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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!
library(futurize)
plan(multisession)
library(crossmap)
xs <- list(1:5, 1:5)
ys <- xmap(xs, ~ .y * .x) |> futurize()
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)
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()These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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