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.
It's seems easy and eloquent to just write "@cached_method" to speed
up and cache your methods. But watch out -- there is a potentially
serious price to pay for doing so, which may or may not matter
depending on your application. I'm writing this, because I really
wish somebody had warned me about this before.
-- William
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