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R High Performance Programming

You're reading from   R High Performance Programming Overcome performance difficulties in R with a range of exciting techniques and solutions

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Product type Paperback
Published in Jan 2015
Publisher
ISBN-13 9781783989263
Length 176 pages
Edition 1st Edition
Languages
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Authors (2):
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Tjhi W Chandra Tjhi W Chandra
Author Profile Icon Tjhi W Chandra
Tjhi W Chandra
Aloysius Shao Qin Lim Aloysius Shao Qin Lim
Author Profile Icon Aloysius Shao Qin Lim
Aloysius Shao Qin Lim
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Toc

Table of Contents (12) Chapters Close

Preface 1. Understanding R's Performance – Why Are R Programs Sometimes Slow? FREE CHAPTER 2. Profiling – Measuring Code's Performance 3. Simple Tweaks to Make R Run Faster 4. Using Compiled Code for Greater Speed 5. Using GPUs to Run R Even Faster 6. Simple Tweaks to Use Less RAM 7. Processing Large Datasets with Limited RAM 8. Multiplying Performance with Parallel Computing 9. Offloading Data Processing to Database Systems 10. R and Big Data Index

Setting up Hadoop on Amazon Web Services

There are many ways to set up a Hadoop cluster. We can install Hadoop on a single server in pseudo-distributed mode to simulate a cluster, or on an actual cluster of servers, or virtual machines in fully distributed mode. There are also several distributions of Hadoop available from the vanilla open source version provided by the Apache Foundation to commercial distributions such as Cloudera, Hortonworks, and MapR. Covering all the different ways of setting up Hadoop is beyond the scope of this book. We instead provide instructions for one way to set up Hadoop and other relevant tools for the purpose of the examples in this chapter. If you are using an existing Hadoop cluster or setting up one in a different way, you might have to modify some of the steps.

Note

Because Hadoop and its associated tools are mostly developed for Linux/Unix based operating systems, the code in this chapter will probably not work on Windows. If you are a Windows user, follow...

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