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Parallel R: Data Analysis in the Distributed World

Parallel R: Data Analysis in the Distributed World

Parallel R: Data Analysis in the Distributed World
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Parallel R: Data Analysis in the Distributed World Paperback - 2011

by McCallum, Q; Weston, Stephen

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O'Reilly Media. 1. Very Good. It's a well-cared-for item that has seen limited use. The item may show minor signs of wear. All the text is legible, with all pages included. It may have slight markings and/or highlighting.
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Details

  • Title Parallel R: Data Analysis in the Distributed World
  • Author McCallum, Q; Weston, Stephen
  • Binding Paperback
  • Edition 1
  • Condition Used - Very good
  • Pages 120
  • Volumes 1
  • Language ENG
  • Publisher O'Reilly Media
  • Publication date 2011-11-29
  • Bookseller's Inventory # 1449309925-11-1
  • ISBN 9781449309923 / 1449309925
  • Weight 0.47 lbs (0.21 kg)
  • Dimensions 9.19 x 7 x 0.27 in (23.34 x 17.78 x 0.69 cm)
  • Themes
    • Aspects (Academic): Science/Technology Aspects
  • Category Computers - Languages / Programming
  • Dewey Decimal Code 005.133
  • Quantity available 1

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Reader reviews for Parallel R: Data Analysis in the Distributed World

From the publisher

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

About the author

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 parallelcomputing 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.

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