Master's project to coerce linux nvidia drivers to run generalised linear models
On 1/23/06, Gabor Grothendieck <ggrothendieck at gmail.com> wrote:
I wonder if it would make more sense to get a relatively low level package to run on it so that all packages that used that low level package would benefit. The Matrix package and the functions runmean and sum.exact in package caTools are some things that come to mind. Others may have other ideas along these lines.
Martin and I are delighted to learn that the Matrix package is a "relatively low-level" package :-) We were of the opinion that the amount of code and design work that went into it made it a little more sophisticated than that. More seriously, the approach to speeding up model fitting that has been most successful to date is to speed up the BLAS (Basic Linear Algebra Subroutines), especially the Level-3 BLAS. The bulk of the computation in the Matrix package takes place in either Lapack (for dense matrices) or CHOLMOD (for sparse matrices) code and those are based on calls to the Levels 1, 2 and 3 BLAS. The Atlas package and K. Goto's BLAS are designed to obtain the highest level of performance possible from the CPU on these routines. I think the easiest way of incorporating the power of the GPU into the model fitting process would be to port the BLAS to the GPU. I also imagine that someone somewhere has already started on that.
On 1/23/06, Oliver LYTTELTON <oliver at bic.mni.mcgill.ca> wrote:
Hi, I am working with a friend on a master's project. Our laboratory does a lot of statistical analysis using the R stats package and we also have a lot of under-utilised nvidia cards sitting in the back of our networked linux machines. Our idea is to coerce the linux nvidia driver to run some of our statistical analysis for us. Our first thought was to specifically code up a version of glm() to run on the nvidia cards... Thinking that this might be of use to the broader community we thought we might ask for feedback before starting? Any ideas... Thanks, Olly
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