Book Image

Distributed Computing with Python

Book Image

Distributed Computing with Python

Overview of this book

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.
Table of Contents (15 chapters)
Distributed Computing with Python
Credits
About the Author
About the Reviewer
www.PacktPub.com
Preface
Index

Cloud computing and AWS


AWS is the leading provider of cloud computing; that is to say, it is a collection of Internet-based, on-demand computing and storage services, usually with a pay-as-you-go pricing model.

Being able to access the vast pool of computing resources (real or virtualized) and storage units of a cloud provider, means that an application can scale both horizontally (by adding more machines) and vertically (by choosing more powerful hardware) on demand. The same application can, therefore, run with no or very little downtime while adapting to its user load, by increasing or reducing the resources it uses dynamically (and therefore, its cost per unit of time).

This ease of scaling resources, the ability to tap into the vast resources of a big cloud provider such as Amazon, the high availability of cloud infrastructure, and the low prices of computing and storage resources, can make the cloud a very attractive option for application deployment, even for a small company or an...