# Protean Compiler: An Agile Framework to Drive Fine-grain Phase Ordering

arXiv: https://arxiv.org/abs/2602.06142 · HTML: https://mlsanity.com/paper/2602.06142/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 50 references that are in the map.
- First posted on arXiv 5 Feb 2026.

## Builds on (most cited first, 11 of 11)

- [Qwen2.5-Coder Technical Report](https://mlsanity.com/paper/2409.12186/) (2024)
- [Large Language Models for Compiler Optimization](https://mlsanity.com/paper/2309.07062/) (2023)
- [CompilerGym: Robust, Performant Compiler Optimization Environments for AI Research](https://mlsanity.com/paper/2109.08267/) (2021)
- [A Survey on Compiler Autotuning using Machine Learning](https://mlsanity.com/paper/1801.04405/) (2018)
- [MLGO: a Machine Learning Guided Compiler Optimizations Framework](https://mlsanity.com/paper/2101.04808/) (2021)
- [NeuroVectorizer: End-to-End Vectorization with Deep Reinforcement Learning](https://mlsanity.com/paper/1909.13639/) (2019)
- [Static Neural Compiler Optimization via Deep Reinforcement Learning](https://mlsanity.com/paper/2008.08951/) (2020)
- [AutoPhase: Compiler Phase-Ordering for High Level Synthesis with Deep Reinforcement Learning](https://mlsanity.com/paper/1901.04615/) (2019)
- [MLGOPerf: An ML Guided Inliner to Optimize Performance](https://mlsanity.com/paper/2207.08389/) (2022)
- [Enabling more efficient and cost-effective AI/ML systems with Collective Mind, virtualized MLOps, MLPerf, Collective Knowledge Playground and reproducible optimization tournaments](https://mlsanity.com/paper/2406.16791/) (2024)
- [Framing AI System Benchmarking as a Learning Task: FlexBench and the Open MLPerf Dataset](https://mlsanity.com/paper/2509.11413/) (2025)

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

- [T-LLM Compiler: Trusted LLM-based Code Optimization and Verification Framework](https://mlsanity.com/paper/2608.14953/) (2026)

## Abstract (arXiv)

The phase ordering problem has been a long-standing challenge since the late 1970s, yet it remains an open problem due to having a vast optimization space and an unbounded nature, making it an open-ended problem without a finite solution, one can limit the scope by reducing the number and the length of optimizations. Traditionally, such locally optimized decisions are made by hand-coded algorithms tuned for a small number of benchmarks, often requiring significant effort to be retuned when the benchmark suite changes. In the past 20 years, Machine Learning has been employed to construct performance models to improve the selection and ordering of compiler optimizations, however, the approaches are not baked into the compiler seamlessly and never materialized to be leveraged at a fine-grained scope of code segments. This paper presents Protean Compiler: An agile framework to enable LLVM with built-in phase-ordering capabilities at a fine-grained scope. The framework also comprises a complete library of more than 140 handcrafted static feature collection methods at varying scopes, and the experimental results showcase speedup gains of up to 4.1% on average and up to 15.7% on select Cbench applications wrt LLVM's O3 by just incurring a few extra seconds of build time on Cbench. Additionally, Protean compiler allows for an easy integration with third-party ML frameworks and other Large Language Models, and two applications of this two-step optimization show a gain of 10.1% and 8.5% speedup w.r.t. -O3 on CBench's Susan and Jpeg applications. Protean compiler is seamlessly integrated into LLVM and can be used as a new, enhanced, full-fledged compiler. We plan to release the project to the open-source community in the near future.