[R] General-purpose GPU computing in statistics (using R)

[Prof Brian Ripley]

On Thu, 3 Jun 2010, Ravi Varadhan wrote:

> Hi All,
>
> I have been reading about general purpose GPU (graphical processing units)
> computing for computational statistics.  I know very little about this, but
> I read that GPUs currently cannot handle double-precision floating points

Not so for a while, and the latest ones are quite fast at it.

> and also that they are not necessarily IEEE compliant.  However, I am not
> sure what the practical impact of this limitation is likely to be on
> computational statistics problems (e.g. optimization, multivariate analysis,
> MCMC, etc.).
>
> What are the main obstacles that are likely to prevent widespread use of
> this technology in computational statistics?

Developing highly parallel algorithms that can exploit the 
architectures.  That's not just in statistics, see e.g.

http://www.microway.com/pdfs/TeslaC2050-Fermi-Performance.pdf

(A Tesla C2050 is the latest generation GPU -- shipping within the 
last month.)

> Can algorithms be coded in R to take advantage of the GPU 
> architecture to speed up computations?  I would appreciate hearing 
> from R sages about their views on the usefulness of general purpose 
> GPU (graphical processing units) computing for computational 
> statistics.  I would also like to hear about views on the future of 
> GPGPU - i.e. is it here to stay or is it just a gimmick that will 
> quietly disappear into the oblivion.

They need a lot of programming work to use, and the R packages 
currently attempting to use them (cudaBayesreg and gputools) are very 
specialized.  It seems likely that they will remain a niche area, In 
much the same way that enhanced BLAS are -- there are problems for 
which the latter can make a big difference, but they are far from 
universally useful.

We've been here several times before: when I was on UK national 
supercomputing committees in the 1980s and 90s there were several 
similar contenders (SIMD arrays, Inmos Transputers ...) and all faded 
away.

That is not to say that general purpose parallelism is not going to be 
central, as we each get (several) machines with many CPU cores.  But 
that sort of parallelism is likely to be exploited in different ways 
from that of GPUs.

>
>
>
> Thanks very much.
>
>
>
> Best regards,
>
> Ravi.
>
> ----------------------------------------------------------------------------
> ------------------------------
>
> Ravi Varadhan, Ph.D.
>
> Assistant Professor,
>
> Center on Aging and Health,
>
> Johns Hopkins University School of Medicine
>
> (410)502-2619
>
> rvaradhan at jhmi.edu
>
> http://www.jhsph.edu/agingandhealth/People/Faculty_personal_pages/Varadhan.h
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-- 
Brian D. Ripley,                  ripley at stats.ox.ac.uk
Professor of Applied Statistics,  http://www.stats.ox.ac.uk/~ripley/
University of Oxford,             Tel:  +44 1865 272861 (self)
1 South Parks Road,                     +44 1865 272866 (PA)
Oxford OX1 3TG, UK                Fax:  +44 1865 272595