High-Performance Open-Source Archive
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 come with futureverse, including
structured concurrency. For example, it ensures that
remaining parallel tasks are cancelled if there is an error or an
interrupt. Also, if the function you parallelize outputs 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.
Using futurize comes with a zero risk buy-in. If
there is ever a parallel universe where futurize() suddenly
stops working, setting futurize <- identical avoids
rewrites while make all code to run sequentially.
The futurize package supports transpilation of functions from multiple packages. The tables below summarize the supported map-reduce (Table 1) and domain-specific (Tables 2 and 3) functions, respectively. To programmatically see which packages are currently supported, use:
futurize_supported_packages()To see which functions are supported for a specific package, use:
futurize_supported_functions("caret")| 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() |
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()You can also futurize calls from a growing set of domain-specific CRAN and Bioconductor 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 |
| DiceKriging | km() |
doFuture |
| ez | ezBoot(), ezPerm(),
ezPlot2() |
doFuture |
| fwb | fwb(), vcovFWB() |
- |
| gamlss | add1All(), add1TGD(),
drop1All(), drop1TGD(),
gamlssCV() |
- |
| glmmTMB | profile() for ‘glmmTMB’ |
- |
| glmnet | cv.glmnet() |
doFuture |
| kernelshap | kernelshap(), permshap() |
doFuture |
| lme4 | allFit(), bootMer(),
influence() and profile() for ‘merMod’ |
- |
| metafor | profile(), rstudent(),
cooks.distance(), dfbetas() for ‘rma’ |
- |
| mgcv | bam(), predict() for ‘bam’ |
- |
| modelsummary | modelsummary(), msummary(),
modelplot() |
future.apply |
| parameters | bootstrap_model(),
bootstrap_parameters() |
- |
| partykit | cforest(), ctree_control(),
mob_control(), varimp() for ‘cforest’ |
future.apply |
| pls | mvr(), plsr(), pcr(),
cppls(), crossval() |
- |
| pvclust | pvclust() |
- |
| riskRegression | Score() for ‘list’ |
doFuture |
| rugarch | arfimacv(), arfimadistribution(),
arfimaroll(), autoarfima(),
multifilter(), multifit(),
multiforecast(), ugarchboot(),
ugarchdistribution(), ugarchroll() |
- |
| sandwich | vcovBS(), vcovJK() |
future.apply |
| seriation | seriate_best(), seriate_rep() |
doFuture |
| shapr | explain(), explain_forecast() |
- |
| Sim.DiffProc | MCM.sde() |
- |
| SimDesign | runSimulation(), runArraySimulation() |
- |
| stars | st_apply() |
future.apply |
| strucchange | breakpoints() for ‘formula’ |
doFuture |
| SuperLearner | CV.SuperLearner() |
- |
| tm | TermDocumentMatrix(), tm_index(),
tm_map() |
- |
| TSP | solve_TSP() |
doFuture |
| vegan | adonis(), adonis2(), anova()
for ‘cca’, anosim(), cascadeKM(),
estaccumR(), mantel(),
mantel.partial(), metaMDSiter(),
mrpp(), oecosimu(),
ordiareatest(), permutest() for ‘betadisper’,
and ‘cca’ |
- |
Table 2: CRAN packages with domain-specific functions currently
supported by futurize() for parallel
transpilation.
