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R is a wonderful thing, indeed: in recent years this free, open-source product has become a popular toolkit for statistical analysis and programming. Two of R's limitations -- that it is single-threaded and memory-bound -- become especially troublesome in the current era of large-scale data analysis. It's possible to break past these boundaries by putting R on the parallel path. Parallel R will describe how to give R parallel muscle. Coverage will include stalwarts such as snow and multicore, and also newer techniques such as Hadoop and Amazon's cloud computing platform.

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Average Rating: 4 out of 5 rating Based on 1 Rating

"Data Scientist @ Civitas Learning" - by Robert_Yerex on 20-MAR-2012
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Excellent overview of some of the current techniques for distributing R processing. Examples and code are great. While it does not go into great detail on any of the methods, it does give enough information to get started in each and allow one to make an informed decision as to which method to pursue in greater detail.
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