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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

Understanding interprocess communication

Interprocess communication (IPC) consists of mechanisms that allow the exchange of information among processes.

There are several means to implement IPC, and in general, they depend on the chosen architecture for the runtime environment. In some cases, for example, where processes run on the same machine, we could use various types of communication, such as shared memory, message queues, and pipes. When processes are physically distributed in clusters, for instance, we could use sockets and Remote Procedure Call (RPC).

In Chapter 5, Using Multiprocessing and ProcessPoolExecutor, we verified the use of regular pipes among other things. We also studied the communication among processes that have a common parent process. But, sometimes it is necessary to perform communication between unrelated processes (processes with different parent processes). We might ask ourselves if the communication between unrelated processes could be done through their addressing...

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