ergm (version 3.2.4)

ergm-parallel: Parallel Processing in the ergm Package

Description

For estimation that require MCMC, ergm can take advantage of multiple CPUs or CPU cores on the system on which it runs, as well as computing clusters. It uses package parallel and snow to facilitate this, and supports all cluster types that they does.

The number of nodes used and the parallel API are controlled using the parallel and parallel.type arguments passed to the control functions, such as control.ergm.

PSOCK clusters

The parallel package is used with PSOCK clusters by default, to utilize multiple cores on a system. The number of cores on a system can be determined with the detectCores function. This method works with the base installation of R on all platforms, and does not require additional software. For more advanced applications, such as clusters that span multiple machines on a network, the clusters can be initialized manually, and passed into ergm using the parallel control argument. See the second example below.

MPI clusters

To use MPI to accelerate ERGM sampling, pass the control parameter parallel.type="MPI". ergm requires the snow and Rmpi packages to communicate with an MPI cluster. Using MPI clusters requires the system to have an existing MPI installation. See the MPI documentation for your particular platform for instructions.

To use {{{ergm}}} across multiple machines in a high performance computing environment, see the section "User initiated clusters" below.

User initiated clusters

A cluster can be passed into ergm with the parallel control parameter. ergm will detect the number of nodes in the cluster, and use all of them for MCMC sampling. This method is flexible: it will accept any cluster type that is compatible with snow or parallel packages. Usage examples for a multiple-machine high performance MPI cluster can be found at the statnet wiki: https://statnet.csde.washington.edu/trac/wiki/ergmParallel

Examples

# Uses 2 SOCK clusters for MCMLE estimation
data(faux.mesa.high)
nw <- faux.mesa.high
fauxmodel.01 <- ergm(nw ~ edges + isolates + gwesp(0.2, fixed=TRUE), 
                     control=control.ergm(parallel=2, parallel.type="PSOCK"))
summary(fauxmodel.01)