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Parallel Programming with Python

You're reading from   Parallel Programming with Python Develop efficient parallel systems using the robust Python environment.

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Product type Paperback
Published in Jun 2014
Publisher
ISBN-13 9781783288397
Length 124 pages
Edition 1st Edition
Languages
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Author (1):
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 Palach Palach
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Palach
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Table of Contents (10) Chapters Close

Preface 1. Contextualizing Parallel, Concurrent, and Distributed Programming 2. Designing Parallel Algorithms FREE CHAPTER 3. Identifying a Parallelizable Problem 4. Using the threading and concurrent.futures Modules 5. Using Multiprocessing and ProcessPoolExecutor 6. Utilizing Parallel Python 7. Distributing Tasks with Celery 8. Doing Things Asynchronously Index

Discovering PP

The previous section introduced a low-level mechanism to establish communication among the processes using system calls directly. This was necessary to contextualize the communication between processes in the Linux and Unix environments. Now, we will use a Python module, PP, to establish IPC communication not only among local processes, but also physically distributed throughout a computer network.

The available PP module documentation is not extensive. We can find the documents and FAQs at http://www.parallelpython.com/component/option,com_smf/. The API provides a wide notion of how this tool should be used; it is simple and straightforward.

The most important advantage of using PP is the abstraction that this module provides. Some important features of PP are as follows:

  • Automatic detection of number of processors to improve load balance
  • Many processors allocated can be changed at runtime
  • Load balance at runtime
  • Auto-discovery resources throughout the network

The PP module implements...

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