PolyGym: Polyhedral Optimizations as an Environment for Reinforcement Learning

Author:

Brauckmann Alexander,Goens Andres,Castrillon Jeronimo

Funder

German Federal Ministry of Education and Research (BMBF)

Publisher

IEEE

Cited by 7 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. An Experimental Analysis of RL based Compiler Optimization Techniques using Compiler GYM;2024 IEEE 9th International Conference for Convergence in Technology (I2CT);2024-04-05

2. The Next 700 ML-Enabled Compiler Optimizations;Proceedings of the 33rd ACM SIGPLAN International Conference on Compiler Construction;2024-02-17

3. Automatic Creation of High-bandwidth Memory Architectures from Domain-specific Languages: The Case of Computational Fluid Dynamics;ACM Transactions on Reconfigurable Technology and Systems;2023-03-11

4. Reinforcement Learning Strategies for Compiler Optimization in High level Synthesis;2022 IEEE/ACM Eighth Workshop on the LLVM Compiler Infrastructure in HPC (LLVM-HPC);2022-11

5. Towards intelligent compiler optimization;2022 45th Jubilee International Convention on Information, Communication and Electronic Technology (MIPRO);2022-05-23

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