So, what would you say is/are the class/classes of problems that would
benefit greatly from a high flops gpu, but without the sort of bus
bandwidth you would like to see?
Almost any problem that is embarrassingly parallel or nearly so is potentially a candidate for low-bandwidth computing. Ray tracing is the primo example. Almost any linear problem is potentially embarrassingly parallel, and, if you don't want to go through the work of exposing the embarrassingly parallel nature of the problem, there are the tricks that make the linpack benchmark so popular for selling "supercomputers" that have absurdly small bisection bandwidths.
My question, though, is, if that's the kind of problem you have, why not do it on a distributed platform and teach students how to use distributed resources? If you're Pixar, I understand why you'd want a well-organized farm of GPU's, but if you just want to replicate what LLNL (Lawrence Livermore) was doing, say, ten years ago, are you doing your students any favor by giving them a GPGPU instead of the challenge of doing real distributed computing? Conceivably, watts per flop (power consumption) makes GPGPU's the hands down winner over distributed computing for problems that are embarrassingly parallel or nearly so. Inevitably, though, people will want clusters of GPUGPU's, so you'll wind up doing distributed computing, anyway.
If you rewrite your applications for the one-off architectures typical of GPU's, so that you have to do it all over again when the next generation comes out, have you really done yourself any favors?
I don't claim that there are simple or obvious answers, but it's just too easy for people to be blown away by huge flops numbers. What I'm afraid of has already started to happen as far as I'm concerned, which is that all problems will be jammed into the low-bandwidth mold, whether it's appropriate or not.
Robert.
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