Parallel R: Data Analysis in the Distributed World
By Q. Ethan McCallum and Stephen Weston
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Currently unavailable
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About this ebook
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
Q. Ethan McCallum
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.
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