[Numpy-discussion] Speed up function on cross product of two sets?

Zachary Pincus zpincus at stanford.edu
Sun Apr 2 17:17:06 CDT 2006

Tim -

Thanks for your suggestions -- that all makes good sense.

It sounds like the general take home message is, as always: "the  
first thing to try is to vectorize your inner loop."


>> I have a inner loop that looks like this:
>> out = []
>> for elem1 in l1:
>>   for elem2 in l2:
>>     out.append(do_something(l1, l2))
> this is do_something(elem1, elem2), correct?
>> result = do_something_else(out)
>> where do_something and do_something_else are implemented with  
>> only  numpy ufuncs, and l1 and l2 are numpy arrays.
>> As an example, I need to compute the median distance from any  
>> element  in one set to any element in another set.
>> What's the best way to speed this sort of thing up with numpy  
>> (e.g.  push as much down into the underlying C as possible)? I  
>> could re- write do_something with the numexpr tools (which are  
>> very cool), but  that doesn't address the fact that I've still got  
>> nested loops living  in Python.
> The exact approach I'd take would depend on the sizes of l1 and l2  
> and a certain amount of trial and error. However, the first thing  
> I'd try is:
>    n1 = len(l1)
>    n2 = len(l2)
>    out = numpy.zeros([n1*n2], appropriate_dtype)
>    for i, elem1 in enumerate(l1):
>        out[i*n2:(i+1)*n2] = do_something(elem1, l1)
>    result = do_something_else(out)
> That may work as is, or you may have to tweak do_something slightly  
> to handle l1 correctly. You might also try to do the operations in  
> place and stuff the results into out directly by using X= and three  
> argument ufuncs. I'd not do that at first though.
> One thing to consider is that, in my experience, numpy works best  
> on chunks of about 10,000 elements. I believe that this is a  
> function of cache size. Anyway, this may choice of which of l1 and  
> l2 you continue to loop over, and which you vectorize. If they both  
> might get really big, you could even consider chopping up l1 when  
> you vectorize it. Again I wouldn't do that unless it really looks  
> like you need it.
> If that all sounds opaque, feel free to ask more questions. Or if  
> you have questions about microoptimizing the guts of do_something,  
> I have a bunch of experience with that and I like a good puzzle.
>> Perhaps there's some way in numpy to make one big honking array  
>> that  contains all the pairs from the two lists, and then just run  
>> my  do_something on that huge array, but that of course scales  
>> poorly.
> I know of at least one way, but it's a bit of a kludge. I don't  
> think I'd try that though. As you said, it scales poorly.  As long  
> as you can vectorize your inner loop, it's not necessary and  
> sometimes makes things worse, to vectorize your outer loop as well.  
> That's assuming your inner loop is large, it doesn't help if your  
> inner loop is 3 elements long for instance, but that doesn't seem  
> like it should be a problem here.
> Regards,
> -tim

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