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Snip1d was taking a very long time to compute for high resolution polar images. Since it is computed on a row-by-row basis, however, I found that parallelizing over the rows using multiprocessing provided a big performance improvement (3x faster for my example). Some other things I tried that did not result in a speed improvement: * Using the latest `convolve` from astropy * Using the latest `convolve_fft` from astropy * Performing multithreading instead of multiprocessing * Parallelizing at a finer grain level in the snip1d loops This also includes a test that verifies that the snip1d output does not change. Signed-off-by: Patrick Avery <patrick.avery@kitware.com>
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saransh13
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We should add same parallelism to the FastSNIP1D kernel in a future PR.
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Snip1d was taking a very long time to compute for high resolution polar images. Since it is computed on a row-by-row basis, however, I found that parallelizing over the rows using multiprocessing provided a big performance improvement (3x faster for my example).
Some other things I tried that did not result in a speed improvement:
convolvefrom astropyconvolve_fftfrom astropyThis also includes a test that verifies that the snip1d output does not change.