[Numpy-discussion] Comparing NumPy/IDL Performance
Thu Sep 29 11:18:44 CDT 2011
I think the remaining delta between the integer and float "boxcar" smoothing is that the integer version (test 21) still uses median_filter(), while the float one (test 22) is using uniform_filter(), which is a boxcar.
Other than that and the slow roll() implementation in numpy, things look pretty solid, yes?
On Sep 29, 2011, at 12:11 PM, Keith Hughitt wrote:
> Thank you all for the comments and suggestions.
> First off, I would like to say that I entirely agree with people's suggestions about lack of objectiveness in the test design, and the caveat about optimizing early. The main reason we put together the Python version of the benchmark was as a quick "sanity check" to make sure that there are no major show-stoppers before we began work on the library. We also wanted to put together something to show other people who are firmly in the IDL camp that this is a viable option.
> We did in fact put together another short test-suite (test_testr.py & time_testr.pro) which consists of operations that would are frequently used by us, but it also is testing a very small portion of the kinds of things our library will eventually do.
> That said, I made a few small changes to the original benchmark, based on people's feedback, and put together a new plot.
> The changes made include:
> 1. Using xrange instead of range
> 2. Using uniform filter instead of median filter
> 3. Fixed a typo for tests 2 & 3 which resulted in slower Python results
> Again, note that some of the tests are testing non-numpy functionality. Several of the results still stand out, but overall the results are much more reasonable than before.
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