Exercise: distributed data analysis with R

In this exercise, you will learn how to submit R jobs in the cluster using the Rscript, the scripting front-end of R.

This exercise is divided into two tasks. The first task is to get you familiar with the flow of running R script as batch jobs in the HPC cluster. The second is more about bookkeeping outputs (R data files) produced by R jobs running concurrently in the cluster.

Note

In this exercise, we will use commands in R and in Linux shell. When you see the commands started with a prompt $, it means a command in Linux shell. If you see >, it implies a command to be typed in a R console.

Preparation

Follow the steps below to download the prepared R scripts.

$ mkdir R_exercise
$ cd R_exercise
$ wget https://github.com/Donders-Institute/hpc-wiki-v2/raw/master/docs/cluster_howto/exercise_R/R_exercise.tgz
$ tar xvzf R_exercise.tgz
$ ls
magic_cal_2.R  magic_cal_3.R  magic_cal.R

Load environment for R version 3.2.2.

$ module unload R
$ module load R/3.2.2
$ which R
/opt/R/3.2.2/bin/R

Task 1: simple job

In this task, we use the script magic_cal.R. This script uses the magic library to genera a magic matrix of a given dimension, and calculate the sum of its diagonal elements. The matrix and the sum are both printed to the standard output.

  1. run the script interactively, for a matrix of dimention 8

    $ export R_LIBS=/opt/R/packages
$ Rscript magic_cal.R 5
WARNING: ignoring environment value of R_HOME
Loading required package: abind
     [,1] [,2] [,3] [,4] [,5]
[1,]    9    2   25   18   11
[2,]    3   21   19   12   10
[3,]   22   20   13    6    4
[4,]   16   14    7    5   23
[5,]   15    8    1   24   17
[1] 65

  2. read and understand the magic_cal.R script

  3. run the script to the cluster as a batch job

    $ echo "Rscript $PWD/magic_cal.R 5" | qsub -N "magic_cal" -l walltime=00:10:00,mem=256mb
11082769.dccn-l029.dccn.nl

  4. wait the job to finish, and check the output of the job. Do you get same results as running interactively?

  5. run five batch jobs in parallel to run the magic_cal.R with matrices in dimention 5,6,7,8,9.

    $ for d in {5 .. 9}; do
    echo "Rscript $PWD/magic_cal.R $d" | qsub -N "magic_cal_$d" -l walltime=00:10:00,mem=256mb;
done

Task 2: job bookkeeping and saving output objects

In the previous task, data objects are just printed to the standard output, which are consequently captured as text in the output files of the jobs. Data stored in this way is hardly be reused for following analyses. A better approach is to store the objects in a R data file (i.e. the RData files), using the save function of R.

Given that batch jobs in the cluster will be executed at the same time, writing objects from different jobs into the same file is not recommanded as the concurrency issue may result in corrupted outputs. A better approach is to write outputs of each job to a seperate file. In implies that running batch jobs in parallel requires an additional bookkeeping strategy on the jobs as well as the output files produced from them.

In this exercise, we are going to use the script magic_cal_2.R in which functions are provided to

  • save objects into data file, and

  • get job/process information that can be used for the bookkeeping purpose.

Follow the steps below:

  1. run the script interactively

    $ Rscript magic_cal_2.R 5
WARNING: ignoring environment value of R_HOME
Loading required package: abind
saving objects  magic_matrix,sum_diagonal  to  magic_cal_2.out.RData  ...done

    From the terminal output, you see two objects are saved into a RData file called magic_cal_2.out.RData. Later on, you can load the object from this file into R or a R script. For example,

    > load("magic_cal_2.out.RData")
> ls()
[1] "magic_matrix" "sum_diagonal"
> magic_matrix
     [,1] [,2] [,3] [,4] [,5]
[1,]    9    2   25   18   11
[2,]    3   21   19   12   10
[3,]   22   20   13    6    4
[4,]   16   14    7    5   23
[5,]   15    8    1   24   17
> q(save="no")

  2. read and understand the magic_cal_2.R script, especially the functions at the top of the script.

  3. try to run magic_cal_2.R as batch jobs as we did in the previous task.

    Tip

    You probably noticed that the functions defined in magic_cal_2.R are so generic that they can be reused for different scripts.

    That is right! In fact, we have factored out those functions into /opt/cluster/share/R so that you could easily make use of those functions in the future.

    In the script magic_cal_3.R, it shows you how to load those functions in your R scripts. It also shows you how to construct the name of the RData file using the job information.