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futurize: Parallelize Common Functions via One Magic Function The 'futurize' hexlogo

TL;DR

The futurize package makes it extremely simple to parallelize your existing map-reduce calls, but also a growing set of domain-specific calls. All you need to know is that there is a single function called futurize() that will take care of everything, e.g.

y <- lapply(x, fcn) |> futurize()
y <- map(x, fcn) |> futurize()
b <- boot(city, ratio, R = 999) |> futurize()

The futurize() function parallelizes via futureverse, meaning your code can take advantage of any supported future backends, whether it be parallelization on your local computer, across multiple computers, in the cloud, or on a high-performance compute (HPC) cluster. The futurize package has only one hard dependency - the future package. All other dependencies are optional “buy-in” dependencies as shown in the below tables.

In addition to getting access to all future-based parallel backends, by using futurize() you also get access to all the benefits that comes with futureverse. Notably, if the function you parallelize output messages and warnings, they will be relayed from the parallel worker to your main R session, just as you get when running sequentially. This is particularly useful when troubleshooting or debugging.

Supported calls

Supported map-reduce packages

The futurize package supports transpilation of functions from multiple packages. The tables below summarize the supported map-reduce (Table 1) and domain-specific (Table 2) functions, respectively. To programmatically see which packages are currently supported, use:

To see which functions are supported for a specific package, use:

Package Functions Requires
base lapply(), sapply(), tapply(), vapply(), mapply(), .mapply(), Map(), eapply(), apply(), by(), replicate(), Filter() future.apply
stats kernapply() future.apply
purrr map() and variants, map2() and variants, pmap() and variants, imap() and variants, modify(), modify_if(), modify_at(), map_if(), map_at(), invoke_map() furrr
crossmap xmap() and variants, xwalk(), map_vec(), map2_vec(), pmap_vec(), imap_vec() -
foreach %do%, e.g. foreach() %do% { }, times() %do% { } doFuture
plyr aaply() and variants, ddply() and variants, llply() and variants, mlply() and variants doFuture
pbapply pblapply(), pbsapply() and variants, pbby(), pbreplicate() and pbwalk() future.apply
BiocParallel bplapply(), bpmapply(), bpvec(), bpiterate(), bpaggregate() doFuture

Table 1: Map-reduce functions currently supported by futurize() for parallel transpilation.

Here are some examples:

library(futurize)
plan(multisession)

xs <- 1:10
ys <- lapply(xs, sqrt) |> futurize()

xs <- 1:10
ys <- purrr::map(xs, sqrt) |> futurize()

xs <- 1:10
ys <- crossmap::xmap_dbl(xs, ~ .y * .x) |> futurize()

library(foreach)
xs <- 1:10
ys <- foreach(x = xs) %do% { sqrt(x) } |> futurize()

xs <- 1:10
ys <- plyr::llply(xs, sqrt) |> futurize()

xs <- 1:10
ys <- pbapply::pblapply(xs, sqrt) |> futurize()

xs <- 1:10
ys <- BiocParallel::bplapply(xs, sqrt) |> futurize()

and

ys <- replicate(3, rnorm(1)) |> futurize()

y <- by(warpbreaks, warpbreaks[,"tension"],
        function(x) lm(breaks ~ wool, data = x)) |> futurize()

xs <- EuStockMarkets[, 1:2]
k <- kernel("daniell", 50)
xs_smooth <- stats::kernapply(xs, k = k) |> futurize()

Supported domain-specific packages

You can also futurize calls from a growing set of domain-specific packages that have optional built-in support for parallelization.

Package Functions Requires
boot boot(), censboot(), tsboot() -
caret bag(), gafs(), nearZeroVar(), rfe(), safs(), sbf(), train() doFuture
fwb fwb(), vcovFWB() -
glmnet cv.glmnet() doFuture
glmmTMB "confint() and profile() for ‘glmmTMB’ -
lme4 allFit(), bootMer(), influence() and profile() for ‘merMod’ -
mgcv bam(), predict() for ‘bam’ -
mice mice() -
partykit cforest(), ctree_control(), mob_control(), varimp() for ‘cforest’ future.apply
seriation seriate_best(), seriate_rep() doFuture
strucchange breakpoints() for ‘formula’ doFuture
tm TermDocumentMatrix(), tm_index(), tm_map() -
TSP solve_RSP() doFuture
vegan adonis(), adonis2(), anosim(), cascadeKM(), estaccumR(), mantel(), mantel.partial(), metaMDSiter(), mrpp(), oecosimu(), ordiareatest(), permutest() for ‘betadisper’, and ‘cca’, simper() -

Table 2: Domain-specific functions currently supported by futurize() for parallel transpilation.

Here are some examples:

ctrl <- caret::trainControl(method = "cv", number = 10)
model <- caret::train(Species ~ ., data = iris, method = "rf", trControl = ctrl) |> futurize()

ratio <- function(d, w) sum(d$x * w)/sum(d$u * w)
b <- boot::boot(boot::city, ratio, R = 999) |> futurize()

f <- fwb::fwb(boot::city, ratio, R = 999) |> futurize()

cv <- glmnet::cv.glmnet(x, y) |> futurize()

m <- lme4::allFit(models) |> futurize()

imp <- mice::mice(nhanes, m = 5) |> futurize()

b <- mgcv::bam(y ~ s(x0, bs = bs) + s(x1, bs = bs), data = dat) |> futurize()

cf <- partykit::cforest(dist ~ speed, data = cars) |> futurize()

o <- seriation::seriate_best(d_supreme) |> futurize()

bp <- strucchange::breakpoints(Nile ~ 1) |> futurize()
  
m <- tm::tm_map(crude, content_transformer(tolower)) |> futurize()

tour <- TSP::solve_TSP(USCA50, method = "nn", rep = 10) |> futurize()

md <- vegan::mrpp(dune, Management) |> futurize()