Synopsis
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"synopsis" may belong to another edition of this title.
About the Authors
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.
Stephen Weston has been working in high performance and parallel computing for over 25 years. He was employed at Scientific Computing Associates in the 90's, working on the Linda programming system, invented by David Gelernter. He was also a founder of Revolution Computing, leading the development of parallel computing packages for R, including nws, foreach, doSNOW, and doMC. He works at Yale University as an HPC Specialist.
"About this title" may belong to another edition of this title.
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Parallel R: Data Analysis in the Distributed World
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