R high performance programming : overcome performance difficulties in R with a range of exciting techniques and solutions /

R high performance programming : overcome performance difficulties in R with a range of exciting techniques and solutions /

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Bibliographic Details
Author / Creator:Lim, Aloysius, author.
Imprint:Birmingham, UK : Packt Publishing, 2015.
Description:1 online resource (1 volume) : illustrations.
Language:English
Series:Community experience distilled
Community experience distilled.
Subject:
R (Computer program language)
Open source software.
Application software -- Development.
Application software -- Development.
Open source software.
R (Computer program language)
Electronic books.
Format: E-Resource Book
URL for this record:http://pi.lib.uchicago.edu/1001/cat/bib/11305749
Hidden Bibliographic Details
Other authors / contributors:Tjhi, William, author.
ISBN:9781783989270
1783989270
1783989270
1783989262
9781783989263
Notes:Includes index.
Online resource; title from cover (Safari, viewed February 23, 2015).
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 non-active data; Summary; Chapter 7: Processing Large Datasets with Limited RAM; Using memory-efficient data structures; Smaller data types; Sparse matrices; Symmetric matrices; Bit vectors; Using memory-mapped files and processing data in chunks; The bigmemory package; The ff package; Summary; Chapter 8: Multiplying Performance with Parallel Computing
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.
Other form:Print version: Lim, Aloysius. R high performance programming : overcome performance difficulties in R with a range of exciting techniques and solutions. Birmingham, England : Packt Publishing, ©2015 iii, 159 pages Community experience distilled. 9781783989263
Table of Contents:
  • Cover; Copyright; Credits; About the Authors; About the Reviewers; www.PacktPub.com; Table of Contents; Preface; 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 the total execution time; Measuring execution time with system.time(); Repeating time measurements with rbenchmark
  • Measuring distribution of execution time with microbenchmarkProfiling the execution time; Profiling a function with Rprof(); The profiling results; Profiling the 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; Seek fast alternative packages in CRAN; Summary
  • Chapter 4: Using Compiled Code for Greater SpeedCompiling R code before execution; Compiling functions; Just-in-time (JIT) compilation of R code; Using compiled languages in R; Prerequisites; Including compiled code inline; Calling external compiled code; Considerations for using compiled code; The R APIs; R data types versus native data types; Creating R objects and garbage collection; Allocating memory for non-R objects; Summary; Chapter 5: Using GPUs to Run R Even Faster; General purpose computing on GPUs; R and GPUs; Installing gputools; Fast statistical modeling in R with gputools
  • Data parallelism versus task parallelismImplementing data parallel algorithms; Implementing task parallel algorithms; Running the same task on workers in a cluster; Running different tasks on workers in a cluster; 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 into SQL; Using dplyr

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