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...

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Vydané v:	2021 30th International Conference on Parallel Architectures and Compilation Techniques (PACT) s. 17 - 29
Hlavní autori:	Brauckmann, Alexander, Goens, Andres, Castrillon, Jeronimo
Médium:	Konferenčný príspevok..
Jazyk:	English
Vydavateľské údaje:	IEEE 01.09.2021
Predmet:	Benchmark testing loop scheduling machine learning Machine learning algorithms Markov processes PolyGym polyhedral optimization Reinforcement learning Schedules Shape Time measurement
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