A reimplementation of MaskedArray
Tim Hochberg
tim.hochberg at ieee.org
Thu Nov 9 11:11:37 CST 2006
A. M. Archibald wrote:
> On 08/11/06, Tim Hochberg <tim.hochberg at ieee.org> wrote:
>
>
>> It has always been my experience (on various flavors or Pentium) that
>> operating on NANs is extremely slow. Does anyone know on what hardware
>> NANs are *not* slow? Of course it's always possible I just never notice
>> NANs on hardware where they aren't slow.
>>
>
> On an opteron machine I have access to, they appear to be no slower
> (and even faster for some transcendental functions) than ordinary
> floats:
>
> In [13]: a=zeros(1000000)
>
> In [14]: %time for i in xrange(1000): a += 1.1
> CPU times: user 6.87 s, sys: 0.00 s, total: 6.87 s
> Wall time: 6.87
>
> In [15]: a *= NaN
>
> In [16]: %time for i in xrange(1000): a += 1.1
> CPU times: user 6.86 s, sys: 0.00 s, total: 6.86 s
> Wall time: 6.87
>
> On my Pentium M, they are indeed significantly slower (three times? I
> didn't really do enough testing to say how much). I am actually rather
> offended by this unfair discrimination against a citizen in good
> standing of the IEEE floating point community.
>
If they're only 3x slower you're doing better than I am. On my core duo
box they appear to be nearly 20x slower for both addition and
multiplication. This more or less matches what I recall from earlier boxes.
>>> Timer("a.prod()", "import numpy as np; a = np.ones(4096); a[0]
= np.nan").timeit(10000)
5.6495738983585397
>>> Timer("a.prod()", "import numpy as np; a =
np.ones(4096)").timeit(10000)
0.34439041833525152
>>> Timer("a.sum()", "import numpy as np; a = np.ones(4096); a[0] =
np.nan").timeit(10000)
6.0985655998179027
>>> Timer("a.sum()", "import numpy as np; a =
np.ones(4096)").timeit(10000)
0.32354363473564263
I've been told that operations on NANs are slow because they aren't
always implemented in the FPU hardware. Instead they are trapped and
implemented software or firmware or something or other. That may well be
bogus 42nd hand information though.
-tim
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