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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
Preface
What this book covers
What you need for this book
Who this book is for
Conventions
Reader feedback
Customer support
Free Chapter
1
1. Understanding R's Performance – Why Are R Programs Sometimes Slow?
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
2. Profiling – Measuring Code's Performance
2. Profiling – Measuring Code's Performance
Measuring total execution time
Profiling the execution time
Profiling memory utilization
Monitoring memory utilization, CPU utilization, and disk I/O using OS tools
Identifying and resolving bottlenecks
Summary
3
3. Simple Tweaks to Make R Run Faster
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
4. Using Compiled Code for Greater Speed
4. Using Compiled Code for Greater Speed
Compiling R code before execution
Using compiled languages in R
Summary
5
5. Using GPUs to Run R Even Faster
5. Using GPUs to Run R Even Faster
General purpose computing on GPUs
R and GPUs
Fast statistical modeling in R with gputools
Summary
6
6. Simple Tweaks to Use Less RAM
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
7. Processing Large Datasets with Limited RAM
7. Processing Large Datasets with Limited RAM
Using memory-efficient data structures
Using memory-mapped files and processing data in chunks
Summary
8
8. Multiplying Performance with Parallel Computing
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8. Multiplying Performance with Parallel Computing
Data parallelism versus task parallelism
Implementing data parallel algorithms
Implementing task parallel algorithms
Executing tasks in parallel on a cluster of computers
Shared memory versus distributed memory parallelism
Optimizing parallel performance
Summary
9
9. Offloading Data Processing to Database Systems
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
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
10. R and Big Data
10. R and Big Data
Understanding Hadoop
Setting up Hadoop on Amazon Web Services
Processing large datasets in batches using Hadoop
Summary
11
Index
Index

Implementing task parallel algorithms

Let's now see how to implement a task parallel algorithm using both socket-based and forked clusters. We will look at how to run the same task and different tasks on workers in a cluster.

Running the same task on workers in a cluster

To demonstrate how to run the same task on a cluster, the task for this example is to generate 500 million Poisson random numbers. We will do this by using L'Ecuyer's combined multiple-recursive generator, which is the only random number generator in base R that supports multiple streams to generate random numbers in parallel. The random number generator is selected by calling the RNGkind() function.

Note

We cannot just use any random number generator in parallel because the randomness of the data depends on the algorithm used to generate random data and the seed value given to each parallel task. Most other algorithms were not designed to produce random numbers in multiple parallel streams, and might produce multiple highly...

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