Hi Marc, I've exactly the same question and it looks like most of the heavy users from the threads I've followed use Unix/Linux/Mac. Some threads have given rationale for a 64bit system due to memory benefits but there seems to be not much buy-in from the guys here (so I'd give that a pass). The CRAN page also isn't very excited about 64bit for now.
As David mentioned, Dirk's work seems to be hungry from speed and I closely (try to) follow his work. >From his blog, he uses a "Debian Linux system" and that is what I've set up for myself. This obviously may just be a matter of coincidence. (But, saves me a lot of time trying to figure out issues related to the other OS's. Also, many authors of the packages that I use really don't have the time or inclination to make is Windoze friendly.) My 2p in transition. -----Original Message----- From: r-help-boun...@r-project.org [mailto:r-help-boun...@r-project.org] On Behalf Of David Winsemius Sent: 22 January 2011 21:02 To: Marc Jekel Cc: r-help@r-project.org Help Subject: Re: [R] which operating system + computer specifications lead to the best performance for R? 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 ______________________________________________ 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.
