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0.1 Overview

  • R provides a large number of packages for parallel evaluations on multi-core, multi-socket and multi-node systems. The latter are usually referred to as computer clusters.
  • MPI is also supported
  • For an overview of parallelization packages available for R see here
  • One of the most comprehensive parallel computing environments for R is batchtools. Older versions of this package were released under the name BatchJobs (Bischl et al. 2015).
  • batchtools supports both multi-core and multi-node computations with and without schedulers. By making use of cluster template files, most schedulers and queueing systems are supported (e.g. Torque, Sun Grid Engine, Slurm).
  • The BiocParallel package (see here) provides similar functionalities as batchtools, but is tailored to use Bioconductor objects.

0.2 Reminder: Traditional Job Submission for R

This topic is covered in more detail in other tutorials. The following only provides a very brief overview of this submission method.

1. Create Slurm submission script, here called script_name.sh with:

#!/bin/bash -l
#SBATCH --nodes=1
#SBATCH --ntasks=1
#SBATCH --cpus-per-task=1
#SBATCH --mem-per-cpu=1G
#SBATCH --time=1-00:15:00 # 1 day and 15 minutes
#SBATCH --mail-user=useremail@address.com
#SBATCH --mail-type=ALL
#SBATCH --job-name="some_test"
#SBATCH -p short # Choose queue/partition from: intel, batch, highmem, gpu, short

Rscript my_script.R

2. Submit R script called my_script.R by above Slurm script with:

sbatch script_name.sh

0.3 Parallel Evaluations on Clusters with batchtools

  • The following introduces the usage of batchtools for a computer cluster using SLURM as scheduler (workload manager). SLURM is the scheduler used by the HPCC at UCR.
  • Similar instructions are provided in HPCC’s manual section covering batchtools here
  • To simplify the evaluation of the R code on the following slides, the corresponding text version is available for download from here.

0.4 Hands-on Demo of batchtools

0.4.1 Set up working directory for SLURM

First login to your cluster account, open R and execute the following lines. This will create a test directory (here mytestdir), redirect R into this directory and then download the required files:

dir.create("mytestdir")
setwd("mytestdir")
download.file("https://bit.ly/3Oh9dRO", "slurm.tmpl")
download.file("https://bit.ly/3KPBwou", ".batchtools.conf.R")

0.4.2 Load package and define some custom function

The following code defines a test function (here myFct) that will be run on the cluster for demonstration purposes.

The test function (myFct) subsets the iris data frame by rows, and appends the host name and R version of each node where the function was executed. The R version to be used on each node can be specified in the slurm.tmpl file (under module load).

library('RenvModule')
module('load','slurm') # Loads slurm among other modules
library(batchtools)
myFct <- function(x) {
    Sys.sleep(10) # to see job in queue, pause for 10 sec
    result <- cbind(iris[x, 1:4,],
                    Node=system("hostname", intern=TRUE),
                    Rversion=paste(R.Version()[6:7], collapse="."))
    return(result)
    }

0.4.3 Submit jobs from R to cluster

The following creates a batchtools registry, defines the number of jobs and resource requests, and then submits the jobs to the cluster via SLURM.

reg <- makeRegistry(file.dir="myregdir", conf.file=".batchtools.conf.R")
Njobs <- 1:4 # Define number of jobs (here 4)
ids <- batchMap(fun=myFct, x=Njobs) 
done <- submitJobs(ids, reg=reg, resources=list(partition="short", walltime=120, ntasks=1, ncpus=1, memory=1024))
waitForJobs() # Wait until jobs are completed

0.4.4 Summarize job status

After the jobs are completed one can inspect their status as follows.

getStatus() # Summarize job status
showLog(Njobs[1])
# killJobs(Njobs) # # Possible from within R or outside with scancel

0.4.5 Access/assemble results

The results are stored as .rds files in the registry directory (here myregdir). One can access them manually via readRDS or use various convenience utilities provided by the batchtools package.

readRDS("myregdir/results/1.rds") # reads from rds file first result chunk
loadResult(1) 
lapply(Njobs, loadResult)
reduceResults(rbind) # Assemble result chunks in single data.frame
do.call("rbind", lapply(Njobs, loadResult))

0.4.6 Remove registry directory from file system

By default existing registries will not be overwritten. If required one can explicitly clean and delete them with the following functions.

clearRegistry() # Clear registry in R session
removeRegistry(wait=0, reg=reg) # Delete registry directory
# unlink("myregdir", recursive=TRUE) # Same as previous line

0.4.7 Load registry into R

Loading a registry can be useful when accessing the results at a later state or after moving them to a local system.

from_file <- loadRegistry("myregdir", conf.file=".batchtools.conf.R")
reduceResults(rbind)

0.5 Conclusions

0.5.1 Advantages of batchtools

  • many parallelization methods multiple cores, and across both multiple CPU sockets and nodes
  • most schedulers supported
  • takes full advantage of a cluster
  • robust job management by organizing results in registry file-based database
  • simplifies submission, monitoring and restart of jobs
  • well supported and maintained package

0.6 Session Info

sessionInfo()
## R version 4.4.0 (2024-04-24)
## Platform: x86_64-pc-linux-gnu
## Running under: Debian GNU/Linux 11 (bullseye)
## 
## Matrix products: default
## BLAS:   /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.9.0 
## LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.9.0
## 
## locale:
##  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C               LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8     LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8    LC_PAPER=en_US.UTF-8       LC_NAME=C                 
##  [9] LC_ADDRESS=C               LC_TELEPHONE=C             LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       
## 
## time zone: America/Los_Angeles
## tzcode source: system (glibc)
## 
## attached base packages:
## [1] stats     graphics  grDevices utils     datasets  methods   base     
## 
## loaded via a namespace (and not attached):
##  [1] digest_0.6.35     R6_2.5.1          fastmap_1.1.1     xfun_0.43         cachem_1.0.8      knitr_1.46        htmltools_0.5.8.1 rmarkdown_2.26    lifecycle_1.0.4   cli_3.6.2         sass_0.4.9        jquerylib_0.1.4  
## [13] compiler_4.4.0    tools_4.4.0       evaluate_0.23     bslib_0.7.0       yaml_2.3.8        rlang_1.1.3       jsonlite_1.8.8

References

Bischl, Bernd, Michel Lang, Olaf Mersmann, Jörg Rahnenführer, and Claus Weihs. 2015. “BatchJobs and BatchExperiments: Abstraction Mechanisms for Using R in Batch Environments.” Journal of Statistical Software. http://www.jstatsoft.org/v64/i11/.