[Numpy-discussion] how is y += x computed when y.strides = (0, 8) and x.strides=(16, 8) ?

Nathaniel Smith njs at pobox.com
Thu Sep 6 08:58:33 EDT 2012

On Thu, Sep 6, 2012 at 1:41 AM, Sebastian Berg
<sebastian at sipsolutions.net> wrote:
> Hey,
> No idea if this is simply not support or just a bug, though I am
> guessing that such usage simply is not planned.

I think that's right... currently numpy simply makes no guarantees
about what order ufunc loops will be performed in, or even if they
will be performed in any strictly sequential order. In ordinary cases
this lets it make various optimizations, but it means that you can't
count on any specific behaviour for the unusual case where different
locations in the output array are stored in overlapping memory.

Fixing this would require two things:
(a) Some code to detect when an array may have internal overlaps (sort
of like np.may_share_memory for axes). Not entirely trivial.
(b) A "fallback mode" for ufuncs where if the code in (a) detects that
we are (probably) dealing with one of these arrays, it processes the
operations in some predictable order without buffering.

I suppose if someone wanted to come up with these two pieces, and it
didn't look like it would cause slowdowns in common cases, the code in
(b) avoided creating duplicate code paths that increased maintenance
burden, etc., then probably no-one would object to making these arrays
act in a better defined way? I don't think most people are that
worried about this though. Your original code would be much clearer if
it just used np.sum...


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