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#!/usr/bin/env python
#
# Author: Mike McKerns (mmckerns @caltech and @uqfoundation)
# Copyright (c) 1997-2016 California Institute of Technology.
# Copyright (c) 2016-2026 The Uncertainty Quantification Foundation.
# License: 3-clause BSD. The full license text is available at:
# - https://github.com/uqfoundation/pathos/blob/master/LICENSE
"""
example of using the 'raw' distributed parallel mapper
To run: python pp_map.py
"""
from pathos.pools import ParallelPool as Pool
pool = Pool()
if __name__ == '__main__':
def add(x, y, z):
"""Add three values"""
return x + y + z
def busybeaver(x):
"""This can take a while"""
for num in range(1000000):
x = x + num
return x
# Immediate evaluation example
import time
start = time.time()
results = pool.map(busybeaver, range(10))
print('Time to queue the jobs: %s' % (time.time() - start))
start = time.time()
# Casting the ppmap generator to a list forces each result to be
# evaluated. When done immediately after the jobs are submitted,
# our program twiddles its thumbs while the work is finished.
print(list(results))
print('Time to get the results: %s' % (time.time() - start))
# Delayed evaluation example
start = time.time()
results = pool.imap(busybeaver, range(10))
print('Time to queue the jobs: %s' % (time.time() - start))
# In contrast with the above example, this time we're submitting a
# batch of jobs then going off to do more work while they're
# processing. Maybe "time.sleep" isn't the most exciting example,
# but it illustrates the point that our main program can do work
# before ppmap() is finished. Imagine that you're submitting some
# heavyweight image processing jobs at the beginning of your
# program, going on to do other stuff like fetching more work to
# do from a remote server, then coming back later to handle the
# results.
time.sleep(5)
start = time.time()
print(list(results))
print('Time to get the first results: %s' % (time.time() - start))
# Built-in map example
print(list(map(add, [1, 2, 3], [4, 5, 6], [7, 8, 9])))
# Trivial ppmap tests
for i in range(10):
print('-' * 30)
start = time.time()
print(pool.map(add, [1, 2, 3], [4, 5, 6], [7, 8, 9]))
print('Iteration time: %s' % (time.time() - start))
# Heavier ppmap tests
for i in range(10):
print('-' * 30)
start = time.time()
print(pool.map(busybeaver, range(10)))
print('Iteration time: %s' % (time.time() - start))
# cleanup
pool.clear()
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