Execute

The [execute] section of a configuration file contains options used to control how tasks are executed within an MPAS-Analysis run:

[execute]
## options related to executing parallel tasks

# the number of parallel tasks (1 means tasks run in serial, the default)
parallelTaskCount = 1

# the parallelism mode in ncclimo ("serial" or "bck")
# Set this to "bck" (background parallelism) if running on a machine that can
# handle 12 simultaneous processes, one for each monthly climatology.
ncclimoParallelMode = serial

Parallel Tasks

By default, MPAS-Analysis runs one task at a time, displaying any logging output directly to the screen, rather than storing it in log files. However, the analysis will run much more quickly if parallel processes are used. For example, to run 12 tasks in parallel, simply set:

parallelTaskCount = 12

MPAS-Analysis currently supports parallelism through spawning processes, rather than with MPI, meaning that MPAS-Analysis should be run on a single node of a multi-node machine such as a cluster or supercomputer. Given that some tasks themselves spawn multiple threads and that some tasks are memory intensive, it may not be desirable to launch one task per core on a node with limited memory.

Because MPAS-Analysis does not use MPI parallelism, it can typically be run on the login nodes of supercomputing facilities. Check with the policies of your center to see if this is permitted and make sure not to run with a large number of parallel tasks so as to overwhelm the shared resource.

Parallelism in NCO

The ncclimo command from the NetCDF Operators (NCO) package is used internally in MPAS-Analysis. This command supports two options for parallelism, that are compatible with MPAS-Analysis: serial or bck. If set to serial, the default, any MPAS-Analysis tasks that use ncclimo will compute climatologies one month and then one season at a time. If bck mode is used, 12 threads are spawned, one for each month, and then separate threads are used to compute each season. Given that computing climatologies takes up a significant portion of the runtime in MPAS-Analysis, the speed-up of nearly a factor of 12 in these computations can be quite noticeable.

Again, when running MPAS-Analysis on login nodes of supercomputing facilities, it is important to be aware of the policies regarding using shared resources, and bck may only be appropriate when running jobs on the compute nodes.