Read Parallel R: Data Analysis in the Distributed World

Parallel R: Data Analysis in the Distributed World

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Description: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 operationsWe have made it easy for you to find a PDF Ebooks without any digging. And by having access to our ebooks online or by storing it on your computer, you have convenient answers with Parallel R: Data Analysis in the Distributed World. To get started finding Parallel R: Data Analysis in the Distributed World, you are right to find our website which has a comprehensive collection of manuals listed.
Our library is the biggest of these that have literally hundreds of thousands of different products represented.

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ISBN: 1449320333

Description: 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 operationsWe have made it easy for you to find a PDF Ebooks without any digging. And by having access to our ebooks online or by storing it on your computer, you have convenient answers with Parallel R: Data Analysis in the Distributed World. To get started finding Parallel R: Data Analysis in the Distributed World, you are right to find our website which has a comprehensive collection of manuals listed.
Our library is the biggest of these that have literally hundreds of thousands of different products represented.

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Parallel R: Data Analysis in the Distributed World

Parallel R: Data Analysis in the Distributed World

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