# Parallel computing information

#### Phil Chalmers

#### 2025-09-18

# 1 Introduction

The purpose of this vignette is to highlight some of the internally defined structures that `SimDesign` supports for distributing the simulation experiment across computing cores, networks, and clusters. In particular, this document includes how the `runSimulation()` function distributes the workload across the `replications` on a per simulation condition basis. Hence, the `replcations` in each each row experiment defined within the `design` object is distributed in parallel, which ensures that the independent replications within each condition follow proper random number generation control.

The logic presented within this document is based on the notion that the front-end user has, in principle, access to each of the computing cores (e.g., can be connected via `ssh`, or are available locally), and that each simulation condition defined in the `design` object reflect independent experiments (default when using `createDesign()`; see `expandDesign()` for the non-independent structure that would not be supported by the approaches described in this document). For situations where the computing architecture is not directly available, such as on high performance computing (HPC) super computers, or simply by picking independent computers and running batches of the simulation code on each computer (with the purpose of collapsing later via `aggregate_simulations()`), see the vignette “Distributing jobs for high-performance computing (HPC)” as managing the random number generation will require additional care.

## 1.1 Local parallel computing

By default, `SimDesign`’s `runSimulation()` function is executed with only a single core. However, setting the argument `runSimulation(..., parallel = TRUE)` will automatically define a cluster object using one core less than the system has available (detect via `parallelly::availableCores()`). This allows a straightforward way to construct a suitable, locally supported cluster object for parallel processing on just the active computer. Depending on the `verbose` and `progress` flags, the progress of each distributed replication will also be printed to the console to indicate the amount of estimated time remaining for the selection simulation condition to complete. This process is then repeated again for each condition in the supplied `design` object until all rows have been evaluated.

This setup is the most painless way to construct and distribute the independent replications per condition, where within each evaluated condition (i.e., each row of the `design` object) high-quality random numbers are automatically used via Pierre L’Ecuyer’s (1999) multiple streams method, limited only by the number of cores that are available. Alternatively, though with a bit of extra effort, users may also define their own cluster computing object by way of the `runSimulation(..., cl)` object, which can be used to link computing resources that are able to communicate via `ssh`, thereby expanding the number of available computing cores detected by `parallel::detectCores()` and friends.

## 1.2 Network computing

If you access have to a set of computers which can be linked via secure-shell (`ssh`) on the same LAN network then Network computing (a.k.a., a Beowulf cluster) may be a viable and useful option. The setup generally requires that the master node has `SimDesign` installed, and the slave/master nodes have all the required R packages pre-installed (Unix utilities such as `dsh` are very useful for this purpose). Finally, the master node must have `ssh` access to the slave nodes, each slave node must have `ssh` access with the master node, and a cluster object (`cl`) from the `parallel` package must be manually defined on the master node.

Setup for network computing is generally straightforward in that it only requires the specification of a) the respective IP addresses within a defined R script, and b) the user name (if different from the master node’s user name; otherwise, only a) is required). On Linux, it may also be important to include relevant information about the host names and IP addresses in the `/etc/hosts` file on the master and slave nodes, and to ensure that the selected port (passed to `parallel::makeCluster()`) on the master node is not hindered by a firewall.

As an example, using the following code the master (primary) node will spawn 7 slave (secondary) and 1 master node, while a separate computer on the network with the associated IP address will spawn an additional 6 slave nodes. Information will be collected on the master node, which is also where the files and objects will be saved using the associated `save`/`filename` inputs in `runSimulation()`.

    library(parallel)
    primary <- '192.168.2.1'
    IPs <- list(list(host=primary, user='myname', ncore=8), list(host='192.168.2.2', user='myname', ncore=6))
    spec <- lapply(IPs, function(IP) rep(list(list(host=IP$host, user=IP$user)), IP$ncore))
    spec <- unlist(spec, recursive=FALSE)
    cl <- makeCluster(master=primary, spec=spec, type = 'PSOCK')
    Final <- runSimulation(..., cl=cl)
    stopCluster(cl)

The object `cl` is passed to `runSimulation()` on the master node and the computations are distributed across the respective IP addresses. Finally, it’s usually good practice to use `stopCluster(cl)` when all the simulations are said and done to release the communication between the computers, which is what the above code shows.

If you have provided suitable names for each respective slave node, as well as the master, then you can define the `cl` object using these instead (rather than supplying the IP addresses in your R script). This requires that the master node has itself and all the slave nodes defined in the `/etc/hosts` and `~/.ssh/config` files, while the slave nodes require themselves and the master node in the same files (only 2 IP addresses required on each slave). Following this setup, and assuming the user name is the same across all nodes, the `cl` object could instead be defined with

    library(parallel)
    primary <- 'master'
    IPs <- list(list(host=primary, ncore=8), list(host='slave', ncore=6))
    spec <- lapply(IPs, function(IP) rep(list(list(host=IP$host)), IP$ncore))
    spec <- unlist(spec, recursive=FALSE)
    cl <- makeCluster(master=primary, spec=spec, type = 'PSOCK')
    Final <- runSimulation(..., cl=cl)
    stopCluster(cl)

As was the case with the local cluster definition in the first section, random numbers are automatically organized via Pierre L’Ecuyer’s (1999) method to ensure quality number generation. A similar setup can also be used via the recently supported `future` interface (see below).

## 1.3 Using the `future` framework

The `future` framework (see `help(future, package = 'future')`) can also be used for distributing the asynchronous function evaluations for each simulation replication by changing the logical input in `runSimulation(..., parallel = TRUE/FALSE)` to the character vector `runSimulation(..., parallel = 'future')`. For this to work, the computation plan must be pre-specified via `future::plan()`. For example, to initialize a local two-worker parallel processing computational plan one can use the follow:

    library(future)
    plan(multisession, workers = 2)

    res <- runSimulation(design=Design, replications=1000, generate=Generate,
                         analyse=Analyse, summarise=Summarise,
                         parallel = 'future')

The benefit of using the `future` framework is the automatic support of many distinct back-ends, such as, for instance, HPC clusters that control the distribution of jobs via Slurm or TORQUE (e.g., see the `future.batchtools` package).

For progress reporting the `progressr` package is required and is intended as a wrapper around `runSimulation()`. Specifically, wrap the function `with_progress()` around `runSimulation()` after having specified the type of `handler()` to use, such as via the following.

    library(progressr)

    # Rstudio style handler (if using RStudio)
    handlers("rstudio")

    # or using the cli package for terminal-based progress
    handlers('cli')

    # See help(progressr) for additional options and details

    # to use progressr, wrap/pipe inside with_progress()
    res <- with_progress(runSimulation(design=Design, replications=1000, generate=Generate,
                         analyse=Analyse, summarise=Summarise,
                         parallel = 'future'))

Finally, when the parallel computations are complete be sure to manually reset the computation plan to free any workers via

    plan(sequential) # release workers

The benefit of the future framework is that it provides a unified distribution framework for parallel (multisession, multicore, cluster) and non-parallel (sequential) processing, manages random number generation correctly for Monte Carlo simulations, and as demonstrated above has a wide variety of tools that can be applied to a interactive sessions.