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Asked: 2026-05-14T21:14:28+00:00

I’m using ParallelPython to develop a performance-critical script. I’d like to share one value

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I’m using ParallelPython to develop a performance-critical script. I’d like to share one value between the 8 processes running on the system. Please excuse the trivial example but this illustrates my question.

def findMin(listOfElements):
    for el in listOfElements:
        if el < min:
             min = el

import pp
min = 0
myList = range(100000)
job_server = pp.Server()
f1 = job_server.submit(findMin, myList[0:25000])
f2 = job_server.submit(findMin, myList[25000:50000])
f3 = job_server.submit(findMin, myList[50000:75000]) 
f4 = job_server.submit(findMin, myList[75000:100000])

The pp docs don’t seem to describe a way to share data across processes. Is it possible?

If so, is there a standard locking mechanism (like in the threading module) to confirm that only one update is done at a time?

l = Lock()
if(el < min):
     l.acquire
     if(el < min):
         min = el
     l.release

I understand I could keep a local min and compare the 4 in the main thread once returned, but by sharing the value I can do some better pruning of my BFS binary tree and potentially save a lot of loop iterations.

Thanks-

Jonathan

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1 Answer

  1. Editorial Team

    Actually, there is an example at http://www.parallelpython.com/content/view/17/31/#CALLBACK and they simply use the locks from the thread module.

    Like JudoWill pointed out, make sure to experiment with how often you should sync the global min in your jobs. If you do it every time you may end up close to serializing your whole calculation.

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