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…mehr
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 operationsHinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.
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.
Inhaltsangabe
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
