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.
Simon R. Chapple is a highly experienced solution architect and lead software engineer with more than 25 years of developing innovative solutions and applications in data analysis and healthcare informatics. He is also an expert in supercomputer HPC and big data processing. Simon is the chief technology officer and a managing partner of Datalytics Technology Ltd, where he leads a team building the next generation of a large scale data analysis platform, based on a customizable set of high performance tools, frameworks, and systems, which enables the entire life cycle 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 that utilized 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). He also built a unique real-time chemotherapy patient mobile-based public cloud-hosted monitoring system undergoing clinical trial in Australia, which is 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 a coauthor 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 employed by EPCC at the University of Edinburgh. She has a degree in Genetics from the University of Glasgow and she now focuses on making high-performance computing accessible to a wider range of users, in particular biologists. Eilidh works on a variety of software projects, including the Simple Parallel R INTerface (SPRINT) and the SEEK for Science web-based data repository.
Thorsten Forster is a data science researcher at University of Edinburgh. With a background in statistics and computer science, he has obtained a PhD in biomedical sciences and has over 10 years of experience in this interdisciplinary research. Conducting research on the data analysis approach to biomedical big data rooted in statistics and machine learning (such as microarrays and next-generation sequencing), Thorsten has been a project manager on the SPRINT project, which is targeted at allowing lay users to make use of parallelized analysis solutions for large biological datasets within the R statistical programming language. He is also a co-founder of Fios Genomics Ltd, a university spun-out company providing biomedical big data research with data-analytical services. Thorsten's 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, investigating the role of cholesterol in immune responses to infections, and investigating the genomic factors that cause childhood wheezing to progress to asthma. Thorsten's complete profile is available at http://tinyurl.com/ThorstenForster-UEDIN.
Terence Sloan is a software development group manager at EPCC, the High Performance Computing Centre at the University of Edinburgh. He has more than 25 years of experience in managing and participating in data science and HPC projects with Scottish SMEs, UK corporations, and European and global collaborations. Terry, was the co-principal investigator on the Wellcome Trust (Award no. 086696/Z/08/Z), the BBSRC (Award no. BB/J019283/1), and the 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 behavior analysis. Terry is a coordinator for the Data Analytics with HPC, Project Preparation, and Dissertation courses on the University of Edinburgh's MSc programme, in HPC with Data Science. He also plays the drums.
