Parallel R

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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

Table of Contents
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
Book Details

Paperback: 122 pages
Publisher: O?Reilly Media (October 2011)
Language: English
ISBN-10: 1449309925
ISBN-13: 978-1449309923

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