I think the general experience is that R is going to be more memory-hungry than other resources so you'll get the best bang for your buck on that end. R also has good parallelization support: that and other high performance concerns are addressed here:
http://cran.r-project.org/web/views/HighPerformanceComputing.html Performance (as it is for most computationally expensive tasks) will likely be better under Linux and you'll get good free help from R-SIG-Fedora and R-SIG-Debian if you pick one of those (in addition to whatever your sys admin can give) Michael On Tue, May 8, 2012 at 6:49 AM, Hugh Morgan <h.mor...@har.mrc.ac.uk> wrote: > Has anyone got any advice about what hardware to buy to run lots of R > analysis? Links to studies or other documents would be great as would be > personal opinion. > > We are not currently certain what analysis we shall be running, but our > first implementation uses the functions lme and gls from the library nlme. > To do one data point currently takes 1.5 seconds on our 3 year old sunfire > box, and the data points are completely independant so the analysis is fully > parallelisable without implmenting multi-threading within each data point. > We have a reasnoble amount of sys admin support in house. We are an > academic institution. We are looking at spending a few thousand to a small > number of tens of thousands of dollars. > > Any help greatly appreciated > > > This email may have a PROTECTIVE MARKING, for an explanation please see: > http://www.mrc.ac.uk/About/Informationandstandards/Documentmarking/index.htm > > ______________________________________________ > R-help@r-project.org mailing list > https://stat.ethz.ch/mailman/listinfo/r-help > PLEASE do read the posting guide http://www.R-project.org/posting-guide.html > and provide commented, minimal, self-contained, reproducible code. ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.