Parallel R: Data Analysis in the Distributed World

by Q. Ethan McCallum, Stephen Weston

It’s tough to argue with R as a high-quality, cross-platform, open source statistical software product―unless you’re in the business of crunching Big Data. This concise book introduces you to several strategies for using R to analyze large datasets, including three chapters on using R and Hadoop together. You’ll learn the basics of Snow, Multicore, Parallel, Segue, RHIPE, and Hadoop Streaming, including how to find them, how to use them, when they work well, and when they don’t. With these packages, you can overcome R’s single-threaded nature by spreading work across multiple CPUs, or offloading work to multiple machines to address R’s memory barrier.

Genres: Programming

126 pages, Paperback

First published January 1, 2011

Reviews

[Arnarn]

Read snow multi core and parallel packages

[liyang · Rating 3 out of 5]

A good big picture of parallel R by introducing multiple packages for parallel and distributed computing, including snow, multicore, parallel and a little about R+Hadoop, RHIPE etc. However, it's just a big picture. Besides, it's more like a simple how-to manual, and lack of details.