[AstroPy] Fast(er) Deeming Periodograms from python via OpenCL
ewald.zietsman at gmail.com
Thu Sep 25 09:37:06 EDT 2014
I'm working with some Kepler data from which I need to extract amplitudes
and frequencies. The dataset is really long and hence the required DFT
takes an awfully long time to calculate (16 hours per DFT if I run 4
threads on my Macbook Pro). I'm getting around this problem by running it
in parallel on my Graphics Processing Unit (on my Linux Box) via PyopenCL.
I did a blog post about the kind of performance increase I get on my setup
doing this. You can read it here:
TL;DR; My Macbook will take around 16 hours to do one DFT which my will
take my GPU about 50 minutes.
I'm also working on a smallish library that will allow me to find and
prewhiten the lightcurve using robust sinusoid fitting after finding
amplitudes/periods using DFTs. That repo is still an infant and will change
quickly but it is here:
It at least shows (in the tests/ folder) how to run my DFT code. There are
3 DFT implementations at the moment, all three using exactly the same slow
algorithm, numpy serial, numpy/fortran/openmp parallel and opencl parallel.
I could work towards getting the numpy serial version into astropy somehow,
it there is interest?
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