MLGO: a Machine Learning Guided Compiler Optimizations Framework

• URL: http://arxiv.org/abs/2101.04808v1
• Date: Wed, 13 Jan 2021 00:02:49 GMT
• Title: MLGO: a Machine Learning Guided Compiler Optimizations Framework
• Authors: Mircea Trofin (1), Yundi Qian (1), Eugene Brevdo (1), Zinan Lin (2), Krzysztof Choromanski (1), David Li (1) ((1) Google, Inc., (2) Carnegie Mellon University)
• Abstract summary: This work is the first full integration of machine learning in a complex compiler pass in a real-world setting. We use two different ML algorithms to train the inlining-for-size model, and achieve up to 7% size reduction. The same model generalizes well to a diversity of real-world targets, as well as to the same set of targets after months of active development.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Leveraging machine-learning (ML) techniques for compiler optimizations has been widely studied and explored in academia. However, the adoption of ML in general-purpose, industry strength compilers has yet to happen. We propose MLGO, a framework for integrating ML techniques systematically in an industrial compiler -- LLVM. As a case study, we present the details and results of replacing the heuristics-based inlining-for-size optimization in LLVM with machine learned models. To the best of our knowledge, this work is the first full integration of ML in a complex compiler pass in a real-world setting. It is available in the main LLVM repository. We use two different ML algorithms: Policy Gradient and Evolution Strategies, to train the inlining-for-size model, and achieve up to 7\% size reduction, when compared to state of the art LLVM -Oz. The same model, trained on one corpus, generalizes well to a diversity of real-world targets, as well as to the same set of targets after months of active development. This property of the trained models is beneficial to deploy ML techniques in real-world settings.

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