Distributed computing in R with Rmpi

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Load modules and install Rmpi

Change to the new software stack and load required modules. Here we need MPI and R libraries.

$ env2lmod
$ module load gcc/6.3.0 openmpi/2.1.1 r/4.0.2
$ R
> install.packages("Rmpi")

Run R in an interactive session

Rmpi assigns one processor to be the master and other processors to be workers. Here we would like to use 5 processors on 2 nodes for computation. Therefore, we request 6 processors

 $ bsub -n 6 -R "span[ptile=3]" -Is bash
 Generic job.
 Job <155200980> is submitted to queue <normal.4h>.
 <<Waiting for dispatch ...>>
 <<Starting on eu-c7-105-05>>

Use Rmpi

1. Load Rmpi which calls mpi.initialize()

 > library(Rmpi)

2. Spawn R-slaves to the host. nslaves = requested number of processors - 1

 > usize <- as.numeric(Sys.getenv("MPI_UNIVERSE_SIZE"))
 > ns <- usize - 1
 > mpi.spawn.Rslaves(nslaves=ns)

3. Set up a variable array

 > var = c(11.0, 22.0, 33.0)

4. Root sends state variables and parameters to other ranks

 > mpi.bcast.data2slave(var, comm = 1, buffunit = 100)

5. Get the rank number of that processor

 > mpi.bcast.cmd(id <- mpi.comm.rank())

6. Check if each rank can use its own value

 > mpi.remote.exec(paste("The variable on rank ",id," is ", var[id]))

7. Root orders other ranks to calculate

 > mpi.bcast.cmd(output <- var[id]*2)

8. Root orders other ranks to gather the output

 > mpi.bcast.cmd(mpi.gather(output, 2, double(1)))

9. Root gathers the output from other ranks

 > mpi.gather(double(1), 2, double(usize))

10. Close down and quit

 > mpi.close.Rslaves(dellog = FALSE)
 > mpi.quit()

Exercises

1. Try replacing mpi.scatter.Robj() instead of mpi.bcast.data2slave() in point 4
2. Create an R script using Rmpi and submit a batch job through BSUB command line
3. Create a BSUB job script and submit a batch job

Further reading

https://cran.r-project.org/web/packages/Rmpi/Rmpi.pdf

< Examples