future1.34.0 package

Unified Parallel and Distributed Processing in R for Everyone

multicore

Create a multicore future whose value will be resolved asynchronously ...

MulticoreFuture-class

A multicore future is a future whose value will be resolved asynchrono...

MultiprocessFuture-class

A multiprocess future is a future whose value will be resolved asynchr...

multisession

Create a multisession future whose value will be resolved asynchronous...

nbrOfWorkers

Get the number of workers available

nullcon

Creates a connection to the system null device

plan

Plan how to resolve a future

private_length

Gets the length of an object without dispatching

re-exports

Functions Moved to 'parallelly'

future

Create a future

readImmediateConditions

Writes and Reads 'immediateCondition' RDS Files

requestCore

Request a core for multicore processing

resetWorkers

Free up active background workers

tweak

Tweak a future function by adjusting its default arguments

find_references

Get the first or all references of an object

Future-class

A future represents a value that will be available at some point in th...

future.options

Options used for futures

backtrace

Back trace the expressions evaluated when an error was caught

cluster

Create a cluster future whose value will be resolved asynchronously in...

clusterExportSticky

Export globals to the sticky-globals environment of the cluster nodes

ClusterFuture-class

A cluster future is a future whose value will be resolved asynchronous...

ConstantFuture-class

A future with a constant value

FutureCondition

A condition (message, warning, or error) that occurred while orchestra...

FutureGlobals

A representation of a set of globals used with futures

futureOf

Get the future of a future variable

FutureResult

Results from resolving a future

futures

Get all futures in a container

futureSessionInfo

Get future-specific session information and validate current backend

mandelbrot

Mandelbrot convergence counts

getExpression

Inject code for the next type of future to use for nested futures

getGlobalsAndPackages

Retrieves global variables of an expression and their associated packa...

grapes-conditions-grapes

Control whether standard output should be captured or not

grapes-globals-grapes

Specify globals and packages for a future assignment

grapes-label-grapes

Specify label for a future assignment

grapes-lazy-grapes

Control lazy / eager evaluation for a future assignment

grapes-plan-grapes

Use a specific plan for a future assignment

grapes-seed-grapes

Set random seed for future assignment

grapes-stdout-grapes

Control whether standard output should be captured or not

grapes-tweak-grapes

Temporarily tweaks the arguments of the current strategy

resolve

Resolve one or more futures synchronously

resolved

Check whether a future is resolved or not

result

Get the results of a resolved future

run

Run a future

save_rds

Robustly Saves an Object to RDS File Atomically

sequential

Create a sequential future whose value will be in the current session

sessionDetails

Outputs details on the current session

signalConditions

Signals Captured Conditions

sticky_globals

Place a sticky-globals environment immediately after the global enviro...

UniprocessFuture-class

An uniprocess future is a future whose value will be resolved synchron...

usedCores

Get number of cores currently used

value

The value of a future or the values of all elements in a container

The purpose of this package is to provide a lightweight and unified Future API for sequential and parallel processing of R expression via futures. The simplest way to evaluate an expression in parallel is to use `x %<-% { expression }` with `plan(multisession)`. This package implements sequential, multicore, multisession, and cluster futures. With these, R expressions can be evaluated on the local machine, in parallel a set of local machines, or distributed on a mix of local and remote machines. Extensions to this package implement additional backends for processing futures via compute cluster schedulers, etc. Because of its unified API, there is no need to modify any code in order switch from sequential on the local machine to, say, distributed processing on a remote compute cluster. Another strength of this package is that global variables and functions are automatically identified and exported as needed, making it straightforward to tweak existing code to make use of futures.

  • Maintainer: Henrik Bengtsson
  • License: LGPL (>= 2.1)
  • Last published: 2024-07-29