I have a possibility to do processing on a super computer, where task managing and resource allocation are controlled by SLURM (Simple Linux Utility for Resource Management) batch job system. However, I have not found the right configurations how to utilize the allocated resources with lidR efficiently. I have tried to allocate 20 CPU's to one task in SLURM and specified  20 workers for a multisession with Future-package within R script. After running a process for a short while, using LAScatalog processing engine, CPU efficiency statistics suggested that with these settings only one of the CPU's was used.

``` 
SLURM BASH SCRIPT:

#!/bin/bash
#SBATCH --nodes=1
#SBATCH --ntasks-per-node=1
#SBATCH --cpus-per-task=20

#load module
module load r-env-singularity

#Run script
srun --threads-per-core=1 singularity_wrapper exec Rscript --no-save pointClouds.R



Allocation within R-script:
plan(multisession, workers=20L)
```
Tried changing the setting around determining 20 tasks per node and one cpu per task. This setting raised CPU utilization, but when looking at the "process outputs" -textfile, it shows that every part of the code was executed 20 times (i.e. every package was loaded 20 times).

Any help on how to correctly allocate resources with a SLURM bash script to achieve efficient parallel processing with lidR LAScatalog?