RL4ReAl: Reinforcement Learning for Register Allocation

Autor:	VenkataKeerthy, S., Jain, Siddharth, Kundu, Anilava, Aggarwal, Rohit, Cohen, Albert, Upadrasta, Ramakrishna
Rok vydání:	2022
Předmět:	Computer Science - Machine Learning Computer Science - Hardware Architecture Computer Science - Programming Languages D.2 I.2.5
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
Externí odkaz:	http://arxiv.org/abs/2204.02013 Zobrazit plný text záznamu View this record from Arxiv