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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

Chapter 4. Using Compiled Code for Greater Speed

So far, we have looked at how to optimize the computational performance of an R code. What if, after optimizing the code, it still runs too slowly? In this chapter, we will look at how to overcome the performance limitations caused by on-the-fly interpretation of an R code using a compiled code. Many CRAN packages use compiled code to offer optimum performance, so a simple way to take advantage of a compiled code is to use these packages. Sometimes, however, a specific task needs to be performed for which no package exists. It is useful to know how to write a compiled code for R in order to make R programs run faster.

We will first see how to compile R code before its execution, then we will explore how to integrate compiled languages like C/C++ into R so that we can run R programs at native CPU speed.

This chapter covers the following topics:

  • Compiling an R code before execution
  • Using compiled languages in R
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