Post-link optimizers (PLOs) such as Propeller and BOLT have demonstrated that precise, profile-guided code layout can extract significant performance gains from heavily optimized binaries. However, these systems are currently restricted to intraprocedural techniques, leaving the global potential of interprocedural layout largely untapped. Interprocedural code layout is historically difficult due to a combinatorially intractable search space and complex call-return semantics that are challenging to model. Consequently, the performance potential of fine-grained interprocedural layout remains unproven in practice. AI-PROPELLER uses Magellan, an agentic workflow that evolves the compiler heuristic in Propeller into a fine-grained interprocedural optimizer and fine-tunes the resulting policy hyperparameters. To ensure high-fidelity, we move away from approximate static cost models and the agentic workflow generates multiple layout variants that are executed on actual hardware to measure real performance counters, providing a precise reward signal for the evolutionary loop. AI-PROPELLER has been evaluated on several benchmarks including large warehouse-scale applications and experiments show performance improvements of 0.23% to 1.6% optimized with state-of-the-art FDO and PLO which is significant for real-world binaries. This is the first time ever that large warehouse-scale applications in industrial settings have been optimized with fine-grained interprocedural code layout.

翻译：后链接优化器（PLO），如Propeller和BOLT，已证明精确的配置文件引导代码布局能从高度优化的二进制文件中提取显著的性能增益。然而，这些系统目前仅限于过程内技术，未能充分利用跨过程布局的全局潜力。由于组合爆炸式的搜索空间和难以建模的复杂调用返回语义，跨过程代码布局历来困难重重。因此，细粒度跨过程布局在实际中的性能潜力尚未得到验证。AI-PROPELLER采用Magellan这一智能体工作流，将Propeller中的编译器启发式方法演化为细粒度的跨过程优化器，并对生成的策略超参数进行微调。为确保高保真度，我们摒弃了近似的静态成本模型，智能体工作流生成多个布局变体，在真实硬件上执行以测量实际性能计数器，为进化循环提供精确的奖励信号。AI-PROPELLER在多个基准测试（包括大型仓库规模应用）上进行了评估，实验表明，使用最先进的FDO和PLO优化后，性能提升达0.23%至1.6%，这对实际二进制文件来说意义重大。这是首次在工业环境中对大型仓库规模应用采用细粒度跨过程代码布局进行优化。