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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(BiocParallel)
slow_fcn <- function(x) {
Sys.sleep(0.1) # emulate work
x^2
}
xs <- 1:1000
ys <- bplapply(xs, slow_fcn) |> futurize()
This vignette demonstrates how to use this approach to parallelize
functions such as bplapply(), bpmapply(), and bpvec() in the
BiocParallel package. For example, consider the bplapply()
function. It works like base-R lapply(), but uses the
BiocParallel framework to process the tasks concurrently. It is
commonly used something like:
library(BiocParallel)
xs <- 1:1000
ys <- bplapply(xs, slow_fcn)
The parallel backend is controlled by the BiocParallel::register(),
similar to how we use future::plan() in futureverse. We can use
the futurize package to tell BiocParallel to hand over the
orchestration of parallel tasks to futureverse. All we need to do is
to pass the expression to futurize() as in:
library(futurize)
library(BiocParallel)
xs <- 1:1000
ys <- bplapply(xs, slow_fcn) |> 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 all
BiocParallel functions that take argument
BPPARAM. Specifically,
bplapply() and .bplapply_impl()bpmapply() and .bpmapply_impl()bpvec()bpaggregate()The following functions are currently not supported:
bpiterate() - technically supported, but because this
function does not support using DoparParam() with it, it
effectively does not work with futurize()bpvectorize()register()