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(Sim.DiffProc) # Define 1D SDE model f <- expression(0) g <- expression(1) mod1d <- snssde1d(drift = f, diffusion = g, x0 = 1, M = 10, N = 100) stat <- function(x, ...) mean(x) res <- MCM.sde(mod1d, statistic = stat, R = 10, time = 0.5) |> futurize() ``` # Introduction This vignette demonstrates how to use this approach to parallelize **[Sim.DiffProc]** functions such as `MCM.sde()`. The **[Sim.DiffProc]** package provides a comprehensive framework for numerical simulation and inference of Stochastic Differential Equations (SDEs) in R. Because Monte Carlo simulation is highly iterative, running multiple replications in parallel can significantly reduce execution times. ## Example: Monte Carlo simulation of SDEs The `MCM.sde()` function performs Monte Carlo simulations for SDEs. For example: ```r library(Sim.DiffProc) f <- expression(0) g <- expression(1) mod1d <- snssde1d(drift = f, diffusion = g, x0 = 1, M = 10, N = 100) stat <- function(x, ...) mean(x) res <- MCM.sde(mod1d, statistic = stat, R = 10, time = 0.5) ``` Here `MCM.sde()` evaluates sequentially. To run in parallel, pipe to `futurize()`: ```r library(futurize) library(Sim.DiffProc) res <- MCM.sde(mod1d, statistic = stat, R = 10, time = 0.5) |> futurize() ``` This will distribute the Monte Carlo 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 **Sim.DiffProc** functions are supported by `futurize()`: * `MCM.sde()` with `seed = TRUE` as the default [Sim.DiffProc]: https://cran.r-project.org/package=Sim.DiffProc [other parallel backends]: https://www.futureverse.org/backends.html