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pyATF: Constraint-Based Auto-Tuning in Python

Richard Schulze, Sergei Gorlatch, Ari Rasch
University of Münster, Muenster, Germany
34th ACM SIGPLAN International Conference on Compiler Construction (CC’25), 2025

@inproceedings{schulze2025pyatf,

   title={pyATF: Constraint-Based Auto-Tuning in Python},

   author={Schulze, Richard and Gorlatch, Sergei and Rasch, Ari},

   booktitle={Proceedings of the 34th ACM SIGPLAN International Conference on Compiler Construction},

   pages={35–47},

   year={2025}

}

We introduce pyATF – a new, language-independent, open-source auto-tuning tool that fully automatically determines optimized values of performance-critical program parameters. A major feature of pyATF is its support for constrained parameters, e.g., the value of one parameter has to divide the value of another parameter. A further major feature of pyATF is its user interface which is designed with a particular focus on expressivity and usability for real-world demands, and which is offered in the increasingly popular Python programming language. We experimentally confirm the practicality of pyATF using real-world studies from the areas of quantum chemistry, image processing, data mining, and deep learning: we show that pyATF auto-tunes the complex parallel implementations of our studies to higher performance than achieved by state-of-practice approaches, including hand-optimized vendor libraries.
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