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: 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