[Numpy-discussion] Byte aligned arrays

Nathaniel Smith njs@pobox....
Thu Dec 20 06:15:15 CST 2012

On Thu, Dec 20, 2012 at 8:12 AM, Henry Gomersall <heng@cantab.net> wrote:
> On Wed, 2012-12-19 at 15:10 +0000, Nathaniel Smith wrote:
> <snip>
>> >> > Is this something that can be rolled into Numpy (the feature, not
>> my
>> >> > particular implementation or interface - though I'd be happy for
>> it to
>> >> > be so)?
>> >> >
>> >> > Regarding (b), I've written a test case that works for Linux on
>> x86-64
>> >> > with GCC (my platform!). I can test it on 32-bit windows, but
>> that's it.
>> >> > Is ARM supported by Numpy? Neon would be great to include as
>> well. What
>> >> > other platforms might need this?
>> >>
>> >> Your code looks simple and portable to me (at least the alignment
>> >> part). I can see a good argument for adding this sort of
>> functionality
>> >> directly to numpy with a nice interface, though, since these kind
>> of
>> >> requirements seem quite common these days. Maybe an interface like
>> >>   a = np.asarray([1, 2, 3], base_alignment=32)  # should this be in
>> >> bits or in bytes?
>> >>   b = np.empty((10, 10), order="C", base_alignment=32)
>> >>   # etc.
>> >>   assert a.base_alignment == 32
>> >> which underneath tries to use posix_memalign/_aligned_malloc when
>> >> possible, or falls back on the overallocation trick otherwise?
>> >>
>> >
>> > There is a thread about this from several years back. IIRC, David
>> Cournapeau
>> > was interested in the same problem. At first glance, the alignment
>> keyword
>> > looks interesting. One possible concern is keeping alignment for
>> rows,
>> > views, etc., which is probably not possible in any sensible way. But
>> people
>> > who need this most likely know what they are doing and just need
>> memory
>> > allocated on the proper boundary.
>> Right, my intuition is that it's like order="C" -- if you make a new
>> array by, say, indexing, then it may or may not have order="C", no
>> guarantees. So when you care, you call asarray(a, order="C") and that
>> either makes a copy or not as needed. Similarly for base alignment.
>> I guess to push this analogy even further we could define a set of
>> array flags, ALIGNED_8, ALIGNED_16, etc. (In practice only power-of-2
>> alignment matters, I think, so the number of flags would remain
>> manageable?) That would make the C API easier to deal with too, no
>> need to add PyArray_FromAnyAligned.
> So, if I were to implement this, I presume the proper way would be
> through modifications to multiarray?

Yes, numpy/core/src/multiarray/ is the code you'd be modifying.

> Would this basic description be a reasonable initial target?

My feeling is that we're at the stage where we need to get more
information and feedback from people with experience in this area
before we'll be able to merge anything into numpy proper (since that
implies nailing down and committing to an API). One way to get that
might be to go ahead and implement something to experiment with, and
this "basic description" does seem like one of the plausible options,
so... yes it seems like a reasonable initial target to work on, but I
don't want to mislead you into thinking that I think it would
necessarily be a reasonable initial target to ship in 1.8 or whatever.
I feel like I don't have enough information to make a judgement there.

There must be other people working with SIMD and numpy, right? If
you're interested in this problem, another thing that might help would
be to spend some effort finding those people and convincing them to
get involved in discussing what they need.


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