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The future package in R provides a unified abstraction for asynchronous and/or parallel computation. It allows R expressions to be scheduled for future evaluation, with the result retrieved later, in a way decoupled from the specific backend used. This lets code be written in a way that works with sequential execution, multicore, multisession, cluster, or remote compute backends, without changing the high-level code. It handles automatic exporting of needed global variables/functions, managing of packages, RNG, etc.
Features
- Unified Future API: same interface for different execution modes (sequential, multicore, multisession, cluster)
- Automatic detection and export of global objects and functions needed by future expressions so user doesn’t need to manage that manually
- Support for various parallel and distributed computation backends (local multicore, remote clusters, etc.)
- Plans and strategies: ability to control how futures are resolved (e.g. whether in background, how many workers, etc.) via plan() etc
- Integration with other packages (future.apply, furrr, etc.) to provide parallel versions of *apply(), mapping functions etc
- Non-blocking or asynchronous evaluation (so main process need not block while future is resolving) and support for querying whether a future is resolved etc
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