[Numpy-discussion] Question about unaligned access

[Francesc Alted]

Oops, forgot to mention my NumPy version:

In [72]: np.__version__
Out[72]: '1.9.2'

Francesc

2015-07-06 17:18 GMT+02:00 Francesc Alted <faltet at gmail.com>:

> 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.
>
> Thanks,
> Francesc
>
> --
> Francesc Alted
>



-- 
Francesc Alted
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