发表机构
University of Chinese Academy of Sciences（中国科学院大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
CompileRover提出三角色（裁判、顾问、操作员）LLM驱动框架，通过控制流分析、结构转换和动态模式识别优化虚拟机编译器，显著提升执行性能并降低开销。
AI 中文摘要
代码优化在虚拟机编译器的开发中扮演着至关重要的角色，优化框架能够显著提升所生成汇编代码的性能。然而，现有的虚拟机编译器输出常常表现出冗余计算、低效的循环结构和次优的函数实现，这些共同损害了执行效率。为了解决这些不足，我们提出了CompileRover，一个专门为虚拟机编译器设计的高级优化框架。CompileRover采用了一种精妙的三角色协作机制，包括裁判（referee）、顾问（advisor）和操作员（operator），通过利用全面的优化算法和新颖的方法论（包括控制流分析、代码结构转换和动态执行模式识别）有效克服性能瓶颈。广泛的评估表明，CompileRover持续超越最先进的虚拟机编译器，在各种基准测试中实现了执行性能的显著提升。此外，性能分析验证了所引入的优化显著减少了执行开销，改善了数据流一致性，并强有力地增强了编译器性能，展示了CompileRover作为优化虚拟机编译器的一种有效且可靠的方法。
英文摘要
Code optimization plays a crucial role in the development of virtual machine compilers, with optimization frameworks significantly enhancing the performance of generated assembly code. However, existing virtual machine compiler outputs frequently exhibit redundant computations, inefficient loop structures, and suboptimal function implementations, which collectively impair execution efficiency. To address these shortcomings, we propose CompileRover, an advanced optimization framework specifically designed for virtual machine compilers. CompileRover employs a sophisticated three-role collaboration mechanism, comprising a referee, an advisor, and an operator, effectively overcoming performance bottlenecks by leveraging comprehensive optimization algorithms and novel methodologies, including control flow analysis, code structure transformations, and dynamic execution pattern recognition. Extensive evaluations demonstrate that CompileRover consistently surpasses state-of-the-art virtual machine compilers, achieving significant improvements in execution performance across various benchmarks. Furthermore, performance analyses validate that the introduced optimizations notably reduce execution overhead, improve dataflow consistency, and robustly enhance compiler performance, showcasing CompileRover as an effective and reliable approach to optimizing virtual machine compilers.
Comments20 pages
