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
具體描述
讀後感
評分
評分
評分
評分
用戶評價
沒怎麼看，差不多就是幾個相關的包的介紹，齣的時候就過時瞭。
评分沒怎麼看，差不多就是幾個相關的包的介紹，齣的時候就過時瞭。
评分沒怎麼看，差不多就是幾個相關的包的介紹，齣的時候就過時瞭。
评分不錯的一本書。其實隻要是並行計算的我都有興趣，唯一的足是，這裏R使用的並行計算架構稍微簡單瞭些，——雖然很多庫
评分還行 就是有點過時瞭
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