# Parallelize 'crossmap' functions

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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

    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,

    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:

    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.

    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.

    plan(future.mirai::mirai_multisession)

and

    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()`