# [Numpy-discussion] Slicing slower than matrix multiplication?

Jasper van de Gronde th.v.d.gronde@hccnet...
Sat Dec 12 05:59:16 CST 2009

Francesc Alted wrote:
> ...
> Yeah, I think taking slices here is taking quite a lot of time:
>
> In [58]: timeit E + Xi2[P/2,:]
> 100000 loops, best of 3: 3.95 µs per loop
>
> In [59]: timeit E + Xi2[P/2]
> 100000 loops, best of 3: 2.17 µs per loop
>
> don't know why the additional ',:' in the slice is taking so much time, but my
> guess is that passing & analyzing the second argument (slice(None,None,None))
> could be the responsible for the slowdown (but that is taking too much time).
> Mmh, perhaps it would be worth to study this more carefully so that an
> optimization could be done in NumPy.

This is indeed interesting! And very nice that this actually works the
way you'd expect it to. I guess I've just worked too long with Matlab :)

>> I think the lesson mostly should be that with so little data,
>> benchmarking becomes a very difficult art.
>
> Well, I think it is not difficult, it is just that you are perhaps
> benchmarking Python/NumPy machinery instead ;-)  I'm curious whether Matlab
> can do slicing much more faster than NumPy.  Jasper?

I had a look, these are the timings for Python for 60x20:
Dot product: 0.051165 (5.116467e-06 per iter)
Add a row: 0.092849 (9.284860e-06 per iter)
Add a column: 0.082523 (8.252348e-06 per iter)
For Matlab 60x20:
Dot product: 0.029927 (2.992664e-006 per iter)
Add a row: 0.019664 (1.966444e-006 per iter)
Add a column: 0.008384 (8.384376e-007 per iter)
For Python 600x200:
Dot product: 1.917235 (1.917235e-04 per iter)
Add a row: 0.113243 (1.132425e-05 per iter)
Add a column: 0.162740 (1.627397e-05 per iter)
For Matlab 600x200:
Dot product: 1.282778 (1.282778e-004 per iter)
Add a row: 0.107252 (1.072525e-005 per iter)
Add a column: 0.021325 (2.132527e-006 per iter)

If I fit a line through these two data points (60 and 600 rows), I get
the following equations:
Python, AR: 3.8e-5 * n + 0.091
Matlab, AC: 2.4e-5 * n + 0.0069