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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

Your typical HPC cluster

HPC systems come in all shapes and sizes; however, they tend to share a few common characteristics. They tend to be homogeneous, with a large number of identical, rack-mounted computers located in the same room and connected with very fast networking. Sometimes, especially between upgrades, an HPC cluster might be split into two processor architectures. In those cases, particular care needs to be taken in scheduling our code if those differences have important performance implications.

Most of the computers in a cluster (called compute nodes) run exactly the same operating system and the same set of software packages and are dedicated to exclusively run computations, as the name implies. Users are not typically allowed to use these machines directly.

A smaller number of nodes are special in that they are usually not as powerful as the compute nodes but do allow users to log in. They are called service nodes (or login nodes or head nodes) and are dedicated to run...

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