Harness the power of multiple computers using Python through this fastpaced 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 dataintensive 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 CPUintensive 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
CPUintensive 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, stepbystep guide will show you how to make the best of your hardware configuration using Python for distributing applications.