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.

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About the Author

Simon Chapple is a highly experienced solution architect and lead software engineer, with 25+ years developing innovative solutions and applications in data analysis and healthcare informatics, and an expert in supercomputer HPC and Big Data processing. Simon is Chief Technology Officer and a managing partner for Datalytics Technology Ltd, where he is leading the team building the next generation of large scale data analysis platform based on a customisable set of high performance tools, frameworks and systems, that enables the entire lifecycle of data processing for real-time analytics, from capture through analysis to presentation, to be encapsulated for easy deployment into any existing operational IT environment. Previously he was Director of Product Innovation at Aridhia Informatics, where he built a number of novel systems for healthcare providers in Scotland, including a unified patient pathway tracking system utilising ten separate data system integrations for both 18-weeks Referral To Treatment and cancer patient management (enabling the provider to deliver best performance on patient waiting times in Scotland); and a unique real-time chemotherapy patient mobile-based public cloud hosted monitoring system undergoing clinical trial in Australia, highly praised by nurses and patients: ""its like having a nurse in your living room… hopefully all chemo patients will one day know the security and comfort of having an around-the-clock angel of their own”. Simon is also co-author of the ROpenCL open source package enabling statistics programs written in R to exploit the parallel computation within graphics accelerator chips. Eilidh Troup is an Applications Consultant at EPCC in the University of Edinburgh. She is interested in making High Performance Computing accessible to new users, particularly biologists. She works on a variety of software projects, including the SPRINT Simple Parallel R INTerface. Thorsten Forster is a data science researcher at the University of Edinburgh. With a background in statistics and computer science he has obtained his PhD in biomedical sciences, and has over 10 years of experience in this interdisciplinary research. Researching statistical and machine learning rooted data analysis approaches to biomedical big data (microarrays, next generation sequencing), he has been project manager on the SPRINT project, which is targeted at allowing lay users to make use of parallelised analysis solutions for large biological data sets within the R statistical programming language. He is also a co-founder of Fios Genomics Ltd, a university spin out company providing data analytical services for biomedical big data research. Current work includes devising a gene transcription classifier for the diagnosis of bacterial infections in newborn babies, transcriptional profiling of interferon gamma activation of macrophages, investigation of the role of cholesterol in immune responses to infections, and the investigation of genomic factors that cause childhood wheezing to progress to asthma. A complete profile is available here: http://tinyurl.com/ThorstenForster-UEDIN Terence Sloan (Terry) is a Software Development Group Manager at EPCC, the University of Edinburgh’s High Performance Computing Centre. He has 25+ years experience managing and participating in Data Science and HPC projects with Scottish SMEs, UK corporations, European and global collaborations. Terry, was the Co-Principal Investigator on the Wellcome Trust (Award no. 086696/Z/08/Z), BBSRC (Award no. BB/J019283/1) and three EPSRC distributed Computational Science awards that have helped develop the SPRINT package for R. He has also held awards from the ESRC (Award nos. RES-189-25-0066, RES-149-25-0005) that investigated the use of operational big data for customer behaviour analysis. Terry is the coordinator for the Data Analytics with HPC, Project Preparation and Dissertation courses on the University of Edinburgh’s HPC with Data Science MSc programme. He also plays the drums.

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Mastering Parallel Programming with R

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