Submitting jobs on the DTU computers¶
Smaller calculations can be run in a Jupyter Notebook, but larger calculations require running on multiple CPU cores for an extended time. Such jobs should be submitted with the MyQueue tool. MyQueue is a unified frontend for a number of different queuing systems available on HPC installations. It supports submittting individual jobs as well as complete workflows.
mq acts as a front-end to the queue system.
mq submit -R CORES:TIME script
Submit a GPAW Python script via the configured queueing system.
- positional arguments:
Command-line argument for Python script.
- selected optional arguments:
- -h, --help
show help message and exit
- -n NAME, --name NAME
Name used for task.
- -R RESOURCES, --resources RESOURCES
Examples: “8:1h”, 8 cores for 1 hour. Use “m” for minutes, “h” for hours and “d” for days. “16:1:30m”: 16 cores, 1 process, half an hour.
- -z, --dry-run
Show what will happen without doing anything.
- -v, --verbose
- -q, --quiet
$ mq submit -R 8:4h script.py # 8 cores, 4 hours $ mq list $ qstat hpc ...
The last command shows the user’s jobs in the hpc queue, which is the
queue we use for the summer school.
mq list and
qstat hpc give
some of the same information.
Choosing the number of processes¶
GPAW parallelizes most efficiently over k-points, so it is a good idea to make the number of processes a divisor of the number of irreducible k-points. If you have 12 irreducible k-points, the calculation parallelizes well on 2, 3, 4, 6 or 12 processes.
If you have very few irreducible k-points you may need to have more processes than k-points; in these cases GPAW choose other parallelization strategies. In this case, it is an advantage to make the number of processes a multiple of the number of irreducible k-points.
Dry run: Let GPAW help you choosing¶
If you run your script with the command:
$ gpaw python --dry-run=1 myscript.py
then your script will execute until the first GPAW calculation. That
calculation will print information into the
.txt file, and then stop. In
the file, you can see the number of irreducible k-points and use it to select
your parallelization strategy.
Once you have decided how many processes you want, run another dry-run to check how GPAW will parallelize:
$ gpaw python --dry-run=PROCESSES myscript.py
PROCESSES is the number of processes you want to use. In this case,
gpaw will print how it will parallelize the calculation when running for real.