parallelism_in_calmr

Running experiments in parallel

With the advent of time-based models, version 0.51 of calmr uses the future package to parallelize some operations. Thanks to the design philosophy of future, running things in parallel will take you a single line of code.

Why run things in parallel?

In many situations we find ourselves having to run a model over many iterations, either because our design contains enough kinds of trials so that order effects are a worry, or because we want to run the same model with different parameters.

Let’s run the HeiDI model (Honey et al., 2020) over a long, random design. Let’s also enable verbosity via calmr_verbosity(), which uses the progressr package.

library(calmr)
# enables progress bars (try it on your computer)
# calmr_verbosity(TRUE)
pav_inhib <- data.frame(
  group = "group",
  phase1 = "50(US)/50AB/50#A",
  rand1 = TRUE
)
# set options to introduce more randomness
pars <- get_parameters(pav_inhib, model = "HDI2020")
exp <- make_experiment(pav_inhib,
  parameters = pars,
  model = "HDI2020",
  iterations = 100,
  miniblocks = FALSE
)

# time it
start <- proc.time()
pav_res <- run_experiment(exp)
end <- proc.time() - start
end
#>    user  system elapsed 
#>   5.753   0.048   3.789

Let’s try parallelizing now.

Running an experiment in parallel

To run the same experiment, but in parallel, you need to enable a future plan. A “plan” is one of many ways the future package can parallelize things (you should really consult their documentation). Regardless, if you are running calmr on a single computer, you’ll be using plan(multisession)

library(future)
plan(multisession)

start <- proc.time()
pav_res <- run_experiment(exp)
end <- proc.time() - start
end
#>    user  system elapsed 
#>   0.937   0.068   6.382

# go back to non-parallel evaluations
plan(sequential)

In this case, the parallel evaluation was faster (see user time above).

The future package trades off ease of use for bulkier overheads, but as the overheads tend to be constant, the parallelization will have a better payoff once you run more and more iterations.

Honey, R. C., Dwyer, D. M., & Iliescu, A. F. (2020). HeiDI: A model for Pavlovian learning and performance with reciprocal associations. Psychological Review, 127, 829–852. https://doi.org/10.1037/rev0000196