CABLE benchmarking

Repository to benchmark CABLE. The benchmark will run the exact same configurations on two CABLE branches specified by the user, e.g. a user branch (with personal changes) against the head of the trunk. The results should be attached with all new tickets.

The code will: (i) check out; (ii) build; and (iii) run branches across N standard science configurations. It is possible to produce some plots locally. But the outputs should be uploaded to the modelevaluation website for further benchmarking and evaluation.

For the moment, the benchmarking only works on NCI supercomputer.

Permissions

To run the benchmarking, you will need access to the following projects at NCI:

• ks32
• wd9
• hh5

You can request access via my.nci.org.au. All of those projects accept all requests by default.

Create a work directory.

1. Choose a work directory. You can run the benchmark from any directory you want under /scratch or /g/data. /scratch is preferred as the data in the run directory does not need to be preserved for the long run. The code will create sub-directories as needed. Please ensure you have enough space to store the CABLE outputs in your directory, at least temporary until you upload them to modelevaluation.org. You will need about 70GB.

2. Create a config file in the work directory. The default name is config.yaml but any name can be specified at run time. This file follows the YAML format. You can find an example configuration file here

3. Create a science configuration file in the work directory. The default name is site_configs.yaml but any name can be specified at run time. This file follows the YAML format. You can find an example science configuration file here

Run the benchmarking

Start the benchmarking process

Once you have a configuration file, you need to load the modules for Python:

module use /g/data/hh5/public/modules
module load conda/analysis3-unstable

Then you simply launch the benchmarking:

benchcab

For help on the available options for the benchmarking:

benchcab -h

Note: This line

module use /g/data/hh5/public/modules

can be safely added anywhere in your $HOME/.bashrc file. You then need to log out and back in for it to take effect. If you do so, you can simply load the Python module with module load and you do not have to type the module use line everytime. It is not recommended to put any module load lines in your $HOME/.bashrc file however.

Check the status

The benchmarking will follow the steps:

1. Checkout both branches. The codes will be stored under src/ directory in your work directory. The directories are created automatically.
2. Compile the source code from both branches
3. Setup and launch a PBS job to run the simulations in parallel. The simulation inputs and outputs are stored under runs/site/. The directories are created automatically.

When the benchmarking will launch the PBS job, it will print out the job id to screen. You can check the status of the job with qstat.

Use modelevaluation.org

Once the benchmarking has finished running all the simulations, you need to upload the output files to modelevaluation.org. The output files can be found under runs/site/outputs.

Process still to be documented

Contacts

Please enter your questions as issues or discussions on the Github repository.