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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

The big picture


Testing and debugging monolithic applications is not simple, as every developer knows. However, there are a number of tools that make the task dramatically easier, including the pdb debugger, various profilers (notable mentions include cProfile and line_profile), linters, static code analysis tools, and a host of test frameworks, a number of which have been included in the standard library of Python 3.3 and higher.

The challenge with distributed applications is that most of the tools and packages that we can use for single-process applications lose much of their power when dealing with multiple processes, especially when these processes run on different computers.

Debugging and profiling distributed applications written in C, C++, and Fortran can be done with tools such as Intel VTune, Allinea MAP, and DDT. Unfortunately, Python developers are left with very few or no options for the time being.

Writing small- or medium-sized distributed systems is not terribly hard, as we saw...

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