1 Cluster computing

SimDesign code may be released to a computing system which supports parallel cluster computations using the industry standard Message Passing Interface (MPI) form. This simply requires that the computers be setup using the usual MPI requirements (typically, running some flavor of Linux, have password-less open-SSH access, IP addresses have been added to the /etc/hosts file or ~/.ssh/config, etc). More generally though, these resources are widely available through professional organizations dedicated to super-computing.

To setup the R code for an MPI cluster the argument MPI = TRUE needs to be added to the extra_options list input, which wraps the appropriate MPI directives around runSimulation. At this point the source files can be submitted using suitable BASH commands to execute the mpirun tool. For example,

cl <- startMPIcluster()
runSimulation(design=Design, replications=1000, filename='mysimulation',
    generate=Generate, analyse=Analyse, summarise=Summarise, extra_options = list(MPI=TRUE))

The necessary SimDesign files must be uploaded to the dedicated master node so that a BASH call to mpirun can be used to distribute the work across slaves. For instance, if the following BASH command is run on the master node then 16 processes will be summoned (1 master, 15 slaves) across the computers named localhost, slave1, and slave2 in the ssh config file.

mpirun -np 16 -H localhost,slave1,slave2 R --slave -f simulation.R

A similar setup can also be used via the recently supported future interface (see below).

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. This approach is similar to MPI computing approach except that it offers more localized control and requires more hands-on administrative access to the master and slave nodes. 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 defined on the master node.

Setup for network computing is generally more straightforward and controlled than the setup for MPI jobs 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). However, on Linux I have found it is also 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 makeCluster()) on the master node is not hindered by a firewall. As an example, using the following code the master node (primary) will spawn 7 slaves and 1 master, while a separate computer on the network with the associated IP address will spawn an additional 6 slaves. Information will be collected on the master node, which is also where the files and objects will be saved using the save inputs.

primary <- ''
IPs <- list(list(host=primary, user='myname', ncore=8), list(host='', 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)

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.

Alternatively, 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

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)

A similar setup can also be used via the recently supported future interface (see below).

3 Poor man’s cluster computing for independent nodes

In the event that you do not have access to a Beowulf-type cluster (described in the section on “Network Computing”) but have multiple personal computers then the simulation code can be manually distributed across each independent computer instead.

This simply requires passing a smaller value to the replications argument on each computer and later aggregating the results using the aggregate_simulations() function. For instance, if you have two computers available on different networks and wanted a total of 500 replications you could pass replications = 300 to one computer andreplications = 200 to the other along with a filename argument (or simply saving the final objects as .rds files manually after runSimulation() has finished). This will create two distinct .rds files which can be combined later with the aggregate_simulations() function. The benefit of this approach over MPI or setting up a Beowulf cluster is that computers need not be linked on the same network, and, should the need arise, the temporary simulation results can be migrated to another computer in case of a complete hardware failure by moving the saved temp files to another node, modifying the suitable compname input to save_details (or, if the filename and tmpfilename were modified, matching those files accordingly), and resuming the simulation as normal.

Note that this is also a useful tactic if the MPI or Network computing options require you to submit smaller jobs due to time and resource constraint-related reasons, where fewer replications/nodes should be requested. After all the jobs are completed and saved to their respective files, aggregate_simulations() can then collapse the files as if the simulations were run all at once. Hence, SimDesign makes submitting smaller jobs to super-computing resources considerably less error prone than managing a number of smaller jobs manually.

4 Using the future framework

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

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.


# Rstudio style handler (if using RStudio)

# or using the cli package for terminal-based progress 

# 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