Machines

One of the major advantages of compass over Legacy COMPASS is that it attempts to be aware of the capabilities of the machine it is running on. This is a particular advantage for so-called “supported” machines with a config file defined for them in the compass package. But even for “unknown” machines, it is not difficult to set a few config options in your user config file to describe your machine. Then, compass can use this data to make sure test cases are configured in a way that is appropriate for your machine.

config options

The config options typically defined for a machine are:

# The paths section describes paths that are used within the ocean core test
# cases.
[paths]

# A shared root directory where MPAS standalone data can be found
database_root = /lcrc/group/e3sm/public_html/mpas_standalonedata

# the path where shared compass environments are deployed
compass_envs = /lcrc/soft/climate/compass/chrysalis/base


# Options related to deploying compass environments on supported
# machines
[deploy]

# the compiler set to use for system libraries and MPAS builds
compiler = intel

# the compiler to use to build software (e.g. ESMF and MOAB) with spack
software_compiler = intel

# the system MPI library to use for intel compiler
mpi_intel = openmpi

# the system MPI library to use for gnu compiler
mpi_gnu = openmpi

# the base path for spack environments used by compass
spack = /lcrc/soft/climate/compass/chrysalis/spack

# whether to use the same modules for hdf5, netcdf-c, netcdf-fortran and
# pnetcdf as E3SM (spack modules are used otherwise)
use_e3sm_hdf5_netcdf = True

The paths section provides local paths to the root of the “databases” (local caches) of data files for each MPAS core. These are generally in a shared location for the project to save space. Similarly, compass_envs is a location where shared environments can be deployed for compass releases for users to share.

The deploy section is used by ./deploy.py to create pixi and Spack environments and load scripts. It says which compiler set is the default, which MPI library is the default for each supported compiler, and where libraries built with system MPI will be placed.

Some config options come from a package, mache that is a dependency of compass. mache is designed to detect and provide a machine-specific configuration for E3SM supported machines. Typical config options provided by mache that are relevant to compass are:

# The parallel section describes options related to running jobs in parallel
[parallel]

# parallel system of execution: slurm, cobalt or single_node
system = slurm

# whether to use mpirun or srun to run a task
parallel_executable = srun

# cores per node on the machine
cores_per_node = 36

# account for running diagnostics jobs
account = e3sm

# quality of service (default is the first)
qos = regular, interactive

The parallel section defined properties of the machine, to do with parallel runs. Currently, machine files are defined for high-performance computing (HPC) machines with multiple nodes. These machines all use Slurm job queueing to submit parallel jobs. They also all use the srun command to run individual tasks within a job. The number of cores_per_node vary between machines, as does the account that typical compass users will have access to on the machine.

Slurm job queueing

Most HPC systems now use the slurm workload manager. Here are some basic commands:

salloc -N 1 -t 2:0:0 # interactive job (see machine specific versions below)
sbatch script # submit a script
squeue # show all jobs
squeue -u <my_username> # show only your jobs
scancel jobID # cancel a job

Supported Machines

On each supported machine, ./deploy.py generates a load script for each compiler and MPI library you deploy, as described in Quick Start for Developers. Most machines support 2 compilers, each with one or more variants of MPI and the required NetCDF, pNetCDF and SCORPIO libraries. Sourcing a load script first activates the pixi environment for compass, then loads modules and sets environment variables that will allow you to build and run the MPAS model.

A table with the full list of supported machines, compilers, MPI variants, and MPAS-model build commands is found in Supported Machines in the Developer’s Guide. The links below give the config options for each machine.

• Chicoma
• Chrysalis
• CompyMcNodeFace
• Perlmutter

Other Machines

If you are working on an “unknown” machine, you will need to define some of the config options that would normally be in a machine’s config file yourself in your user config file:

# This file contains some common config options you might want to set

# The paths section describes paths to databases and shared compass environments
[paths]

# A root directory where MPAS standalone data can be found
database_root = /home/xylar/data/mpas/mpas_standalonedata

# The parallel section describes options related to running tests in parallel
[parallel]

# parallel system of execution: slurm or single_node
system = single_node

# whether to use mpirun or srun to run the model
parallel_executable = mpirun -host localhost

# cores per node on the machine, detected automatically by default
# cores_per_node = 4

The paths for the MPAS core “databases” can be any emtpy path to begin with. If the path doesn’t exist, compass will create it.

If you’re not working on an HPC machine, you will probably not have multiple nodes or Slurm job queueing. You will probably use MPICH or OpenMPI from the deployed pixi environment. In this case, the parallel_executable is mpirun.

To deploy compass on an unknown machine, run ./deploy.py --no-spack from the root of a clone of the repository, as described in Other Machines. You will then need to build the MPAS component with the compilers and libraries from the pixi environment.

On an unknown HPC machine, you would also need to load modules and set environment variables so that MPAS components can be built with system NetCDF, pNetCDF and SCORPIO. This will likely require working with an MPAS developer to add the machine to compass and mache, see Adding a New Supported Machine.