This vignette provides a short example on how to use the R package bhmbasket in a high performance computing (HPC) environment using the R packages doFuture and future.batchtools.
library(bhmbasket)
library(doFuture)
library(future.batchtools)
rng_seed <- 5440
set.seed(rng_seed)The code below provides an example for specifying a parallel backend using the future framework with the SLURM job scheduler. Kindly see the documentation of the R packages doFuture and future.batchtools for further options. This code is to be run on the master node.
registerDoFuture()
job_time <- 24 # time for job in hours
n_workers <- 10 # number of worker nodes
n_cpus <- 16 # number of cpus per worker node
gb_memory <- 1 # memory [GB] per worker node
slurm <- tweak(batchtools_slurm,
template = system.file('templates/slurm-simple.tmpl',
package = 'batchtools'),
workers = n_workers,
resources = list(
walltime = 60 * 60 * job_time,
ncpus = n_cpus,
memory = 1000 * gb_memory))
plan(slurm)The R package bhmbasket makes use of the foreach framework and runs with every applicable parallel backend. Therefore, no further adjustments to the code are necessary, which can be run on the master node. With a parallel backend registered as shown above, the job scheduler will automatically distribute the parallel jobs to the worker nodes.
Below is some example code, which was taken from the examples section of ?bhmbasket::getEstimates. Kindly note that running this small example on a HPC environment will most likely not result in a performance improvement.
scenarios_list <- simulateScenarios(
n_subjects_list = list(c(10, 20, 30)),
response_rates_list = list(c(0.1, 0.2, 3)),
n_trials = 10)
analyses_list <- performAnalyses(
scenario_list = scenarios_list,
target_rates = c(0.1, 0.1, 0.1),
calc_differences = matrix(c(3, 2, 2, 1), ncol = 2),
n_mcmc_iterations = 100)
getEstimates(analyses_list)