[Numpy-discussion] Question about unaligned access
Julian Taylor
jtaylor.debian at googlemail.com
Mon Jul 6 14:11:47 EDT 2015
On 06.07.2015 18:21, Francesc Alted wrote:
> 2015-07-06 18:04 GMT+02:00 Jaime Fernández del Río <jaime.frio at gmail.com
> <mailto:jaime.frio at gmail.com>>:
>
> On Mon, Jul 6, 2015 at 10:18 AM, Francesc Alted <faltet at gmail.com
> <mailto: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.
>
>
> Yes, my guess is that you are right. I suppose that it is possible to
> improve the numpy codebase to accelerate this particular access pattern
> on Intel platforms, but provided that structured arrays are not that
> used (pandas is probably leading this use case by far, and as far as I
> know, they are not using structured arrays internally in DataFrames),
> then maybe it is not worth to worry about this too much.
>
> Thanks anyway,
> Francesc
>
>
>
> Jaime
>
>
>
> Thanks,
> Francesc
>
> --
> Francesc Alted
>
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> --
> Francesc Alted
>
>
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