[R] (Parallel) Random number seed question...

[Blair Christian]

Hi All,

I have k identical parallel pieces of code running, each using n.rand
random numbers.  I would like to use the same RNG (for now), and set
the seeds so that I can guarantee that there are no overlaps in the
random numbers sampled by the k pieces of code.  Another side goal is
to have reproducibility of my results.  In the past I have used C with
SPRNG for this task, but I'm hoping that there is an easy way to do
this in R with poor man's parallelization (eg running multiple Rs on
multiple processors without the overhead of setting up any mpi or
using snow(fall)).  It is not clear from the documentation if set.seed
arguments are sequential or not for a given RNG, eg if set.seed(1) on
processor 1, set.seed(1+n.rand) on processor 2, set.seed(1+2*n.rand)
on processor 3, etc for the default RNG "Mersenne-Twister".  An easy
approach would be to simply write a script to generate n.rand numbers,
records the .Random.seed and proceeds in that manner- inelegant, but
effective.  My question here is "Is there a better way?"


(mvtnorm part directed to Torsten Hothorn)
To further clarify, it seems there is a different RNG for normal
(rnorm) than for everything else? (eg RNGKind( ..,
normal.kind="Inversion"); further, does anybody know if mvtnorm uses
this generator?


Further, some RNGs seem to be based on the archictecture (eg the
Knuth-TAOCP-2002 for example)- is the period really related to 2^32,
or is it dependent the architecture, 2^64 for 64 bit R and 2^32 for 32
bit R?


I noticed there are several packages related to RNG- please direct me
to a vignette/R news article/previous post if this has been covered ad
nauseum.  I have skimmed vignettes/docs for rsprng package, RNG doc in
base, setRNG package, mvtnorm package vignette  (Or am I setting
myself up to write a current RNG doc?)


(directed to Gregory Warnes)
I found a presentation by Gregory Warnes from 1999 addressing these
same questions (and uses a "collings generator" in some C code).
http://www.r-project.org/conferences/DSC-1999/slides/warnes.ps.gz
Have you turned to the snowfall related parallel implementations, did
your Collings generator work well, or have you discovered another
trick you might like to share?

Thank you all for your time and excellent contributions to the open
source community,
Blair