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Python Parallel Programming Cookbook

You're reading from   Python Parallel Programming Cookbook Master efficient parallel programming to build powerful applications using Python

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Product type Paperback
Published in Aug 2015
Publisher
ISBN-13 9781785289583
Length 286 pages
Edition 1st Edition
Languages
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Author (1):
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Giancarlo Zaccone Giancarlo Zaccone
Author Profile Icon Giancarlo Zaccone
Giancarlo Zaccone
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Table of Contents (8) Chapters Close

Preface 1. Getting Started with Parallel Computing and Python 2. Thread-based Parallelism FREE CHAPTER 3. Process-based Parallelism 4. Asynchronous Programming 5. Distributed Python 6. GPU Programming with Python Index

How to manage a state between processes


Python multiprocessing provides a manager to coordinate shared information between all its users. A manager object controls a server process that holds Python objects and allows other processes to manipulate them.

A manager has the following properties:

  • It controls the server process that manages a shared object

  • It makes sure the shared object gets updated in all processes when anyone modifies it

How to do it...

Let's see an example of how to share a state between processes:

  1. First, the program creates a manager list, shares it between n number of taskWorkers, and every worker updates an index.

  2. After all workers finish, the new list is printed to stdout:

    import multiprocessing
    
    def worker(dictionary, key, item):
        dictionary[key] = item
    
    if __name__ == '__main__':
        mgr = multiprocessing.Manager()
        dictionary = mgr.dict()
        jobs = [ multiprocessing.Process\
                 (target=worker, args=(dictionary, i, i*2))
                 for i in range(10) 
      ...
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