Array job based on R [closed] - r

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I am trying to construct and submit an array job based on R in the HPC of my university.
I'm used to submit array jobs based on Matlab and I have some doubts on how to translate the overall procedure to R. Let me report a very simple Matlab example and then my questions.
The code is based on 3 files:
"main" which does some preliminary operations.
"subf" which should be run by each task and uses some matrices created by "main".
a bash file which I qsub in the terminal.
1. main:
clear
%% Do all the operations that are common across tasks
% Here, as an example, I create
% 1) a matrix A that I will sum to the output of each task
% 2) a matrix grid; each task will use some rows of the matrix grid
m=1000;
A=rand(m,m);
grid=rand(m,m);
%% Tasks
tasks=10; %number of tasks
jobs=round(size(grid,1)/tasks); %I split the number of rows of the matrix grid among the tasks
2. subf:
%% Set task ID
idtemp=str2double(getenv('SGE_TASK_ID'));
%% Select local grid
if idtemp<tasks
grid_local= grid(jobs*(idtemp-1)+1: idtemp*jobs,:);
else
grid_local= grid(jobs*(idtemp-1)+1: end,:); %for the last task, we should take all the rows of grid that have been left
end
sg_local=size(grid_local,1);
%% Do the task
output=zeros(sg_local,1);
for g=1:sg_local
output(g,:)=sum(sum(A+repmat(grid_local(g,:),m,1)));
end
%% Save output by keeping track of task ID
filename = sprintf('output.%d.mat', ID);
save(filename,'output')
3. bash
#$ -S /bin/bash
#$ -l h_vmem=6G
#$ -l tmem=6G
#$ -l h_rt=480:0:0
#$ -cwd
#$ -j y
#Run 10 tasks where each task has a different $SGE_TASK_ID ranging from 1 to 10
#$ -t 1-10
#$ -N Example
date
hostname
#Output the Task ID
echo "Task ID is $SGE_TASK_ID"
export PATH=/xx/xx/matlab/bin:$PATH
matlab -nodisplay -nodesktop -nojvm -nosplash -r "main; ID = $SGE_TASK_ID; subf; exit"
These are my questions:
Suppose I'm able to translate "main" and "subf" into R language. Should I be extra-careful about anything in particular concerning the parallelisation? For example, do I have to declare some parallel environment, such as parLapply or dopar?
In the "main" file I should also install some R packages. Can I do them locally in my folder directly at the beginning of the "main" file, or should I contact the HPC administrator to install them globally?
I could not find any example of bash file for R in the instructions given by my university. Therefore, I have doubts on how to re-adapt the above bash file. I suppose that the only lines to change are:
export PATH=/xx/xx/matlab/bin:$PATH
matlab -nodisplay -nodesktop -nojvm -nosplash -r "main; ID = $SGE_TASK_ID; subf; exit"
Could you give some hints on how I should change them?

Answer 1

The parallelization is handled by the HPC, right? In which case, I think "no", nothing special required.
It depends on how they allow/enable R. In a HPC that I use (not your school), the individual nodes do not have direct internet access, so it would require special care; this might be the exception, I don't know.
Recommendation: if there is a shared filesystem that both you and all of the nodes can access, then create an R "library" there that contains the installed packages you need, then use .libPaths(...) in your R scripts here to add that to the search path for packages. The only gotcha to this might be if there are non-R shared library (e.g., .dll, .so, .a) requirements. For this, either "docker" or "ask admins".
If you don't have a shared filesystem, then you might ask the cluster admins if they use/prefer docker images (you might provide an image or a DOCKERFILE to create one) or if they have preferred mechanisms for enabling various packages.
I do not recommend asking them to install the packages, for two reasons: First, think about them needing to do this with every person who has a job to run, for any number of programming languages, and then realize that they may have no idea how to do it for that language. Second, package versions are very important, and you asking them to install a package may install either a too-new package or overwrite an older version that somebody else is relying on. (See packrat and renv for discussions on reproducible environments.)
Bottom line, the use of a path you control (and using .libPaths) enables you to have complete control over package versions. If you have not been bitten by unintended consequences of newer-versioned packages, just wait ... congratulations, you've been lucky.
I suggest you can add source("main.R") to the beginning of subf.R, which would make your bash file perhaps as simple as
export PATH=/usr/local/R-4.x.x/bin:$PATH
Rscript /path/to/subf.R
(Noting that you'll need to reference Sys.getenv("SGE_TASK_ID") somewhere in subf.R.)