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Goal: Introduction some high-level aspects of using R in parallel relating to the cluster.

Note

In the same spirit as this is not a course on learning the R language, this is not a section on developing parallelized code with any of the 10s of parallel related packages.

Instead it will detail some aspects to consider regards using our cluster.

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Table of Contents
stylenone

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Parallel Programming with R

The are 10s of potential packages that could be used, as a starting point we’d direct your to here: CRAN Task View: High-Performance and Parallel Computing with R.

One thing to consider with respect to what package you wish to explore is whether it provides multi-node functionality (such as Rmpi) or just multicore (parallel) on a single compute node, and/or cluster features.

Note

Remember: Just asking for multiple nodes (and GPUs) won’t actually make your code run faster unless the underlying package can actually utilize them.

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R parallel Package: Overview

Info

The parallel package is not part of the core R installation and is now a base package.

It does not need to be installed.

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Building Rmpi from Source

Info

If you wish to try to install Rmpi, you should use the latest implementation of OpenMPI on the cluster to build against.

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Multicore: Detecting Cores

Note

Typically, using parallel::detectCores() to detect the number of available cores on a cluster node is a slight red herring. This returns the entire total number of cores of the node your job is allocated and not the actual number of cores you requested/allocated.

For example, if you're sbatch script defines the following,

Code Block
#SBATCH --nodes=1
#SBATCH --cpus-per-task=8

and you're allocated a standard compute node that has 32 cores, parallel::detectCores() will return a value of 32 and not 8 which is what you requested!
This will probably lead to unexpected results/failures when you try and run a function expecting 32 cores when only 8 are actually available.
To remove this problem you can use, and need to pass into your R script, the value of the $SLURM_JOB_CPUS_PER_NODE slurm environment variable.

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Detect Cores Example

Expand
titler_multicore.R
Code Block
args <- commandArgs(trailingOnly = TRUE)
if (!is.na(args[1])) {
  num_of_cores <- args[1]
  print(paste0("Num of Cores: ", num_of_cores))
}

print(paste0("detectCores: ", parallel::detectCores()))

options(mc.cores = num_of_cores)
print(paste0("mc.cores: ", getOption("mc.cores", 1L)))
Code Block
# Create an interactive session that uses 8 cores:
[salexan5@mblog2 ~]$ salloc -A arcc -t 10:00 -c 8
salloc: Granted job allocation 861904
salloc: Nodes mbcpu-001 are ready for job
[salexan5@mbcpu-001 ~]$ module load gcc/13.2.0 r/4.4.0

# Check the slurm environment variable: SLURM_JOB_CPUS_PER_NODE
[salexan5@mbcpu-001 ~]$ echo $SLURM_JOB_CPUS_PER_NODE
8

# What does R detect?
[salexan5@mbcpu-001 ~]$ Rscript r_multicore.R $SLURM_JOB_CPUS_PER_NODE
[1] "Num of Cores: 8"
[1] "detectCores: 96"
[1] "mc.cores: 8"

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Create an R Kernel for a Jupyter Notebook

 

Using R and RStudio on the Cluster

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