Dear all, Probably many of you experience long computation times when estimating large number of parameters using maximum likelihood with functions that reguire numerical methods such as integration or root-finding. Maximum likelihood is an example of paralellization that could sucessfully utilize GPU. The general algorithm is described here: http://openlab-mu-internal.web.cern.ch/openlab-mu-internal/03_Documents/4_Presentations/Slides/2010-list/CHEP-Maximum-likelihood-fits-on-GPUs.pdf. Is it possible to implement this algorithm in R ? Kind regards, Oyvind Foshaug -- View this message in context: http://r.789695.n4.nabble.com/Max-likelihood-using-GPU-tp3532034p3532034.html Sent from the R devel mailing list archive at Nabble.com.
Max likelihood using GPU
3 messages · oyvfos, Robert Lowe, Øyvind Foshaug
Hi Oyvind, I believe this is possible to implement. There is already some work ongoing in using the GPU in R and they use the CUDA toolkit as the reference you supplied do. http://brainarray.mbni.med.umich.edu/Brainarray/rgpgpu/ Thanks, Rob
On 18 May 2011, at 10:07, oyvfos wrote:
Dear all, Probably many of you experience long computation times when estimating large number of parameters using maximum likelihood with functions that reguire numerical methods such as integration or root-finding. Maximum likelihood is an example of paralellization that could sucessfully utilize GPU. The general algorithm is described here: http://openlab-mu-internal.web.cern.ch/openlab-mu-internal/03_Documents/4_Presentations/Slides/2010-list/CHEP-Maximum-likelihood-fits-on-GPUs.pdf. Is it possible to implement this algorithm in R ? Kind regards, Oyvind Foshaug -- View this message in context: http://r.789695.n4.nabble.com/Max-likelihood-using-GPU-tp3532034p3532034.html Sent from the R devel mailing list archive at Nabble.com.
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