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  • Book Overview & Buying R High Performance Programming
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R High Performance Programming

R High Performance Programming

By : Aloysius Shao Qin Lim, Tjhi W Chandra
4.4 (14)
Buy this Book
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R High Performance Programming

R High Performance Programming

4.4 (14)
By: Aloysius Shao Qin Lim, Tjhi W Chandra
Buy this Book

Overview of this book

This book is for programmers and developers who want to improve the performance of their R programs by making them run faster with large data sets or who are trying to solve a pesky performance problem.
Table of Contents (12 chapters)
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Preface
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Preface
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What this book covers
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What you need for this book
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Who this book is for
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Conventions
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Reader feedback
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Customer support
Free Chapter
1
1. Understanding R's Performance – Why Are R Programs Sometimes Slow?
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1. Understanding R's Performance – Why Are R Programs Sometimes Slow?
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Three constraints on computing performance – CPU, RAM, and disk I/O
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R is interpreted on the fly
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R is single-threaded
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R requires all data to be loaded into memory
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Algorithm design affects time and space complexity
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Summary
2
2. Profiling – Measuring Code's Performance
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2. Profiling – Measuring Code's Performance
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Measuring total execution time
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Profiling the execution time
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Profiling memory utilization
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Monitoring memory utilization, CPU utilization, and disk I/O using OS tools
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Identifying and resolving bottlenecks
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Summary
3
3. Simple Tweaks to Make R Run Faster
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3. Simple Tweaks to Make R Run Faster
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Vectorization
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Use of built-in functions
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Preallocating memory
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Use of simpler data structures
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Use of hash tables for frequent lookups on large data
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Seeking fast alternative packages in CRAN
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Summary
4
4. Using Compiled Code for Greater Speed
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4. Using Compiled Code for Greater Speed
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Compiling R code before execution
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Using compiled languages in R
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Summary
5
5. Using GPUs to Run R Even Faster
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5. Using GPUs to Run R Even Faster
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General purpose computing on GPUs
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R and GPUs
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Fast statistical modeling in R with gputools
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Summary
6
6. Simple Tweaks to Use Less RAM
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6. Simple Tweaks to Use Less RAM
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Reusing objects without taking up more memory
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Removing intermediate data when it is no longer needed
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Calculating values on the fly instead of storing them persistently
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Swapping active and nonactive data
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Summary
7
7. Processing Large Datasets with Limited RAM
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7. Processing Large Datasets with Limited RAM
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Using memory-efficient data structures
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Using memory-mapped files and processing data in chunks
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Summary
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8. Multiplying Performance with Parallel Computing
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8. Multiplying Performance with Parallel Computing
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Data parallelism versus task parallelism
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Implementing data parallel algorithms
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Implementing task parallel algorithms
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Executing tasks in parallel on a cluster of computers
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Shared memory versus distributed memory parallelism
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Optimizing parallel performance
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Summary
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9. Offloading Data Processing to Database Systems
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9. Offloading Data Processing to Database Systems
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Extracting data into R versus processing data in a database
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Preprocessing data in a relational database using SQL
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Converting R expressions to SQL
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Running statistical and machine learning algorithms in a database
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Using columnar databases for improved performance
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Using array databases for maximum scientific-computing performance
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Summary
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10. R and Big Data
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10. R and Big Data
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Understanding Hadoop
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Setting up Hadoop on Amazon Web Services
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Processing large datasets in batches using Hadoop
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Summary
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Index
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Index

Chapter 6. Simple Tweaks to Use Less RAM

So far, we have learned the techniques to overcome CPU limitations and improve the speed of R programs. As you can recall from Chapter 1, Understanding R's Performance – Why Are R Programs Sometimes Slow? that another key constraint of R is memory. All the data that an R program needs to perform its tasks on must be loaded into the computer's memory or RAM. RAM is also needed for any intermediate computations, so the amount of RAM needed to process a given dataset can be many times the size of the dataset, depending on the type of tasks or algorithms being executed. This can become a problem when a large dataset needs to be processed, or when there is little RAM available to complete the tasks.

In this chapter and the next, we will learn how to optimize the RAM utilization of R programs so that memory-intensive tasks can be executed successfully.

This chapter covers:

  • Reusing objects without taking up more memory
  • Removing intermediate...

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