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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 FREE CHAPTER 2. Designing Parallel Algorithms 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

Using asyncio

We can define asyncio as a module that came to reboot asynchronous programming in Python. The asyncio module allows the implementation of asynchronous programming using a combination of the following elements:

  • Event loop: This was already defined in the previous section. The asyncio module allows an event loop per process.
  • Coroutines: As mentioned in the official documentation of asyncio, "A coroutine is a generator that follows certain conventions." Its most interesting feature is that it can be suspended during execution to wait for external processing (some routine in I/O) and return from the point it had stopped when the external processing is done.
  • Futures: The asyncio module defines its own object Future. Futures represent a processing that has still not been accomplished.
  • Tasks: This is a subclass of asyncio.Future to encapsulate and manage coroutines.

Beyond these mechanisms, asyncio provides a series of other features for the developing of applications, such...

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