On May 9, 2009, at 8:17 PM, Eric Firing wrote: > Eric Firing wrote:
> > A part of the slowdown is what looks to me like unnecessary copying > in _MaskedBinaryOperation.__call__. It is using getdata, which > applies numpy.array to its input, forcing a copy. I think the copy > is actually unintentional, in at least one sense, and possibly two: > first, because the default argument of getattr is always evaluated, > even if it is not needed; and second, because the call to np.array > is used where np.asarray or equivalent would suffice. Yep, good call. the try/except should be better, and yes, I forgot to force copy=False (thought it was on by default...). I didn't know that getattr always evaluated the default, the docs are scarce on that subject... > Pierre, > > ... I pressed "send" too soon. There are test failures with the > patch I attached to my last message. I think the basic ideas are > correct, but evidently there are wrinkles to be worked out. Maybe > putmask() has to be used instead of where() (putmask is much faster) > to maintain the ability to do *= and similar, and maybe there are > other adjustments. Somehow, though, it should be possible to get > decent speed for simple multiplication and division; a 10x penalty > relative to ndarray operations is just too much. Quite agreed. It was a shock to realize that we were that slow. I gonna have to start testing w/ large arrays... I'm confident we can significantly speed up the _MaskedOperations without losing any of the features. Yes, putmask may be a better option. We could probably use the following MO: * result = a.data/b.data * putmask(result, m, a) However, I gonna need a good couple of weeks before being able to really look into it... _______________________________________________ Numpy-discussion mailing list Numpy-discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
