Many simulations in R are long running. Analysis of statistical algorithms can generate workloads that run for hours if not days tying up a single computer. Given the amount of time R programmers can spend waiting for results, getting acquainted parallelism makes sense.

In this first in a series of blogs, we describe an approach to achieving parallelism in R using BatchJobs, a framework that provides Map, Reduce and Filter variants to generate jobs on batch computing systems running across clustered computing environments.