Download Distributed Computing with Python by Francesco Pierfederici PDF

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By Francesco Pierfederici

Key Features

  • You'll learn how to write info processing courses in Python which are hugely to be had, trustworthy, and fault tolerant
  • Make use of Amazon net providers besides Python to set up a robust distant computation system
  • Train Python to deal with data-intensive and source hungry applications

Book Description

CPU-intensive information processing initiatives became the most important contemplating the complexity of a number of the large information purposes which are used this present day. lowering the CPU usage in step with procedure is essential to enhance the final velocity of applications.

This booklet will train you the way to accomplish parallel execution of computations via dispensing them throughout a number of processors in one computing device, hence enhancing the final functionality of a large information processing job. we are going to conceal synchronous and asynchronous types, shared reminiscence and dossier platforms, conversation among a variety of methods, synchronization, and more.

What you'll Learn

  • Get an advent to parallel and allotted computing
  • See synchronous and asynchronous programming
  • Explore parallelism in Python
  • Distributed program with Celery
  • Python within the Cloud
  • Python on an HPC cluster
  • Test and debug disbursed applications

About the Author

Francesco Pierfederici is a software program engineer who loves Python. He has been operating within the fields of astronomy, biology, and numerical climate forecasting for the final 20 years.

He has outfitted huge allotted structures that utilize tens of millions of cores at a time and run on many of the quickest supercomputers on the planet. He has additionally written loads of purposes of doubtful usefulness yet which are nice enjoyable. generally, he simply loves to construct things.

Table of Contents

  1. An advent to Parallel and allotted Computing
  2. Asynchronous Programming
  3. Parallelism in Python
  4. Distributed functions – with Celery
  5. Python within the Cloud
  6. Python on an HPC Cluster
  7. Testing and Debugging disbursed Applications
  8. The street Ahead

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Extra resources for Distributed Computing with Python

Example text

I would like to thank my wife, Alicia, for her unreasonable patience during the gestation of this book. I would also like to thank Parshva Sheth and Aaron Lazar at Packt Publishing and the technical reviewer, James King, who were all instrumental in making this a better book. About the Reviewer James King is a software developer with a broad range of experience in distributed systems. He is a contributor to many open source projects including OpenStack and Mozilla Firefox. He enjoys mathematics, horsing around with his kids, games, and art.

In reality, however, only one task was running at any given time. The typical tools used in parallel applications are threads. On systems such as Python (as we will see in Chapter 3, Parallelism in Python) where threads have significant limitations, programmers resort to launching (oftentimes, by means of forking) subprocesses instead. These subprocesses replace (or complement) threads and run alongside the main application process. The first technique is called multithreaded programming. The second is called multiprocessing.

This allowed, for the first time, a potentially enormous number of computers to communicate with each other. These networked machines can either be located in the same office/building (the so-called Local Area Network (LAN)) or be spread out across different buildings, cities, or even across the planet (that is, WAN or wide area networking). By now, most of us are familiar with multiprocessor/multicore computers, and indeed, the chances are pretty high that the phone in our pocket, the tablet in our hands, or the laptop we take on the road has a handful of cores already.

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