scattered data.
"""
pass
[docs] @abstractmethod
def scatter_list(self, l):
"""Distribute list of objects evenly among the workers.
On each worker a `list` is created holding an (up to rounding) equal
amount of objects of `l`. The returned |RemoteObject| therefore refers
to different data on each of the workers.
Parameters
----------
l
The list (sequence) of objects to distribute.
Returns
-------
A |RemoteObject| referring to the scattered data.
"""
pass
[docs] @abstractmethod
def apply(self, function, *args, **kwargs):
"""Apply function in parallel on each worker.
This calls `function` on each worker in parallel, passing `args` as
positional and `kwargs` as keyword arguments. Keyword arguments
which are |RemoteObjects| are automatically mapped to the
respective object on the worker. Moreover, keyword arguments which
are |immutable| objects that have already been pushed to the workers
will not be transmitted again. (|Immutable| objects which have not
been pushed before will be transmitted and the remote copy will be
destroyed after function execution.)
Parameters
----------
function
The function to execute on each worker.
args
The positional arguments for `function`.
kwargs
The keyword arguments for `function`.
Returns
-------
List of return values of the function executions, ordered by
worker number (from `0` to `len(pool) - 1`).
"""
pass
[docs] @abstractmethod
def apply_only(self, function, worker, *args, **kwargs):
"""Apply function on a single worker.
This calls `function` on on the worker with number `worker`, passing
`args` as positional and `kwargs` as keyword arguments. Keyword arguments
which are |RemoteObjects| are automatically mapped to the
respective object on the worker. Moreover, keyword arguments which
are |immutable| objects that have already been pushed to the workers
will not be transmitted again. (|Immutable| objects which have not
been pushed before will be transmitted and the remote copy will be
destroyed after function execution.)
Parameters
----------
function
The function to execute.
worker
The worker on which to execute the function. (Number between
`0` and `len(pool) - 1`.)
args
The positional arguments for `function`.
kwargs
The keyword arguments for `function`.
Returns
-------
Return value of the function execution.
"""
pass
[docs] @abstractmethod
def map(self, function, *args, **kwargs):
"""Parallel version of the builtin :func:`map` function.
Each positional argument (after `function`) must be a sequence
of same length n. `map` calls `function` in parallel on each of these n
positional argument combinations, always passing `kwargs` as keyword
arguments. Keyword arguments which are |RemoteObjects| are automatically
mapped to the respective object on the worker. Moreover, keyword arguments
which are |immutable| objects that have already been pushed to the workers
will not be transmitted again. (|Immutable| objects which have not
been pushed before will be transmitted and the remote copy will be
destroyed after function execution.)
Parameters
----------
function
The function to execute on each worker.
args
The sequences of positional arguments for `function`.
kwargs
The keyword arguments for `function`.
Returns
-------
List of return values of the function executions, ordered by
the sequence of positional arguments.
"""
pass
[docs]class RemoteObject:
"""Handle to remote data on the workers of a |WorkerPool|.
See documentation of :class:`WorkerPool` for usage
of these handles in conjunction with :meth:`~WorkerPool.apply`,
:meth:`~WorkerPool.scatter_array`,
:meth:`~WorkerPool.scatter_list`.
Remote objects can be used as a context manager: when leaving the
context, the remote object's :meth:`~RemoteObject.remove`
method is called to ensure proper cleanup of remote resources.
Attributes
----------
removed
`True`, after :meth:`remove` has been called.
"""
removed = False
[docs] @abstractmethod
def _remove(self):
"""Actual implementation of 'remove'."""
pass
[docs] def remove(self):
"""Remove the remote object from the workers.
Remove the object this handle refers to from all workers.
Note that the object will only be destroyed if no other
object on the worker holds a reference to that object.
Moreover, |immutable| objects will only be destroyed if
:meth:`remove` has been called on *all* |RemoteObjects|
which refer to the object (see :meth:`~WorkerPool.push`).
"""
if self.removed:
return
self._remove()
self.removed = True
def __enter__(self):
return self
def __exit__(self, exc_type, exc_val, exc_tb):
self.remove()
def __del__(self):
self.remove()