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arXiv Machine Learning By Mingqiao Mo, Yunlong Tan, Hao Zhang

CompileRover: Revolutionizing Virtual Machine Compiler Optimization with a Tri-Role LLM-Driven Framework

Read the original on arXiv Machine Learning →

CompileRover is a new optimization framework for virtual machine compilers that uses a tri‑role LLM‑driven collaboration mechanism involving a referee, an advisor, and an operator. It tackles common issues such as redundant computations, inefficient loops, and suboptimal function implementations by applying control‑flow analysis, code‑structure transformations, and dynamic execution pattern recognition. Benchmark evaluations show that CompileRover consistently outperforms existing virtual machine compilers, reducing execution overhead, improving data‑flow consistency, and enhancing overall compiler performance.

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