Distributed Computing with Python

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.