In this chapter, you were introduced to three simple yet different techniques of utilizing parallelism in R, operating both FORK and PSOCK implemented clusters with the base R parallel package, which exploit the multicore processing capability of your own computer, and using larger-scale AWS EMR Hadoop clusters hosted remotely in the cloud directly from your computer through the segue package.
Along the way, you learned how to split a problem efficiently into independent parallelizable tasks and how imbalanced computation can be dealt with through dynamic load-balancing task management. You also saw how to effectively instrument, benchmark, and measure the runtime of your code in order to identify areas for both serial and parallel performance improvement. In fact, as an extra challenge, the current implementation of evaluateCell() can itself be improved upon and sped up….
You have also now solved Aristotle's Number Puzzle(!), and if this piqued your interest, then you can find out more about the magic hexagon at http://en.wikipedia.org/wiki/Magic_hexagon.
Who knows, you may even be able to apply your new parallel R skills to discover a new magic hexagon solution….
This chapter gave you a significant grounding in the simplest methods of parallelism using R. You should now be able to apply this knowledge directly to your own context and accelerate your own R code. In the remainder of this book, we will look at other forms of parallelism and frameworks that can be used to approach more data-intensive problems on a larger scale. You can either read the book linearly from here to the concluding one, Chapter 6, The Art of Parallel Programming, which summarizes the key learning for successful parallel programming, or you can drop into specific chapters for particular technologies, such as Chapter 2, Introduction to Message Passing, Chapter 3, Advanced Message Passing, and Chapter 4, Developing SPRINT an MPI-based R package for Supercomputers for explicit message-passing-based parallelism using MPI and Chapter 5, The Supercomputer in your Laptop for GPU-accelerated parallelism using OpenCL.
There is also a bonus chapter that will introduce you to Apache Spark, one of the newest and most popular frameworks implementing distributed parallel computation that supports complex analytics and is arguably the successor to the established, Hadoop-based Map/Reduce, which can also be applied to real-time data analysis.