Hi, I was very surprised to find that sage is much slower than ipython for doing the same numerical task (monte carlo simulation using numpy arrays). Following is summary of the time taken -
In [1]: from layered_ising_mc import layered_ising_mc In [2]: a = layered_ising_mc(n=16, J_p=20, h=19.94, beta=0.5) In [3]: a.set_initial_condition() In [4]: time data = a.monte_carlo(1000); a.check_consistency() CPU times: user 16.89 s, sys: 0.03 s, total: 16.93 s Wall time: 17.43 s sage: from layered_ising_mc import layered_ising_mc sage: a = layered_ising_mc(n=16, J_p=20, h=19.94, beta=0.5) sage: a.set_initial_condition() sage: time data = a.monte_carlo(1000); a.check_consistency() CPU times: user 191.94 s, sys: 0.11 s, total: 192.04 s Wall time: 194.03 s I was surprised that the difference is a factor of 10. I hope I am not comparing apples to oranges. Best wishes, Rajeev -- To post to this group, send email to sage-support@googlegroups.com To unsubscribe from this group, send email to sage-support+unsubscr...@googlegroups.com For more options, visit this group at http://groups.google.com/group/sage-support URL: http://www.sagemath.org
