Google Research

RL4ReAl: Reinforcement Learning for Register Allocation

  • S. VenkataKeerthy
  • Siddharth Jain
  • Anilava Kundu
  • Rohit Aggarwal
  • Albert Cohen
  • Ramakrishna Upadrasta
CC 2023, ACM


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.

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