Communications Backend for R

1. Mirai Parallel Clusters

mirai provides an alternative communications backend for R. This functionality was developed to fulfil a request by R Core at R Project Sprint 2023.

A ‘miraiCluster’ is recognised as one of the official cluster types in R 4.5, and may be created by parallel::makeCluster(type = "MIRAI"). This function calls make_cluster(), which may also be used to create a ‘miraiCluster’ directly.

• Specify ‘n’ to launch nodes on the local machine.
• Specify ‘url’ for receiving connections from remote nodes.
• Optionally, specify ‘remote’ to launch remote daemons using a remote configuration generated by 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(parallel)
library(mirai)

cl <- makeCluster(6, type = "MIRAI")
cl
#> < miraiCluster | ID: `26` nodes: 6 active: TRUE >
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] 6
#> 
#> $daemons
#> [1] "abstract://eb809f13097f9a03ba5cb5bb"
stopCluster(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://192.168.1.71:45111",dispatcher=FALSE,cleanup=FALSE,rs=c(10407,2115398811,-2011965664,-789584735,1666662894,1025958295,-751939348))'
#> 
#> [2]
#> Rscript -e 'mirai::daemon("tcp://192.168.1.71:45111",dispatcher=FALSE,cleanup=FALSE,rs=c(10407,1495377764,161492535,-451945023,2093847167,-1105055737,1737365375))'
stop_cluster(cl)

2. Foreach Support

A ‘miraiCluster’ may also be registered by doParallel for use with the foreach package.

Running some parallel examples for the foreach() function:

library(doParallel)
library(foreach)

cl <- makeCluster(6, type = "MIRAI")
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 5.989529  1.1456413 -4.1351703
#> result.2 3.745199 -0.5989935 -0.1462058
#> result.3 3.007812  2.0183943 -2.0262067

# simple parallel matrix multiply
a <- matrix(1:16, 4, 4)
b <- t(a)
foreach(b = iterators::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

stopCluster(cl)