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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

General purpose computing on GPUs


Historically, GPUs were designed and used to render high-resolution graphics such as for video games. To be able to render millions of pixels every second, GPUs utilize a highly parallel architecture that specializes in the types of computations required to render graphics. At a high level, the architecture of a GPU is similar to that of a CPU—it has its own multi-core processor and memory. However, because GPUs are not designed for general computation, individual cores are much simpler with slower clock speeds and limited support for complex instructions, compared to CPUs. In addition, they typically have less RAM than CPUs. To achieve real-time rendering, most GPU computations are done in a highly parallel manner, with many more cores than CPUs—a modern GPU might have more than 2,000 cores. Given that one core can run multiple threads, it is possible to run tens of thousands of parallel threads on a GPU.

In 1990s, programmers began to realize that certain...

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