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, ...
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
0 有用 nwind 2012-07-15 17:33:59
看得不是很仔细，以后说不定有用吧，就是有点短
0 有用 黑仪 2016-05-28 10:43:10
只看了snow package
0 有用 人在鹭洲 2016-03-02 10:45:37
还行 就是有点过时了
0 有用 大刀 2013-06-29 16:20:04
以前觉得不明觉厉的东西，尝试便也掌握了
0 有用 步踏空虚 2013-01-06 10:39:07
就眼下的使用只读了multicore的部分。意料之外的幽默， 深浅合宜，非常不错。