[Numpy-discussion] "aligned" matrix / ctypes

Zachary Pincus zachary.pincus@yale....
Thu Apr 24 11:29:14 CDT 2008


Hi all,

> It's worth mentioning that there was some discussion of adding an
> allocator for aligned arrays. It got sidetracked into a discussion of
> SSE support for numpy, but there are all sorts of reasons to want
> aligned arrays in numpy, as this post demonstrates. Seeing as it's
> just a few lines worth of pure python, is anyone interested in writing
> an aligned_empty() for numpy?

Here's my attempt at aligned_empty(). It only accepts alignments that  
are an integer multiple (or divisor) of the dtype's itemsize, because  
of limitations in ndarray.view().

I've also attached aligned_empty_arbitrary(), which is similar, but  
allows any bizarro alignment requested. Or at least it would, if  
ndarray.view() were able to handle strided arrays.

It could probably be tightened a bit -- I tried to make the code err  
on the "demonstrative" side rather than "terse".

Here are some basic tests:

import aligned_allocator as aa

a = aa.aligned_empty((9,7), dtype=numpy.uint8, byte_alignment=64,  
order='C')
for i in range(a.shape[0]):
     assert(a[i,:].ctypes.data % 64 == 0)

a = aa.aligned_empty((9,7), dtype=numpy.float32, byte_alignment=16,  
order='C')
for i in range(a.shape[0]):
     assert(a[i,:].ctypes.data % 32 == 0)

a = aa.aligned_empty((9,7), dtype=numpy.uint16, byte_alignment=32,  
order='F')
for i in range(a.shape[1]):
     assert(a[:,i].ctypes.data % 32 == 0)

Best,
Zach



  
    
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