registerDoFuture: Use the Foreach %dopar% Adapter with Futures

In doFuture: Use Foreach to Parallelize via the Future Framework

Use the Foreach ⁠%dopar%⁠ Adapter with Futures

Description

The registerDoFuture() function makes the %dopar% operator of the foreach package to process foreach iterations via any of the future backends supported by the future package, which includes various parallel and distributed backends. In other words, if a computational backend is supported via the Future API, it'll be automatically available for all functions and packages making using the foreach framework. Neither the developer nor the end user has to change any code.

Usage

registerDoFuture()

Value

registerDoFuture() returns, invisibly, the previously registered foreach ⁠%dopar%⁠ backend.

Parallel backends

To use futures with the foreach package and its %dopar% operator, use doFuture::registerDoFuture() to register doFuture to be used as a ⁠%dopar%⁠ adapter. After this, ⁠%dopar%⁠ will parallelize with whatever future backend is set by future::plan().

The built-in future backends are always available, e.g. sequential (sequential processing), multicore (forked processes), multisession (background R sessions), and cluster (background R sessions on local and remote machines). For example, plan(multisession) will make ⁠%dopar%⁠ parallelize via R processes running in the background on the local machine, and plan(cluster, workers = c("n1", "n2", "n2", "n3")) will parallelize via R processes running on external machines.

Additional backends are provided by other future-compliant packages. For example, the future.batchtools package provides support for high-performance compute (HPC) cluster schedulers such as SGE, Slurm, and TORQUE / PBS. As an illustration, plan(batchtools_slurm) will parallelize by submitting the foreach iterations as tasks to the Slurm scheduler, which in turn will distribute the tasks to one or more compute nodes.

Global variables and packages

Unless running locally in the global environment (= at the R prompt), the foreach package requires you do specify what global variables and packages need to be available and attached in order for the "foreach" expression to be evaluated properly. It is not uncommon to get errors on one or missing variables when moving from running a res <- foreach() %dopar% { ... } statement on the local machine to, say, another machine on the same network. The solution to the problem is to explicitly export those variables by specifying them in the .export argument to foreach::foreach(), e.g. foreach(..., .export = c("mu", "sigma")). Likewise, if the expression needs specific packages to be attached, they can be listed in argument .packages of foreach().

When using registerDoFuture(), the above becomes less critical, because by default the Future API identifies all globals and all packages automatically (via static code inspection). This is done exactly the same way regardless of future backend. This automatic identification of globals and packages is illustrated by the below example, which does not specify .export = c("my_stat"). This works because the future framework detects that function my_stat() is needed and makes sure it is exported. If you would use, say, cl <- parallel::makeCluster(2) and doParallel::registerDoParallel(cl), you would get a run-time error on Error in { : task 1 failed - \"could not find function "my_stat" ....

Having said this, note that, in order for your "foreach" code to work everywhere and with other types of foreach adapters as well, you may want to make sure that you always specify arguments .export and .packages.

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().

The value chunk.size specifies the average number of elements processed per future ("chunks"). If +Inf, then all elements are processed in a single future (one worker). If NULL, then argument future.scheduling is used.

The value scheduling specifies the average number of futures ("chunks") that each worker processes. If 0.0, then a single future is used to process all iterations; none of the other workers are not used. If 1.0 or TRUE, then one future per worker is used. If 2.0, then each worker will process two futures (if there are enough iterations). If +Inf or FALSE, then one future per iteration is used. The default value is scheduling = 1.0.

The name of foreach() argument .options.future follows the naming conventions of the doMC, doSNOW, and doParallel packages, This argument should not be mistaken for the R options of the future package.

For backward-compatibility reasons with existing foreach code, one may also use arguments ⁠.options.multicore = list(preschedule = <logical>)⁠ and ⁠.options.snow = list(preschedule = <logical>)⁠ when using doFuture. .options.multicore = list(preschedule = TRUE) is equivalent to .options.future = list(scheduling = 1.0) and .options.multicore = list(preschedule = FALSE) is equivalent to .options.future = list(scheduling = +Inf). and analogously for .options.snow. Argument .options.future takes precedence over argument .option.multicore which takes precedence over argument .option.snow, when it comes to chunking.

Random Number Generation (RNG)

The doFuture adapter registered by registerDoFuture() does not itself provide a framework for generating proper random numbers in parallel. This is a deliberate design choice based on how the foreach ecosystem is set up and to align it with other foreach adapters, e.g. doParallel. To generate statistically sound parallel RNG, it is recommended to use the doRNG package, where the %dorng% operator is used in place of %dopar%. For example,

y <- foreach(i = 1:3) %dorng% {
  rnorm(1)
}

This works because doRNG is designed to work with any type of foreach ⁠%dopar%⁠ adapter including the one provided by doFuture.

If you forget to use ⁠%dorng%⁠ instead of ⁠%dopar%⁠ when the foreach iteration generates random numbers, doFuture will detect the mistake and produce an informative warning.

For package developers

Please refrain from modifying the foreach backend inside your package or functions, i.e. do not call any registerNnn() in your code. Instead, leave the control on what backend to use to the end user. This idea is part of the core philosophy of the foreach framework.

However, if you think it necessary to register the doFuture backend in a function, please make sure to undo your changes when exiting the function. This can be done using:

  oldDoPar <- registerDoFuture()
  on.exit(with(oldDoPar, foreach::setDoPar(fun=fun, data=data, info=info)), add = TRUE)
  [...]

This is important, because the end-user might have already registered a foreach backend elsewhere for other purposes and will most likely not known that calling your function will break their setup. Remember, your package and its functions might be used in a greater context where multiple packages and functions are involved and those might also rely on the foreach framework, so it is important to avoid stepping on others' toes.

Examples

library(iterators)  # iter()
registerDoFuture()  # (a) tell %dopar% to use the future framework
plan(multisession)  # (b) parallelize futures on the local machine


## Example 1
A <- matrix(rnorm(100^2), nrow = 100)
B <- t(A)

y1 <- apply(B, MARGIN = 2L, FUN = function(b) {
  A %*% b
})

y2 <- foreach(b = iter(B, by = "col"), .combine = cbind) %dopar% {
  A %*% b
}
stopifnot(all.equal(y2, y1))



## Example 2 - Chunking (4 elements per future [= worker])
y3 <- foreach(b = iter(B, by = "col"), .combine = cbind,
              .options.future = list(chunk.size = 10)) %dopar% {
  A %*% b
}
stopifnot(all.equal(y3, y1))


## Example 3 - Simulation with parallel RNG
library(doRNG)

my_stat <- function(x) {
  median(x)
}

my_experiment <- function(n, mu = 0.0, sigma = 1.0) {
  ## Important: use %dorng% whenever random numbers
  ##            are involved in parallel evaluation
  foreach(i = 1:n) %dorng% {
    x <- rnorm(i, mean = mu, sd = sigma)
    list(mu = mean(x), sigma = sd(x), own = my_stat(x))
  }
}

## Reproducible results when using the same RNG seed
set.seed(0xBEEF)
y1 <- my_experiment(n = 3)

set.seed(0xBEEF)
y2 <- my_experiment(n = 3)

stopifnot(identical(y2, y1))

## But only then
y3 <- my_experiment(n = 3)
str(y3)
stopifnot(!identical(y3, y1))

doFuture documentation built on April 1, 2023, 12:22 a.m.