R High Performance Programming电子书

R High Performance Programming

Table of Contents

R High Performance Programming

Credits

About the Authors

About the Reviewers

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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