Mircea Trofin
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From scheduling, to resource allocation to optimization of complex workflows, systems are replete with decision-making problems which are typically addressed with hand-designed heuristics. Recent literature studies pose these setups as Reinforcement Learning (RL) problems owing to a natural fit, with several successes in simulated benchmark environments. However, bringing the RL approach to any complex system in practice is full of challenges in integrating the system into the act-observe-learn paradigm of RL, which has limited the adoption of these techniques. In this work, we present an alternative approach which uses offline data collected using multiple existing baseline policies to simultaneously improve upon them. By repeating multiple iterations of this improvement process, including any learned policies into the set of baselines, we show how performance can be quickly bootstrapped using our approach. We demonstrate the practicality of our approach through evaluation in optimizing the inlining decisions for the LLVM compiler, and obtain significant improvements even over prior RL-based policies.
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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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