parcels-benchmarks

This repository houses performance benchmarks for Parcels.

Development instructions

• install Pixi curl -fsSL https://pixi.sh/install.sh | bash
• pixi install
• pixi run asv run

You can run the linting with pixi run lint

Important

The default path for the benchmark data is set by pooch.os_cache, which typically is a subdirectory of your home directory. Currently, you will need at least 50GB of disk space available to store the benchmark data. To change the location of the benchmark data cache, you can set the environment variable PARCELS_DATADIR to a preferred location to store the benchmark data.

To view the benchmark data

• pixi run asv publish
• pixi run asv preview

Contributing benchmark runs

We value seeing how Parcels benchmarks perform on a variety of systems. When you run the benchmarks, this adds data to the results/ subdirectory in this repository. After running the benchmarks, you can commit the changes made to the results/ subdirectory and open a pull request to contribute your benchmark results.

Parcels Community Members

Members of the Parcels community can contribute benchmark data using the following steps

1. Create a fork of this repository

2. Clone your fork onto your system

git clone [email protected]:<your-github-handle>/parcels-benchmarks.git ~/parcels-benchmarks

3. Run the benchmarks

cd ~/parcels-benchmarks
pixi run asv run

4. Commit your benchmark data and push the changes back to your fork, e.g.

git add results
git commit -m "Add benchmark data"
git push origin main

5. Open a pull request from your fork

Adding benchmarks

Adding benchmarks for parcels typically involves adding a dataset and defining the benchmarks you want to run.

Data is hosted remotely on a SurfDrive managed by the Parcels developers. You will need to open an issue on this repository to start the process of getting your data hosted in the shared SurfDrive. Once your data is hosted, you can add an entry to the parcels_benchmarks.benchmark_setup.DATA_FILES list. Each entry has the following attributes

{
      "name": str # Name of the dataset that you can reference in the benchmarks
      "file": str, # Path, relative to the data_url, to the .zip file containing the benchmark data
      "known_hash": str | None # Pooch hash of the zip file; set to None if it is unknown
},

This repository uses ASV for running benchmarks. You can add benchmarks by including a python script in the benchmarks/ subdirectory. Within each benchmarks/*.py file, we ask that you define a class for the set of benchmarks you plan to run for your dataset. You can use the existing benchmarks as a good starting point for writing your benchmarks.

To learn more about writing benchmarks compatible with ASV, see the ASV "Writing Benchmarks" documentation