[R] which operating system + computer specifications lead to the best performance for R?

[David Winsemius]

On Jan 22, 2011, at 10:03 AM, Sascha Vieweg wrote:

> On 11-01-22 14:56, Marc Jekel wrote:
>
>> I have the opportunity to buy a new computer for my simulations in  
>> R. My goal is to get the execution of R code as fast as possible. I  
>> know that the number of cores and the working memory capacity are  
>> crucial for computer performance but maybe someone has experience/ 
>> knowledge which comp specifications are especially crucial  
>> (especially in relation to R). Is there any knowledge on the  
>> performance of R for different operating systems (Linux, Win, Mac  
>> etc.) resp. is performance dependent on the operating system at  
>> all? Even small differences in performance (i.e., speed of  
>> calculations) matter for me (quite large datasets + repeated  
>> calculations etc.).
>
> Not really a recommendation, just my considerations: That depends on  
> your budget, Mac Pro (5k$ in the U.S.) would probably serve your  
> needs for a long time ;-). I am running R 2.12.0 on a MacBook Pro,  
> 2.4 Dual Core with (only) 2G ram, together with (paid) TextMate as  
> editor, and Sweave. 2G ram is few! And I noted remarkable  
> improvements whan I was lucky to use a MBP Intel Core i5 for a  
> couple of days. Whatever processor and memory, I like the easy  
> interplay between R and the Unix environment (things like passing  
> shell commands from R to my system or other interpreters), easy  
> graphics etc.

I also use a MacPro (circa early 1998) R 2.12.1 with 24 GB and still  
find it generally very capable for a dataset of 5.5 MM rows and about  
150 variables using the survival and rms packages. I seem to remember  
a price of 4KUS$ but I didn't write that check. I haven't succeeded in  
getting the multi-processor applications to work, however, and my  
guess is that Linux boxes (and Linux users) may be more likely to  
offer paths to success if that is an expectation. I am mostly  
interested in having adequate memory space for one core anyway, as  
most of the packages I use don't seem to be set up for parallel  
execution.

It may depend on what development system you use and which packages  
you expect to install. I know there are people with the StatET- 
equipped systems out there but I have never been able to get a working  
setup on my Mac. Too many moving parts and the gears don't seem to  
mesh out of the box. Same with GTK2+ and its R friends.

This would be better posted on the HPC mailing list anyway:
https://stat.ethz.ch/mailman/listinfo/r-sig-hpc

You might want to search with "Dirk Eddelbuettel" in your search  
string, since he seems to share your "need for speed" and has  
championed various approaches to High Performance Computing with R:
http://dirk.eddelbuettel.com/bio/presentations.html
>

--

David Winsemius, MD
West Hartford, CT