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

# Foreach Iteration using Futures via %dopar%

## Introduction

The doFuture package provides a `%dopar%` adapter for the foreach package that works with any type of future backend. The doFuture package is cross platform just as the future package.

Below is an example showing how to make `%dopar%` work with multisession futures. A multisession future will be evaluated in parallel using background R process.

    library("doFuture")
    registerDoFuture()
    plan(multisession)

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

## Futures bring foreach to the HPC cluster

To do the same on a high-performance computing (HPC) cluster, the future.batchtools package can be used. Assuming batchtools has been configured correctly, then the following foreach iterations will be submitted to the HPC job scheduler and distributed for evaluation on the compute nodes.

    library("doFuture")
    registerDoFuture()
    plan(future.batchtools::batchtools_slurm)

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

## Futures for plyr

The plyr package uses foreach as a parallel backend. This means that with doFuture any type of futures can be used for asynchronous (and synchronous) plyr processing including multicore, multisession, MPI, ad hoc clusters and HPC job schedulers. For example,

    library("doFuture")
    registerDoFuture()
    plan(multisession)
    library("plyr")

    cutoff <- 0.10
    y <- llply(mtcars, mean, trim = cutoff, .parallel = TRUE)
    ## $a
    ##  25%  50%  75%
    ## 3.25 5.50 7.75
    ##
    ## $beta
    ##       25%       50%       75%
    ## 0.2516074 1.0000000 5.0536690
    ##
    ## $logic
    ## 25% 50% 75%
    ## 0.0 0.5 1.0

## Futures and BiocParallel

The BiocParallel package supports any `%dopar%` adapter as a parallel backend. This means that with doFuture, BiocParallel supports any type of future. For example,

    library("doFuture")
    registerDoFuture()
    plan(multisession)
    library("BiocParallel")
    register(DoparParam(), default = TRUE)

    cutoff <- 0.10
    x <- bplapply(mtcars, mean, trim = cutoff)

## doFuture takes care of exports and packages automatically

The foreach package itself has some support for automated handling of globals but unfortunately it does not work in all cases. Specifically, if `foreach()` is called from within a function, you do need to export globals explicitly. For example, although global `cutoff` is properly exported when we do

    library("doParallel")
    registerDoParallel(parallel::makeCluster(2))

    cutoff <- 0.10
    y <- foreach(x = mtcars) %dopar% {
      mean(x, trim = cutoff)
    }
    names(y) <- colnames(mtcars)

it falls short as soon as we try to do the same from within a function:

    my_mean <- function() {
      y <- foreach(x = mtcars) %dopar% {
        mean(x, trim = cutoff)
      }
      names(y) <- colnames(mtcars)
      y
    }

    x <- my_mean()
    ## Error in { : task 1 failed - "object 'cutoff' not found"

The solution is to explicitly export global variables, e.g.

    my_mean <- function() {
      y <- foreach(x = mtcars, .export = "cutoff") %dopar% {
        mean(x, trim = cutoff)
      }
      names(y) <- colnames(mtcars)
      y
    }

    y <- my_mean()

In contrast, when using the `%dopar%` adapter of doFuture, all of the future machinery comes into play including automatic handling of global variables, e.g.

    library("doFuture")
    registerDoFuture()
    plan(multisession, workers = 2)

    my_mean <- function() {
      y <- foreach(x = mtcars) %dopar% {
        mean(x, trim = cutoff)
      }
      names(y) <- colnames(mtcars)
      y
    }

    x <- my_mean()

will indeed work.

Another advantage with doFuture is that, contrary to doParallel, packages that need to be attached are also automatically taken care of, e.g.

    registerDoFuture()
    library("tools")
    ext <- foreach(file = c("abc.txt", "def.log")) %dopar% file_ext(file)
    unlist(ext)
    ## [1] "txt" "log"

whereas

    registerDoParallel(parallel::makeCluster(2))
    library("tools")
    ext <- foreach(file = c("abc.txt", "def.log")) %dopar% file_ext(file)
    ## Error in file_ext(file) :
    ##   task 1 failed - "could not find function "file_ext""

Having said all this, in order to write foreach code that works everywhere, it is better to be conservative and not assume that all end users will use a doFuture backend. Because of this, it is still recommended to explicitly specify all objects that need to be exported whenever using the foreach API. The doFuture framework can help you identify what should go into the `.export` argument. By setting `options(doFuture.foreach.export = ".export-and-automatic-with-warning")`, doFuture will warn if it finds globals not listed in `.export` and produce an informative warning message suggesting that those should be added. To assert that argument `.export` is correct, test the code with `options(doFuture.foreach.export = ".export")`, which will disable automatic identification of globals such that only the globals specified by the `.export` argument are used.

## doFuture replaces existing doNnn packages

Due to the generic nature of futures, the doFuture package provides the same functionality as many of the existing doNnn packages combined, e.g. doMC, doParallel, doMPI, and doSNOW.

doNnn usage | doFuture alternative
--- | ---
    library("foreach")
    registerDoSEQ()
    library("doFuture")
    registerDoFuture()
    plan(sequential)
    library("doMC")
    registerDoMC()
    library("doFuture")
    registerDoFuture()
    plan(multicore)
    library("doParallel")
    registerDoParallel()
    library("doFuture")
    registerDoFuture()
    plan(multisession)  ## on MS Windows
    plan(multicore)     ## on Linux, Solaris, and macOS
N/A |
    library("doFuture")
    registerDoFuture()
    plan(future.callr::callr)
    library("doParallel")
    cl <- makeCluster(4)
    registerDoParallel(cl)
    library("doFuture")
    registerDoFuture()
    cl <- makeCluster(4)
    plan(cluster, workers = cl)
    library("doMPI")
    cl <- startMPIcluster(count = 4)
    registerDoMPI(cl)
    library("doFuture")
    registerDoFuture()
    cl <- makeCluster(4, type = "MPI")
    plan(cluster, workers = cl)
    library("doSNOW")
    cl <- makeCluster(4)
    registerDoSNOW(cl)
    library("doFuture")
    registerDoFuture()
    cl <- makeCluster(4)
    plan(cluster, workers = cl)
N/A | High-performance compute (HPC) schedulers, e.g. SGE, Slurm, and TORQUE / PBS.
    library("doFuture")
    registerDoFuture()
    plan(future.batchtools::batchtools_sge)
    library("doRedis")
    registerDoRedis("jobs")
    startLocalWorkers(n = 4, queue = "jobs")
| N/A. There is currently no known Redis-based future backend and therefore no known doFuture alternative to the doRedis package.