knitr::opts_chunk$set( eval = FALSE, # en- / disables R code evaluation globally cache = FALSE, # en- / disables R code caching globally collapse = TRUE, comment = "#>" )
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.
## Adapt the SLURM template to requirements job_time <- 1 # time for job in hours n_workers <- 24 # number of worker nodes n_cpus <- 16 # number of cpus per worker node gb_memory <- 2 # memory [GB] per cpu 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)) ## Register the parallel backend registerDoFuture() ## Specify how the futures should be resolved plan(list(slurm, multisession))
The R package bhmbasket
makes use of the foreach framework and runs with every applicable parallel backend.
With a parallel backend registered as shown above and running the code on the master node, the job scheduler will automatically distribute the jobs to the worker nodes via plan(slurm)
, and with the nested parallelization built into performAnalyses()
, each worker node makes use of its CPUs via plan(multisession)
.
Below is some example code, which was taken from the examples section of ?bhmbasket::getEstimates
.
Due to the foreach framework, no adjustments to the code are necessary.
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)
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