Parallel CASA for radio astronomy¶

A Bit About CASA¶

CASA (Common Astronomy Software Applications) is the primary software for reducing radio interferometry data from telescopes such as the Jansky Very Large Array (VLA) and the Atacama Large Millimeter/submillimeter Array (ALMA).

Getting CASA: module on Hopper, direct path on Easley¶

On Hopper, CASA is a working module — just load it:

module load casa/6.5.2

On Easley, there is currently no casa module at all (module avail casa and module spider casa both come back empty — not broken, just not provided there). The same CASA install is still present on disk though, so set up an alias to it instead:

CASA_DIR=/opt/local/casa/6.5.2/casa-6.5.2-26-py3.8
alias casa="$CASA_DIR/bin/casa"
alias mpicasa="$CASA_DIR/bin/mpicasa"

Either way, confirm you have a working casa before going further:

casa --version
# CASA 6.5.2.26

Getting Compute Resources with Slurm¶

Both clusters use Slurm, not PBS — there is no $PBS_NODEFILE.

Easley's partitions (per sinfo): general, bigmem, h100, l40s, interactive, debug, scavenger. Hopper's: general, debug. CASA doesn't need a GPU, so general or debug (for short test runs) is the right choice on either cluster — there is no singleGPU partition on either, and you don't need one.

Request an interactive session to test in:

srun --partition debug --time=00:30:00 --ntasks=1 --cpus-per-task=2 --pty bash

Then load the module (Hopper) or set the alias (Easley) as shown above.

Running CASA Non-Interactively¶

CASA scripts are plain Python. Save a script, e.g. hello.py:

print("Hello from inside CASA")
print(2 + 2)

Run it with -c, --nogui, and --log2term (so output goes to your terminal/log instead of a GUI logger window):

casa --nologger --nogui --log2term -c hello.py

Tested end-to-end on both clusters (srun --partition debug, single task) and produces output including:

CASA 6.5.2.26 -- Common Astronomy Software Applications [6.5.2.26]
Hello from inside CASA
4

Running MPI-Parallel CASA with `mpicasa`¶

CASA ships its own mpicasa wrapper (a thin layer over the OpenMPI build bundled with CASA) for running CASA in parallel across multiple MPI ranks/nodes. Request more than one task from Slurm and pass the rank count to mpicasa with -n:

srun --partition debug --time=00:30:00 --ntasks=2 --cpus-per-task=2 --pty bash

mpicasa -n 2 casa --nologger --nogui --log2term -c hello.py

Tested end-to-end on Easley across 2 tasks (Slurm placed them on two separate nodes) and each rank correctly ran the script and printed:

Hello from inside CASA
4

Note: at the end of the run you'll likely see a message like:

mpirun has exited due to process rank 0 with PID ... exiting improperly...

This is expected/benign — it happens because CASA's script mode doesn't call MPI_Finalize() cleanly on exit, not because anything failed. As long as your script's own output appears for every rank (as it does above), the run succeeded.

Getting Data to Work With¶

For real reduction work, download a dataset from the NRAO Science Data Archive at data.nrao.edu — pick something small (a few GB) to start with. This QuickByte only demonstrates the pipeline (Slurm → CASA → your script); it does not cover calibration/imaging workflows, which are extensively documented in NRAO's own CASA docs.

This quickbyte was tested against casa/6.5.2 on both Easley (direct path) and Hopper (module).

Migrated from UNM-CARC QuickBytes (last source update 2021-03-08). Spotted a problem? Open an issue or pull request.

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