mirai
provides an alternative communications backend for R. This functionality was developed to fulfil a request by R Core at R Project Sprint 2023.
make_cluster()
creates a cluster object of class ‘miraiCluster’, which is fully-compatible with parallel
cluster types.
remote_config()
or ssh_config()
.Created clusters may be used for any function in the parallel
base package such as parallel::clusterApply()
or parallel::parLapply()
, or the load-balanced versions such as parallel::parLapplyLB()
.
library(mirai)
cl <- make_cluster(4)
cl
#> < miraiCluster | ID: `0` nodes: 4 active: TRUE >
parallel::parLapply(cl, iris, mean)
#> $Sepal.Length
#> [1] 5.843333
#>
#> $Sepal.Width
#> [1] 3.057333
#>
#> $Petal.Length
#> [1] 3.758
#>
#> $Petal.Width
#> [1] 1.199333
#>
#> $Species
#> [1] NA
status()
may be called on a ’miraiCluster` to query the number of connected nodes at any time.
status(cl)
#> $connections
#> [1] 4
#>
#> $daemons
#> [1] "abstract://e755083adb3ac002d81d87c7"
stop_cluster(cl)
Making a cluster specifying ‘url’ without ‘remote’ causes the shell commands for manual deployment of nodes to be printed to the console.
cl <- make_cluster(n = 2, url = host_url())
#> Shell commands for deployment on nodes:
#>
#> [1]
#> Rscript -e 'mirai::daemon("tcp://hostname:41567",rs=c(10407,-1229812554,-355079233,-704283916,613800293,-1049741598,-1259508485))'
#>
#> [2]
#> Rscript -e 'mirai::daemon("tcp://hostname:41567",rs=c(10407,-936602309,482053993,1829342693,1529156475,2009204106,1430761999))'
stop_cluster(cl)
A ‘miraiCluster’ may also be registered by doParallel
for use with the foreach
package.
Running some parallel examples for the foreach()
function:
library(foreach)
library(iterators)
cl <- make_cluster(4)
doParallel::registerDoParallel(cl)
# normalize the rows of a matrix
m <- matrix(rnorm(9), 3, 3)
foreach(i = 1:nrow(m), .combine = rbind) %dopar%
(m[i, ] / mean(m[i, ]))
#> [,1] [,2] [,3]
#> result.1 0.6168828 1.1209518 1.2621654
#> result.2 1.9631338 0.3970546 0.6398116
#> result.3 1.8112756 8.5974163 -7.4086918
# simple parallel matrix multiply
a <- matrix(1:16, 4, 4)
b <- t(a)
foreach(b = iter(b, by='col'), .combine = cbind) %dopar%
(a %*% b)
#> [,1] [,2] [,3] [,4]
#> [1,] 276 304 332 360
#> [2,] 304 336 368 400
#> [3,] 332 368 404 440
#> [4,] 360 400 440 480