Raul,
Thanks for doing this work -- both the profiling and actual
suggestions for how to improve the code -- whoo hoo!
In general, it seem that numpy performance for scalars and very small
arrays (i.e (2,), (3,) maybe (3,3), the kind of thing that you'd use
to hold a coordinate point or the like, not small as in "fits in
cache") is pretty slow. In principle, a basic array scalar operation
could be as fast as a numpy native numeric type, and it would be great
is small array operations were, too.
It may be that the route to those performance improvements is
special-case code, which is ugly, but I think could really be worth it
for the common types and operations.
I'm really out of my depth for suggesting (or contributing) actual
soluitons, but +1 for the idea!
-Chris
NOTE: Here's a example of what I'm talking about -- say you are
scaling an (x,y) point by a (s_x, s_y) scale factor:
def numpy_version(point, scale):
return point * scale
def tuple_version(point, scale):
return (point[0] * scale[0], point[1] * scale[1])
In [36]: point_arr, sca
scale scale_arr
In [36]: point_arr, scale_arr
Out[36]: (array([ 3., 5.]), array([ 2., 3.]))
In [37]: timeit tuple_version(point, scale)
1000000 loops, best of 3: 397 ns per loop
In [38]: timeit numpy_version(point_arr, scale_arr)
100000 loops, best of 3: 2.32 us per loop
It would be great if numpy could get closer to tuple performance for
this sor tof thing...
-Chris
--
Christopher Barker, Ph.D.
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