def test2(obj):
from pathos.pp import ParallelPythonPool
p = ParallelPythonPool(2)
x = [1, 2, 3]
assert map(obj, x) == p.map(obj, x)
#!/usr/bin/env python
#
# Author: Mike McKerns (mmckerns @caltech and @uqfoundation)
# Copyright (c) 1997-2014 California Institute of Technology.
# License: 3-clause BSD. The full license text is available at:
# - http://trac.mystic.cacr.caltech.edu/project/pathos/browser/pathos/LICENSE
from pathos.pp import stats
from pathos.pp import ParallelPythonPool as Pool
pool = Pool()
def host(id):
import socket
import time
time.sleep(1.0)
return "Rank: %d -- %s" % (id, socket.gethostname())
print "Evaluate 10 items on 2 cpus" #FIXME: reset lport below
pool.ncpus = 2
pool.servers = ('localhost:5653',)
res5 = pool.map(host, range(10))
print pool
print '\n'.join(res5)
print stats()
print ''
# end of file
_d = [delay]*len(_x)
from pathos.python import PythonSerial as PS
basic = PS()
print basic
start = time.time()
res = basic.map(busy_add, _x, _y, _d)
print "time to queue:", time.time() - start
start = time.time()
_basic = list(res)
print "time to results:", time.time() - start
print ""
from pathos.pp import ParallelPythonPool as PPP
#from pathos.pp import stats
pp_pool = PPP(4, servers=('localhost:5653','localhost:2414'))
print pp_pool
start = time.time()
res = pp_pool.map(busy_add, _x, _y, _d)
print "time to queue:", time.time() - start
start = time.time()
_pp_pool = list(res)
print "time to results:", time.time() - start
#print stats()
assert _basic == _pp_pool
print ""
from pathos.multiprocessing import ProcessingPool as MPP
mp_pool = MPP(4)
print mp_pool
#!/usr/bin/env python
#
# Author: Mike McKerns (mmckerns @caltech and @uqfoundation)
# Copyright (c) 1997-2014 California Institute of Technology.
# License: 3-clause BSD. The full license text is available at:
# - http://trac.mystic.cacr.caltech.edu/project/pathos/browser/pathos/LICENSE
from pathos.pp import stats
from pathos.pp import ParallelPythonPool as Pool
pool = Pool()
def host(id):
import socket
import time
time.sleep(1.0)
return "Rank: %d -- %s" % (id, socket.gethostname())
print "Evaluate 10 items on 2 cpus" #FIXME: reset lport below
pool.ncpus = 2
pool.servers = ('localhost:5653', )
res5 = pool.map(host, range(10))
print pool
print '\n'.join(res5)
print stats()
print ''
# end of file
#!/usr/bin/env python
from pathos.pp import stats
from pathos.pp import ParallelPythonPool as Pool
pool = Pool()
def host(id):
import socket
return "Rank: %d -- %s" % (id, socket.gethostname())
print "Evaluate 10 items on 1 cpu"
pool.ncpus = 1
res3 = pool.map(host, range(10))
print pool
print '\n'.join(res3)
print stats()
print "Evaluate 10 items on 2 cpus"
pool.ncpus = 2
res5 = pool.map(host, range(10))
print pool
print '\n'.join(res5)
print stats()
# end of file
def test_ppmap(obj):
from pathos.pp import ParallelPythonPool
p = ParallelPythonPool(2)
x = [1, 2, 3]
assert map(obj, x) == p.map(obj, x)
#!/usr/bin/env python
#
# Author: Mike McKerns (mmckerns @caltech and @uqfoundation)
# Copyright (c) 1997-2014 California Institute of Technology.
# License: 3-clause BSD. The full license text is available at:
# - http://trac.mystic.cacr.caltech.edu/project/pathos/browser/pathos/LICENSE
"""
example of using the 'raw' distributed parallel mapper
To run: python pp_map.py
"""
from pathos.pp import ParallelPythonPool 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))
#!/usr/bin/env python
#
# Author: Mike McKerns (mmckerns @caltech and @uqfoundation)
# Copyright (c) 1997-2014 California Institute of Technology.
# License: 3-clause BSD. The full license text is available at:
# - http://trac.mystic.cacr.caltech.edu/project/pathos/browser/pathos/LICENSE
from pathos.pp import stats
from pathos.pp import ParallelPythonPool as Pool
pool = Pool()
def host(id):
import socket
return "Rank: %d -- %s" % (id, socket.gethostname())
print "Evaluate 10 items on 1 cpu"
pool.ncpus = 1
res3 = pool.map(host, range(10))
print pool
print '\n'.join(res3)
print stats()
print "Evaluate 10 items on 2 cpus"
pool.ncpus = 2
res5 = pool.map(host, range(10))
print pool
print '\n'.join(res5)
print stats()
#!/usr/bin/env python
#
# Author: Mike McKerns (mmckerns @caltech and @uqfoundation)
# Copyright (c) 1997-2014 California Institute of Technology.
# License: 3-clause BSD. The full license text is available at:
# - http://trac.mystic.cacr.caltech.edu/project/pathos/browser/pathos/LICENSE
"""
example of using the 'raw' distributed parallel mapper
To run: python pp_map.py
"""
from pathos.pp import ParallelPythonPool 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))
from numpy import sin
return sin(x - xp)
# print the input to screen
x = np.arange(N * nodes, dtype=np.float64)
xp = np.arange(N * nodes, dtype=np.float64)[::-1]
print("Input: %s\n" % x)
# map sin_diff to the workers, then print to screen
print("Running serial python ...")
y = map(sin_diff, x, xp)
print("Output: %s\n" % np.asarray(y))
# map sin_diff to the workers, then print to screen
print("Running mpi4py on %d cores..." % nodes)
y = MpiPool(nodes).map(sin_diff, x, xp)
print("Output: %s\n" % np.asarray(y))
# map sin_diff to the workers, then print to screen
print("Running multiprocesing on %d processors..." % nodes)
y = ProcessingPool(nodes).map(sin_diff, x, xp)
print("Output: %s\n" % np.asarray(y))
# map sin_diff to the workers, then print to screen
print("Running parallelpython on %d cpus..." % nodes)
y = ParallelPythonPool(nodes).map(sin_diff, x, xp)
print("Output: %s\n" % np.asarray(y))
# EOF
"""sin squared of all data"""
import numpy as np
return np.sin(xi)**2
# print the input to screen
x = np.arange(N * nodes, dtype=np.float64)
print("Input: %s\n" % x)
# run sin2 in series, then print to screen
print("Running serial python ...")
y = map(sin2, x)
print("Output: %s\n" % np.asarray(y))
# map sin2 to the workers, then print to screen
print("Running mpi4py on %d cores..." % nodes)
y = MpiPool(nodes).map(sin2, x)
print("Output: %s\n" % np.asarray(y))
# map sin2 to the workers, then print to screen
print("Running multiprocesing on %d processors..." % nodes)
y = ProcessingPool(nodes).map(sin2, x)
print("Output: %s\n" % np.asarray(y))
# map sin2 to the workers, then print to screen
print("Running parallelpython on %d cpus..." % nodes)
y = ParallelPythonPool(nodes).map(sin2, x)
print("Output: %s\n" % np.asarray(y))
# EOF