Distributed Computing with Python

Nonfiction, Computers, Programming, Parallel Programming, Advanced Computing, Parallel Processing, Programming Languages
Cover of the book Distributed Computing with Python by Francesco Pierfederici, Packt Publishing
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Author: Francesco Pierfederici ISBN: 9781785887048
Publisher: Packt Publishing Publication: April 12, 2016
Imprint: Packt Publishing Language: English
Author: Francesco Pierfederici
ISBN: 9781785887048
Publisher: Packt Publishing
Publication: April 12, 2016
Imprint: Packt Publishing
Language: English

Harness the power of multiple computers using Python through this fast-paced informative guide

About This Book

  • You'll learn to write data processing programs in Python that are highly available, reliable, and fault tolerant
  • Make use of Amazon Web Services along with Python to establish a powerful remote computation system
  • Train Python to handle data-intensive and resource hungry applications

Who This Book Is For

This book is for Python developers who have developed Python programs for data processing and now want to learn how to write fast, efficient programs that perform CPU-intensive data processing tasks.

What You Will Learn

  • Get an introduction to parallel and distributed computing
  • See synchronous and asynchronous programming
  • Explore parallelism in Python
  • Distributed application with Celery
  • Python in the Cloud
  • Python on an HPC cluster
  • Test and debug distributed applications

In Detail

CPU-intensive data processing tasks have become crucial considering the complexity of the various big data applications that are used today. Reducing the CPU utilization per process is very important to improve the overall speed of applications.

This book will teach you how to perform parallel execution of computations by distributing them across multiple processors in a single machine, thus improving the overall performance of a big data processing task. We will cover synchronous and asynchronous models, shared memory and file systems, communication between various processes, synchronization, and more.

Style and Approach

This example based, step-by-step guide will show you how to make the best of your hardware configuration using Python for distributing applications.

View on Amazon View on AbeBooks View on Kobo View on B.Depository View on eBay View on Walmart

Harness the power of multiple computers using Python through this fast-paced informative guide

About This Book

Who This Book Is For

This book is for Python developers who have developed Python programs for data processing and now want to learn how to write fast, efficient programs that perform CPU-intensive data processing tasks.

What You Will Learn

In Detail

CPU-intensive data processing tasks have become crucial considering the complexity of the various big data applications that are used today. Reducing the CPU utilization per process is very important to improve the overall speed of applications.

This book will teach you how to perform parallel execution of computations by distributing them across multiple processors in a single machine, thus improving the overall performance of a big data processing task. We will cover synchronous and asynchronous models, shared memory and file systems, communication between various processes, synchronization, and more.

Style and Approach

This example based, step-by-step guide will show you how to make the best of your hardware configuration using Python for distributing applications.

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