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(crossmap) xs <- list(1:5, 1:5) ys <- xmap(xs, ~ .y * .x) |> futurize() ``` # Introduction This vignette demonstrates how to use this approach to parallelize **[crossmap]** functions such as `xmap()` and `xwalk()`. The **crossmap** `xmap()` function can be used to iterate over every combination of elements in an input list. For example, ```r library(crossmap) xs <- list(1:5, 1:5) ys <- xmap(xs, ~ .y * .x) ``` Here `xmap()` evaluates sequentially over each combination of (.y, .x) elements. We can easily make it evaluate in parallel, by using: ```r library(futurize) library(crossmap) xs <- list(1:5, 1:5) ys <- xmap(xs, ~ .y * .x) |> futurize() ``` This will distribute the calculations across the available parallel workers, given that we have set parallel workers, e.g. ```r plan(multisession) ``` The built-in `multisession` backend parallelizes on your local computer and it 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 `futurize()` function supports parallelization of the following **crossmap** functions: * `imap_vec()`, `map_vec()`, `map2_vec()`, `pmap_vec()`, `xmap_vec()` * `xmap()` * `xmap_chr()`, `xmap_dbl()`, `xmap_int()`, `xmap_lgl()`, `xmap_raw()` * `xmap_dfc()`, `xmap_dfr()` * `xmap_mat()`, `xmap_arr()` * `xwalk()` [crossmap]: https://cran.r-project.org/package=crossmap