<%@meta language="R-vignette" content="-------------------------------- %\VignetteIndexEntry{Foreach Iteration using Futures via %dopar%} %\VignetteAuthor{Henrik Bengtsson} %\VignetteKeyword{R} %\VignetteKeyword{package} %\VignetteKeyword{vignette} %\VignetteKeyword{foreach} %\VignetteKeyword{future} %\VignetteKeyword{promise} %\VignetteKeyword{lazy evaluation} %\VignetteKeyword{synchronous} %\VignetteKeyword{asynchronous} %\VignetteKeyword{parallel} %\VignetteKeyword{cluster} %\VignetteEngine{R.rsp::rsp} %\VignetteTangle{FALSE} --------------------------------------------------------------------"%> # 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. ```r 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. ```r 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, ```r 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, ```r 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 ```r 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: ```r 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. ```r 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. ```r 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. ```r registerDoFuture() library("tools") ext <- foreach(file = c("abc.txt", "def.log")) %dopar% file_ext(file) unlist(ext) ## [1] "txt" "log" ``` whereas ```r 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 usagedoFuture 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.
[batchtools]: https://cran.r-project.org/package=batchtools [BiocParallel]: https://bioconductor.org/packages/release/bioc/html/BiocParallel.html [callr]: https://cran.r-project.org/package=callr [doFuture]: https://cran.r-project.org/package=doFuture [doMC]: https://cran.r-project.org/package=doMC [doMPI]: https://cran.r-project.org/package=doMPI [doParallel]: https://cran.r-project.org/package=doParallel [doRedis]: https://cran.r-project.org/package=doRedis [doSNOW]: https://cran.r-project.org/package=doSNOW [foreach]: https://cran.r-project.org/package=foreach [future]: https://cran.r-project.org/package=future [future.callr]: https://cran.r-project.org/package=future.callr [future.batchtools]: https://cran.r-project.org/package=future.batchtools [plyr]: https://cran.r-project.org/package=plyr