Parallel R

Autorentext
Q Ethan McCallum is a consultant, writer, and technology enthusiast, though perhaps not in that order. His work has appeared online on The O'Reilly Network and Java.net, and also in print publications such as C/C++ Users Journal, Doctor Dobb's Journal, and Linux Magazine. In his professional roles, he helps companies to make smart decisions about data and technology.

Klappentext
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.
* 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


Zusammenfassung
R is a wonderful thing, indeed: in recent years this free, open-source product has become a popular toolkit for statistical analysis and programming. Two of R's limitations -- that it is single-threaded and memory-bound -- become especially troublesome in the current era of large-scale data analysis.

Inhalt
Preface Chapter 1: Getting Started Chapter 2: snow Chapter 3: multicore Chapter 4: parallel Chapter 5: A Primer on MapReduce and Hadoop Chapter 6: R+Hadoop Chapter 7: RHIPE Chapter 8: Segue Chapter 9: New and Upcoming

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