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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

Running a Python job using PBS


The Portable Batch System (PBS) was developed for NASA in the beginning of the 90s. It now exists in three variants: OpenPBS, Torque, and PBS Pro. These are all forks of the original codebase, and have a very similar look and feel from the user perspective.

Here, we will look at PBS Pro (which is a commercial product by Altair Engineering and is available at http://www.pbsworks.com), but most of the features and directives we will mention should work on Torque and OpenPBS with just some minor differences. Also, in the interest of brevity, we will mostly concentrate on the differences between HTCondor (described earlier) and PBS.

Conceptually, PBS is just like HTCondor, a resource manager and job scheduling system. Both have a similar architecture, with a master node (pbs_server), a negotiator and scheduler (pbs_sched), and job supervisors (pbs_mom) on the execution nodes.

Users submit jobs to a queue. Usually, there are multiple job queues for different types...

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