It looks like on my Pentium M multiplication with NaNs is slow, but using a masked array ranges from slightly faster (with only one value masked) to twice as slow (with all values masked): In [15]:Timer("a.prod()", "import numpy as np; aa = np.ones(4096); a = np.ma.masked_greater(aa,0)").timeit(10000) Out[15]:9.4012830257415771 In [16]:Timer("a.prod()", "import numpy as np; a = np.ones(4096); a[0]= np.nan").timeit(10000) Out[16]:5.5737850666046143 In [17]:Timer("a.prod()", "import numpy as np; a = np.ones(4096)").timeit(10000) Out[17]:0.40796804428100586 In [18]:Timer("a.prod()", "import numpy as np; aa = np.ones(4096); aa[0] = 2; a = np.ma.masked_greater(aa,1)").timeit(10000) Out[18]:4.1544749736785889 In [19]:Timer("a.prod()", "import numpy as np; a = np.ones(4096); a[:]= np.nan").timeit(10000)Out[19]:5.8589630126953125 For transcendentals, nans or masks don't make much difference, although masks are slightly faster than nans: In [20]:Timer("np.sin(a)", "import numpy as np; a = np.ones(4096); a[:]= np.nan").timeit(10000) Out[20]:4.5575671195983887 In [21]:Timer("np.ma.sin(a)", "import numpy as np; aa = np.ones(4096); a = np.ma.masked_greater(aa,0)").timeit(10000) Out[21]:4.4125270843505859 In [22]:Timer("b=np.sin(a)", "import numpy as np; a = np.ones(4096)").timeit(10000) Out[22]:3.5793929100036621 Eric Tim Hochberg wrote:
A. M. Archibald wrote:
On 08/11/06, Tim Hochberg <tim.hochberg@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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