Your verification ID is: guDlT7MCuIOFFHSbB3jPFN5QLaQ Big Computing: Revolution R Open

Monday, November 10, 2014

Revolution R Open

Recently Revolution Analytics released Revolution R Open. This is their Open Source version of R that does have some enhancements over basic Open Source R. To me the most significant enhancement is the use of intel's MKL libraries which will speed up your computations especially if you use Windows as your operating system. Here is the Link to Revolution R Open where you can read more about it and download the software.

For those of you too lazy to click a link here is the text from Revolution Analytics blog announcing the release:

What is Revolution R Open

Revolution R Open is the enhanced distribution of R from Revolution Analytics.
Revolution R Open is based on the statistical software R (version 3.1.1) and adds additional capabilities for performance, reproducibility and platform support.
Just like R, Revolution R Open is open source and free to download, use, and share.
Revolution R Open includes:
Get Revolution R Open today! You can download and install Revolution R Open, free of charge.

R: A Complete Environment

R is a complete environment for data scientists.
Revolution R Open is built on R 3.1.1 from the R Foundation for Statistical Computing. R is the most widely-used language for statistics and data science, and is ranked the 9th most popular of all data science languages by the IEEE. R is used by leading companies around the world as part of data-driven applications in industries including finance, healthcare, technology, scientific research, media, government and academia.
The R language includes every data type, data manipulation, statistical model, and chart that the modern data scientist could ever need. Learn more about R here.

Total Compatibility

R developers have contributed thousands of free add-on packages for R, to further extend its capabilities for data handling, data visualization, statistical analysis and much more. Learn more about R packages here. Almost 6000 packages are available in CRAN (the Comprehensive R Archive Network), and you can browse packages by name or by topic area at MRAN. Even more packages can be found at GitHub (including the RHadoop packages to integrate R and Hadoop) or in theBioconductor repository. All packages that support R 3.1.1 are compatible with Revolution R Open.
Revolution R Open is also compatible with R user interfaces including RStudio, which we recommend as an excellent IDE for R developers. Applications that include the capability to call out to R are also compatible with Revolution R Open. If you would like to integrate R into your own application, DeployR Open is designed to work with Revolution R Open.

Multithreaded Performance (MKL)

From the very beginning R from the R Foundation was designed to use only a single thread (processor) at a time. Even today, R still works that way unless linked with a multi-threaded BLAS/LAPACK libraries.
The machines of today offer so much more in terms of processing power. To take advantage of this, Revolution R Open includes by default the Intel Math Kernel Library (MKL), which provides these BLAS and LAPACK library functions used by R. Intel MKL is multi-threaded and makes it possible for so many common R operations, such as matrix multiply/inverse, matrix decomposition, and some higher-level matrix operations, to use all of the processing power available.
Our tests show that linking to MKL improves the performance of your R code, especially where many vector/matrix operations are used. See these benchmarks. Performance improves with additional cores, meaning you can expect better results on a four-core laptop than on a two-core laptop--even on non-Intel hardware.
MKL's default behavior is to use as many parallel threads as there are available cores. There’s nothing you need to do to benefit from this performance improvement--not a single change to your R script is required. Learn how to control or restrict the number of threads.

Reliable R code (RRT)

Most R scripts rely on one or more CRAN packages, but packages on CRAN change daily. It can be difficult to write a script in R and then share it with others, or even run it on another system, and get the same results. Changes in package versions can result in your code generating errors or, even worse, generating incorrect results without warning.
Revolution R Open includes the Reproducible R Toolkit. The MRAN server archives the entire contents of CRAN on a daily basis, and the checkpoint function makes it easy to install the package versions required to reproduce your results reliably.

Platform Support

Supported Platforms. Revolution R Open is built and tested on the following 64-bit platforms:
  • Ubuntu 12.04, 14.04
  • CentOS / Red Hat Enterprise Linux 5.8, 6.5, 7.0
  • OS X Mavericks (10.9)
  • Windows® 7.0 (SP 1), 8.0, 8.1, Windows Server® 2008 R2 (SP1) and 2012
Experimental Platforms. Revolution R Open is also available for these Experimental platforms. While we expect it to work, RRO hasn’t been completely tested on these platforms. Let us know if you encounter problems.
  • OpenSUSE 13.1
  • OS X Yosemite (10.10)

To learn about other system requirements, read more in our installation guide.

Help and Resources

Revolution R Open provides detailed installation instructions and learning resources to help you get started with R.
Visit the Revolution R Open Google Group for discussions with other users.
Technical support and a limited warranty for Revolution R Open is available with a subscription to Revolution R Plus from Revolution Analytics.