Robert Kern <robert.k...@gmail.com> wrote: > No one needs small jumps of arbitrary size. The real use case for > jumping is to make N parallel streams that won't overlap. You pick a > number, let's call it `jump_steps`, much larger than any single run of > your system could possibly consume (i.e. the number of core PRNG > variates pulled is << `jump_steps`). Then you can initializing N > parallel streams by initializing RandomState once with a seed, storing > that RandomState, then jumping ahead by `jump_steps`, storing *that* > RandomState, by `2*jump_steps`, etc. to get N RandomState streams that > will not overlap. Give those to your separate processes and let them > run. > > So the alternative may actually be to just generate and distribute > *one* set of these jump coefficients for a really big jump size but > still leaves you enough space for a really large number of streams > (fortunately, 2**19937-1 is a really big number).
DCMT might be preferred in this case. It works the same, except you have N "random state" streams with characteristic polynomials that are distinct and relatively prime to each other. Thus each of the N processes will get an independent stream of random numbers, without any chance of overlap. http://www.math.sci.hiroshima-u.ac.jp/∼m-mat/MT/DC/dc.html Jump-ahead is easier to accomplish with MRG-32k3a than MT19937. Another generator with an efficient jump-ahead is XORWOW. Sturla _______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
