<%@meta language="R-vignette" content="-------------------------------- %\VignetteIndexEntry{A Future for R: Available Future Backends} %\VignetteAuthor{Henrik Bengtsson} %\VignetteKeyword{R} %\VignetteKeyword{package} %\VignetteKeyword{vignette} %\VignetteKeyword{future} %\VignetteKeyword{synchronous} %\VignetteKeyword{asynchronous} %\VignetteKeyword{parallel} %\VignetteKeyword{cluster} %\VignetteKeyword{callr} %\VignetteKeyword{mirai} %\VignetteKeyword{batchtools} %\VignetteKeyword{future.callr} %\VignetteKeyword{future.mirai} %\VignetteKeyword{future.batchtools} %\VignetteEngine{R.rsp::rsp} %\VignetteTangle{FALSE} --------------------------------------------------------------------"%> # A Future for R: Available Future Backends The **future** package comes with built-in future backends that leverage parallel frameworks available in the **parallel** package, which is part of R itself. In addition to these backends, others exist in package extensions, e.g. **[future.callr]**, **[future.mirai]**, and **[future.batchtools]**. Below is an overview of the most common backends that you as an end-user can choose from. | Package / Backend | Features | How futures are evaluated |:----------------|:------------|:----------------------------------------------------- | **future**
`sequential` | 📶
♻️
| sequentially and in the current R process; default
Example: `plan(sequential)` | **future**
`multisession` | 📶
♻️
| parallelly via background R sessions on current machine
Examples: `plan(multisession)` and `plan(multisession, workers = 2)` | **future**
`cluster` | 📶
♻️*
| parallelly in external R sessions on current, local, and/or remote machines
Examples: `plan(cluster, workers = "raspberry-pi")`, `plan(cluster, workers = c("localhost", "n1", "n1", "pi.example.org"))` | **future**
`multicore` | 📶
♻️
| (not recommended) parallelly via forked R processes on current machine; not with GUIs like RStudio; not on Windows
Examples: `plan(multicore)` and `plan(multicore, workers = 2)` | **[future.callr]**
`callr` | 📶
♻️
| parallelly via transient **[callr]** background R sessions on current machine; all memory is returned when as each future is resolved
Examples: `plan(callr)` and `plan(callr, workers = 2)` | **[future.mirai]**
`mirai_multisession` | 📶
♻️
| parallelly via **[mirai]** background R sessions on current machine; low latency
Examples: `plan(mirai_multisession)` and `plan(mirai_multisession, workers = 2)` | **[future.mirai]**
`mirai_cluster` |♻️
| parallelly via **[mirai]** daemons running locally or remotely
Example: `plan(mirai_cluster)` | **[future.batchtools]**
`batchtools_lsf`
`batchtools_openlava`
`batchtools_sge`
`batchtools_slurm`
`batchtools_torque` | 📶(soon)
♻️
| parallelly on HPC job schedulers (Load Sharing Facility [LSF], OpenLava, TORQUE/PBS, Son/Sun/Oracle/Univa Grid Engine [SGE], Slurm) via **[batchtools]**; for long-running tasks; high latency | 📶: futures relay progress updates in real-time, e.g. **[progressr]**
♻️: futures are interruptible and restartable; * interrupts are disabled by default
(next): next release; (soon): in a near-future release [batchtools]: https://cran.r-project.org/package=batchtools [callr]: https://cran.r-project.org/package=callr [mirai]: https://cran.r-project.org/package=mirai [future]: https://future.futureverse.org [future.apply]: https://future.apply.futureverse.org [future.batchtools]: https://future.batchtools.futureverse.org [future.callr]: https://future.callr.futureverse.org [future.mirai]: https://future.mirai.futureverse.org [future.tests]: https://future.tests.futureverse.org [furrr]: https://furrr.futureverse.org [doFuture]: https://doFuture.futureverse.org [progressr]: https://progressr.futureverse.org