On Thu, Jul 26, 2012 at 7:59 AM, Carrie Wager <cgwager at 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
>
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>
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