The purpose of this package is to provide worry-free parallel alternatives to base-R "apply" functions, e.g.
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:
Future-versions of common goto
The following function is yet not implemented:
|All future backends|
||Built-in and conditional on operating system|
||All future backends via doFuture|
||All future backends|
Bioconductor's parallel mappers:
||All future backends via doFuture (because it supports foreach) or via BiocParallel.FutureParam (direct BiocParallelParam support; prototype)|
||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.
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.
future_*apply() versions for all common
*apply() functions that exist in base R. This also involves writing a large set of package tests asserting the correctness and the same behavior as the corresponding
future_*apply() functions with each other, e.g. the future-specific arguments.
future_*apply() functions and features that fit in this package but don't necessarily have a corresponding function in base R. Examples of this may be "apply" functions that return futures rather than values, mechanisms for benchmarking, and richer control over load balancing.
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:
To install the pre-release version that is available in Git branch
develop on GitHub, use:
This will install the package from source.
This Git repository uses the Git Flow branching model (the
git flow extension is useful for this). The
develop branch contains the latest contributions and other code that will appear in the next release, and the
master branch contains the code of the latest release, which is exactly what is currently on CRAN.
Contributing to this package is easy. Just send a pull request. When you send your PR, make sure
develop is the destination branch on the future.apply repository. Your PR should pass
R CMD check --as-cran, which will also be checked by Travis CI and AppVeyor CI when the PR is submitted.
|Resource||CRAN||GitHub Actions||Travis CI||AppVeyor CI|
|Platforms:||Multiple||Multiple||Linux & macOS||Windows|
|R CMD check|