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R High Performance Programming

更新時間：2021-08-06 19:17:18

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ThisbookisforprogrammersanddeveloperswhowanttoimprovetheperformanceoftheirRprogramsbymakingthemrunfasterwithlargedatasetsorwhoaretryingtosolveapeskyperformanceproblem.

目錄(77章)

倒序

coverpage
R High Performance Programming
Credits
About the Authors
About the Reviewers
www.PacktPub.com
Support files eBooks discount offers and more
Preface
What this book covers
What you need for this book
Who this book is for
Conventions
Reader feedback
Customer support
Chapter 1. Understanding R's Performance – Why Are R Programs Sometimes Slow?
Three constraints on computing performance – CPU RAM and disk I/O
R is interpreted on the fly
R is single-threaded
R requires all data to be loaded into memory
Algorithm design affects time and space complexity
Summary
Chapter 2. Profiling – Measuring Code's Performance
Measuring total execution time
Profiling the execution time
Profiling memory utilization
Monitoring memory utilization CPU utilization and disk I/O using OS tools
Identifying and resolving bottlenecks
Summary
Chapter 3. Simple Tweaks to Make R Run Faster
Vectorization
Use of built-in functions
Preallocating memory
Use of simpler data structures
Use of hash tables for frequent lookups on large data
Seeking fast alternative packages in CRAN
Summary
Chapter 4. Using Compiled Code for Greater Speed
Compiling R code before execution
Using compiled languages in R
Summary
Chapter 5. Using GPUs to Run R Even Faster
General purpose computing on GPUs
R and GPUs
Fast statistical modeling in R with gputools
Summary
Chapter 6. Simple Tweaks to Use Less RAM
Reusing objects without taking up more memory
Removing intermediate data when it is no longer needed
Calculating values on the fly instead of storing them persistently
Swapping active and nonactive data
Summary
Chapter 7. Processing Large Datasets with Limited RAM
Using memory-efficient data structures
Using memory-mapped files and processing data in chunks
Summary
Chapter 8. Multiplying Performance with Parallel Computing
Data parallelism versus task parallelism
Implementing data parallel algorithms
Implementing task parallel algorithms
Executing tasks in parallel on a cluster of computers
Shared memory versus distributed memory parallelism
Optimizing parallel performance
Summary
Chapter 9. Offloading Data Processing to Database Systems
Extracting data into R versus processing data in a database
Preprocessing data in a relational database using SQL
Converting R expressions to SQL
Running statistical and machine learning algorithms in a database
Using columnar databases for improved performance
Using array databases for maximum scientific-computing performance
Summary
Chapter 10. R and Big Data
Understanding Hadoop
Setting up Hadoop on Amazon Web Services
Processing large datasets in batches using Hadoop
Summary
Index 更新時間：2021-08-06 19:17:18

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R High Performance Programming