[R] R on Multicore for Linux

stephen sefick ssefick at gmail.com
Thu Jul 21 03:44:21 CEST 2011

Make this reproducible.

On Wed, Jul 20, 2011 at 6:44 PM, Madana_Babu <madana_babu at infosys.com> wrote:
> Hi all,
> I have R installed on a box, which is running on a machine with 16 core and
> Redhat - Linux. I am handling huge (size of dataset will be 5 GB) dataset.
> Lets assume that my data is in the form of structured (multiple) logs. I
> access the data by using all.files(). Since by default basic version of R
> utilizes single core, the processing of my analysis code is taking too much
> time. I got to know that mclapply() can be used to use all cores
> (processors) to make R much faster when we have multicores. Can anyone help
> me in understanding how to use mclapply() function in the above situation.
> Thanks in advance
> Regards,
> Madana
> --
> View this message in context: http://r.789695.n4.nabble.com/R-on-Multicore-for-Linux-tp3682318p3682318.html
> Sent from the R help mailing list archive at Nabble.com.
> ______________________________________________
> R-help at r-project.org mailing list
> 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.

Stephen Sefick
| Auburn University                                         |
| Biological Sciences                                      |
| 331 Funchess Hall                                       |
| Auburn, Alabama                                         |
| 36849                                                           |
| sas0025 at auburn.edu                                  |
| http://www.auburn.edu/~sas0025                 |

Let's not spend our time and resources thinking about things that are
so little or so large that all they really do for us is puff us up and
make us feel like gods.  We are mammals, and have not exhausted the
annoying little problems of being mammals.

                                -K. Mullis

"A big computer, a complex algorithm and a long time does not equal science."

                              -Robert Gentleman

More information about the R-help mailing list