Clustering Computing

smcsmc uses a lot of compute time. To ammeliorate this, it is highly recommend that users should make use of compute clusters, whether academic or otherwise. In this growing guide, we show how to leverage various compute cluster environments to best use smcsmc. Please help us to fill in this section if you use a different cluster computing system.

SGE / qsub

The code ships with native support for SGE clusters and usage is relatively straightforward. Procedure for PBS clusters should be almost identical, and only minor tweaking of model.py will give identical functionality.

Tip

It’s a good idea to run smcsmc in a tmux or screen session on the head node of your compute cluster. This will ensure that if your connection is interupted the program will not stopped.

1. If you wish to use a queue other than your default, place a qsub.conf file in the same directory that you want to conduct your analysis.
2. Specify a number of chunks in your input arguements. A good number to start with for typical human sequences is 100.
3. Tell smcsmc that you wish to spawn cluster jobs by specifying the c option. Note that this overrides the use of multithreading. If you wish to use specific parameters for your cluster session, the C flag can be used to provide specific directives and will take priority over all instructions given in qsub.conf.

That’s it!

As an example, the following would be added to any other input arguements that you wish to use:

cluster_args = {
   'c':      '',
   'chunks': '100',
   'C':      '-P your.prc -q short.prj'
}

For more information about how input arguements are handled by smcsmc see