[SciPy-dev] Indexing array performance

[Travis Oliphant]

Francesc Altet wrote:

>however, the next code seems to be slower in scipy_core:
>
>In [114]: t3 = timeit.Timer('a=empty(shape=10000);a[arange(10000)]','from 
>scipy.base import empty, arange')
>
>In [115]: t3.repeat(3,1000)
>Out[115]: [3.5126161575317383, 3.5309510231018066, 3.5558919906616211]
>
>In [116]: t4 = timeit.Timer('a=array(None,shape=10000);a[arange(10000)]','from 
>numarray import array, arange')
>
>In [117]: t4.repeat(3,1000)
>Out[117]: [2.0824751853942871, 2.1258058547973633, 2.0946059226989746]
>
>It seems like if the index array feature can be further optimized in
>scipy_core.
>
>  
>
I just did some simple tests.  It looks like 2-d indexing is quite a bit 
faster in scipy_core.

Try these:

t4 = 
timeit.Timer('a=array(None,shape=(1000,1000));a[arange(1000),arange(1000)]','from 
numarray import array, arange')

t3 = 
timeit.Timer('a=empty(shape=(1000,1000));a[arange(1000),arange(1000)]','from 
scipy.base import empty, arange')

My results:

 >>> t3.repeat(3,100)
[0.18409419059753418, 0.19265508651733398, 0.18711185455322266]

 >>> t4.repeat(3,100)
[4.0139532089233398, 3.9884538650512695, 4.405332088470459]


However, as you noticed 1-d indexing is a bit slower.  However if you 
use flat indexing (which is special-cased) it is faster:

Thus:

t4 = timeit.Timer('a=array(None,shape=10000);a[arange(10000)]','from 
numarray import array, arange')

t3 = timeit.Timer('a=empty(shape=10000);a.flat[arange(10000)]','from 
scipy.base import empty, arange')

Gives:

 >>> t3.repeat(3,100)
[0.18227100372314453, 0.16614699363708496, 0.16269397735595703]

 >>> t4.repeat(3,100)
[0.40496301651000977, 0.34369301795959473, 0.3347930908203125]

Thus, I think it might be wise to use the flattened indexing code when 
the array is already 1-d.   I could add this in automatically.

-Travis