Here are some examples:
ratio <- function(d, w) sum(d$x * w)/sum(d$u * w)
b <- boot::boot(boot::city, ratio, R = 999) |> futurize()
ctrl <- caret::trainControl(method = "cv", number = 10)
model <- caret::train(Species ~ ., data = iris, method = "rf", trControl = ctrl) |> futurize()
rt <- ez::ezBoot(data = ANT, dv = rt, wid = subnum, within = .(cue, flank), between = group) |> futurize()
f <- fwb::fwb(boot::city, ratio, R = 999) |> futurize()
m <- DiceKriging::km(~., design = design, response = response, multistart = 8L) |> futurize()
cv <- gamlss::gamlssCV(y ~ pb(x), data = abdom, K.fold = 10) |> futurize()
cv <- glmnet::cv.glmnet(x, y) |> futurize()
ks <- kernelshap::kernelshap(model, X = x_explain, bg_X = bg_X) |> futurize()
m <- lme4::allFit(models) |> futurize()
fit <- metafor::rma(yi, vi)
pr <- profile(fit) |> futurize()
b <- mgcv::bam(y ~ s(x0, bs = bs) + s(x1, bs = bs), data = dat) |> futurize()
fit <- parameters::bootstrap_model(model, iterations = 1000) |> futurize()
cf <- partykit::cforest(dist ~ speed, data = cars) |> futurize()
m <- pls::plsr(density ~ NIR, ncomp = 10, data = yarn, validation = "CV") |> futurize()
fit <- pvclust::pvclust(mtcars, nboot = 1000) |> futurize()
v <- sandwich::vcovBS(fm) |> futurize()
sc <- riskRegression::Score(list("CSC" = fit), data = d,
formula = Hist(time, event) ~ 1, times = 5, B = 100,
split.method = "bootcv") |> futurize()
roll <- rugarch::ugarchroll(spec, sp500ret, n.start = 1000,
refit.window = "moving", refit.every = 100) |> futurize()
result <- shapr::explain(model, x_explain, x_train, approach = "empirical", phi0 = phi0) |> futurize()
o <- seriation::seriate_best(d_supreme) |> futurize()
res <- Sim.DiffProc::MCM.sde(model, statistic = stat, R = 100) |> futurize()
res <- SimDesign::runSimulation(Design, replications = 1000,
generate = Generate, analyse = Analyse, summarise = Summarise) |> futurize()
s <- stars::st_as_stars(matrix(1:20, nrow = 5, ncol = 4))
res <- stars::st_apply(s, MARGIN = 1, FUN = mean) |> futurize()
bp <- strucchange::breakpoints(Nile ~ 1) |> futurize()
res <- SuperLearner::CV.SuperLearner(Y, X, SL.library = SL.library) |> 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()| Package | Functions | Requires |
|---|---|---|
| DESeq2 | DESeq(), lfcShrink(),
results() |
doFuture |
| fgsea | fgsea(), fgseaMultilevel(),
fgseaSimple(), fgseaLabel(),
geseca(), gesecaSimple(),
collapsePathwaysGeseca() |
doFuture |
| GenomicAlignments | summarizeOverlaps() |
doFuture |
| GSVA | gsva(), gsvaRanks(),
gsvaScores(), spatCor() |
doFuture |
| Rsamtools | countBam(), scanBam() |
doFuture |
| scater | calculatePCA(), calculateTSNE(),
calculateUMAP(), runPCA(),
runTSNE(), runUMAP(),
runColDataPCA(), nexprs(),
getVarianceExplained(), plotRLE() |
doFuture |
| scuttle | calculateAverage(), logNormCounts(),
normalizeCounts(), perCellQCMetrics(),
perFeatureQCMetrics(), addPerCellQCMetrics(),
addPerFeatureQCMetrics(), addPerCellQC(),
addPerFeatureQC(), numDetectedAcrossCells(),
numDetectedAcrossFeatures(),
sumCountsAcrossCells(),
sumCountsAcrossFeatures(),
summarizeAssayByGroup(),
aggregateAcrossCells(),
aggregateAcrossFeatures(),
librarySizeFactors(), computeLibraryFactors(),
geometricSizeFactors(),
computeGeometricFactors(),
medianSizeFactors(), computeMedianFactors(),
pooledSizeFactors(), computePooledFactors(),
fitLinearModel() |
doFuture |
| SingleCellExperiment | applySCE() |
doFuture |
| sva | ComBat(), read.degradation.matrix() |
doFuture |
Table 3: Bioconductor packages with domain-specific functions
currently supported by futurize() for parallel
transpilation.
Here are some examples:
dds <- DESeq2::DESeq(dds) |> futurize()
res <- fgsea::fgsea(pathways, stats) |> futurize()
se <- GenomicAlignments::summarizeOverlaps(features, bam_files) |> futurize()
es <- GSVA::gsva(GSVA::gsvaParam(expr, geneSets)) |> futurize()
counts <- Rsamtools::countBam(bamViews) |> futurize()
sce <- scater::runPCA(sce) |> futurize()
qc <- scuttle::perFeatureQCMetrics(sce) |> futurize()
result <- SingleCellExperiment::applySCE(sce, scuttle::perFeatureQCMetrics) |> futurize()
adjusted <- sva::ComBat(dat = dat, batch = batch) |> futurize()
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