R devel - May 2011 - Max likelihood using GPU

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

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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.
> 
> ______________________________________________
> R-devel at r-project.org mailing list
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