On Sunday, December 4, 2016 at 12:00:58 AM UTC+1, Pierre wrote: > > I tried naive things like setting x and y to be integers of a certain > type, and then > > sage: %timeit x^y > > for example, but I always get "" The slowest run took 59.81 times longer > than the fastest. This could mean that an intermediate result is being > cached. "" >
Yes, the first computation is relevant so instead of timeit I would use time. > This makes sense, but I'm not sure what else to try. Individual " %time > x^y " statements seem to show no difference between ZZ and numpy.int, for > example, which puzzles me (overhead?). Exact same issues when defining the > factorial via > Both ZZ and numpy use libgmp internally so if you subtract other factors the times should be similar. > fac= lambda n : 1 if n == 0 else n*fac(n-1) > Note however that most of the time for fac is used for the Python loop. sage: %time _=fac(500) CPU times: user 397 µs, sys: 0 ns, total: 397 µs Wall time: 344 µs sage: %time _=factorial(500) CPU times: user 14 µs, sys: 6 µs, total: 20 µs Wall time: 21.9 µs Also note that time for converting the internal result to string output can be significant as well. So here is my question: does anybody know of a basic test/piece of code > that would illustrate the difference in speed between various types of > integers and/or floats? > As shown the differences are irrelevant if you allow other factors. -- You received this message because you are subscribed to the Google Groups "sage-devel" group. To unsubscribe from this group and stop receiving emails from it, send an email to sage-devel+unsubscr...@googlegroups.com. To post to this group, send email to sage-devel@googlegroups.com. Visit this group at https://groups.google.com/group/sage-devel. For more options, visit https://groups.google.com/d/optout.
