#
##
## This file is part of pyFormex 1.0.7 (Mon Jun 17 12:20:39 CEST 2019)
## pyFormex is a tool for generating, manipulating and transforming 3D
## geometrical models by sequences of mathematical operations.
## Home page: http://pyformex.org
## Project page: http://savannah.nongnu.org/projects/pyformex/
## Copyright 2004-2019 (C) Benedict Verhegghe (benedict.verhegghe@ugent.be)
## Distributed under the GNU General Public License version 3 or later.
##
## This program is free software: you can redistribute it and/or modify
## it under the terms of the GNU General Public License as published by
## the Free Software Foundation, either version 3 of the License, or
## (at your option) any later version.
##
## This program 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 General Public License for more details.
##
## You should have received a copy of the GNU General Public License
## along with this program. If not, see http://www.gnu.org/licenses/.
##
#
"""Framework for multi-processing in pyFormex
This module contains some functions to perform multiprocessing inside
pyFormex in a unified way.
"""
from __future__ import absolute_import, division, print_function
import pyformex as pf
from pyformex import zip
from pyformex.arraytools import splitar
from multiprocessing import Pool, cpu_count, Process, Queue
import numpy as np
[docs]def splitArgs(args, mask=None, nproc=-1, close=False):
"""Split data blocks over multiple processors.
Parameters:
- `args`: a list or tuple of data blocks. All items in the list
that need to be split should be arrays with the same first dimension.
- `mask`: list of bool, with same length as `args`. It flags which
items in the `args` list are to be split. If not specified,
all array type items will be split.
- `nproc`: number of processors intended. If negative (default),
it is set equal to the number of processors detected.
- `close`: bool. If True, the elements where the arrays are
split are included in both blocks delimited by the element.
Returns a list of `nproc` tuples. Each tuple contains the same
number of items as the input `args` and in the same order, whereby
the (nonmasked) arrays are replaced by a slice of the array along
its first axis, and the masked and non-array items are replicated
as is.
This function uses :meth:`arraytools.splitar` for the splitting
of the arrays.
Example:
>>> splitArgs([np.arange(5),'abcde'],nproc=3)
[(array([0, 1]), 'abcde'), (array([2]), 'abcde'), (array([3, 4]), 'abcde')]
>>> for i in splitArgs([np.eye(5),'=>',np.arange(5)],nproc=3):
... print("%s %s %s" % i)
[[ 1. 0. 0. 0. 0.]
[ 0. 1. 0. 0. 0.]] => [0 1]
[[ 0. 0. 1. 0. 0.]] => [2]
[[ 0. 0. 0. 1. 0.]
[ 0. 0. 0. 0. 1.]] => [3 4]
>>> for i in splitArgs([np.eye(5),'=>',np.arange(5)],mask=[1,0,0],nproc=3):
... print("%s %s %s" % i)
[[ 1. 0. 0. 0. 0.]
[ 0. 1. 0. 0. 0.]] => [0 1 2 3 4]
[[ 0. 0. 1. 0. 0.]] => [0 1 2 3 4]
[[ 0. 0. 0. 1. 0.]
[ 0. 0. 0. 0. 1.]] => [0 1 2 3 4]
"""
if nproc < 0:
nproc = cpu_count()
if mask is None:
mask = [isinstance(a, np.ndarray) for a in args]
split = [
splitar(a, nproc, close=close) if m else [a] * nproc
for a, m in zip(args, mask)
]
return list(zip(*split))
[docs]def dofunc(arg):
"""Helper function for the multitask function.
It expects a tuple with (function,args) as single argument.
"""
func, args = arg
return func(*args)
[docs]def multitask(tasks, nproc=-1):
"""Perform tasks in parallel.
Runs a number of tasks in parallel over a number of subprocesses.
Parameters:
- `tasks` : a list of (function,args) tuples, where function is a
callable and args is a tuple with the arguments to be passed to the
function.
- ` nproc`: the number of subprocesses to be started. This may be
different from the number of tasks to run: processes finishing a
task will pick up a next one. There is no benefit in starting more
processes than the number of tasks or the number of processing units
available. The default will set `nproc` to the minimum of these two
values.
"""
if nproc < 0:
nproc = min(len(tasks), cpu_count())
pf.debug("Multiprocessing using %s processors" % nproc, pf.DEBUG.MULTI)
# if pf.scriptMode == 'script':
if __name__ == '__draw__':
if pf.warning(
"""..
Multiprocessing in 'script' mode
================================
You are trying to use multiprocessing while running in 'script' mode.
Multiprocessing in 'script' mode may cause pyFormex to hang indefinitely.
We strongly advice you to cancel the operation now and to run your
application in 'app' mode. Multiprocessing runs fine in 'app' mode.
""",
actions=['Cancel', 'I know the risks and insist on continuing']
) == 'Cancel':
return
pool = Pool(nproc)
res = pool.map(dofunc, tasks)
pool.close()
return res
### Following is an alternative using Queues
[docs]def worker(input, output):
"""Helper function for the multitask function.
This is the function executed by any of the processes started
by the multitask function. It takes tuples (function,args) from
the input queue, computes the results of the call function(args),
and pushes these results on the output queue.
Parameters:
- `input`: Queue holding the tasks to be performed
- `output`: Queue where the results are to be delivered
"""
for func, args in iter(input.get, 'STOP'):
result = func(*args)
output.put(result)
[docs]def multitask2(tasks, nproc=-1):
"""Perform tasks in parallel.
Runs a number of tasks in parallel over a number of subprocesses.
Parameters:
- `tasks` : a list of (function,args) tuples, where function is a
callable and args is a tuple with the arguments to be passed to the
function.
- ` nproc`: the number of subprocesses to be started. This may be
different from the number of tasks to run: processes finishing a
task will pick up a next one. There is no benefit in starting more
processes than the number of tasks or the number of processing units
available. The default will set `nproc` to the minimum of these two
values.
"""
if nproc < 0:
nproc = min(len(tasks), cpu_count())
# Create queues
task_queue = Queue()
done_queue = Queue()
# Submit tasks
for task in tasks:
task_queue.put(task)
# Start worker processes
for i in range(nproc):
Process(target=worker, args=(task_queue, done_queue)).start()
# Get results
res = [done_queue.get() for i in range(len(tasks))]
# Tell child processes to stop
for i in range(nproc):
task_queue.put('STOP')
# Return result
return res
# End
