High level functions for parallel programming with 'Rcpp'. For example, the 'parallelFor()' function can be used to convert the work of a standard serial "for" loop into a parallel one and the 'parallelReduce()' function can be used for accumulating aggregate or other values.

Documentation

Manual: RcppParallel.pdf
Vignette: None available.

Maintainer: Kevin Ushey <kevin at rstudio.com>

Author(s): JJ Allaire*, Romain Francois*, Kevin Ushey*, Gregory Vandenbrouck*, Marcus Geelnard* (TinyThread library, http://tinythreadpp.bitsnbites.eu/), RStudio*, Intel* (Intel TBB library, https://www.threadingbuildingblocks.org/), Microsoft*

Install package and any missing dependencies by running this line in your R console:

install.packages("RcppParallel")

Depends R (>= 3.0.2)
Imports
Suggests Rcpp, RUnit, knitr, rmarkdown
Enhances
Linking to BH(>=1.60.0-1)

Package RcppParallel
Materials
URL http://rcppcore.github.io/RcppParallel https://github.com/RcppCore/RcppParallel
Task Views HighPerformanceComputing
Version 4.3.20
Published 2016-08-16
License GPL-2
BugReports
SystemRequirements GNU make, Windows: cmd.exe and cscript.exe, Solaris: g++ is required
NeedsCompilation yes
Citation
CRAN checks RcppParallel check results
Package source RcppParallel_4.3.20.tar.gz