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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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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

Reusing objects without taking up more memory


The first tweak takes advantage of how R manages the memory of objects using a copy-on-modification model. In this model, when a copy of an object x is made, for example with y <- x, it is not actually copied in the memory. Rather, the new variable y simply points to the same block of memory that contains x. The first time when y is modified, R copies the data into a new block of memory so that x and y have their own copies of the data. That is why this model of memory management is called copy-on-modification. What this means is that new objects can sometimes be created from existing objects without taking up additional memory. To identify potential memory bottlenecks and manage the memory utilization of R programs, it is helpful to understand when R copies data and when it does not.

Take for example the following code, which generates a numeric vector x with 1 million elements and creates a list y that contains two copies of x. We can examine...

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