Bay Area engineer Vineet Abraham recently ran some benchmarks for Revolution R Open (RRO) running on Mac OS X and on Ubuntu. Thanks to the multi-threaded processing capabilites of RRO, several operations ran much faster than R downloaded from CRAN, without having to change any code:
For the most part, RRO performs significantly faster than standard R both locally and on the server. RRO performs really well on the matrix operations as seen in column group mm (over 90% faster than standard R); this is probably due to the addition of the Intel Math Kernel library.
(In fact, while the Intel MKL is used on Ubunti, on OS X the standard Accelerate Framework provides the multi-threading capability, with similar results.) As Vineet's benchmarks show, RRO doesn't improve things for every benchmark, but with some mathematically-intensive operations the difference can be dramatically.
On a related note, I've been doing some benchmarks on RRO 8.0.3 (based on R 3.1.3), due to be released very soon. On my 2-core Surface Pro (yes, it runs fine on a Surface), using the multi-threading reduced the computation for the Urbanek benchmarks from 32 seconds to 8 seconds.
Numbr Crunch: Benchmarking R/RRO is OSX and Ubuntu on the cloud