Oops, now with link to Ahmed's page http://cs.anu.edu.au/people/Ahmed.ElZein/doku.php?id=research:more
On Tue, Nov 18, 2008 at 11:25 PM, Mose <[EMAIL PROTECTED]> wrote: > GPU architecture is different enough from CPU architecture that you > don't need 10s of GPUs to see a performance benefit over today's, say, > 8 core CPUs. Lots of GPUs now give you a (relatively cheap) > "supercomputer" -- look up nVidia's Tesla marketing mumbo jumbo. One > GPU still gives you a 'heckuva job'. > > From Wikipedia's GPU page, speaking on modern general purpose GPUs: > > http://en.wikipedia.org/wiki/Graphics_processing_unit > > "Typically the performance advantage is only obtained by running the > single active program simultaneously on many example problems in > parallel using the GPU's SIMD architecture[11]. However, substantial > acceleration can also be obtained by not compiling the programs but > instead transferring them to the GPU and interpreting them there[12]. > Acceleration can then be obtained by either interpreting multiple > programs simultaneously, simultaneously running multiple example > problems, or combinations of both. A modern GPU (e.g. 8800 GTX) can > readily simultaneously interpret hundreds of thousands of very small > programs." > > The first sentence, you can imagine, applies to some a lot of matrix work. > > There are BLAS libraries for some GPUs (e.g. CUDA BLAS). You can > probably imagine having R use it. Ahmed El Zein has a poster about > his presentation "Performance Evaluation of the NVIDIA GeForce 8800 > GTX GPU for Machine Learning" that gives some more interesting info. > > -Mose > > > On Tue, Nov 18, 2008 at 10:56 PM, Prof Brian Ripley > <[EMAIL PROTECTED]> wrote: >> On Tue, 18 Nov 2008, Emmanuel Levy wrote: >> >>> Dear All, >>> >>> I just read an announcement saying that Mathematica is launching a >>> version working with Nvidia GPUs. It is claimed that it'd make it >>> ~10-100x faster! >>> http://www.physorg.com/news146247669.html >> >> Well, lots of things are 'claimed' in marketing (and Wolfram is not shy to >> claim). I think that you need lots of GPUs, as well as the right problem. >> >>> I was wondering if you are aware of any development going into this >>> direction with R? >> >> It seems so, as users have asked about using CUDA in R packages. >> >> Parallelization is not at all easy, but there is work on making R better >> able to use multi-core CPUs, which are expected to become far more common >> that tens of GPUs. >> >>> Thanks for sharing your thoughts, >>> >>> Best wishes, >>> >>> Emmanuel >> >> PS: R-devel is the list on which to discuss the development of R. >> >> -- >> Brian D. Ripley, [EMAIL PROTECTED] >> 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 >> >> ______________________________________________ >> [email protected] 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. >> > ______________________________________________ [email protected] 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.
