On Wednesday 19 December 2007 23:42, Bill Hart wrote: > Here is a graph that uses slightly bigger coefficients, up to 10000 > bits. > > http://sage.math.washington.edu/home/wbhart/flint-trunk/graphing/factor2.pn >g > > I don't see the speed increase you mentioned for large coefficients, > but this might be due to some special properties of the polynomials > you are factoring, or perhaps due to the particular architecture you > have. Prime field arithmetic is probably quite architecture dependent, > so that may contribute, I'm not sure.
Yeah, in the end, I don't think I believe the speed increase for large coefficients either. I think what I did observe was probably a result of accidents with semi-random polynomials. Here's a snippet from my patched branch: Loading SAGE library. Current Mercurial branch is: uni-factoring sage: R.<x>=ZZ[] sage: f=x^2-1 sage: timeit f._factor_ntl() ######## NTL wins 10000 loops, best of 3: 159 µs per loop sage: timeit f._factor_pari() 1000 loops, best of 3: 794 µs per loop sage: f=R.random_element(6) sage: timeit f._factor_ntl() ######### NTL wins 1000 loops, best of 3: 316 µs per loop sage: timeit f._factor_pari() 1000 loops, best of 3: 533 µs per loop I'm not seeing this NTL win on your chart. I'm wondering if this might be because of data-conversion costs to pari. Of course, that data conversion is a cost of the sage implementation so when making the cutoff choices for sage we need to include the conversion costs. -- Joel --~--~---------~--~----~------------~-------~--~----~ To post to this group, send email to sage-devel@googlegroups.com To unsubscribe from this group, send email to [EMAIL PROTECTED] For more options, visit this group at http://groups.google.com/group/sage-devel URLs: http://sage.scipy.org/sage/ and http://modular.math.washington.edu/sage/ -~----------~----~----~----~------~----~------~--~---
