The purpose of this package is to provide worry-free parallel alternatives to base-R "apply" functions, e.g. apply(), lapply(), and vapply(). The goal is that one should be able to replace any of these in the core with its futurized equivalent and things will just work. For example, instead of doing:
library(datasets)
library(stats)
y <- lapply(mtcars, FUN = mean, trim = 0.10)one can do:
library(future.apply)
plan(multisession) ## Run in parallel on local computer
library(datasets)
library(stats)
y <- future_lapply(mtcars, FUN = mean, trim = 0.10)Reproducibility is part of the core design, which means that perfect, parallel random number generation (RNG) is supported regardless of the amount of chunking, type of load balancing, and future backend being used. To enable parallel RNG, use argument future.seed = TRUE.
Where does the future.apply package fit in the software stack? You can think of it as a sibling to foreach, furrr, BiocParallel, plyr, etc. Just as parallel provides parLapply(), foreach provides foreach(), BiocParallel provides bplapply(), and plyr provides llply(), future.apply provides future_lapply(). Below is a table summarizing this idea:
| Package | Functions | Backends |
|---|---|---|
|
future.apply |
Future-versions of common goto *apply() functions available in base R (of the base package):future_apply(),
future_by(),
future_eapply(),
future_lapply(),
future_Map(),
future_mapply(),
future_.mapply(),
future_replicate(),
future_sapply(),
future_tapply(), and
future_vapply().
The following function is not implemented: future_rapply() |
All future backends |
| parallel |
mclapply(), mcmapply(),
clusterMap(), parApply(), parLapply(), parSapply(), ...
|
Built-in and conditional on operating system |
| foreach |
foreach(),
times()
|
All future backends via doFuture |
| furrr |
future_imap(),
future_map(),
future_pmap(),
future_map2(),
...
|
All future backends |
| BiocParallel |
Bioconductor's parallel mappers:bpaggregate(),
bpiterate(),
bplapply(), and
bpvec()
|
All future backends via doFuture (because it supports foreach) or via BiocParallel.FutureParam (direct BiocParallelParam support; prototype) |
| plyr |
**ply(..., .parallel = TRUE) functions:aaply(),
ddply(),
dlply(),
llply(), ...
|
All future backends via doFuture (because it uses foreach internally) |
Note that, except for the built-in parallel package, none of these higher-level APIs implement their own parallel backends, but they rather enhance existing ones. The foreach framework leverages backends such as doParallel, doMC and doFuture, and the future.apply framework leverages the future ecosystem and therefore backends such as built-in parallel, future.callr, and future.batchtools.
By separating future_lapply() and friends from the future package, it helps clarifying the purpose of the future package, which is to define and provide the core Future API, which higher-level parallel APIs can build on and for which any futurized parallel backends can be plugged into.
The API and identity of the future.apply package will be kept close to the *apply() functions in base R. In other words, it will neither keep growing nor be expanded with new, more powerful apply-like functions beyond those core ones in base R. Such extended functionality should be part of a separate package.
R package future.apply is available on CRAN and can be installed in R as:
install.packages("future.apply")To install the pre-release version that is available in Git branch develop on GitHub, use:
remotes::install_github("futureverse/future.apply", ref="develop")This will install the package from source.
To contribute to this package, please see CONTRIBUTING.md.