[Beowulf] What class of PDEs/numerical schemes suitable for GPU clusters

[Peter St. John]

Regarding the hyperbolic (etc) classification of PDEs:  you may want
qualitative theory instead of (or in additon to) number crunching. I'd
suggest stopping over at UC Davis, where I count at least half a dozen PDE
folks in the applied math program (more if you count the turbulence and
fluid dynamics folks). Never miss an opportunity to buy a mathematician
lunch :-)

Peter

On Tue, Nov 18, 2008 at 2:55 PM, Finch, Ralph <rfinch at water.ca.gov> wrote:

> As we know by now GPUs can run some problems many times faster than CPUs
> (e.g. http://folding.stanford.edu/English/FAQ-highperformance). From
> what I understand GPUs are useful only with certain classes of numerical
> problems and discretization schemes, and of course the code must be
> rewritten to take advantage of the GPU.
>
> I'm part of a group that is purchasing our first beowulf cluster for a
> climate model and an estuary model using Chombo
> (http://seesar.lbl.gov/ANAG/chombo/). Getting up to speed (ha) on
> clusters I started wondering if packages like Chombo, and numerical
> problems generally, would be rewritten to take advantage of GPUs and GPU
> clusters, if the latter exist.  From decades ago when I actually knew
> something I vaguely recall that PDEs can be classed as to parabolic,
> hyperbolic or elliptic. And there are explicit and implicit methods in
> time.  Are some of these classifications much better suited for GPUs
> than others? Given the very substantial speed improvements with GPUs,
> will there be a movement to GPU clusters, even if there is a substantial
> cost in problem reformulation?  Or are GPUs only suitable for a rather
> narrow range of numerical problems?
>
> Ralph Finch, P.E.
> California Dept. of Water Resources
> Delta Modeling Section, Bay-Delta Office
> Room 215-13
> 1416 9th Street
> Sacramento CA  95814
> 916-653-7552
> rfinch at water.ca.gov
>
>
>
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