pyATF: Constraint-Based Auto-Tuning in Python

Schulze, Richard; Gorlatch, Sergei; Rasch, Ari

Forschungsartikel in Online-Sammlung (Konferenz) | Peer reviewed

Zusammenfassung

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.

Details zur Publikation

Name des RepositoriumsACM Digital Library
StatusVeröffentlicht
Veröffentlichungsjahr2025
Konferenz34th ACM SIGPLAN International Conference on Compiler Construction, Las Vegas, Vereinigte Staaten
DOI10.1145/3708493.3712682
StichwörterCompilers, Auto-tuning; Performance optimization; Constrained parameters;

Autor*innen der Universität Münster

Gorlatch, Sergei
Professur für Praktische Informatik (Prof. Gorlatch)
Institut für Informatik
Rasch, Ari
Professur für Praktische Informatik (Prof. Gorlatch)
Institut für Informatik
Schulze, Richard Heinrich Hermann
Professur für Praktische Informatik (Prof. Gorlatch)
Institut für Informatik