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#
# A library to manage ARCFIRE experiments
#
# Copyright (C) 2017-2018 Nextworks S.r.l.
# Copyright (C) 2017-2018 imec
#
# Sander Vrijders <sander.vrijders@ugent.be>
# Dimitri Staessens <dimitri.staessens@ugent.be>
# Vincenzo Maffione <v.maffione@nextworks.it>
# Marco Capitani <m.capitani@nextworks.it>
#
# This library is free software; you can redistribute it and/or
# modify it under the terms of the GNU Lesser General Public
# License as published by the Free Software Foundation; either
# version 2.1 of the License, or (at your option) any later version.
#
# This library 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
# Lesser General Public License for more details.
#
# You should have received a copy of the GNU Lesser General Public
# License along with this library; if not, write to the Free Software
# Foundation, Inc., http://www.fsf.org/about/contact/.
#
import multiprocessing.dummy as multiprocessing
import sys
import rumba.log as log
if sys.version_info[0] >= 3:
import contextlib
else:
import contextlib2 as contextlib
logger = log.get_logger(__name__)
def call_in_parallel(name_list, argument_list, executor_list):
"""
Calls each executor in executor_list with the corresponding
argument in argument_list
Assumes that the three lists are the same length, will fail otherwise.
Is equivalent to
for i, e in enumerate(executor_list):
e(argument_list[i])
but all calls will be executed in parallel.
If successful, no output will be given. Otherwise, this will raise
the exception raised by one failed call at random.
:param name_list: list of names of the executors (used for logging)
:param argument_list: list of arguments to the executors
:param executor_list: list of executors (as functions)
"""
if len(executor_list) != len(name_list) \
or len(executor_list) != len(argument_list):
raise ValueError("Names, arguments and executors lists "
"must have the same length")
def job(executor, name, m_queue, argument):
try:
# m_queue.cancel_join_thread()
logger.debug('Starting process "%s".'
% (name,))
executor(argument)
m_queue.put("DONE")
except BaseException as e:
logger.error('Setup failed. %s: %s',
type(e).__name__,
str(e))
m_queue.put(e)
logger.debug('About to start spawning processes.')
queue = multiprocessing.Queue()
with contextlib.ExitStack() as stack:
# This is a composite context manager.
# After exiting the 'with' block, the __exit__ method of all
# processes that have been registered will be called.
msg_to_be_read = 0
for i, e in enumerate(executor_list):
stack.enter_context(ProcessContextManager(
target=job,
args=(e, name_list[i], queue, argument_list[i])
))
msg_to_be_read += 1
results = []
for _ in range(len(executor_list)):
result = queue.get() # This blocks until results are available
msg_to_be_read -= 1
results.append(result)
for result in results:
if result != "DONE":
raise result
# If we get here, we got a success for every node, hence we are done.
class ProcessContextManager(object):
def __init__(self, target, args=None, kwargs=None):
if args is None:
args = ()
if kwargs is None:
kwargs = {}
self.process = multiprocessing.Process(
target=target,
args=tuple(args),
kwargs=kwargs
)
def __enter__(self):
self.process.start()
return self.process
def __exit__(self, exc_type, exc_val, exc_tb):
if exc_tb is not None or exc_val is not None or exc_tb is not None:
logger.error('Subprocess error: %s.' % (type(exc_val).__name__,))
try:
self.process.terminate()
self.process.join()
except AttributeError:
# We are using multiprocessing.dummy, so no termination.
# We trust the threads will die with the application
# (since we are shutting down anyway)
pass
return False
else:
self.process.join()
return True
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