A Future for R: Available Future Backends

Henrik Bengtsson

The future package comes with built-in future backends that leverage the parallel package 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 chose from.

Package / Backend	Features	How futures are evaluated
future sequential	📶 ♻️	sequentially and in the current R process; default
future multisession	📶 ♻️	parallelly via background R sessions on current machine
future cluster	📶 ♻️*	parallelly in external R sessions on current, local, and/or remote machines
future multicore	📶 ♻️	(not recommended) parallelly via forked R processes on current machine; not with GUIs like RStudio; not on Windows
future.callr callr	📶(next) ♻️(next)	parallelly via transient callr background R sessions on current machine; all memory is returned when as each future is resolved
future.mirai mirai_multisession	📶(next) ♻️(next)	parallelly via mirai background R sessions on current machine; low latency
future.mirai mirai_cluster	♻️(next)	parallelly via mirai daemons running locally or remotely
future.batchtools batchtools_lsf batchtools_openlava batchtools_sge batchtools_slurm batchtools_torque	📶(soon) ♻️(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; * disabled by default
(next): next release; (soon): in a near-future release