Foreach Iteration using Futures via %dofuture%

In addition to providing a foreach adapter to be used with the %dopar% operator of foreach, the doFuture package provides an alternative foreach() operator called %dofuture% that ties more directly into the future framework. For example,

library(doFuture)
plan(multisession)

cutoff <- 0.10
y <- foreach(x = mtcars, .export = c("cutoff")) %dofuture% {
  mean(x, trim = cutoff)
}
names(y) <- colnames(mtcars)

There are several advantages of using %dofuture% instead of %dopar%. When you use %dofuture%,

there is no need to use registerDoFuture()
there is no need to use %dorng% of the doRNG package (but you need to specify .options.future = list(seed = TRUE) whenever using random numbers in the expr expression)
global variables and packages are identified automatically by the future framework
errors are relayed as-is (with %dopar% they captured and modified)

This makes foreach(...) %dofuture% { ... } more in line with how sibling packages future.apply and furrr work.

Global variables and packages

When using %dofuture%, the future framework identifies globals and packages automatically (via static code inspection).

However, there are cases where it fails to find some of the globals or packages. When this happens, one can specify the future() arguments globals and packages via foreach argument .options.future. For example, if you specify argument .options.future = list(globals = structure(TRUE, ignore = "b", add = "a")) then globals are automatically identified (TRUE), but it ignores b and always adds a.

An alternative to specifying the globals and the packages options via .options.future, is to use the %globals% and %packages% operators.

For further details and instructions, see help("future", package = "future").

Random Number Generation (RNG)

The %dofuture% uses the future ecosystem to generate proper random numbers in parallel in the same way they are generated in, for instance, future.apply and furrr. For this to work, you need to specify .options.future = list(seed = TRUE). For example,

y <- foreach(i = 1:3, .options.future = list(seed = TRUE)) %dofuture% {
  rnorm(1)
}

An alternative to specifying the seed option via .options.future, is to use the %seed% operator.

y <- foreach(i = 1:3) %dofuture% {
  rnorm(1)
} %seed% TRUE

For further details and instructions, see help("future", package = "future").

Load balancing (“chunking”)

Whether load balancing (“chunking”) should take place or not can be controlled by specifying either argument .options.future = list(scheduling = <ratio>) or .options.future = list(chunk.size = <count>) to foreach(). For example,

y <- foreach(x = 1:10, .options.future = list(scheduling = 2.0)) %dofuture% {
  slow_fcn(x)
}

For further details and instructions, see help("future_lapply", package = "future.apply").

- Foreach Iteration using Futures via %dofuture%