parallel library's random numbers
Ross, Consider writing this SPMD style and run as Rscript. Here is a short example: library(pbdMPI, quiet=TRUE) init() comm.set.seed(1234567, diff=TRUE) r <- runif(5) comm.print(r, all.rank=TRUE) finalize() Put the above 6 lines of R code into file rand.r and then run mpirun -np 2 Rscript rand.r Experiment with diff=FALSE and with more than 2 nodes. Then start reading the vignette for the pbdDEMOpackage. If diff=TRUE, the streams are independent. This uses the package rlecuyer. Good luck! George P.S. I assume your 50 nodes are in a cluster, probably managed with PBS. Then the mpirun is issued after a "qsub -I ..." interactive allocation.
On 12/19/13 5:08 PM, Ross Boylan wrote:
On Thu, 2013-12-19 at 13:28 -0800, Ross Boylan wrote:
Conceptually, I have 2,000 tasks and I want each task to have access to an independent, repeatable random number stream. Many of the tasks will run on the same node. For example, a typical job would be to do tasks 200 to 400. I might do one run with 10 nodes and a later one with 50, but I want the same streams for each task. The facilities in parallel seem designed to achieve repeatable randomization for a fixed number of nodes. Is there a way to get them to do what I want?
I'm thinking maybe I should just ignore the parallel random number stuff and do set.seed(t) before task t. It's easy to imagine that is not an entirely safe method, however. Ross
The documentation does not explain what nextRNGStream and nextRNGSubStream do nor how that relates to the initialization done by clusterSetRNGStream. For example, if I do nextRNGStream on node 1, do I get the same stream as is being used on node 2? The documentation does not say that calling nextRNGStream actually resets the seed, though that seems to be implicit in the example (and is explicit in one book I found on the net). I'm also unsure if calling mc.reset.stream() is necessary after calling clusterSetRNGStream. Ross Boylan P.S. Using R 3.0.1
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