Stefan Behnel <stefan...@behnel.de> added the comment:
Thanks for your review and ideas, Serhiy. I added a couple of test cases, but failed to find any case where the new implementation is not much faster. I also tried "divmod(n_div, d_div)" for implementing __divmod__(), and the results are mixed, e.g. Arithmetic operators: $ ./python -m timeit -s 'from fractions import Fraction as F; a = F(-7, 3); b = F(3, 2)' 'divmod(a,b)' 100000 loops, best of 5: 3.11 usec per loop $ ./python -m timeit -s 'from fractions import Fraction as F; a, b = F(10**15+1, 10**27+1), F(10**9-1, 10**7-1)' 'divmod(a, b)' 100000 loops, best of 5: 3.48 usec per loop $ ./python -m timeit -s 'from fractions import Fraction as F; a, b = F(10**350+1, 10**207+1), F(10**89-1, 10**62-1)' 'divmod(a, b)' 20000 loops, best of 5: 17.7 usec per loop $ ./python -m timeit -s 'from fractions import Fraction as F; a, b = F(10**89-1, 10**611-1), F(10**350+1, 10**207+1)' 'divmod(a, b)' 20000 loops, best of 5: 18.2 usec per loop $ ./python -m timeit -s 'from fractions import Fraction as F; a, b = F(10**350+1, 10**207+1), F(10**89-1, 10**612-1)' 'divmod(a, b)' 5000 loops, best of 5: 34.4 usec per loop divmod(): $ ./python -m timeit -s 'from fractions import Fraction as F; a = F(-7, 3); b = F(3, 2)' 'divmod(a,b)' 100000 loops, best of 5: 3.04 usec per loop $ ./python -m timeit -s 'from fractions import Fraction as F; a, b = F(10**15+1, 10**27+1), F(10**9-1, 10**7-1)' 'divmod(a, b)' 100000 loops, best of 5: 3.56 usec per loop $ ./python -m timeit -s 'from fractions import Fraction as F; a, b = F(10**350+1, 10**207+1), F(10**89-1, 10**62-1)' 'divmod(a, b)' 20000 loops, best of 5: 17.3 usec per loop $ ./python -m timeit -s 'from fractions import Fraction as F; a, b = F(10**89-1, 10**611-1), F(10**350+1, 10**207+1)' 'divmod(a, b)' 20000 loops, best of 5: 18.2 usec per loop $ ./python -m timeit -s 'from fractions import Fraction as F; a, b = F(10**350+1, 10**207+1), F(10**89-1, 10**612-1)' 'divmod(a, b)' 10000 loops, best of 5: 31.7 usec per loop Current master, for comparison: $ ./python -m timeit -s 'from fractions import Fraction as F; a = F(-7, 3); b = F(3, 2)' 'divmod(a,b)' 20000 loops, best of 5: 14.1 usec per loop $ ./python -m timeit -s 'from fractions import Fraction as F; a, b = F(10**15+1, 10**27+1), F(10**9-1, 10**7-1)' 'divmod(a, b)' 20000 loops, best of 5: 16 usec per loop $ ./python -m timeit -s 'from fractions import Fraction as F; a, b = F(10**350+1, 10**207+1), F(10**89-1, 10**62-1)' 'divmod(a, b)' 5000 loops, best of 5: 61.2 usec per loop $ ./python -m timeit -s 'from fractions import Fraction as F; a, b = F(10**89-1, 10**611-1), F(10**350+1, 10**207+1)' 'divmod(a, b)' 5000 loops, best of 5: 65.3 usec per loop $ ./python -m timeit -s 'from fractions import Fraction as F; a, b = F(10**350+1, 10**207+1), F(10**89-1, 10**612-1)' 'divmod(a, b)' 2000 loops, best of 5: 120 usec per loop Definitely not an obvious decision, although there is a tendency towards faster execution for very large numbers. Whether it's faster or slower would probably depend on the data and the application at hand. I could live with either choice, but would use divmod() for now since it simplifies the implementation. ---------- _______________________________________ Python tracker <rep...@bugs.python.org> <https://bugs.python.org/issue35588> _______________________________________ _______________________________________________ Python-bugs-list mailing list Unsubscribe: https://mail.python.org/mailman/options/python-bugs-list/archive%40mail-archive.com
