About performance of R

a) Base R already includes the "parallel" package. Deciding to use more than one processor for a particular computation is a very high level decision that can require knowledge of computing time cost, importance of other tasks on the system, and interdependence of computation results. It is not a decision that R should automatically make.

b) Most performance issues with R arise due to users choosing inefficient algorithms. Inserting parallelism inside existing algorithms will not fix that.
---------------------------------------------------------------------------
Jeff Newmiller                        The     .....       .....  Go Live...
DCN:<jdnewmil at dcn.davis.ca.us>        Basics: ##.#.       ##.#.  Live Go...
                                      Live:   OO#.. Dead: OO#..  Playing
Research Engineer (Solar/Batteries            O.O#.       #.O#.  with
/Software/Embedded Controllers)               .OO#.       .OO#.  rocks...1k
--------------------------------------------------------------------------- 
Sent from my phone. Please excuse my brevity.

Hi there,

Now that R has grown up with a vibrant community. It's no 1 statistical
package used by scientists. It's graphics capabilities are amazing.
Now it's time to provide native support in "R core" for distributed and
parallel computing for high performance in massive datasets.
And may be base R functions should be replaced with best R packages
like data.table, dplyr, reader for fast and efficient operations.


Thanks

Sent from my iPad

______________________________________________
R-help at r-project.org mailing list -- To UNSUBSCRIBE and more, see
https://stat.ethz.ch/mailman/listinfo/r-help
PLEASE do read the posting guide
http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.