Parallel R [NOOK Book]

Overview

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....

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Parallel R

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Overview

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
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Product Details

  • ISBN-13: 9781449320331
  • Publisher: O'Reilly Media, Incorporated
  • Publication date: 10/21/2011
  • Sold by: Barnes & Noble
  • Format: eBook
  • Edition number: 1
  • Pages: 126
  • File size: 5 MB

Meet 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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Table of Contents

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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Sort by: Showing 1 Customer Reviews
  • Anonymous

    Posted May 21, 2012

    Very useful introduction to parallel processing with R

    A client asked about parallel processing with R, and this book got me started easily. I used parallel on windows, which was quick and easy, but limited to the cores on one machine. The book has information on Hadoop, but I did not pursue that approach.

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