Parallel Monte Carlo Simulation


Carry out parallel Monte Carlo simulation on R slaves spawned by using slavedaemon.R script and all executed results are returned back to master.


mpi.parSim(n=100, rand.gen=rnorm, rand.arg=NULL,statistic, 
nsim=100, run=1, slaveinfo=FALSE, sim.seq=NULL, simplify=TRUE, comm=1, ...)  



sample size.


the random data generating function. See the details section


additional argument list to rand.gen.


the statistic function to be simulated. See the details section


the number of simulation carried on a slave which is counted as one slave job.


the number of looping. See the details section.


if TRUE, the numbers of jobs finished by slaves will be displayed.


if reproducing the same simulation is desirable, set it to the integer vector .mpi.parSim generated in previous simulation.


logical; should the result be simplified to a vector or matrix if possible?


a communicator number


optional arguments to statistic


It is assumed that one simulation is carried out as statistic(rand.gen(n)), where rand.gen(n) can return any values as long as statistic can take them. Additional arguments can be passed to rand.gen by rand.arg as a list. Optional arguments can also be passed to statistic by the argument ....

Each slave job consists of replicate(nsim,statistic(rand.gen(n))), i.e., each job runs nsim number of simulation. The returned values are transported from slaves to master.

The total number of simulation (TNS) is calculated as follows. Let slave.num be the total number of slaves in a comm and it is mpi.comm.size(comm)-1. Then TNS=slave.num*nsim*run and the total number of slave jobs is slave.num*run, where run is the number of looping from master perspective. If run=1, each slave will run one slave job. If run=2, each slave will run two slaves jobs on average, and so on.

The purpose of using run has two folds. It allows a tuneup of slave job size and total number of slave jobs to deal with two different cluster environments. On a cluster of slaves with equal CPU power, run=1 is often enough. But if nsim is too big, one can set run=2 and the slave jog size to be nsim/2 so that TNS=slave.num*(nsim/2)*(2*run). This may improve R computation efficiency slightly. On a cluster of slaves with different CPU power, one can choose a big value of run and a small value of nsim so that master can dispatch more jobs to slaves who run faster than others. This will keep all slaves busy so that load balancing is achieved.

The sequence of slaves who deliver results to master are saved into .mpi.parSim. It keeps track which part of results done by which slaves. .mpi.parSim can be used to reproduce the same simulation result if the same seed is used and the argument sim.seq is equal to .mpi.parSim.

See the warning section before you use mpi.parSim.


The returned values depend on values returned by replicate of statistic(rand.gen(n)) and the total number of simulation (TNS). If statistic returns a single value, then the result is a vector of length TNS. If statistic returns a vector (list) of length nrow, then the result is a matrix of dimension c(nrow, TNS).


It is assumed that a parallel RNG is used on all slaves. Run mpi.setup.rngstream on the master to set up a parallel RNG. Though mpi.parSim works without a parallel RNG, the quality of simulation is not guarantied.

mpi.parSim will automatically transfer rand.gen and statistic to slaves. However, any functions that rand.gen and statistic reply on but are not on slaves must be transfered to slaves before using mpi.parSim. You can use mpi.bcast.Robj2slave for that purpose. The same is applied to required packages or C/Fortran codes. You can use either mpi.bcast.cmd or put required(package) and/or dyn.load(so.lib) into rand.gen and statistic.

If simplify is TRUE, sapply style simplication is applied. Otherwise a list of length slave.num*run is returned.


Hao Yu

See Also

mpi.setup.rngstream mpi.bcast.cmd mpi.bcast.Robj2slave

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