(This article was first published on

**cloudnumbers.com » R-project**, and kindly contributed to R-bloggers)In view of open-source parallel computing with R this week presents a big step to the future.

**R 2.14.0**was released at October 31th, 2011.

Now, R base ships with a parallel computing package called “parallel”.library(parallel)

It combines advantages of the packages multicore and snow and it contains support for multiple RNG streams. The version released for R 2.14.0 contains base functionality, higher-level convenience functions are planned. If you are used to snow or multicore there isn’t much to learn. Test it and enjoy parallel computing with R!

More information about the new release you can find at http://cran.r-project.org/src/base/NEWS.html- O’Reilly – “Parallel R, Data Analysis in the Distributed World” book by Q. Ethan McCallum and Stephen Weston is now available!

Announced in the beginning of the year as a book for everyone “doing large-scale work with R”, the book is available since the end of October 2011. It covers packages as snow and multicore, and also newer techniques such as Hadoop and cloud computing. All chapters have the same and very hands-on section structure. With a lot of code examples and a good selection of important theoretical background information about parallel computing it is a well written book for beginners and experts in parallel computing with R. Especially, Chapter 4 introduces the new package “parallel” and therefore provides a lot of useful input to the R community. Unfortunately, chapters 4 to 9 cover very new packages and technologies where we will see a lot of changes in the next year(s). I am looking forward for a second edition at the end of 2012.

I am very happy that cloudnumbers.com got his own section in Chapter 9 as an “out-of-the-box support for R” service “for scientific HPC” in the Cloud.

For more details go to the O’Reilly webpage or get a free chapter about the new package “parallel” at Safari Books.

I am convinced that parallel computing is now becoming mainstream in statistical programming with R. Keep on coding in parallel!

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