using parallel's parLapply: unable to access variables within parallel code

Question

I recently got a computer with several cores and am learning to use parallel computing. I\'m fairly proficient with lapply and was told parLapply

Answer 1

An alternate method provided by Martin Morgan would work here as well.

This method supplies the objects to each node in the cluster directly in parLapply call with no need to use cluster export:

library(parallel)
text.var <- rep("I like cake and ice cream so much!", 20)
ntv <- length(text.var)
gc.rate <- 10

pos <-  function(i) {
    paste(sapply(strsplit(tolower(i), " "), nchar), collapse=" | ")
}

cl <- makeCluster(mc <- getOption("cl.cores", 4))
parLapply(cl, seq_len(ntv), function(i, pos, text.var, ntv, gc.rate) {
        x <- pos(text.var[i])
        if (i%%gc.rate==0) gc()
        return(x)
    }, pos, text.var, ntv, gc.rate
)

Answer 2

out1<-lapply(seq_len(ntv), function(i) {x <- pos(text.var[i]);if (i%%gc.rate==0) gc();return(x)})
out2<-parLapply(cl, seq_len(ntv), function(i) {x <- pos(text.var[i]);if (i%%gc.rate==0) gc();return(x)})

>     identical(out1,out2)
# [1] TRUE
require(rbenchmark)
benchmark(lapply(seq_len(ntv), function(i) {x <- pos(text.var[i]);if (i%%gc.rate==0) gc();return(x)}),parLapply(cl, seq_len(ntv), function(i) {x <- pos(text.var[i]);if (i%%gc.rate==0) gc();return(x)}))


                                                                                       test
#1        lapply(seq_len(ntv), function(i) {\n    x <- pos(text.var[i])\n    if (i%%gc.rate == 0) \n        gc()\n    return(x)\n})
#2 parLapply(cl, seq_len(ntv), function(i) {\n    x <- pos(text.var[i])\n    if (i%%gc.rate == 0) \n        gc()\n    return(x)\n})
#  replications elapsed relative user.self sys.self user.child sys.child
#1          100   20.03 3.453448     20.31     0.05         NA        NA
#2          100    5.80 1.000000      0.22     0.03         NA        NA

> cl
socket cluster with 2 nodes on host ‘localhost’

Answer 3

You need to export those variables to the other R processes in the cluster:

cl <- makeCluster(mc <- getOption("cl.cores", 4))
clusterExport(cl=cl, varlist=c("text.var", "ntv", "gc.rate", "pos"))