Book Image

Mastering Parallel Programming with R

By : Simon R. Chapple, Terence Sloan, Thorsten Forster, Eilidh Troup
Book Image

Mastering Parallel Programming with R

By: Simon R. Chapple, Terence Sloan, Thorsten Forster, Eilidh Troup

Overview of this book

R is one of the most popular programming languages used in data science. Applying R to big data and complex analytic tasks requires the harnessing of scalable compute resources. Mastering Parallel Programming with R presents a comprehensive and practical treatise on how to build highly scalable and efficient algorithms in R. It will teach you a variety of parallelization techniques, from simple use of R’s built-in parallel package versions of lapply(), to high-level AWS cloud-based Hadoop and Apache Spark frameworks. It will also teach you low level scalable parallel programming using RMPI and pbdMPI for message passing, applicable to clusters and supercomputers, and how to exploit thousand-fold simple processor GPUs through ROpenCL. By the end of the book, you will understand the factors that influence parallel efficiency, including assessing code performance and implementing load balancing; pitfalls to avoid, including deadlock and numerical instability issues; how to structure your code and data for the most appropriate type of parallelism for your problem domain; and how to extract the maximum performance from your R code running on a variety of computer systems.
Table of Contents (13 chapters)

What does the future hold?


Obviously, this final section is at a considerable risk of "crystal ball gazing" and getting it wrong. However, there is a number of clear directions in which we can see how both hardware and software will develop that make it clear that parallel programming will play an ever more important and increasing role in our computational future. Besides, it has now become critical for us to be able to process vast amounts of information within a short window of time in order to ensure our own individual and collective safety. For example, we are experiencing an increased momentum toward significant climate change and extreme weather events and will, therefore, require increasingly accurate weather prediction to help us deal with this; this will only be possible with highly efficient parallel algorithms.

In order to gaze into the future, we need to look back at the past. The hardware technology available to parallel computing has evolved at a phenomenal pace through the...