Agentic Auto-Scheduling: An Experimental Study of LLM-Guided Loop Optimization

2025-11-01

AI Summary

Automated optimization of complex nested loops on modern hardware remains challenging due to the combinatorial complexity of legal and profitable loop transformations. Method: This paper proposes an LLM-guided closed-loop compilation optimization framework that leverages a general-purpose large language model—without fine-tuning or in-context examples—as an intelligent agent. Grounded in the polyhedral model, the LLM generates loop transformation schedules, which are iteratively refined using real-time compiler feedback on both performance speedup and semantic correctness. Contribution/Results: To our knowledge, this is the first zero-shot, feedback-driven autonomous scheduling approach, introducing the embodied intelligence paradigm into compiler optimization. Evaluated on the PolyBench benchmark, it achieves an average 2.66× speedup per run and up to 3.54× after five iterations—substantially outperforming state-of-the-art tools such as Pluto—demonstrating the feasibility and superiority of LLM–compiler co-optimization for efficient, reliable automatic loop optimization.

Technology Category

• Search and Optimization: Learning to Search
• Planning, Routing, and Scheduling: Planning with Language Models
• Natural Language Processing: Learning & Optimization for NLP

Application Category

• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

Automatic code optimization remains a difficult challenge, particularly for complex loop nests on modern hardware. This paper investigates a novel approach to code optimization where Large Language Models (LLMs) guide the process through a closed-loop interaction with a compiler. We present ComPilot, an experimental framework that leverages off-the-shelf LLMs, without any task-specific fine-tuning, as interactive optimization agents. ComPilot establishes a feedback loop where an LLM proposes transformations for a given loop nest to a compiler. The compiler attempts the transformations, reporting back legality status and measured speedup or slowdown. The LLM utilizes this concrete feedback to iteratively refine its optimization strategy. Our extensive evaluation across the PolyBench benchmark suite demonstrates the effectiveness of this zero-shot approach. ComPilot achieves geometric mean speedups of 2.66x (single run) and 3.54x (best-of-5 runs) over the original code. Furthermore, ComPilot demonstrates competitive performance against the state-of-the-art Pluto polyhedral optimizer, outperforming it in many cases. This experimental study demonstrates that general-purpose LLMs can effectively guide the code optimization process when grounded by compiler feedback, opening promising research directions for agentic AI in code optimization.

Problem

• LLMs guide loop optimization via compiler feedback loop
• Zero-shot approach achieves speedups over original code
• Competes with state-of-the-art polyhedral optimizer performance

Innovation

• LLMs guide code optimization via closed-loop compiler interaction
• Compiler feedback refines LLM optimization strategies iteratively
• Zero-shot approach achieves competitive performance against state-of-the-art