The doFuture package provides mechanisms for using the
foreach package together with the future package such that
foreach()
and times()
parallelizes via any future backend.
The future package provides a generic API for using futures in R. A future is a simple yet powerful mechanism to evaluate an R expression and retrieve its value at some point in time. Futures can be resolved in many different ways depending on which strategy is used. There are various types of synchronous and asynchronous futures to choose from in the future package. Additional future backends are implemented in other packages. For instance, the future.batchtools package provides futures for any type of backend that the batchtools package supports. For an introduction to futures in R, please consult the vignettes of the future package.
The foreach package implements a map-reduce API with functions
foreach()
and times()
that provides us with powerful methods for
iterating over one or more sets of elements with the option to do it
in parallel.
The doFuture package provides two alternatives for using futures with foreach:
y <- foreach(...) %dofuture% { ... }
registerDoFuture()
+ y <- foreach(...) %dopar% { ... }
.
%dofuture%
The first alternative (recommended), which uses %dofuture%
, avoids
having to use registerDoFuture()
. The %dofuture%
operator
provides a more consistent behavior than %dopar%
, e.g. there is a
unique set of foreach arguments instead of one per possible adapter.
Identification of globals, random number generation (RNG), and error
handling is handled by the future ecosystem, just like with other
map-reduce solutions such as future.apply and furrr. An
example is:
library(doFuture)
plan(multisession)
y <- foreach(x = 1:4, y = 1:10) %dofuture% {
z <- x + y
slow_sqrt(z)
}
This alternative is the recommended way to let foreach()
parallelize
via the future framework, especially if you start out from scratch.
See help("%dofuture%", package = "doFuture")
for more details and
examples on this approach.
registerDoFuture()
+ %dopar%
The second alternative is based on the traditional foreach
approach where one registers a foreach adapter to be used by
%dopar%
. A popular adapter is doParallel::registerDoParallel()
,
which parallelizes on the local machine using the parallel
package. This package provides registerDoFuture()
, which
parallelizes using the future package, meaning any
future-compliant parallel backend can be used.
An example is:
library(doFuture)
registerDoFuture()
plan(multisession)
y <- foreach(x = 1:4, y = 1:10) %dopar% {
z <- x + y
slow_sqrt(z)
}
This alternative is useful if you already have a lot of R code that
uses %dopar%
and you just want to switch to using the future
framework for parallelization. Using registerDoFuture()
is also
useful when you wish to use the future framework with packages and
functions that uses foreach()
and %dopar%
internally,
e.g. caret, plyr, NMF, and glmnet. It can
also be used to configure the Bioconductor BiocParallel package,
and any package that rely on it, to parallelize via the future
framework.
See help("registerDoFuture", package = "doFuture")
for more details
and examples on this approach.