Parallel computation using latrend

Description

The model estimation functions support parallel computation through the use of the foreach mechanism. In order to make use of parallel execution, a parallel back-end must be registered.

Windows

On Windows, the parallel-package can be used to define parallel socket workers.

nCores <- parallel::detectCores(logical = FALSE)
cl <- parallel::makeCluster(nCores)

Then, register the cluster as the parallel back-end using the doParallel package:

doParallel::registerDoParallel(cl)

If you defined your own lcMethod or lcModel extension classes, make sure to load them on the workers as well. This can be done, for example, using:

parallel::clusterEvalQ(cl,
  expr = setClass('lcMethodMyImpl', contains = "lcMethod"))

Unix

On Unix systems, it is easier to setup parallelization as the R process is forked. In this example we use the doMC package:

nCores <- parallel::detectCores(logical = FALSE)
doMC::registerDoMC(nCores)

See Also

latrendRep, latrendBatch, latrendBoot, latrendCV

Examples

data(latrendData)

# parallel latrendRep()
method <- lcMethodLMKM(Y ~ Time, id = "Id", time = "Time")
models <- latrendRep(method, data = latrendData, .rep = 5, parallel = TRUE)

# parallel latrendBatch()
methods <- lcMethods(method, nClusters = 1:3)
models <- latrendBatch(methods, data = latrendData, parallel = TRUE)