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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(mgcv)
## Adopted from example("bam", package = "mgcv")
dat <- gamSim(1, n = 25000, dist = "normal", scale = 20)
bs <- "cr"
k <- 12
b <- bam(y ~ s(x0, bs = bs) + s(x1, bs = bs) + s(x2, bs = bs, k = k) +
s(x3, bs = bs), data = dat) |> futurize()
This vignette demonstrates how to use this approach to parallelize mgcv
functions such as bam().
The mgcv package is one of the "recommended" packages in R. It
provides methods for fitting Generalized Additive Models (GAMs). The
bam() function can be used to fit GAMs for massive datasets
("Big Additive Models") with many thousands of observations, making
it an excellent candidate for parallelization.
The bam() function supports parallel processing by setting up a
parallel cluster and passing it as argument cluster. This is
abstracted away by futurize:
library(mgcv)
## Adopted from example("bam", package = "mgcv")
dat <- gamSim(1, n = 25000, dist = "normal", scale = 20)
bs <- "cr"
k <- 12
b <- bam(y ~ s(x0, bs = bs) + s(x1, bs = bs) + s(x2, bs = bs, k = k) +
s(x3, bs = bs), data = dat) |> futurize()
This will distribute the calculations across the available parallel workers, given that we have set up parallel workers, e.g.
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.
plan(future.mirai::mirai_multisession)
and
plan(future.batchtools::batchtools_slurm)
The following mgcv functions are supported by futurize():
bam()predict() for 'bam'For comparison, here is what it takes to parallelize bam() using
the parallel package directly, without futurize:
library(mgcv)
library(parallel)
## Adopted from example("bam", package = "mgcv")
dat <- gamSim(1, n = 25000, dist = "normal", scale = 20)
bs <- "cr"
k <- 12
## Set up a PSOCK cluster
ncpus <- 4L
cl <- makeCluster(ncpus)
## Fit the model in parallel
b <- bam(y ~ s(x0, bs = bs) + s(x1, bs = bs) + s(x2, bs = bs, k = k) +
s(x3, bs = bs), data = dat, cluster = cl)
## Tear down the cluster
stopCluster(cl)
This requires you to manually create and manage the cluster
lifecycle. If you forget to call stopCluster(), or if your code
errors out before reaching it, you leak background R processes. You
also have to decide upfront how many CPUs to use and what cluster
type to use. Switching to another parallel backend, e.g. a Slurm
cluster, would require a completely different setup. With
futurize, all of this is handled for you - just pipe to
futurize() and control the backend with plan().