Difference between revisions of "Distributed computing in R with Rmpi"
Line 55: | Line 55: | ||
== Exercise == | == Exercise == | ||
− | What happens when replacing mpi.scatter.Robj() instead of mpi.bcast.data2slave() in point 4? | + | 1. What happens when 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 Read == | == Further Read == | ||
https://cran.r-project.org/web/packages/Rmpi/Rmpi.pdf | https://cran.r-project.org/web/packages/Rmpi/Rmpi.pdf |
Revision as of 08:42, 9 December 2020
Load modules
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/4.0.2 r/4.0.2
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 4 processors for computation. Therefore, we request 5 processors
$ bsub -n 5 -W 02:00 -I bash Generic job. Job <155200980> is submitted to queue <normal.4h>. <<Waiting for dispatch ...>> <<Starting on eu-c7-105-05>>
Define available global number of processors with the environment parameter MPI_UNIVERSE_SIZE.
$ export MPI_UNIVERSE_SIZE=5
Start R
$ R >
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()
Exercise
1. What happens when 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