I am using the multiprocessing with apply_async to do some work. Each task
takes a few seconds but I have several thousand tasks. I was wondering if
there is a more efficient method and especially when I plan to operate on a
large memory arrays (numpy)
Here is what I have now
import multiprocessing as mp
import random
def f(x):
count=0
for i in range(x):
x=random.random()
y=random.random()
if x*x + y*y<=1:
count+=1
return count
def main():
resultObj=[]
n=10000
P=mp.Pool(2)
for arg in xrange(n):
resultObj.append(P.apply_async(f,(arg,)))
P.close()
P.join()
result = [ i.get() for i in resultObj ]
print sum(result)/(n)
if __name__=="__main__":
main()
1) Does multiprocessing do a fork for each task?
2) If so, I assume thats costly due to setup and teardown. Would this be
the case?
3) I plan to pass large arrays to function,f, therefore is there a more
efficient method to achieve this?
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