Synergy-Guided Compiler Auto-Tuning of Nested LLVM Pass Pipelines

2025-10-15

AI Summary

Existing automated pass-tuning methods assume linear pass sequences, rendering them incompatible with LLVM’s new Pass Manager—which employs a nested hierarchical structure—thus yielding syntactically invalid pipelines. Method: We propose the first automated tuning framework for nested optimization pipelines: (i) we formally define a context-free grammar specifying syntactically valid pipelines; (ii) we introduce a forest-based data structure to natively represent nested pass topologies; and (iii) we design a structure-aware, co-evolutionary genetic algorithm augmented with a local refinement mechanism. Results: Evaluated across seven benchmark suites, our generated optimization policies reduce instruction count by 13.62% on average over `opt -Oz`, demonstrating substantial improvement in discovering high-performance, syntactically valid pass combinations under complex structural constraints.

Technology Category

• Search and Optimization: Sampling/Simulation-based Search
• Machine Learning: Optimization
• Natural Language Processing: Code Generation / Program Synthesis from Natural Language

Application Category

• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Compiler optimization relies on sequences of passes to improve program performance. Selecting and ordering these passes automatically, known as compiler auto-tuning, is challenging due to the large and complex search space. Existing approaches generally assume a linear sequence of passes, a model compatible with legacy compilers but fundamentally misaligned with the hierarchical design of the LLVM New Pass Manager. This misalignment prevents them from guaranteeing the generation of syntactically valid optimization pipelines. In this work, we present a new auto-tuning framework built from the ground up for the New Pass Manager. We introduce a formal grammar to define the space of valid nested pipelines and a forest-based data structure for their native representation. Upon this foundation, we develop a structure-aware Genetic Algorithm whose operators manipulate these forests directly, ensuring that all candidate solutions are valid by construction. The framework first mines synergistic pass relationships to guide the search. An optional refinement stage further explores subtle performance variations arising from different valid structural arrangements. We evaluate our approach on seven benchmark datasets using LLVM 18.1.6. The discovered pipelines achieve an average of 13.62% additional instruction count reduction compared to the standard opt -Oz optimization level, showing that our framework is capable of navigating this complex, constrained search space to identify valid and effective pass pipelines.

Problem

• Automating selection and ordering of LLVM compiler passes
• Ensuring generation of syntactically valid nested optimization pipelines
• Navigating large complex search space for performance improvements

Innovation

• Formal grammar defines valid nested pass pipelines
• Forest-based data structure represents optimization pipelines natively
• Structure-aware genetic algorithm ensures valid pipeline construction