Mastering Parallel Programming with R: Master the robust features of R parallel programming to accelerate your data science computations

Simon R. Chapple

Sloan

Forster

Troup

$38.99

5 (4 Ratings)

Paperback May 2016 244 pages 1st Edition

Simon R. Chapple

Sloan

Forster

Troup

$38.99

5 (4 Ratings)

Paperback May 2016 244 pages 1st Edition

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Key benefits

*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

Description

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.

Who is this book 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

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Richard Seitz Jan 04, 2019

Ich hatte bisher nur Grundlegende Kenntnisse.Da ich nun für div. Auswertungen auf R aufbauen wollte und viel Wert auf Performance lege, habe ich mir dieses Buch zugelegt.Es ist alles was man für paralleles Programmieren benötigt gut und ausführlich beschrieben.Gerade für mich als Anfänger war das Buch sehr hilfreich und ich habe einen Performancegewinn von über 200 % erziehlt.Kann das Buch deshalb guten Gewissens weiter empfehlen.

Amazon Verified review

Steven P. Sanderson Jun 23, 2016

This book gives nice clear concise examples of how to perform parallel programming in R. There are many great examples to follow that help you get an understanding of how this paradigm of programming does and does not work. The author understands that the material can be difficult to grasp but gives a clear explanation that will sink in with readers. He discusses the pro's and the con's of parallel programming, where it is beneficial and where it is not. For anyone wanting to obtain an understanding of parallel programming inside of R, I would recommend this book.

Sven-Ivar Fjeld Nov 05, 2016

My knowledge of parallel computing is, in other languages, decent. It was therefore great to read this book which introduced me to the R ways of doing things in a parallel fashion, along with some great custom functions, examples, and solutions. As a personal example of how this book benefitted me, I was able to reduce the time taken by a particular computational heavy asset allocation algorithm of mine from an average of 4.6 mins to 2.3 mins. Looking forward to how this can be brought down further with the use of GPUs and other interesting methods.

Mastering Parallel Programming with R: Master the robust features of R parallel programming to accelerate your data science computations

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