> Feature - the calls all happen at once starting with the same state of >> sage including the random state. >> >> > > how one would then run a randomised algorithm in parallel? (e.g. testing > property with random input) > In such a case one certainly need something orthogonal to this. >
Calling the same function f with the parallel decorator iteratively gives: sage: [f(a) for a in range(10)] [0.5456150826695888, 1.5274761438193447, 2.6261975603956875, 3.311480135021058, 4.622068879474614, 5.056304819780978, 6.850777826232956, 7.652224224628873, 8.419067022860219, 9.356877740213488] That's why, as a user, I was expecting the same behavior for f(range(10)). Also, I realized using @parallel(p_iter='reference') gives what I was expecting and @parallel(p_iter='multiprocessing') gives a intermediate (pairs of results with same random part, two because I have two cpus I guess). So between using p_iter='fork' with a random input or using p_iter='reference', is there some reason to choose one instead of the other? Sébastien -- 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 http://groups.google.com/group/sage-devel. For more options, visit https://groups.google.com/d/optout.
