[Numpy-discussion] object array alignment issues

Francesc Alted faltet@pytables....
Fri Oct 16 07:20:05 CDT 2009


A Friday 16 October 2009 14:02:03 David Cournapeau escrigué:
> On Fri, Oct 16, 2009 at 8:53 PM, Pauli Virtanen <pav+sp@iki.fi> wrote:
> > Fri, 16 Oct 2009 12:07:10 +0200, Francesc Alted wrote:
> > [clip]
> >
> >> IMO, NumPy can be improved for unaligned data handling.  For example,
> >> Numexpr is using this small snippet:
> >>
> >> from cpuinfo import cpu
> >> if cpu.is_AMD() or cpu.is_Intel():
> >>     is_cpu_amd_intel = True
> >> else:
> >>     is_cpu_amd_intel = False
> >>
> >> for detecting AMD/Intel architectures and allowing the code to avoid
> >> memcpy() calls for the unaligned arrays.
> >>
> >> The above code uses the excellent ``cpuinfo.py`` module from Pearu
> >> Peterson, which is distributed under NumPy, so it should not be too
> >> difficult to take advantage of this for avoiding unnecessary copies in
> >> this scenario.
> >
> > I suppose this kind of check is easiest to do at compile-time, and
> > defining a -DFORCE_ALIGNED? This wouldn't cause performance penalties for
> > those architectures for which they are not necessary.
>
> I wonder whether we could switch at runtime (import time) - it could
> be useful for testing.
>
> That being said, I agree that the cpu checks should be done at compile
> time - we had quite a few problems with cpuinfo in the past with new
> cpu/unhandled cpu, I think a compilation-based method is much more
> robust (and simpler) here. There are things where C is just much
> easier than python :)

Agreed.  I'm relaying in ``cpuinfo.py`` just because it provides what I need 
in an easy way.  BTW, the detection of AMD/Intel (just the vendor) processors 
seems to work flawlessly for the platforms that I've checked (but I suppose 
that you are talking about other characteristics, like SSE version, etc).

-- 
Francesc Alted


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