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
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.
It's OK, but it's pretty obvious early on that R is not as robust when it comes to something you might want to run in a multi process application. Yes. You can program your own use cases, but if your going to do that, use a language that is more complete like python, or Java which have the same or better multiprocessing Capabilities.
Bottom Line No, I would not recommend this to a friend
This is a short guide to a handful of parallel R libraries. There's also alot of emphasis on how to connect R to the world of big data, three chapters are dedicated to running R and Hadoop; R+Hadoop, RHIPE and Seque. I think it's a good, short read for the more experienced R-programmer and I also like the small size.