On Wed, Jul 20, 2011 at 3:57 AM, eat <e.antero.ta...@gmail.com> wrote:
> 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
I think that's the effect of the timer having a low resolution, and
the repeat value being 10, instead of the default 1000000. For the
huge array operations, a small repeat value wasn't a problem. But my
mistake made it a simple python assignment, and for such a quick
operation you need to repeat it a great many times between timer calls
to get a meaningful result:
In [1]: %timeit m = -0.5
10000000 loops, best of 3: 39.1 ns per loop
In [2]: import timeit
In [3]: t=timeit.Timer('m = -0.5')
In [4]: t.timeit(number=1000000000)
Out[35]: 38.36219096183777
So, directly repeating the assignment a billion times puts it into
'nanoseconds per assignment' units, and the results from ipython
%timeit and the timeit() call are comparable (approx 39 nanoseconds
per loop).
-Chad
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