Parallel Workers on the Local Machine

Introduction

This vignettes illustrates how to launch parallel workers on the current, local machine. This works the same on all operating systems where R is supported, e.g. Linux, macOS, and MS Windows.

Examples

Example: Launching two parallel workers

The below illustrates how to launch a cluster of two parallel workers on the current machine, run some basic calculations in paralllel, and then shut down the cluster.

library(parallelly)
library(parallel)

cl <- makeClusterPSOCK(2)
print(cl)
#> Socket cluster with 2 nodes where 2 nodes are on host 'localhost'
#> (R version 4.4.2 (2024-10-31), platform x86_64-pc-linux-gnu)

y <- parLapply(cl, X = 1:100, fun = sqrt)
y <- unlist(y)
z <- sum(y)
print(z)
#> [1] 671.4629

parallel::stopCluster(cl)

Comment: In the parallel package, a parallel worker is referred to a parallel node, or short node, which is why we use the same term in the parallelly package.

An alternative to specifying the number of parallel workers is to specify a character vector with that number of "localhost" entries, e.g.

cl <- makeClusterPSOCK(c("localhost", "localhost"))

Example: Launching as many parallel workers as allotted

The availableCores() function will return the number of workers that the system allows. It respects many common settings that controls the number of CPU cores that the current R process is alloted, e.g. R options, environment variables, and CGroups settings. For details, see help("availableCores"). For example,

library(parallelly)
cl <- makeClusterPSOCK(availableCores())
print(cl)
#> Socket cluster with 8 nodes where 8 nodes are on host 'localhost'
#> (R version 4.4.2 (2024-10-31), platform x86_64-pc-linux-gnu)