Using R Parallel with other R packages

Question

I am working on a very time intensive analysis using the LQMM package in R. I set the model to start running on Thursday, it is now Monday, and is still running. I am confid

Answer 1

The dependent libraries will need to be evaluated on all your nodes. The function clusterEvalQ is foreseen inside the parallel package for this purpose. You might also need to export some of your data to the global environments of your subnodes: For this you can use the clusterExport function. Also view this page for more info on other relevant functions that might be useful to you.

In general, to speed up your application by using multiple cores you will have to split up your problem in multiple subpieces that can be processed in parallel on different cores. To achieve this in R, you will first need to create a cluster and assign a particular number of cores to it. Next, You will have to register the cluster, export the required variables to the nodes and then evaluate the necessary libraries on each of your subnodes. The exact way that you will setup your cluster and launch the nodes will depend on the type of sublibraries and functions that you will use. As an example, your clustersetup might look like this when you choose to utilize the doParallel package (and most of the other parallelisation sublibraries/functions):

library(doParallel)
nrCores <- detectCores()
cl <- makeCluster(nrCores)
registerDoParallel(cl); 
clusterExport(cl,c("g1data"),envir=environment());
clusterEvalQ(cl,library("lqmm"))

The cluster is now prepared. You can now assign subparts of the global task to each individual node in your cluster. In the general example below each node in your cluster will process subpart i of the global task. In the example we will use the foreach %dopar% functionality that is provided by the doParallel package:

The doParallel package provides a parallel backend for the foreach/%dopar% function using the parallel package of R 2.14.0 and later.

Subresults will automatically be added to the resultList. Finally, when all subprocesses are finished we merge the results:

resultList <- foreach(i = 1:nrCores) %dopar%
{
   #process part i of your data.
}
stopCluster(cl)
#merge data..

Since your question was not specifically on how to split up your data I will let you figure out the details of this part for yourself. However, you can find a more detailed example using the doParallel package in my answer to this post.