Parallel R

• Date de parution : 26/10/2011
• Editeur : O'reilly Media
• EAN : 9781449309916
• Série : (-)
• Support : Livre numérique

• Nombre de pages : (-)
• Collection : (-)
• Genre : (-)
• Thème : Non attribué
• Prix littéraire(s) : (-)

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,... Voir plus

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