Difference between revisions of "R 2016"

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   mpirun -np $NSLOTS R --slave -f ''SCRIPTNAME'' ''SCRIPT_CMDLINE_OPTIONS''
   mpirun -np $NSLOTS R --slave -f ''SCRIPTNAME'' ''SCRIPT_CMDLINE_OPTIONS''


to enable SGE to control all processes of your script. Please do not use the batch starting sequence ''R CMD BATCH''.
to enable SLURM to control all processes of your script. Please '''do not''' use the batch starting sequence ''R CMD BATCH''!


The corresponding parallel environment in the SGE submission script is specified by
The corresponding parallel environment in the SLURM submission script is specified by


   #$ -pe impi NUMBER_OF_CORES
   #SBATCH --ntasks=NUMBER_OF_TASKS
  #$ -R y


=== Usage of NetCDF and R ===
=== Usage of NetCDF and R ===

Revision as of 09:24, 27 March 2017

Introduction

R is a free software environment for statistical computing and graphics.

Using R on the HPC cluster

If you want to use R on the HPC cluster, you will have to load its module. You can do that by using the command

module load R

Since there is only one version of R installed, you dont need to specify a version. If you use the command

module spider R

you will find more informations about the module.

Usage of R and MPI

For parallelization the packages doMPI and Rmpi are installed. To launch an parallel R script inside a SLURM script please use command line

  mpirun -np $NSLOTS R --slave -f SCRIPTNAME SCRIPT_CMDLINE_OPTIONS

to enable SLURM to control all processes of your script. Please do not use the batch starting sequence R CMD BATCH!

The corresponding parallel environment in the SLURM submission script is specified by

 #SBATCH --ntasks=NUMBER_OF_TASKS

Usage of NetCDF and R

A package for NetCDF has been installed together with R. In order to use it, please add the command

module load netCDF

to your job script before starting R. Your R-script should include a line

library(ncdf)

to load the NetCDF library. Please refer to the documentations of NetCDF and R for more informations.

Installed version

The currently installed version of R is 3.3.1.

Additional installed packages

The R release contains a lot of additional packages. After loading and starting R ("module load R" and simply "R" on the command line), you can generate a list of all of them by using the following commands

ip <- as.data.frame(installed.packages()[,c(1,3:4)])
rownames(ip) <- NULL
ip <- ip[is.na(ip$Priority),1:2,drop=FALSE]
print(ip, row.names=FALSE)

You will receive a list of every package and its related version. It should look like this:

       Package     Version
           abc         2.1
      abc.data         1.0
         abind       1.4-3
       acepack     1.3-3.3
        adabag         4.1

Documentation

You can look up anything about R on their