[R] Reasons to Use R
brown_emu at yahoo.com
Sat Apr 7 01:52:21 CEST 2007
I wonder if this information is too outdated or not relevant when scaled up
to larger problems...
--- Ramon Diaz-Uriarte <rdiaz02 at gmail.com> wrote:
> Dear Lorenzo,
> I'll try not to repeat what other have answered before.
> On 4/5/07, Lorenzo Isella <lorenzo.isella at gmail.com> wrote:
> > The institute I work for is organizing an internal workshop for High
> > Performance Computing (HPC).
> > (1)Institutions (not only academia) using R
> You can count my institution too. Several groups. (I can provide more
> details off-list if you want).
> > (2)Hardware requirements, possibly benchmarks
> > (3)R & clusters, R & multiple CPU machines, R performance on different
> We do use R in commodity off-the shelf clusters; our two clusters are
> running Debian GNU/Linux; both 32-bit machines ---Xeons--- and 64-bit
> machines ---dual-core AMD Opterons. We use parallelization quite a
> bit, with MPI (via Rmpi and papply packages mainly). One convenient
> feature is that (once the lam universe is up and running) whether we
> are using the 4 cores in a single box, or the max available 120, is
> completeley transparent. Using R and MPI is, really, a piece of cake.
> That said, there are things that I miss; in particular, oftentimes I
> wish R were Erlang or Oz because of the straightforward fault-tolerant
> distributed computing and the built-in abstractions for distribution
> and concurrency. The issue of multithreading has come up several times
> in this list and is something that some people miss.
> I am not sure how much R is used in the usual HPC realms. It is my
> understanding that the "traditional HPC" is still dominated by things
> such as HPF, and C with MPI, OpenMP, or UPC or Cilk. The usual answer
> to "but R is too slow" is "but you can write Fortran or C code for the
> bottlenecks and call it from R". I guess you could use, say, UPC in
> that C that is linked to R, but I have no experience. And I think this
> code can become a pain to write and maintain (specially if you want to
> play around with what you try to parallelize, etc). My feeling (based
> on no information or documentation whatsoever) is that how far R can
> be stretched or extended into HPC is still an open question.
> > (4)finally, a list of the advantages for using R over commercial
> > statistical packages. The money-saving in itself is not a reason good
> > enough and some people are scared by the lack of professional support,
> > though this mailing list is simply wonderful.
> (In addition to all the already mentioned answers)
> Complete source code availability. Being able to look at the C source
> code for a few things has been invaluable for me.
> And, of course, and extremely active, responsive, and vibrant
> community that, among other things, has contributed packages and code
> for an incredible range of problems.
> P.S. I'd be interested in hearing about the responses you get to your
> > Kind Regards
> > Lorenzo Isella
> > ______________________________________________
> > R-help at stat.math.ethz.ch mailing list
> > https://stat.ethz.ch/mailman/listinfo/r-help
> > PLEASE do read the posting guide
> > and provide commented, minimal, self-contained, reproducible code.
> Ramon Diaz-Uriarte
> Statistical Computing Team
> Structural Biology and Biocomputing Programme
> Spanish National Cancer Centre (CNIO)
> R-help at stat.math.ethz.ch mailing list
> PLEASE do read the posting guide
> and provide commented, minimal, self-contained, reproducible code.
TV dinner still cooling?
More information about the R-help