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. You'll learn the basics of Snow, Multicore, Parallel, and some Hadoop-related tools, 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. Snow: works well in a traditional cluster environment Multicore: popular for multiprocessor and multicore computers Parallel: part of the upcoming R 2.14.0 release R+Hadoop: provides low-level access to a popular form of cluster computing RHIPE: uses Hadoop's power with R's language and interactive shell Segue: lets you use Elastic MapReduce as a backend for lapply-style operations

Genres: Programming

126 pages, Kindle Edition

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.