[Numpy-discussion] Question about unaligned access
Jaime Fernández del Río
jaime.frio at gmail.com
Mon Jul 6 12:04:11 EDT 2015
On Mon, Jul 6, 2015 at 10:18 AM, Francesc Alted <faltet at gmail.com> wrote:
> Hi,
>
> I have stumbled into this:
>
> In [62]: sa = np.fromiter(((i,i) for i in range(1000*1000)), dtype=[('f0',
> np.int64), ('f1', np.int32)])
>
> In [63]: %timeit sa['f0'].sum()
> 100 loops, best of 3: 4.52 ms per loop
>
> In [64]: sa = np.fromiter(((i,i) for i in range(1000*1000)), dtype=[('f0',
> np.int64), ('f1', np.int64)])
>
> In [65]: %timeit sa['f0'].sum()
> 1000 loops, best of 3: 896 µs per loop
>
> The first structured array is made of 12-byte records, while the second is
> made by 16-byte records, but the latter performs 5x faster. Also, using an
> structured array that is made of 8-byte records is the fastest (expected):
>
> In [66]: sa = np.fromiter(((i,) for i in range(1000*1000)), dtype=[('f0',
> np.int64)])
>
> In [67]: %timeit sa['f0'].sum()
> 1000 loops, best of 3: 567 µs per loop
>
> Now, my laptop has a Ivy Bridge processor (i5-3380M) that should perform
> quite well on unaligned data:
>
>
> http://lemire.me/blog/archives/2012/05/31/data-alignment-for-speed-myth-or-reality/
>
> So, if 4 years-old Intel architectures do not have a penalty for unaligned
> access, why I am seeing that in NumPy? That strikes like a quite strange
> thing to me.
>
I believe that the way numpy is setup, it never does unaligned access,
regardless of the platform, in case it gets run on one that would go up in
flames if you tried to. So my guess would be that you are seeing chunked
copies into a buffer, as opposed to bulk copying or no copying at all, and
that would explain your timing differences. But Julian or Sebastian can
probably give you a more informed answer.
Jaime
>
> Thanks,
> Francesc
>
> --
> Francesc Alted
>
> _______________________________________________
> NumPy-Discussion mailing list
> NumPy-Discussion at scipy.org
> http://mail.scipy.org/mailman/listinfo/numpy-discussion
>
>
--
(\__/)
( O.o)
( > <) Este es Conejo. Copia a Conejo en tu firma y ayúdale en sus planes
de dominación mundial.
-------------- next part --------------
An HTML attachment was scrubbed...
URL: <http://mail.python.org/pipermail/numpy-discussion/attachments/20150706/58f0bb28/attachment.html>
More information about the NumPy-Discussion
mailing list