# 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 = list(map(sin2, x))
print("Output: %s\n" % np.asarray(y))
if HAS_PYINA:
# 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 = ProcessPool(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 threads..." % nodes)
y = ThreadPool(nodes).map(sin2, x)
print("Output: %s\n" % np.asarray(y))
freeze_support()
# 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 = list(map(sin_diff, x, xp))
print("Output: %s\n" % np.asarray(y))
if HAS_PYINA:
# 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 = ProcessPool(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 threads..." % nodes)
y = ThreadPool(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 = ParallelPool(nodes).map(sin_diff, x, xp)
import scipy.integrate
scipy.integrate.quad(lambda x: 4.0/(1+x*x), 0, 1)
"""
from numpy import arange
# default # of rectangles
n = 20000
integration_points = (arange(1, n + 1) - 0.5) / n
def f(x):
return 4.0 / (1.0 + x * x)
#from pyina.launchers import MpiScatter as Mpi
from pyina.launchers import MpiPool as Mpi
if __name__ == '__main__':
work = Mpi(2)
out = work.map(f, integration_points)
from pyina import mpi
if mpi.world.rank == 0:
print("approxmiate pi : ", sum(out) / n)
print("calculated on %d nodes " % work.nodes)
# end of file
def play(Q):
id, l = Q
import numpy
return "3 x %d = %d" % (id, numpy.sum(l))
def play2(id,l):
import numpy
return "3 x %d = %d" % (id, numpy.sum(l))
args = [ (i, range(3)*i) for i in range(5) ]
arg1 = [ i for i in range(5) ]
arg2 = [ range(3)*i for i in range(5) ]
print "Using 12 nodes and a worker pool..."
print 'Evaluate a function that expects a n-tuple argument "map(f,args)"'
pool = MpiPool(12)
res1 = pool.map(play, args)
#res1 = map(play, args)
print pool
print '\n'.join(res1)
print ''
print 'Evaluate a function that expects n arguments "map(f,arg1,arg2)"'
res2 = pool.map(play2, arg1, arg2)
#res2 = map(play2, arg1, arg2)
print pool
print '\n'.join(res2)
# end of file
#!/usr/bin/env python
#
# Author: Mike McKerns (mmckerns @caltech and @uqfoundation)
# Copyright (c) 1997-2016 California Institute of Technology.
# Copyright (c) 2016-2018 The Uncertainty Quantification Foundation.
# License: 3-clause BSD. The full license text is available at:
# - https://github.com/uqfoundation/pyina/blob/master/LICENSE
from pyina.launchers import MpiScatter, MpiPool
def host(id):
import socket
return "Rank: %d -- %s" % (id, socket.gethostname())
print("Evaluate 10 items on 3 nodes using a worker pool:")
pool = MpiPool(3)
res1 = pool.map(host, range(10))
print(pool)
print('\n'.join(res1))
print('')
print("Evaluate 10 items on 3 nodes using scatter-gather:")
scat = MpiScatter(3)
res2 = scat.map(host, range(10))
print(scat)
print('\n'.join(res2))
print('')
print("Evaluate 5 items on 2 nodes using a worker pool:")
pool.nodes = 2
res3 = pool.map(host, range(5))
#!/usr/bin/env python
#
# Author: Mike McKerns (mmckerns @caltech and @uqfoundation)
# Copyright (c) 1997-2016 California Institute of Technology.
# Copyright (c) 2016-2019 The Uncertainty Quantification Foundation.
# License: 3-clause BSD. The full license text is available at:
# - https://github.com/uqfoundation/pyina/blob/master/LICENSE
from pyina.launchers import MpiScatter, MpiPool
def host(id):
import socket
return "Rank: %d -- %s" % (id, socket.gethostname())
print("Evaluate 10 items on 3 nodes using a worker pool:")
pool = MpiPool(3)
res1 = pool.map(host, range(10))
print(pool)
print('\n'.join(res1))
print('')
print("Evaluate 10 items on 3 nodes using scatter-gather:")
scat = MpiScatter(3)
res2 = scat.map(host, range(10))
print(scat)
print('\n'.join(res2))
print('')
print("Evaluate 5 items on 2 nodes using a worker pool:")
pool.nodes = 2
res3 = pool.map(host, range(5))
kill_all()
elif len(sys.argv) > 2:
if sys.argv[1] == "--workers":
print("seting up mpi...")
MASTERINFO = set_master()
nodes = sys.argv[2:]
nodes = [node.strip('[()]').strip(',').strip() for node in nodes]
#nodes = nodes.strip('[()]').split(',')
set_workers(nodes, MASTERINFO)
#elif sys.argv[1] == "--alias":
# print "setting up mpi python..."
# nodes = sys.argv[2:]
# nodes = [node.strip('[()]').strip(',').strip() for node in nodes]
# for node in nodes:
# alias(int(node))
elif sys.argv[1] == "--fetch":
nnodes = int(sys.argv[2])
try:
pool = MpiPool()
pool.nodes = nnodes
hostnames = pool.map(host, range(nnodes))
print('\n'.join(hostnames))
except: # "--help"
print(__doc__)
else: # "--help"
print(__doc__)
else: # "--help"
print(__doc__)
# End of file
import scipy.integrate
scipy.integrate.quad(lambda x: 4.0/(1+x*x), 0, 1)
"""
from numpy import arange
# default # of rectangles
n = 20000
integration_points = (arange(1,n+1)-0.5)/n
def f(x):
return 4.0/(1.0+x*x)
#from pyina.launchers import MpiScatter as Mpi
from pyina.launchers import MpiPool as Mpi
if __name__ == '__main__':
work = Mpi(2)
out = work.map(f, integration_points)
from pyina import mpi
if mpi.world.rank == 0:
print "approxmiate pi : ", sum(out)/n
print "calculated on %d nodes: " % work.nodes
# end of file
id, l = Q
import numpy
return "3 x %d = %d" % (id, numpy.sum(l))
def play2(id, l):
import numpy
return "3 x %d = %d" % (id, numpy.sum(l))
args = [(i, range(3) * i) for i in range(5)]
arg1 = [i for i in range(5)]
arg2 = [range(3) * i for i in range(5)]
print("Using 12 nodes and a worker pool...")
print('Evaluate a function that expects a n-tuple argument "map(f,args)"')
pool = MpiPool(12)
res1 = pool.map(play, args)
#res1 = map(play, args)
print(pool)
print('\n'.join(res1))
print('')
print('Evaluate a function that expects n arguments "map(f,arg1,arg2)"')
res2 = pool.map(play2, arg1, arg2)
#res2 = map(play2, arg1, arg2)
print(pool)
print('\n'.join(res2))
# end of file
elif len(sys.argv) > 2:
if sys.argv[1] == "--workers":
print("seting up mpi...")
MASTERINFO = set_master()
nodes = sys.argv[2:]
nodes = [node.strip('[()]').strip(',').strip() for node in nodes]
#nodes = nodes.strip('[()]').split(',')
set_workers(nodes,MASTERINFO)
#elif sys.argv[1] == "--alias":
# print "setting up mpi python..."
# nodes = sys.argv[2:]
# nodes = [node.strip('[()]').strip(',').strip() for node in nodes]
# for node in nodes:
# alias(int(node))
elif sys.argv[1] == "--fetch":
nnodes = int(sys.argv[2])
try:
pool = MpiPool()
pool.nodes = nnodes
hostnames = pool.map(host, range(nnodes))
print('\n'.join(hostnames))
except: # "--help"
print(__doc__)
else: # "--help"
print(__doc__)
else: # "--help"
print(__doc__)
# End of file