r - doParallel, cluster vs cores

Question

What is the difference between cluster and cores in registerDoParallel when using doParallel package?

Is my understanding correct that on single machine these are interchangeable and I will get same results for :

cl <- makeCluster(4)
registerDoParallel(cl)    

and

registerDoParallel(cores = 4)

The only difference I see that makeCluster() has to be stopped explicitly using stopCluster().

Answer 1

I think the chosen answer is too general and actually not accurate, since it didn't touch the detail of doParallel package itself. If you read the vignettes, it's actually pretty clear.

The parallel package is essentially a merger of the multicore package, which was written by Simon Urbanek, and the snow package, which was written by Luke Tierney and others. The multicore functionality supports multiple workers only on those operating systems that support the fork system call; this excludes Windows. By default, doParallel uses multicore functionality on Unix-like systems and snow functionality on Windows.

We will use snow-like functionality in this vignette, so we start by loading the package and starting a cluster

To use multicore-like functionality, we would specify the number of cores to use instead

In summary, this is system dependent. Cluster is the more general mode cover all platforms, and cores is only for unix-like system.

To make the interface consistent, the package used same function for these two modes.

> library(doParallel)
> cl <- makeCluster(4)
> registerDoParallel(cl)
> getDoParName()
[1] "doParallelSNOW"

> registerDoParallel(cores=4)
> getDoParName()
[1] "doParallelMC"