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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
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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 use a process pool


The multiprocessing library provides the Pool class for simple parallel processing tasks. The Pool class has the following methods:

  • apply(): It blocks until the result is ready.

  • apply_async(): This is a variant of the apply() method, which returns a result object. It is an asynchronous operation that will not lock the main thread until all the child classes are executed.

  • map(): This is the parallel equivalent of the map() built-in function. It blocks until the result is ready, this method chops the iterable data in a number of chunks that submits to the process pool as separate tasks.

  • map_async(): This is a variant of the map() method, which returns a result object. If a callback is specified, then it should be callable, which accepts a single argument. When the result becomes ready, a callback is applied to it (unless the call failed). A callback should be completed immediately; otherwise, the thread that handles the results will get blocked.

How to do it…

This example...

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