The Difference Between Domc and Doparallel in R

the difference between doMC and doParallel in R

The doParallel package is a merger of doSNOW and doMC, much as parallel is a merger of snow and multicore. But although doParallel has all the features of doMC, I was told by Rich Calaway of Revolution Analytics that they wanted to keep doMC around because it was more efficient in certain circumstances, even though doMC now uses parallel just like doParallel. I haven't personally run any benchmarks to determine if and when there is a significant difference.

I tend to use doMC on a Linux or Mac OS X computer, doParallel on a Windows computer, and doMPI on a Linux cluster, but doParallel does work on all of those platforms.

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As for the different registration methods, if you execute:

registerDoParallel(cores=3)

on a Windows machine, it will create a cluster object implicitly for later use with clusterApplyLB, whereas on Linux and Mac OS X, no cluster object is created or used. The number of cores is simply remembered and used as the value of the mc.cores argument later when calling mclapply.

If you execute:

cl <- makeCluster(3)
registerDoParallel(cl)

then the registered cluster object will be used with clusterApplyLB regardless of the platform. You are correct that in this case, it is your responsibility to shutdown the cluster object since you created it, whereas the implicit cluster object is automatically shutdown.

What's the difference between using the doParallel package with type = MPI and using doMPI directly?

The "doParallel" package acts as a wrapper around the "clusterApplyLB" function which is implemented by calling functions from the "Rmpi" package when using an MPI cluster.

The "doMPI" package uses "Rmpi" functions directly and includes some features that aren't available in "clusterApplyLB":

• supports fetching inputs and combining outputs on-the-fly to efficiently handle a large number of loop iterations;

• supports MPI broadcast to initialize workers;

• allows workers to be started either by mpirun or MPI spawn function.

R: Parallelization with doParallel and foreach

If you want to output something when using parallelism, use makeCluster(no_cores, outfile = "").

doParallel, cluster vs cores

The behavior of doParallel::registerDoParallel(<numeric>) depends on the operating system, see print(doParallel::registerDoParallel) for details.

On Windows machines,

doParallel::registerDoParallel(4)

effectively does

cl <- makeCluster(4)
doParallel::registerDoParallel(cl)

i.e. it set up four ("PSOCK") workers that run in background R sessions. Then, %dopar% will basically utilize the parallel::parLapply() machinery. With this setup, you do have to worry about global variables and packages being attached on each of the workers.

However, on non-Windows machines,

doParallel::registerDoParallel(4)

the result will be that %dopar% will utilize the parallel::mclapply() machinery, which in turn relies on forked processes. Since forking is used, you don't have to worry about globals and packages.

Difference between 'foreach' and 'parallel' in R?

foreach can execute using either %do% or %dopar% ... it only runs in parallel with %dopar%

More information available here: https://cran.r-project.org/web/packages/foreach/vignettes/foreach.pdf

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