mirai

ミライ

みらい 未来 Minimalist Async Evaluation Framework for R → Designed for simplicity, a ‘mirai’ evaluates an R expression asynchronously in a parallel process, locally or distributed over the network.

→ Modern networking and concurrency, built on nanonext and NNG (Nanomsg Next Gen), ensures reliable and efficient scheduling over fast inter-process communications or TCP/IP secured by TLS. Distributed computing can launch remote resources via SSH or cluster managers.

→ A queued architecture readily handles more tasks than available processes, requiring no storage on the file system. Innovative features include event-driven promises, asynchronous parallel map, and automatic serialization of otherwise non-exportable reference objects.

Quick Start

Use mirai() to evaluate an expression asynchronously in a separate, clean R process.

The following mimics an expensive calculation that eventually returns a vector of random values.

library(mirai)

m <- mirai({Sys.sleep(n); rnorm(n, mean)}, n = 5L, mean = 7)

The mirai expression is evaluated in another process and hence must be self-contained, not referring to variables that do not already exist there. Above, the variables n and mean are passed as part of the mirai() call.

A ‘mirai’ object is returned immediately - creating a mirai never blocks the session.

m
#> < mirai [] >

Whilst the async operation is ongoing, attempting to access a mirai’s data yields an ‘unresolved’ logical NA.

m$data
#> 'unresolved' logi NA

To check whether a mirai remains unresolved (yet to complete):

unresolved(m)
#> [1] TRUE

To wait for and collect the return value, use the mirai’s [] method:

m[]
#> [1] 6.288799 7.337810 6.767335 7.435713 7.628763

As a mirai represents an async operation, it is never necessary to wait for it. Once it completes, the return value is automatically available at $data.

while (unresolved(m)) {
  # do work here that does not depend on `m`
}
m$data
#> [1] 6.288799 7.337810 6.767335 7.435713 7.628763

Daemons