[Numpy-discussion] Really cruel draft of vbench setup for NumPy (.add.reduce benchmarks since 2011)

[Yaroslav Halchenko]

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-isnan-a-10types

- 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-right-shift-a-a-3types

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 <sebastian at sipsolutions.net> 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).
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-- 
Yaroslav O. Halchenko, Ph.D.
http://neuro.debian.net http://www.pymvpa.org http://www.fail2ban.org
Senior Research Associate,     Psychological and Brain Sciences Dept.
Dartmouth College, 419 Moore Hall, Hinman Box 6207, Hanover, NH 03755
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