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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

Preallocating memory

Most strongly typed programming languages like C, C++, and Java generally require a vector (or array) to be declared prior to any operation applied on it. This declaration in effect preallocates the memory space that the vector requires. There are special occasions where dynamic memory allocation is used, but this is seldom the first choice mainly because dynamic memory allocation slows down a program. Every time a vector is resized, the program needs to perform extra steps that include copying the vector to a larger or smaller memory block and deleting the old vector. These steps are not needed if the memory is preallocated.

When it comes to preallocating memory, R is no different from the other programming languages. However, being an interpreted language, it imposes less control, thus it is easy for users to overlook this—R will not throw any compilation error if a vector's memory is not preallocated. Nevertheless, not preallocating memory in R can result...

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