[R-sig-hpc] parallel random numbers: set.seed(i), rsprng, rlecuyer, [one solution]

[Hana Sevcikova]

You should get the same results much more easily, using just one 
function from snowFT:

performParallel(cluster.size, 1:1000, fun=your.simulation.function, 
cltype='MPI', gentype='SPRNG', seed=1066 )

If you set gentype='RNGstream' it will do the same thing using rlecuyer.

Hana


On 1/17/11 1:52 PM, Ross Boylan wrote:
> On Tue, 2010-12-14 at 13:51 -0800, Ross Boylan wrote:
>> The question is how to generate reproducible streams of parallel
>> random
>> numbers in a way that is insensitive to the number of nodes used.  If
>> I
>> can run 10 jobs one time, and 60 the next, I want to get the same
>> random
>> numbers.
>>
>> Scenarios
>> A) Each job generates its own random numbers.
>> B) A specialized subset of jobs generates random numbers; the subset
>> expands as the total number of jobs expands.
>> C) A fixed subset, e.g., 5 jobs, are responsible for generating the
>> random number.
>>
>> C) seems the most likely to be achievable. rsprng, which we use,
>> initializes streams with a call that includes the stream number and
>> the
>> total number of streams.  I don't know if the stream number alone
>> determines the sequence, or if the stream number, and possibly
>> messaging
>> between processes, comes into play.  Even if rsprng came with some
>> guarantees, other parallel generators (e.g., rlecuyer) might not.
> I have some good news to report with rsprng, based on sprng v2.
> Scenario A) is quite possible.  That is, if you have 1,000 simulations
> you can operate using 1,000 streams.  You just have to remember to kill
> the old stream if the same process is handling multiple simulations.
>
> The stream number one to pass to the initialization routine is the job
> or simulation number, rather than the rank of the process on which it is
> running.
>
> According to the sprng docs, MPI is used only in in transmitting a seed
> to all nodes.  My code set the seed though other means, and so there was
> never any messaging.  This made it possible to start the streams at
> different times.
>
> I also did a small experiment to see if stream 1 was the same regardless
> of whether the total number of streams was 500 or 1000.  It appeared to
> be.  However, the documentation provides no guarantees that this is the
> case.
>
> Here are some excerpts from my code, which runs under snow:
> # master and slave setup
> setup0<- function(){
>    library("rsprng")
>    # set these in the global environment for use by slaveDo
>    nTotalSimulations<<- 1000
>    seed<<- 1066
> }
>
> # setup to run on master
> # cl is cluster
> setupMaster<- function(cl, nsim, fname){
>    clusterEvalQ(cl, source("ascertain.R")) # file with this code
>    clusterEvalQ(cl, setup0())
>    system.time(r<- clusterApplyLB(cl, seq(nsim), "slaveDo"))
>    invisible(save(r, file=fname))
>    stopCluster(cl)
> }
>
>
> # sample slave job
> # uses global nTotalSimulation and seed
> slaveDo<- function(k){
>    free.sprng()
>    init.sprng(nTotalSimulations, k-1, seed)
>    # do and return simulation results
> }
>
> cl<- getMPIcluster()
> if (mpi.comm.rank(0) == 0) {
>    setupMaster(cl, 1000, "r1.RData")
> }
>
>