Mastering Parallel Programming with R

Master the robust features of R parallel programming to accelerate your data science computations

About This Book

• Create R programs that exploit the computational capability of your cloud platforms and computers to the fullest
• Become an expert in writing the most efficient and highest performance parallel algorithms in R
• Get to grips with the concept of parallelism to accelerate your existing R programs

Who This Book Is For

This  book is for R programmers who want to step beyond its inherent  single-threaded and restricted memory limitations and learn how to  implement highly accelerated and scalable algorithms that are a  necessity for the performant processing of Big Data. No previous  knowledge of parallelism is required. This book also provides for the  more advanced technical programmer seeking to go beyond high level  parallel frameworks.

What You Will Learn

• Create and structure efficient load-balanced parallel computation in R, using R's built-in parallel package
• Deploy  and utilize cloud-based parallel infrastructure from R, including  launching a distributed computation on Hadoop running on Amazon Web  Services (AWS)
• Get  accustomed to parallel efficiency, and apply simple techniques to  benchmark, measure speed and target improvement in your own code
• Develop  complex parallel processing algorithms with the standard Message  Passing Interface (MPI) using RMPI, pbdMPI, and SPRINT packages
• Build and extend a parallel R package (SPRINT) with your own MPI-based routines
• Implement  accelerated numerical functions in R utilizing the vector processing  capability of your Graphics Processing Unit (GPU) with OpenCL
• Understand  parallel programming pitfalls, such as deadlock and numerical  instability, and the approaches to handle and avoid them
• Build a task farm master-worker, spatial grid, and hybrid parallel R programs

In Detail

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.

Style and approach

This  book leads you chapter by chapter from the easy to more complex forms  of parallelism. The author's insights are presented through clear  practical examples applied to a range of different problems, with  comprehensive reference information for each of the R packages employed.  The book can be read from start to finish, or by dipping in chapter by  chapter, as each chapter describes a specific parallel approach and  technology, so can be read as a standalone.