Chapter 1. Simple Parallelism with R

In this chapter, you will start your journey toward mastery of parallelism in R by quickly learning to exploit the multicore processing capability of your own laptop and travel onward to our first look at how you can most simply exploit the vast computing capacity of the cloud.

You will learn about lapply() and its variations supported by R's core parallel package as well as about the segue package that enables us to utilize Amazon Web Services (AWS) and the Elastic Map Reduce (EMR) service. For the latter, you will need to have an account set up with AWS.

Our worked example throughout this chapter will be an iterative solver for an ancient puzzle known as Aristotle's Number Puzzle. Hopefully, this will be something new to you and pique your interest. It has been specifically chosen to demonstrate an important issue that can arise when running code in parallel, namely imbalanced computation. It will also serve to help develop our performance benchmarking skills—an important consideration in parallelism—measuring overall computational effectiveness.

The examples in this chapter are developed using RStudio version 0.98.1062 with the 64-bit R version 3.1.0 (CRAN distribution) running on a mid-2014 generation Apple MacBook Pro OS X 10.9.4 with a 2.6 GHz Intel Core i5 processor and 16 GB memory. Some of the examples in this chapter will not be able to run with Microsoft Windows, but should run without problem on all variants of Linux.