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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
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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
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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
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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

Summary

In this chapter, we learned how to set up a Hadoop cluster on Amazon Elastic MapReduce, and how to use the RHadoop family of packages in order to analyze data in HDFS using MapReduce. We saw how the performance of the MapReduce task improves dramatically as more servers are added to the Hadoop cluster, but the performance eventually reaches a limit due to Amdahl's law (Chapter 8, Multiplying Performance with Parallel Computing).

Hadoop and its ecosystem of tools is rapidly evolving. Other tools are being actively developed to make Hadoop perform even better. For example, Apache Spark (http://spark.apache.org/) provides Resilient Distributed Datasets (RDDs) that store data in memory across a Hadoop cluster. This allows data to be read from HDFS once and to be used many times in order to dramatically improve the performance of interactive tasks like data exploration and iterative algorithms like gradient descent or k-means clustering. Another example is Apache Storm (http://storm...

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