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
def test_ppmap(obj):
from pathos.pp import ParallelPythonPool
p = ParallelPythonPool(2)
x = [1, 2, 3]
assert map(obj, x) == p.map(obj, x)
"""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
