# AI-PROPELLER: Warehouse-Scale Interprocedural Code Layout Optimization with AlphaEvolve

arXiv: https://arxiv.org/abs/2606.00131 · HTML: https://mlsanity.com/paper/2606.00131/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 32 references that are in the map.
- First posted on arXiv 28 May 2026.

## Builds on (most cited first, 8 of 8)

- [AlphaEvolve: A coding agent for scientific and algorithmic discovery](https://mlsanity.com/paper/2506.13131/) (2025)
- [Learning Performance-Improving Code Edits](https://mlsanity.com/paper/2302.07867/) (2023)
- [MLGO: a Machine Learning Guided Compiler Optimizations Framework](https://mlsanity.com/paper/2101.04808/) (2021)
- [Magellan: Autonomous Discovery of Novel Compiler Optimization Heuristics with AlphaEvolve](https://mlsanity.com/paper/2601.21096/) (2026)
- [BOLT: A Practical Binary Optimizer for Data Centers and Beyond](https://mlsanity.com/paper/1807.06735/) (2018)
- [Hardware Counted Profile-Guided Optimization](https://mlsanity.com/paper/1411.6361/) (2014)
- [Improved Basic Block Reordering](https://mlsanity.com/paper/1809.04676/) (2018)
- [Codestitcher: Inter-Procedural Basic Block Layout Optimization](https://mlsanity.com/paper/1810.00905/) (2018)

## Built on by (most cited first, 1 of 1)

- [Recursive Self-Improvement in AI: From Bounded Self-Refinement to Autonomous Research Loops](https://mlsanity.com/paper/2607.07663/) (2026)

## Abstract (arXiv)

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.