Author: Henrik Bengtsson
Keywords: R, package, vignette, foreach, future, promise, lazy evaluation, synchronous, asynchronous, parallel, cluster

# doFuture: An Overview on using Foreach to Parallelize via the Future Framework

## TL;DR

To run `foreach()` in parallel, install R packages doFuture and futurize, and call:

    library(futurize)
    plan(multisession)

    y <- foreach(x = 1:4, y = 1:10) %do% {
      z <- x + y
      slow_sqrt(z)
    } |> futurize()

That’s it - easy!

## Introduction

The foreach package implements a map-reduce API with functions `foreach()` and `times()` that provide us with powerful methods for iterating over one or more sets of elements with options to do it in parallel.

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. You can resolve them sequential, in parallel on your local computer, on remove computers, in the cloud, on a high-performance compute (HPC) cluster, or via any future backend available.

The doFuture package provides a bridge between foreach and the future parallelization framework. Specifically, the doFuture package provides three alternatives for using futures with foreach:

1. `y <- foreach(...) %do% { ... } |> futurize()`

2. `y <- foreach(...) %dofuture% { ... }`

3. `registerDoFuture()` + `y <- foreach(...) %dopar% { ... }`.

### Alternative 1: `futurize()` (recommended)

The first alternative (recommended) uses `futurize()` of the futurize package. An example is:

    library(futurize)
    plan(multisession)

    y <- foreach(x = 1:4, y = 1:10) %do% {
      z <- x + y
      slow_sqrt(z)
    } |> futurize()

This alternative is the recommended and most clean way to let `foreach()` parallelize via the future framework, especially if you start out from scratch. All you need to remember is to pipe it to `futurize()`, and, yes, it is correct to use `%do%` here. In addition to `multisession`, parallelization can be done via any compliant future backend. Identification of globals, random number generation (RNG), and error handling is handled the same way as elsewhere in the future ecosystem. We recommend to use `futurize()`, because it is consistent with how we parallelize `lapply()` and `purrr::map()` using futurize. With `futurize()`, you do not have to explicitly load doFuture - instead doFuture will serve `futurize()` under the hood.

See `help("futurize", package = "futurize")` for more details and examples on this approach.

### Alternative 2: `%dofuture%`

The second alternative (formely 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. An example is:

    library(doFuture)
    plan(multisession)

    y <- foreach(x = 1:4, y = 1:10) %dofuture% {
      z <- x + y
      slow_sqrt(z)
    }

This alternative was the recommended way to let `foreach()` parallelize via the future framework, but now we recommend using `futurize()` instead, especially if you start out from scratch.

See `help("%dofuture%", package = "doFuture")` for more details and examples on this approach.

### Alternative 3: `registerDoFuture()` + `%dopar%`

The third 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 use `foreach()` and `%dopar%` internally, but still do not support `futurize()`, e.g. NMF.

See `help("registerDoFuture", package = "doFuture")` for more details and examples on this approach.