mcapply.hack.r

mclapply.hack <- function(...) {
  library(parallel)
  ## Create a cluster
  ## ... How many workers do you need?
  ## ... N.B. list(...)[[1]] returns the first 
  ##          argument passed to the function. In
  ##          this case it is the list to iterate over
  size.of.list <- length(list(...)[[1]])
  cl <- makeCluster( min(size.of.list, detectCores()) )
  
  ## Find out the names of the loaded packages 
  loaded.package.names <- c(
    ## Base packages
    sessionInfo()$basePkgs,
    ## Additional packages
    names( sessionInfo()$otherPkgs ))
  
  ## N.B. tryCatch() allows us to properly shut down the 
  ##      cluster if an error in our code halts execution
  ##      of the function. For details see: help(tryCatch)
  tryCatch( {
    
    ## Copy over all of the objects within scope to
    ## all clusters. 
    ## 
    ## The approach is as follows: Beginning with the 
    ## current environment, copy over all objects within
    ## the environment to all clusters, and then repeat
    ## the process with the parent environment. 
    ##
    this.env <- environment()
    while( identical( this.env, globalenv() ) == FALSE ) {
      clusterExport(cl,
                    ls(all.names=TRUE, env=this.env),
                    envir=this.env)
      this.env <- parent.env(environment())
    }
    ## repeat for the global environment
    clusterExport(cl,
                  ls(all.names=TRUE, env=globalenv()),
                  envir=globalenv())
    
    ## Load the libraries on all the clusters
    ## N.B. length(cl) returns the number of clusters
    parLapply( cl, 1:length(cl), function(xx){
      lapply(loaded.package.names, function(yy) {
        ## N.B. the character.only option of 
        ##      require() allows you to give the 
        ##      name of a package as a string. 
        require(yy , character.only=TRUE)})
    })
    
    ## Run the lapply in parallel 
    return( parLapply( cl, ...) )
  }, finally = {        
    ## Stop the cluster
    stopCluster(cl)
  })
}