RL4ReAl: Reinforcement Learning for Register Allocation
Autor: | VenkataKeerthy, S., Jain, Siddharth, Kundu, Anilava, Aggarwal, Rohit, Cohen, Albert, Upadrasta, Ramakrishna |
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Rok vydání: | 2022 |
Předmět: | |
Druh dokumentu: | Working Paper |
DOI: | 10.1145/3578360.3580273 |
Popis: | We aim to automate decades of research and experience in register allocation, leveraging machine learning. We tackle this problem by embedding a multi-agent reinforcement learning algorithm within LLVM, training it with the state of the art techniques. We formalize the constraints that precisely define the problem for a given instruction-set architecture, while ensuring that the generated code preserves semantic correctness. We also develop a gRPC based framework providing a modular and efficient compiler interface for training and inference. Our approach is architecture independent: we show experimental results targeting Intel x86 and ARM AArch64. Our results match or out-perform the heavily tuned, production-grade register allocators of LLVM. Comment: Published in CC'23 |
Databáze: | arXiv |
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