R High Performance Programming

Aloysius Lim, William Tjhi

• Publisher: Packt Publishing
• Year: 2015
• ISBN:
  9781783989263
• Topic: Computer Science
• Subtopic: Open Source Programming
• Index: Computer Science

R High Performance Programming

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Table of Contents

R High Performance Programming

Credits

About the Authors

About the Reviewers

www.PacktPub.com

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Free access for Packt account holders

Preface

What this book covers

What you need for this book

Who this book is for

Conventions

Reader feedback

Customer support

Downloading the example code

Errata

Piracy

Questions

1. Understanding R's Performance – Why Are R Programs Sometimes Slow?

Three constraints on computing performance – CPU, RAM, and disk I/O

R is interpreted on the fly

R is single-threaded

R requires all data to be loaded into memory

Algorithm design affects time and space complexity

Summary

2. Profiling – Measuring Code's Performance

Measuring total execution time

Measuring execution time with system.time()

Repeating time measurements with rbenchmark

Measuring distribution of execution time with microbenchmark

Profiling the execution time

Profiling a function with Rprof()

The profiling results

Profiling memory utilization

Monitoring memory utilization, CPU utilization, and disk I/O using OS tools

Identifying and resolving bottlenecks

Summary

3. Simple Tweaks to Make R Run Faster

Vectorization

Use of built-in functions

Preallocating memory

Use of simpler data structures

Use of hash tables for frequent lookups on large data

Seeking fast alternative packages in CRAN

Summary

4. Using Compiled Code for Greater Speed

Compiling R code before execution

Compiling functions

Just-in-time (JIT) compilation of R code

Using compiled languages in R

Prerequisites

Including compiled code inline

Calling external compiled code

Considerations for using compiled code

R APIs

R data types versus native data types

Creating R objects and garbage collection

Allocating memory for non-R objects

Summary

5. Using GPUs to Run R Even Faster

General purpose computing on GPUs

R and GPUs

Installing gputools

Fast statistical modeling in R with gputools

Summary

6. Simple Tweaks to Use Less RAM

Reusing objects without taking up more memory

Removing intermediate data when it is no longer needed

Calculating values on the fly instead of storing them persistently

Swapping active and nonactive data

Summary

7. Processing Large Datasets with Limited RAM

Using memory-efficient data structures

Smaller data types

Sparse matrices

Symmetric matrices

Bit vectors

Using memory-mapped files and processing data in chunks

The bigmemory package

The ff package

Summary

8. Multiplying Performance with Parallel Computing

Data parallelism versus task parallelism

Implementing data parallel algorithms

Implementing task parallel algorithms

Running the same task on workers in a cluster

Running different tasks on workers in a cluster

Executing tasks in parallel on a cluster of computers

Shared memory versus distributed memory parallelism

Optimizing parallel performance

Summary

9. Offloading Data Processing to Database Systems

Extracting data into R versus processing data in a database

Preprocessing data in a relational database using SQL

Converting R expressions to SQL

Using dplyr

Using PivotalR

Running statistical and machine learning algorithms in a database

Using columnar databases for improved performance

Using array databases for maximum scientific-computing performance

Summary

10. R and Big Data

Understanding Hadoop

Setting up Hadoop on Amazon Web Services

Processing large datasets in batches using Hadoop

Uploading data to HDFS

Analyzing HDFS data with RHadoop

Other Hadoop packages for R

Summary

Index

R High Performance Programming

Copyright © 2015 Packt Publishing

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First published: January 2015

Production reference: 1230115

Published by Packt Publishing Ltd.

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ISBN 978-1-78398-926-3

www.packtpub.com

Credits

Authors

Aloysius Lim

William Tjhi

Reviewers

Richard Cotton

Kirill Müller

John Silberholz

Commissioning Editor

Kunal Parikh

Acquisition Editor

Richard Brookes-Bland

Content Development Editor

Susmita Sabat

Technical Editor

Shiny Poojary

Copy Editor

Neha Vyas

Project Coordinator

Milton Dsouza

Proofreaders

Ameesha Green

Clyde Jenkins

Jonathan Todd

Indexer

Tejal Soni

Graphics

Sheetal Aute

Valentina D'silva

Production Coordinator

Komal Ramchandani

Cover Work

Komal Ramchandani

About the Authors

Aloysius Lim has a knack for translating complex data and models into easy-to-understand insights. As cofounder of About People, a data science and design consultancy, he loves solving problems and helping others to find practical solutions to business challenges using data. His breadth of experience—7 years in the government, education, and retail industries—equips him with unique perspectives to find creative solutions.

My deepest thanks go to God for the opportunity to write this book and share the knowledge that I have been given. My lovely wife, Bethany, has been a tremendous source of support and encouragement throughout this project. Thank you dear, for all your love. Many thanks to my partner William for his wonderful friendship. He has been a source of inspiration and insights throughout this journey.

William Tjhi is a data scientist with years of experience working in academia, government, and industry. He began his data science journey as a PhD candidate researching new algorithms to improve the robustness of high-dimensional data clustering. Upon receiving his doctorate, he moved from basic to applied research, solving problems among others in molecular biology and epidemiology using machine learning. He published some of his research in peer-reviewed journals and conferences. With the rise of Big Data, William left academia for industry, where he started practicing data science in both business and public sector settings. William is passionate about R and has been using it as his primary analysis tool since his research days. He was once part of Revolution Analytics, and there he contributed to make R more suitable for Big Data.

I would like to thank my coauthor, Aloysius. Your hard work, patience, and determination made this book possible.

About the Reviewers

Richard Cotton is a data scientist with a mixed background in proteomics, debt collection, and chemical health and safety, and he has worked extensively on tools to give nontechnical users access to stat...