On 03/25/2010 08:49 PM, William Stein wrote:
Hi,I was trying to understand why certain modular symbols code was too slow, and ran across a method that looked like this (in modular/modsym/element.py): def modular_symbol_rep(self): try: return self.__modular_symbols except AttributeError: A = self.parent() v = self.manin_symbol_rep() if v == 0: return v w = [c * x.modular_symbol_rep() for c, x in v] return sum(w) return self.__modular_symbols Of course, on multiple calls, the above is slow, because the __modular_symbols cache never gets set. So I though, ah ha!, this is a great example where I should use @cached_method, now that we have it. So I tried that, but doing timeit('...') showed that it still took about 20microsend/call to call the above function. Replacing @cached_method with the try/except as above, but actually settin gself.__modular_symbols correctly resulted in the speedup I wanted (e.g., each call takes a few hundred nanoseconds). MORAL: If you care about speed, don't use the cached_method decorator yet. It entails massive overheard.
See #8611 for the start of a patch which speeds up cached_function by about 30x in your case (no arguments), and @cached_method by about 3x. The ticket has before/after timing information.
I think someone more familiar with Python method decorators (in particular, when you have an instance, etc.) could probably speed up the @cached_method a lot more. For example, inst.__dict__.setdefault gets called on every call right now.
@cached_method still isn't nearly as fast as a simple cache like what you have above (see the ticket for details), but it's a start.
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