The **futurize** package allows you to easily turn sequential code into parallel code by piping the sequential code to the `futurize()` function. Easy! # TL;DR ```r library(futurize) plan(multisession) library(SimDesign) res <- runSimulation( design = Design, replications = 1000, generate = Generate, analyse = Analyse, summarise = Summarise ) |> futurize() ``` # Introduction This vignette demonstrates how to use this approach to parallelize **[SimDesign]** functions such as `runSimulation()`. The **[SimDesign]** package provides a comprehensive framework for organizing Monte Carlo simulation experiments in R. It uses a structured generate-analyse-summarise workflow for designing, executing, and summarizing simulation studies. The replication-based nature of simulations makes them excellent candidates for parallelization. ## Example: Monte Carlo simulation The `runSimulation()` function runs Monte Carlo simulations over a design of experimental conditions. For example: ```r library(SimDesign) Design <- createDesign( sample_size = c(10, 20, 40), distribution = c("norm", "chi") ) Generate <- function(condition, fixed_objects) { N <- condition$sample_size dist <- condition$distribution if (dist == "norm") rnorm(N) else rchisq(N, df = 5) } Analyse <- function(condition, dat, fixed_objects) { c(mean_est = mean(dat)) } Summarise <- function(condition, results, fixed_objects) { obs_bias <- bias(results[, "mean_est"], parameter = ifelse(condition$distribution == "norm", 0, 5)) obs_RMSE <- RMSE(results[, "mean_est"], parameter = ifelse(condition$distribution == "norm", 0, 5)) c(bias = obs_bias, RMSE = obs_RMSE) } res <- runSimulation( design = Design, replications = 100, generate = Generate, analyse = Analyse, summarise = Summarise ) ``` Here `runSimulation()` evaluates sequentially. To run in parallel, pipe to `futurize()`: ```r library(futurize) library(SimDesign) res <- runSimulation( design = Design, replications = 100, generate = Generate, analyse = Analyse, summarise = Summarise ) |> futurize() ``` This will distribute the replications across the available parallel workers, given that we have set up parallel workers, e.g. ```r plan(multisession) ``` The built-in `multisession` backend parallelizes on your local computer and works on all operating systems. There are [other parallel backends] to choose from, including alternatives to parallelize locally as well as distributed across remote machines, e.g. ```r plan(future.mirai::mirai_multisession) ``` and ```r plan(future.batchtools::batchtools_slurm) ``` # Supported Functions The following **SimDesign** functions are supported by `futurize()`: * `runSimulation()` * `runArraySimulation()` [SimDesign]: https://cran.r-project.org/package=SimDesign [other parallel backends]: https://www.futureverse.org/backends.html