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

Dirk Eddelbuettel edd at debian.org
Fri Jun 4 03:15:25 CEST 2010


On 3 June 2010 at 09:43, Ravi Varadhan wrote:
| 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
| 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.).  

This recent paper

  A. R. Brodtkorb, C. Dyken, T. R. Hagen, J. M. Hjelmervik and
  O. O. Storaasli: State-of-the-Art in Heterogeneous Computing, Scientific
  Programming, 18(1) (2010), pp.  1-33. 

  Node level heterogeneous architectures have become attractive during the
  last decade for several reasons: compared to traditional symmetric CPUs,
  they offer high peak performance and are energy and/or cost efficient. With
  the increase of fine-grained parallelism in high-performance computing, as
  well as the introduction of parallelism in workstations, there is an acute
  need for a good overview and understanding of these architectures. We give
  an overview of the state-of-the-art in heterogeneous computing, focusing on
  three commonly found architectures: the Cell Broadband Engine Architecture,
  graphics processing units (GPUs), and field programmable gate arrays
  (FPGAs).We present a review of hardware, available software tools, and an
  overview of state-of-the-art techniques and algorithms. Furthermore, we
  present a qualitative and quantitative comparison of the architectures, and
  give our view on the future of heterogeneous computing.


is pretty thorough on some of the architectural aspects. 

| What are the main obstacles that are likely to prevent widespread use of
| this technology in computational statistics? 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. 

A hybrid Intel Xeon / Nvidia Tesla computer appeared this week in the most
recent Top500 as entry number two.  GPU aspects may also get integrated into
cpus so this may not be a flash in the pan.  Then again, it won't be a
cure-all either.

I find the gpgpu.org quite useful to keep up with news on GPUs. That is also
how I came across the paper cited above.

Hth,  Dirk

  Regards, Dirk

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