##
# Source: http://www.umbc.edu/hpcf/resources-tara/how-to-run-R.html
# filename: snow-test.R
# 
# SNOW quick ref: http://www.sfu.ca/~sblay/R/snow.html
#
# Notes:
#   - Library loading order matters
#   - system.time([function]) is an easy way to test optimizations
#   - parApply is snow parallel version of 'apply'
#
##

library(Rmpi)
library(snow)

# Initialize SNOW using MPI communication. The first line will get the number of
# MPI processes the scheduler assigned to us. Everything else is standard SNOW
np <- mpi.universe.size() - 1
cluster <- makeMPIcluster(np)

# Print the hostname for each cluster member
sayhello <- function() {
  info <- Sys.info()[c("nodename", "machine")]
  paste("Hello from", info[1], "with CPU type", info[2])
}

names <- clusterCall(cluster, sayhello)
print(unlist(names))

# Compute row sums in parallel using all processes, then a grand sum at the end
# on the master process
parallelSum <- function(m, n) {
  A <- matrix(rnorm(m*n), nrow = m, ncol = n)
  # Parallelize the summation
  row.sums <- parApply(cluster, A, 1, sum)
  print(sum(row.sums))
}

# Run the operation over different size matricies
system.time(parallelSum(5000, 5000))

# Always stop your cluster and exit MPI to ensure resources are properly freed
stopCluster(cluster)
mpi.exit()