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Distributed Computing with Python

You're reading from   Distributed Computing with Python Harness the power of multiple computers using Python through this fast-paced informative guide

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Product type Paperback
Published in Apr 2016
Publisher
ISBN-13 9781785889691
Length 170 pages
Edition 1st Edition
Languages
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Author (1):
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Rasheedh B Rasheedh B
Author Profile Icon Rasheedh B
Rasheedh B
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Table of Contents (10) Chapters Close

Preface 1. An Introduction to Parallel and Distributed Computing 2. Asynchronous Programming FREE CHAPTER 3. Parallelism in Python 4. Distributed Applications – with Celery 5. Python in the Cloud 6. Python on an HPC Cluster 7. Testing and Debugging Distributed Applications 8. The Road Ahead Index

Summary

Python has had support for asynchronous programming since version 1.5.2, with the introduction of the asyncore and asynchat modules for asynchronous network programming. Version 2.5 introduced the ability to send data to coroutines via yield expressions, allowing us to write asynchronous code in a simpler but more powerful way. Python 3.4 introduced a new library for asynchronous I/O called asyncio.

Python 3.5 introduced true coroutine types via async def and await. Interested readers are encouraged to explore these new developments. One word of warning though: asynchronous programming is a powerful tool that can dramatically improve the performance of I/O-intensive code. It does not come without issues, though, the main of which is complexity.

Any important asynchronous code has to carefully select nonblocking libraries in order to avoid using blocking code. It has to implement a coroutine scheduler (since the OS does not schedule coroutines for us like it does with threads), which...

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Distributed Computing with Python
Published in: Apr 2016
Publisher:
ISBN-13: 9781785889691
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