Parallel R.

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.

Table of Contents; Preface; Conventions Used in This Book; Using Code Examples; Safari® Books Online; How to Contact Us; Acknowledgments; Q. Ethan McCallum; Stephen Weston; Chapter 1. Getting Started; Why R?; Why Not R?; The Solution: Parallel Execution; A Road Map for This Book; What We'll Cover; Looking Forward ... ; What We'll Assume You Already Know; In a Hurry?; snow; multicore; parallel; R+Hadoop; RHIPE; Segue; Summary; Chapter 2. snow; Quick Look; How It Works; Setting Up; Working with It; Creating Clusters with makeCluster; Parallel K-Means; Initializing Workers.

Load Balancing with clusterApplyLBTask Chunking with parLapply; Vectorizing with clusterSplit; Load Balancing Redux; Functions and Environments; Random Number Generation; snow Configuration; Installing Rmpi; Executing snow Programs on a Cluster with Rmpi; Executing snow Programs with a Batch Queueing System; Troubleshooting snow Programs; When It Works ... ; ... And When It Doesn't; The Wrap-up; Chapter 3. multicore; Quick Look; How It Works; Setting Up; Working with It; The mclapply Function; The mc.cores Option; The mc.set.seed Option; Load Balancing with mclapply; The pvec Function.

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