Required Packages

Please install the following packages:

install.packages(c("foreach", "doParallel", "doRNG", 
                   "snowFT", "extraDistr", "ggplot2", 
                   "reshape2", "wpp2017"), 
                 dependencies = TRUE)

Structure of Statistical Simulations

Many statistical simulations have the following structure:

for (iteration in 1:N) {
    result[iteration] <- myfunc(...)

If calls of myfunc are independent of one another, we can transform the simulation as follows:

Master-slave Paradigm

There are many packages in R that work in this fashion. One of the first packages, snow (Simple Network of Workstations) has been recently re-implemented as an R core package called parallel.

Package parallel


  • Load the package and check how many cores you have:

    detectCores() # counts hyperthreaded cores
    P <- detectCores(logical = FALSE) # physical cores

Start and Stop a Cluster

  • Start and stop a pool of workers with one worker per core:

    cl <- makeCluster(P)
    # cl[[1]] # gives an error

Types of Clusters

  • Socket communication (default):

    cl <- makeCluster(P, type = "PSOCK")
    • Workers start with an empty environment (i.e. new R process).
    • Available for all OS platforms.
  • Fork: type = "FORK"
    • Workers are complete copies of the master process.
    • Not available for Windows.
  • MPI: type = "MPI"
    • Requires the Rmpi package (and MPI) to be installed.
  • NetWorkSpaces: type = "NWS"
    • Requires the nws package (from Revolution Computing) to be installed.

Evaluating a Function on the Cluster

  • Start a cluster that will be used to solve multiple tasks:

    cl <- makeCluster(P)
  • Let’s get each worker to generate as many normally distributed random numbers as its position in the list:

    clusterApply(cl, 1:P, fun = rnorm)

    The second argument is a sequence where each element gets passed to the corresponding worker, namely as the first argument to the function fun. In this example, the first worker got the number 1, second 2 etc. which is passed as the first argument to the rnorm function. Thus, the node cl[[4]] for example evaluates rnorm(4, mean = 0, sd = 1).

  • Pass additional arguments to fun:

    clusterApply(cl, 1:P, fun = rnorm, 
                mean = 10, sd = 2)
  • Evaluate a function more times than the number of workers: Generate 20 sets of 100,000 random numbers from N(mean=5, sd=1) and return the average of each set:

    res <- clusterApply(cl, rep(100000, 20), 
            fun = function(x) mean(rnorm(x, mean = 5)))