Hi Guys, not quite the recommendations you expressed, but here is my ugly attempt to improve benchmarks coverage: http://www.onerussian.com/tmp/numpy-vbench-20130701/index.html initially I also ran those ufunc benchmarks per each dtype separately, but then resulting webpage is loong which brings my laptop on its knees by firefox. So I commented those out for now, and left only "summary" ones across multiple datatypes. There is a bug in sphinx which forbids embedding some figures for vb_random "as is", so pardon that for now... I have not set cpu affinity of the process (but ran it at nice -10), so may be that also contributed to variance of benchmark estimates. And there probably could be more of goodies (e.g. gc control etc) to borrow from https://github.com/pydata/pandas/blob/master/vb_suite/test_perf.py which I have just discovered to minimize variance. nothing really interesting was pin-pointed so far, besides that - svd became a bit faster since few months back ;-) http://www.onerussian.com/tmp/numpy-vbench-20130701/vb_vb_linalg.html - isnan (and isinf, isfinite) got improved http://www.onerussian.com/tmp/numpy-vbench-20130701/vb_vb_ufunc.html#numpy-i... - right_shift got a miniscule slowdown from what it used to be? http://www.onerussian.com/tmp/numpy-vbench-20130701/vb_vb_ufunc.html#numpy-r... As before -- current code of those benchmarks collection is available at http://github.com/yarikoptic/numpy-vbench/pull/new/master if you have specific snippets you would like to benchmark -- just state them here or send a PR -- I will add them in. Cheers, On Tue, 07 May 2013, Daπid wrote:
On 7 May 2013 13:47, Sebastian Berg
wrote: Indexing/assignment was the first thing I thought of too (also because fancy indexing/assignment really could use some speedups...). Other then that maybe some timings for small arrays/scalar math, but that might be nice for that GSoC project.
Why not going bigger? Ufunc operations on big arrays, CPU and memory bound.
Also, what about interfacing with other packages? It may increase the compiling overhead, but I would like to see Cython in action (say, only last version, maybe it can be fixed). _______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
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