🚀 go-pugleaf

daveg@synaptics.com (Dave Gillespie) writes:

>In article <h_8yzkk@rpi.edu> maniattb@cs.rpi.edu (Bill Maniatty) writes:
>> I have a large distributed stochastic model (runs on MasPar MP-1 with 2,048
>> processing elements).  I'm not an expert on random number generation, but
>> I need to simultaneously g enerate different ``Random'' sequences on each
>> node.

>If the nodes must be independent, then it would probably work pretty
>well to use one random number generator, say, linear congruential,
>to create a sequence of 2048 seeds for a different type of generator,
>say additive congruential, running in parallel on the various nodes.

This idea is really bad, because it ensures that no two generators will
give the same number at one moment (or even always the same). Besides, random
number generators are usually not designed to produce uncorrelated
numbers at ARBITRARY distances in their sequence, so you would probably
fail even simple tests terribly. As I said before, the only mathematically
safe way is to use ONE (and only one) generator of very high quality to
produce values in turn. This is because the mathematics for a good RNG
can only be ensured with regard to itself, not to other instances of it
(different seeds), and certainly not to different RNGs without further
information.