Following on from my previous post about improving performance of R by linking with optimized linear algebra libraries, I thought it would be useful to try out the five benchmarks Revolutions Analytics have on their Revolutionary Performance pages.
For convenience I collected their tests into a single script revolution_benchmark.R that I can simply run with Rscript vanilla revolution_benchmark.R
.
The results, compared with the speedup factors Revolution claims for their version:
R  R + ATLAS  Speedup  Revolution’s claimed speedup 


Matrix Multiply  360.96  9.30  37.8  41.0 
Cholesky Factorization  27.28  5.65  3.8  21.0 
Singular Value Decomposition  98.73  23.57  3.2  12.6 
Principal Components Analysis  454.55  40.92  10.1  15.2 
Linear Discriminant Analysis  271.44  79.61  2.4  4.4 
In all instances Revolution’s claimed speedup is greater, though probably not significantly so for the Matrix Multiply test and hardly so for the Principal Components Analysis. (Of course, I do not have a copy of Revolution Analytics’ product, so I can’t verify their claims or make a comparable test.)
Whether saving 48 seconds on a linear discriminant analysis is enough to justify buying the product is a decision I leave to you: you know what analysis you do. For me, there are (many) orders of magnitudes to be gained by better algorithms and better variable selections so I am not too worried about factors of 2 or even 10. For extra raw power, I run R on a cloud service like AWS which scales well for many problems and is easy to do with stock R while I guess there are some sort of license implications if you wanted to do the same with Revolution’s product. (But I like Revolution and am still trying to find an excuse to use their product.)
Your mileage may vary.
Jump to comments.
You may also like these posts:

Can we make our analysis using the R statistical computing and analysis platform run faster? Usually the answer is yes, and the best way is to improve your algorithm and variable selection. But recently David Smith was suggesting that a big benefit of their (commercial) version of R was that it was linked to a to a better linear algebra library. So I decided to investigate. The quick summary is that it only really makes a difference for fairly artificial benchmark tests. For “normal” work you are unlikely to see a difference most of the time.

R tips: Eliminating the “save workspace image” prompt on exitWhen using R , the statistical analysis and computing platform, I find it really annoying that it always prompts to save the workspace when I exit. This is how I turn it off.
Rbloggers.com offers daily email updates about R news and tutorials on topics such as: visualization (ggplot2, Boxplots, maps, animation), programming (RStudio, Sweave, LaTeX, SQL, Eclipse, git, hadoop, Web Scraping) statistics (regression, PCA, time series, trading) and more...