用大模型+传统编译器+验证工具，提升代码优化准确率至83.3%。
T-LLM Compiler: Trusted LLM-based Code Optimization and Verification Framework
- 大模型生成优化代码，结合传统编译器与验证工具协同工作。
- 在PolyBench/C上实现最高16.1%加速，平均提速26.7%，正确率达83.3%。
- 适合关注代码优化自动化与可信性研究的开发者和研究人员。
大语言模型（LLMs）为代码优化提供了新可能，但受限于代码复杂性和缺乏独立验证能力，难以广泛落地。本文提出可信大模型编译框架T-LLM Compiler，融合高层大模型代码变换、传统编译器与验证工具，实现可验证的迭代优化。在PolyBench/C基准测试中，该方法将代码优化准确率提升至83.3%，最高获得16.1%的性能加速，平均相比标准基线提升26.7%。项目源码已开源。
原文摘要 · Abstract (English)
Recent advances in Large Language Models (LLMs) have opened opportunities to apply high-level code transformations to the field of code optimization, and it has since emerged as one of the most fundamental tasks for LLMs to perform; however, at present, LLMs struggle to apply wide-ranging code optimization tasks due to both the complexity of the code and the inability to independently verify the correctness of the transformations. In this paper, we present the Trusted LLM (T-LLM) Compiler, which proposes an advancement in compiler technology through a collaborative effort involving high-level LLM code transformations, traditional compilers, and verification tools. Experimental results reveal that it can significantly improve code correctness when tested on a set of PolyBench/C benchmarks. Our approach facilitates iterative code optimization efforts with verification strategies that enable corrective actions. Through this approach, T-LLM Compiler achieves code optimization accuracy of up to 83.3% and a speedup of up to 16.1\% on the PolyBench/C benchmarks, with the transformed code reaching an average of 26.7% speedup wrt standard baselines. Additionally, we release the project's source code to the open-source community.
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