RL4ReAl: Reinforcement Learning for Register Allocation

• URL: http://arxiv.org/abs/2204.02013v1
• Date: Tue, 5 Apr 2022 06:30:03 GMT
• Title: RL4ReAl: Reinforcement Learning for Register Allocation
• Authors: S. VenkataKeerthy, Siddharth Jain, Rohit Aggarwal, Albert Cohen, Ramakrishna Upadrasta
• Abstract summary: We propose a novel solution for the Register Allocation problem, leveraging multi-agent hierarchical Reinforcement Learning. 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.
• Score: 2.449909275410288
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We propose a novel solution for the Register Allocation problem, leveraging multi-agent hierarchical Reinforcement Learning. 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. Experimental results match or outperform the LLVM register allocators, targeting Intel x86 and ARM AArch64.

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