Book description
R is a wonderful thing, indeed: in recent years this free, open-source product has become a popular toolkit for statistical analysis and programming. Two of R's limitations -- that it is single-threaded and memory-bound -- become especially troublesome in the current era of large-scale data analysis. It's possible to break past these boundaries by putting R on the parallel path. Parallel R will describe how to give R parallel muscle. Coverage will include stalwarts such as snow and multicore, and also newer techniques such as Hadoop and Amazon's cloud computing platform.
Publisher resources
Table of contents
- Parallel R
- A Note Regarding Supplemental Files
- Preface
- 1. Getting Started
-
2. snow
- Quick Look
- How It Works
- Setting Up
-
Working with It
- Creating Clusters with makeCluster
- Parallel K-Means
- Initializing Workers
- Load Balancing with clusterApplyLB
- Task 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
- 3. multicore
- 4. parallel
- 5. A Primer on MapReduce and Hadoop
- 6. R+Hadoop
- 7. RHIPE
- 8. Segue
- 9. New and Upcoming
- About the Authors
- Copyright
Product information
- Title: Parallel R
- Author(s):
- Release date: October 2011
- Publisher(s): O'Reilly Media, Inc.
- ISBN: 9781449309923
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