snow: bnlearn - snow/parallel package integration

In vspinu/bnlearn: Bayesian network structure learning, parameter learning and inference

Description

How to use the bnlearn package with the parallel computing environment provided by the snow or parallel packages.

Parallel computing for constraint-based algorithms

# load snow, bnlearn and rsprng (for parallel random number
# generation, just in case it's needed); start LAM/MPI via
# lamboot if using an MPI cluster.
> library(snow) # or library(parallel)
> library(bnlearn)
> library(rsprng)
# initialize the cluster ("socket" and "PVM" clusters are fine, too).
> cl <- makeCluster(2, type = "MPI")
Loading required package: Rmpi
        2 slaves are spawned successfully. 0 failed.
> clusterSetupSPRNG(cl)
# load the data.
> data(learning.test)
# call a learning function passing the cluster object (the
# return value of the previous makeCluster() call) as a
# parameter.
> res = gs(learning.test, cluster = cl)
# note that the number of test is evenly divided between
# the two nodes of the cluster.
> clusterEvalQ(cl, bnlearn:::.test.counter())
[[1]]
[1] 47

[[2]]
[1] 42
# a few tests are still executed by this process.
> bnlearn:::.test.counter()
[1] 4
# stop the cluster.
> stopCluster(cl)
[1] 1

Author(s)

Marco Scutari

vspinu/bnlearn documentation built on May 3, 2019, 7:08 p.m.