On Thu, Jul 26, 2012 at 7:59 AM, Carrie Wager <cgwa...@gmail.com> wrote: > I'm currently developing several tools in R that I'd like to deploy for use > by multiple analysts in my research group. Many of the analysts have no > background in using R (but have plenty of experience with SAS), so part of > my effort will be in training them to use the new tools. Some of the > analyses will be too computationally intensive for our regulation 32 bit > Windows desktops, however they will run on a 64 bit machine. > > I would like to provide our IT department with advice regarding a suitable > R server that can handle multiple users. R-studio offers a client/server > model that would allow us to run the server under a linux platform without > requiring users to learn linux. An alternative (less preferable) solution > would be to run a windows server (as we currently do for SAS) which would > require users to log on to the server via a Windows session in order to run > R. > > While I've already searched the R newsgroups and obtained some ideas toward > a solution, I'm wondering if anyone out there has more recent or current > advice (particularly regarding suitable hardware choice and server setup). > I would like to find a solution that would allow me to rapidly deploy R > solutions to collaborators (all within my computer network) who do not > necessarily have much background in R or linux. All data files and data > would be accessed via our networked filesystem (unless, of course they are > so huge that moving them locally to the server would impact processing > time). I'm trying to avoid minimal per-user setup hassle and perceived > inconvenience of running R. > > The system should be able to handle about 5 intensive jobs and up to 20 > users simultaneously. > > Any advice would be appreciated!
Just a few short points: I think the RStudio Server on Linux backend makes a lot of sense. If your coworkers eventually do start using R (just to prototype), they can use desktop RStudio so they have a single unified interface. R is generally very memory hungry so when picking hardware specs, keep that in mind. Also, if you get a multi-core server, note that you'll have to use explicit parallelization in writing scripts if you only have a single R process running at a time. If you have as many processes as cores, it might be better to avoid parallelizing [someone else with more HPC knowledge than me should comment definitively] Also, for scientific / heavy matrix work, go to the effort of building locally and using a tuned BLAS. It really does make a noticeable difference for work on big matrices (which linear models are internally). Your IT folks should not be too unfamiliar with this. For a slightly different take, you might also look at Simon's work on FastRWeb et al. which take much more of a client-server model so you can hide as much as you want (everything?) behind a webpage. Not sure where the best documentation on that is, but I've seen a live demo and it's awesome. Best, Michael > > Thanks, > Carrie Greene Wager, PhD > New England Research Institutes > Watertown, MA > > [[alternative HTML version deleted]] > > ______________________________________________ > 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.
