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RL4ReAl: Reinforcement Learning for Register Allocation
- Publication Year :
- 2022
-
Abstract
- 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.<br />Comment: Published in CC'23
Details
- Database :
- arXiv
- Publication Type :
- Report
- Accession number :
- edsarx.2204.02013
- Document Type :
- Working Paper
- Full Text :
- https://doi.org/10.1145/3578360.3580273