# Profile Guided Optimization without Profiles: A Machine Learning Approach

arXiv: https://arxiv.org/abs/2112.14679 · HTML: https://mlsanity.com/paper/2112.14679/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 31 references that are in the map.
- First posted on arXiv 24 Dec 2021.

## Builds on (most cited first, 9 of 9)

- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [Ansor: Generating High-Performance Tensor Programs for Deep Learning](https://mlsanity.com/paper/2006.06762/) (2020)
- [Neural Code Comprehension: A Learnable Representation of Code Semantics](https://mlsanity.com/paper/1806.07336/) (2018)
- [Ithemal: Accurate, Portable and Fast Basic Block Throughput Estimation using Deep Neural Networks](https://mlsanity.com/paper/1808.07412/) (2018)
- [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)
- [BOLT: A Practical Binary Optimizer for Data Centers and Beyond](https://mlsanity.com/paper/1807.06735/) (2018)

## Built on by (most cited first, 2 of 2)

- [Supersonic: Learning to Generate Source Code Optimizations in C/C++](https://mlsanity.com/paper/2309.14846/) (2023)
- [MLGOPerf: An ML Guided Inliner to Optimize Performance](https://mlsanity.com/paper/2207.08389/) (2022)

## Abstract (arXiv)

Profile guided optimization is an effective technique for improving the optimization ability of compilers based on dynamic behavior, but collecting profile data is expensive, cumbersome, and requires regular updating to remain fresh. We present a novel statistical approach to inferring branch probabilities that improves the performance of programs that are compiled without profile guided optimizations. We perform offline training using information that is collected from a large corpus of binaries that have branch probabilities information. The learned model is used by the compiler to predict the branch probabilities of regular uninstrumented programs, which the compiler can then use to inform optimization decisions. We integrate our technique directly in LLVM, supplementing the existing human-engineered compiler heuristics. We evaluate our technique on a suite of benchmarks, demonstrating some gains over compiling without profile information. In deployment, our technique requires no profiling runs and has negligible effect on compilation time.