[Numpy-discussion] Array vectorization in numpy

[eat]

Hi,

On Wed, Jul 20, 2011 at 2:42 AM, Chad Netzer <chad.netzer at gmail.com> wrote:

> On Tue, Jul 19, 2011 at 6:10 PM, Pauli Virtanen <pav at iki.fi> wrote:
>
> >        k = m - 0.5
> >
> > does here the same thing as
> >
> >        k = np.empty_like(m)
> >        np.subtract(m, 0.5, out=k)
> >
> > The memory allocation (empty_like and the subsequent deallocation)
> > costs essentially nothing, and there are no temporaries or copying
> > in `subtract`.
>
> As verification:
>
> >>> import timeit
> >>> import numpy as np
> >>> t=timeit.Timer('k = m - 0.5', setup='import numpy as np;m =
> np.ones([8092,8092],float)')
> >>> np.mean(t.repeat(repeat=10, number=1))
> 0.53904647827148433
>
> >>> t=timeit.Timer('k = np.empty_like(m);np.subtract(m, 0.5, out=k)',
> setup='import numpy as np;m = np.ones([8092,8092],float)')
> >>> np.mean(t.repeat(repeat=10, number=1))
> 0.54006035327911373
>
> The trivial difference is expected as extra python parsing overhead, I
> think.
>
> Which leads me to apologize, since in my previous post I clearly meant
> to type "m -= 0.5", not "m =- 0.5", which is *quite* a different
> operation...  Carlos, and Lutz, please take heed. :)  In fact, as Lutz
> pointed out, that example was not at all what I intended to show
> anyway.
>
>
> So, just to demonstrate how it was wrong

Perhaps slightly OT, but here is something very odd going on. I would expect
the performance to be in totally different ballpark.

> >>> t=timeit.Timer('m =- 0.5', setup='import numpy as np;m =
> np.ones([8092,8092],float)')
> >>> np.mean(t.repeat(repeat=10, number=1))
> 0.058299207687377931
>
More like:
In []: %timeit m =- .5
10000000 loops, best of 3: 35 ns per loop

-eat

>
> >>> t=timeit.Timer('m -= 0.5', setup='import numpy as np;m =
> np.ones([8092,8092],float)')
> >>> np.mean(t.repeat(repeat=10, number=1))
> 0.28192551136016847
>
> >>> t=timeit.Timer('np.subtract(m, 0.5, m)', setup='import numpy as np;m =
> np.ones([8092,8092],float)')
> >>> np.mean(t.repeat(repeat=10, number=1))
> 0.27014491558074949
>
> >>> t=timeit.Timer('np.subtract(m, 0.5, k)', setup='import numpy as np;m =
> np.ones([8092,8092],float); k = np.empty_like(m)')
> >>> np.mean(t.repeat(repeat=10, number=1))
> 0.54962997436523442
>
> Perhaps the difference in the last two simply comes down to cache
> effects (having to iterate over two different large memory blocks,
> rather than one)?
>
> -Chad
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