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

AI-PROPELLER introduces the first warehouse-scale interprocedural code layout optimization system, using an evolutionary AI workflow to improve binary performance by 0.23-1.6% beyond existing post-link optimizers. This breakthrough applies machine learning to compiler optimization in industrial production environments, achieving measurable real-world performance gains.

AI-PROPELLER represents a meaningful advancement in compiler optimization by tackling interprocedural code layout—a historically unsolved problem due to combinatorial complexity and modeling challenges. Previous post-link optimizers like Propeller and BOLT succeeded with intraprocedural techniques, but the global optimization potential across function boundaries remained inaccessible. This system bridges that gap through an agentic workflow powered by AlphaEvolve, which iteratively refines compiler heuristics and hyperparameters using actual hardware execution rather than approximate static models.

The research emerges from years of optimization theory hitting diminishing returns. As binaries become increasingly optimized through traditional methods, extracting additional performance requires increasingly sophisticated approaches. The shift toward hardware-measured reward signals rather than theoretical cost models demonstrates a maturing field recognizing that real-world performance diverges from static predictions.

For enterprise infrastructure operators, these incremental gains matter substantially. In warehouse-scale computing, even 0.5% performance improvement translates to measurable power consumption reductions and throughput increases across thousands of servers. The methodology's deployment on production applications validates its practical viability, unlike academic techniques that fail under real constraints.

The approach signals a broader trend: compiler optimization is transitioning from hand-crafted heuristics toward AI-driven policy learning. This has implications for infrastructure teams seeking to maximize efficiency from existing hardware without costly upgrades. Further development could enable automatic adaptation to specific workload characteristics, making optimization more responsive to dynamic environments than current static approaches.

• →AI-PROPELLER achieves 0.23-1.6% performance gains on heavily optimized warehouse-scale binaries using interprocedural code layout optimization.
• →The system uses actual hardware execution to measure performance rather than static cost models, enabling more precise optimization feedback.
• →This is the first successful application of fine-grained interprocedural layout optimization in industrial production environments at scale.
• →The evolutionary workflow improves upon existing post-link optimizers by handling global function-level layout previously considered combinatorially intractable.
• →Results demonstrate that AI-driven compiler optimization can extract meaningful gains even from state-of-the-art profile-guided code binaries.