PolyGym: Polyhedral Optimizations as an Environment for Reinforcement Learning

The polyhedral model allows a structured way of defining semantics-preserving transformations to improve the performance of a large class of loops. Finding profitable points in this space is a hard problem which is usually approached by heuristics that generalize from domain-expert knowledge. Existi...

Ausführliche Beschreibung

Bibliographische Detailangaben

Veröffentlicht in:	2021 30th International Conference on Parallel Architectures and Compilation Techniques (PACT) S. 17 - 29
Hauptverfasser:	Brauckmann, Alexander, Goens, Andres, Castrillon, Jeronimo
Format:	Tagungsbericht
Sprache:	Englisch
Veröffentlicht:	IEEE 01.09.2021
Schlagworte:	Benchmark testing loop scheduling machine learning Machine learning algorithms Markov processes PolyGym polyhedral optimization Reinforcement learning Schedules Shape Time measurement
Online-Zugang:	Volltext
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!