Multicore Execution

This section will cover how take advantage of multiple cores on your machine. To use the multicore mode in OpenMx, you must declare independent submodels. A model is declared independent by using the argument ‘independent=TRUE’ in the mxModel() function. An independent model and all of its dependent children are executed in a separate optimization environment. An independent model shares no free parameters with either its sibling models or its parent model. An independent model may not refer to matrices or algebras in either its sibling models or its parent model. A parent model may access the final results of optimization from an independent child model.

To use the snowfall library, you must start your R environment with the following commands:

sfInit(parallel = TRUE, cpus = 8)

sfInit will initialize the snowfall cluster. You must specify either the number of CPUs on your machine or the cluster environment (see snowfall package documentation). sfLibrary exports the OpenMx library to the client nodes in the cluster. At the end of your script, use the command:


To Improve Performance

Any sequential portions of your script will quickly become the performance bottleneck (Amdahl’s Law). Avoid iteration over large data structures. Use the functions omxLapply() and omxSapply() instead of iteration. These two functions invoke the snowfall sfLapply() and sfSapply() functions if the snowfall library has been loaded. Otherwise they invoke the sequential functions lapply() and sapply(). To hunt for bottelnecks in your script, run your script with multicore settings enabled and use Rprof to profile a reasonable size test case. Ignore the calls to sfLapply() and sfSapply() in the results of profiling. Any other time-consuming calls represent potential sequential bottlenecks.

Some of the functions provided by the OpenMx library can be bottlenecks. Iterative use of the mxModel() function in order to add submodels can be time consuming. Use the following unsafe idiom to improve performance:

topModel <- mxModel('container')
# generate a list of independent submodels
submodels <-  omxLapply(1:100, generateNewSubmodels)
names(submodels) <- imxExtractNames(submodels)
topModel@submodels <- submodels

An Example

The following script can be found with demo(BootstrapParallel)

# parameters for the simulation: lambda = factor loadings,
# specifics = specific variances
lambda <- matrix(c(.8, .5, .7, 0), 4, 1)
nObs <- 500
nReps <- 10
nVar <- nrow(lambda)
specifics <- diag(nVar)
chl <- chol(lambda %*% t(lambda) + specifics)

# indices for parameters and hessian estimate in results
pStrt <- 3
pEnd <- pStrt + 2*nVar - 1
hStrt <- pEnd + 1
hEnd <- hStrt + 2*nVar - 1

# dimension names for OpenMx
dn <- list()
dn[[1]] <- paste("Var", 1:4, sep="")
dn[[2]] <- dn[[1]]

# function to get a covariance matrix
randomCov <- function(nObs, nVar, chl, dn) {
        x <- matrix(rnorm(nObs*nVar), nObs, nVar)
        x <- x %*% chl
        thisCov <- cov(x)
        dimnames(thisCov) <- dn

createNewModel <- function(index, prefix, model) {
        modelname <- paste(prefix, index, sep='')
        data <- mxData(randomCov(nObs, nVar, chl, dn), type="cov", numObs=nObs)
        model <- mxModel(model, data)
        model <- mxRename(model, modelname)

getStats <- function(model) {
        retval <- c(model@output$status[[1]],

# initialize obsCov for MxModel
obsCov <- randomCov(nObs, nVar, chl, dn)

# results matrix: get results for each simulation
results <- matrix(0, nReps, hEnd)
dnr <- c("inform", "maxAbsG", paste("lambda", 1:nVar, sep=""),
        paste("specifics", 1:nVar, sep=""),
        paste("hessLambda", 1:nVar, sep=""),
        paste("hessSpecifics", 1:nVar, sep=""))
dimnames(results)[[2]] <- dnr

# instantiate MxModel
template <- mxModel("stErrSim",
               mxMatrix(name="lambda", type="Full", nrow=4, ncol=1,
                        free=TRUE, values=c(.8, .5, .7, 0)),
               mxMatrix(name="specifics", type="Diag", nrow=4,
                        free=TRUE, values=rep(1, 4)),
               mxAlgebra(lambda %*% t(lambda) + specifics,
                         name="preCov", dimnames=dn),
               mxData(observed=obsCov, type="cov", numObs=nObs),
               independent = TRUE)

topModel <- mxModel("container")

submodels <- lapply(1:nReps, createNewModel, "stErrSim", template)

names(submodels) <- imxExtractNames(submodels)
topModel@submodels <- submodels

modelResults <- mxRun(topModel, silent=TRUE, suppressWarnings=TRUE)

results <- t(omxSapply(modelResults@submodels, getStats))

# get rid of bad covergence results
results2 <- data.frame(results[which(results[,1] <= 1),])

# summarize the results
means <- mean(results2)
stdevs <- sd(results2)
sumResults <- data.frame(matrix(dnr[pStrt:pEnd], 2*nVar, 1,
                        dimnames=list(NULL, "Parameter")))
sumResults$mean <- means[pStrt:pEnd]
sumResults$obsStDev <- stdevs[pStrt:pEnd]
sumResults$meanHessEst <- means[hStrt:hEnd]
sumResults$sqrt2meanHessEst <- sqrt(2) * sumResults$meanHessEst

# print results

Table Of Contents

Previous topic

File Checkpointing

Next topic

Full Information Maximum Likelihood, Row Objective Specification

This Page