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  • Book Overview & Buying R High Performance Programming
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R High Performance Programming

R High Performance Programming

By : Aloysius Shao Qin Lim, Tjhi W Chandra
4.4 (14)
Buy this Book
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R High Performance Programming

R High Performance Programming

4.4 (14)
By: Aloysius Shao Qin Lim, Tjhi W Chandra
Buy this Book

Overview of this book

This book is for programmers and developers who want to improve the performance of their R programs by making them run faster with large data sets or who are trying to solve a pesky performance problem.
Table of Contents (12 chapters)
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Preface
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Preface
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What this book covers
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What you need for this book
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Who this book is for
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Conventions
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Reader feedback
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Customer support
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1
1. Understanding R's Performance – Why Are R Programs Sometimes Slow?
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1. Understanding R's Performance – Why Are R Programs Sometimes Slow?
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Three constraints on computing performance – CPU, RAM, and disk I/O
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R is interpreted on the fly
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R is single-threaded
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R requires all data to be loaded into memory
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Algorithm design affects time and space complexity
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Summary
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2. Profiling – Measuring Code's Performance
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2. Profiling – Measuring Code's Performance
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Measuring total execution time
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Profiling the execution time
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Profiling memory utilization
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Monitoring memory utilization, CPU utilization, and disk I/O using OS tools
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Identifying and resolving bottlenecks
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Summary
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3. Simple Tweaks to Make R Run Faster
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3. Simple Tweaks to Make R Run Faster
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Vectorization
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Use of built-in functions
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Preallocating memory
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Use of simpler data structures
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Use of hash tables for frequent lookups on large data
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Seeking fast alternative packages in CRAN
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Summary
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4. Using Compiled Code for Greater Speed
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4. Using Compiled Code for Greater Speed
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Compiling R code before execution
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Using compiled languages in R
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Summary
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5. Using GPUs to Run R Even Faster
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5. Using GPUs to Run R Even Faster
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General purpose computing on GPUs
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R and GPUs
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Fast statistical modeling in R with gputools
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Summary
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6. Simple Tweaks to Use Less RAM
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6. Simple Tweaks to Use Less RAM
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Reusing objects without taking up more memory
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Removing intermediate data when it is no longer needed
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Calculating values on the fly instead of storing them persistently
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Swapping active and nonactive data
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Summary
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7. Processing Large Datasets with Limited RAM
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7. Processing Large Datasets with Limited RAM
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Using memory-efficient data structures
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Using memory-mapped files and processing data in chunks
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Summary
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8. Multiplying Performance with Parallel Computing
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8. Multiplying Performance with Parallel Computing
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Data parallelism versus task parallelism
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Implementing data parallel algorithms
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Implementing task parallel algorithms
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Executing tasks in parallel on a cluster of computers
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Shared memory versus distributed memory parallelism
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Optimizing parallel performance
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Summary
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9. Offloading Data Processing to Database Systems
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9. Offloading Data Processing to Database Systems
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Extracting data into R versus processing data in a database
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Preprocessing data in a relational database using SQL
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Converting R expressions to SQL
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Running statistical and machine learning algorithms in a database
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Using columnar databases for improved performance
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Using array databases for maximum scientific-computing performance
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Summary
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10. R and Big Data
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10. R and Big Data
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Understanding Hadoop
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Setting up Hadoop on Amazon Web Services
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Processing large datasets in batches using Hadoop
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Summary
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Index
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Index

Profiling the execution time

So far, we have seen how to measure the execution time of a whole R expression. What about a more complex expression with multiple parts such as calls to other functions? Is there a way to dig deeper and profile the execution time of each of the parts that make up the expression? R comes with the profiling tool Rprof() that allows us to do just that. Let's see how it works.

Profiling a function with Rprof()

In this example, we write the following sampvar() function to calculate the unbiased sample variance of a numeric vector. This is obviously not the best way to write this function (in fact R provides the var() function to do this), but it serves to illustrate how code profiling works:

# Compute sample variance of numeric vector x
sampvar <- function(x) {
    # Compute sum of vector x
    my.sum <- function(x) {
        sum <- 0
        for (i in x) {
            sum <- sum + i
        }
        sum
    }
    
    # Compute sum of squared variances...

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