*This is a response to a post on GPUs on the R-sig-finance list. Dirk E. appropriately requested the topic be swung over here... ## <Original edited question from Gero Schwenk> Now my question: Does anybody have experience using this package or GPU- resp. parallel-processing for exploration? Or do you use other environments, resp. approaches? <Brian Peterson's highly edited response> I know firms in finance that are making extensive use of different GPU architectures. They are *all* doing a lot of low level C programming to do it, using the API directly in many cases, or reference implementations of linear and matrix algebra packages tuned for the GPU they've chosen. I appreciate the approach if you have the resources to engage in it. ## Low level C is not necessarily required for the GPU. Besides Josh Buckner's phenomenal early strides, Andreas Klockner at Brown has done extensive work with PyCuda, Nicolas Pinto and his students @ MIT has done yeoman's work bringing educational tools to the fore and blending CUDA/PyCUDA and select CUDA developers are pretty far along building out Thrust, a C++ like library. These make life a whole lot easier for those either not used to programming closer to the metal or getting there progressively. That having been said, I am partial to an R path as I am sure many are. http://mathema.tician.de/software/pycuda http://sites.google.com/site/cudaiap2009/ http://code.google.com/p/thrust/ HTH, V.
Vince Fulco, CFA, CAIA 612.424.5477 (universal) vfulco1 at gmail.com A posse ad esse non valet consequentia ?the possibility does not necessarily lead to materialization?