Some of the couple-of-years-old charts I've looked at suggest that
speedups for some of the
most important transforms we use vary between modest and disappointing.
Cross-over points for things like FFTs are usually up in the atmospheric
levels of FFT sizes before
a CUDA-based transform would win even slightly against a
multi-threaded CPU-based FFTW, for
example. But that was a couple of years ago. Anything new along
those lines?
It seems like the kinds of things that do well on a GPU are ones that
take a small amount of input
data, compute ferociously, and produce modest amounts of output data.
Or schemes that might
consume deluges of input data, but produce output data only
occasionally--a flow that did
a bunch of FFTs and produced averaged mag-squared outputs only "once
in a while" might fare
well on a GPU.
On a related note, has anyone looked at enabling the multi-threaded FFTW
stuff? The cross-over
points there (between FFTW in a single-thread and FFTW in
multiple-threads) seem to be lower-down
on the FFT-size curve.
--
Principal Investigator
Shirleys Bay Radio Astronomy Consortium
http://www.sbrac.org
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