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
qsub.py acts as a GPAW-aware front-end to the queue system.
qsub.py [-h] [-p PROCESSES] [-t TIME] [-z] script [argument [argument ...]]
Submit a GPAW Python script via qsub.
- positional arguments:
- Python script
- Command-line argument for Python script.
- optional arguments:
-h, --help show this help message and exit -p PROCESSES, --processes PROCESSES Number of processes. -t TIME, --time TIME Max running time in hours. -z, --dry-run Don’t actually submit script.
$ qsub.py -p 8 -t 4 script.py # 8 cores, 4 hours $ qstat -u <username> ...
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:
python3 myscript.py --gpaw dry-run=1
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:
python3 myscript.py --gpaw dry-run=PROCESSES
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.