Ravi, 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. Abstract: 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. URL: http://babrodtk.at.ifi.uio.no/files/publications/brodtkorb_etal_star_heterocomp_final.pdf 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 ______________________________________________ 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.
