Book Image

Python Parallel Programming Cookbook

By : Giancarlo Zaccone
Book Image

Python Parallel Programming Cookbook

By: Giancarlo Zaccone

Overview of this book

This book will teach you parallel programming techniques using examples in Python and will help you explore the many ways in which you can write code that allows more than one process to happen at once. Starting with introducing you to the world of parallel computing, it moves on to cover the fundamentals in Python. This is followed by exploring the thread-based parallelism model using the Python threading module by synchronizing threads and using locks, mutex, semaphores queues, GIL, and the thread pool. Next you will be taught about process-based parallelism where you will synchronize processes using message passing along with learning about the performance of MPI Python Modules. You will then go on to learn the asynchronous parallel programming model using the Python asyncio module along with handling exceptions. Moving on, you will discover distributed computing with Python, and learn how to install a broker, use Celery Python Module, and create a worker. You will understand anche Pycsp, the Scoop framework, and disk modules in Python. Further on, you will learnGPU programming withPython using the PyCUDA module along with evaluating performance limitations.
Table of Contents (13 chapters)
Python Parallel Programming Cookbook
Credits
About the Author
About the Reviewers
www.PacktPub.com
Preface
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) 
      ...