I think I know the answer to this, but I'm going to ask it anyway... I know that there is a general policy of trying to write code in the standard library that does not disadvantage other implementations. How far does that go the other way? Should the standard library accept slower code because it will be much faster in other implementations?
Briefly, I have a choice of algorithm for the median function in the statistics module. If I target CPython, I will use a naive but simple O(N log N) implementation based on sorting the list and returning the middle item. (That's what the module currently does.) But if I target PyPy, I will use an O(N) algorithm which knocks the socks off the naive version even for smaller lists. In CPython that's typically 2-5 times slower; in PyPy it's typically 3-8 times faster, and the bigger the data set the more the advantage. For the specific details, see http://bugs.python.org/issue21592 My feeling is that the CPython standard library should be written for CPython, that is, it should stick to the current naive implementation of median, and if PyPy wants to speed the function up, they can provide their own version of the module. I should *not* complicate the implementation by trying to detect which Python the code is running under and changing algorithms accordingly. However, I should put a comment in the module pointing at the tracker issue. Does this sound right to others? Thanks, -- Steve _______________________________________________ Python-Dev mailing list Python-Dev@python.org https://mail.python.org/mailman/listinfo/python-dev Unsubscribe: https://mail.python.org/mailman/options/python-dev/archive%40mail-archive.com
