Rmpi

### Required packages
 
# Load
library(Rmpi)
library(doMPI)
library(rlecuyer)
 
### Cluster management
 
# Start cluster
cl = startMPIcluster()
 
# Register cluster
registerDoMPI(cl)
 
# Check cluster size
clusterSize(cl)
 
# Set file name
fileName <- 'full1forCI.csv'
 
# Define the number of replications
nRep <- 100
 
### for loop for simulation
 
# Ensure different random numbers are generated for each replication
RNGkind("L'Ecuyer-CMRG")
 
# foreach loop
m <- foreach (j = 1:nRep) %dopar% {
 
  # Load
  library(OpenMx)
 
  # Try to avoid optimization problems by switching the default optimizer
  mxOption(model = NULL,
           key = 'Default optimizer',
           value = 'NPSOL')
 
  # Try to avoid problems by turning off parallelization
  mxOption(model = NULL,
           key = 'Number of Threads',
           value = 1)
 
  # The code here has been removed.
  # I am simulating data and running LCGA on those data.
  # The code here works if I run it on the cluster using only one processor,
  # or if I parallelize on my own computer using doParallel.
 
  # Returns the result vector
  result
 
}
 
### Write data
 
# Convert matrix to data frame
m <- as.data.frame(x = do.call(what = rbind,
                               args = m))
 
# Write data frame to CSV
write.csv(x = m,
          file = fileName)
 
### Close down cluster
 
closeCluster(cl)
mpi.quit()

#!/bin/bash
#
#$ -cwd
#$ -V
#$ -j y
#$ -S /bin/bash
#
mpirun -n 1 R --vanilla < example-rmpi.R > example-rmpi.Rout

qsub -pe orte 4 example-rmpi.sh