Hi,
On Sun, 27 Feb 2011 11:48:28 -0500 (EST)
"Robert G. Brown" <[email protected]> wrote:
> The solution for nearly anyone needing large numbers of fast, high
> quality random numbers is going to be: Use a fast, high quality
> random number generator from e.g. the Gnu Scientific Library, and
> >>seed<< it from /dev/random, ensuring uniqueness of the seed(s)
> >>across the cluster
I agree that for Monte Carlo simulations a fast, high quality (pseudo)
random number generator (PRNG) is more appropriate than /dev/{u}random.
However, seeding a PRNG randomly is imho a missconception. Even though
Monte Carlo algorithms utilize a pseudo random resource the final
result of a Monte Carlo simulation should be deterministic and
reproducible. Therefore, for scientific Monte Carlo applications one
should use a known seed. Parallel Monte Carlo applications may derive
streams of pseudo random numbers from a common base sequence by
splitting and leap frogging, see also
http://arxiv.org/abs/cond-mat/0609584
Regards,
Heiko
--
-- Number Crunch Blog @ http://numbercrunch.de
-- Cluster Computing @ http://www.clustercomputing.de
-- Random numbers @ http://trng.berlios.de
-- Heiko Bauke @ http://www.mpi-hd.mpg.de/personalhomes/bauke
_______________________________________________
Beowulf mailing list, [email protected] sponsored by Penguin Computing
To change your subscription (digest mode or unsubscribe) visit
http://www.beowulf.org/mailman/listinfo/beowulf
