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]

# The root to a location where the mesh_database, initial_condition_database,
# and bathymetry_database for MPAS-Ocean will be cached
ocean_database_root = /usr/projects/regionalclimate/COMMON_MPAS/ocean/grids/

# The root to a location where the mesh_database and initial_condition_database
# for MALI will be cached
landice_database_root = /usr/projects/regionalclimate/COMMON_MPAS/mpas_standalonedata/mpas-albany-landice

# the path to the base conda environment where compass environments have
# been created
compass_envs = /usr/projects/climate/SHARED_CLIMATE/compass/badger/base


# Options related to deploying a compass conda environment on supported
# machines
[deploy]

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

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

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

# the base path for spack environments used by compass
spack = /usr/projects/climate/SHARED_CLIMATE/compass/badger/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 = False

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 conda environments will be created for compass releases for users to share.

The deploy section is used to help compass create development and release conda environments and activation 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, users will be able to source a script to activate the appropriate compass environment and compilers. Most machines support 2 compilers, each with one or more variants of MPI and the required NetCDF, pNetCDF and SCORPIO libraries. These scripts will first load the conda environment for compass, then it will load modules and set 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. In the links below, we list only the commands needed to use the default MPI variant for each compiler on each machine.

• Anvil
• Badger
• Chrysalis
• CompyMcNodeFace
• Cori

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:

[paths]

# The root to a location where the mesh_database, initial_condition_database,
# and bathymetry_database for MPAS-Ocean will be cached
ocean_database_root = /home/xylar/data/mpas/mpas_standalonedata/mpas-ocean

# The root to a location where the mesh_database and initial_condition_database
# for MALI will be cached
landice_database_root = /home/xylar/data/mpas/mpas_standalonedata/mpas-albany-landice


# 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 install MPICH or OpenMPI, probably via a conda environment. In this case, the parallel_executable is mpirun.

To install the compass package into a conda environment, you will first need to install Miniconda3 (if miniconda is not already installed), then add the conda-forge channel:

conda config --add channels conda-forge
conda config --set channel_priority strict

Then, you will run one of the following three commands, depending on how you would like to handle MPI support in the conda packages.

MPICH

To create a conda environment called “compass” with MPI from the mpich package, run:

conda create -n compass -c conda-forge -c e3sm/label/compass python=3.9 "compass=*=mpi_mpich*"

This is the recommended default for single-node Linux and OSX machines.

OpenMPI

To create a conda environment called “compass” with MPI from the openmpi package, run:

conda create -n compass -c conda-forge -c e3sm/label/compass python=3.9 "compass=*=mpi_openmpi*"

No MPI from conda-forge

To create a conda environment called “compass” without any MPI package from conda-forge, run:

conda create -n compass -c conda-forge -c e3sm/label/compass python=3.9 "compass=*=nompi*"

This would be the starting point for working with compass on an unknown HPC machine. From there, 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.