Hi,
thanks for your response.
I checked out multiprocessing.value, however from what I can make out, it works
with object of only a very limited type. Is there a way to do this for more
complex objects? (In reality, my object is a large multi-dimensional numpy
array).
Thanks,
Elsa.
Date: Wed, 6 Apr 2011 22:20:06 -0700
Subject: Re: multiprocessing
From: drsali...@gmail.com
To: kerensael...@hotmail.com
CC: python-list@python.org
On Wed, Apr 6, 2011 at 9:06 PM, elsa <kerensael...@hotmail.com> wrote:
Hi guys,
I want to try out some pooling of processors, but I'm not sure if it
is possible to do what I want to do. Basically, I want to have a
global object, that is updated during the execution of a function, and
I want to be able to run this function several times on parallel
processors. The order in which the function runs doesn't matter, and
the value of the object doesn't matter to the function, but I do want
the processors to take turns 'nicely' when updating the object, so
there are no collisions. Here is an extremely simplified and trivial
example of what I have in mind:
from multiprocessing import Pool
import random
p=Pool(4)
myDict={}
def update(value):
global myDict
index=random.random()
myDict[index]+=value
total=1000
p.map(update,range(total))
After, I would also like to be able to use several processors to
access the global object (but not modify it). Again, order doesn't
matter:
p1=Pool(4)
def getValues(index):
global myDict
print myDict[index]
p1.map(getValues,keys.myDict)
Is there a way to do this
This should give you a synchronized wrapper around an object in shared memory:
http://docs.python.org/library/multiprocessing.html#multiprocessing.Value
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