MLComp: A Methodology for Machine Learning-based Performance Estimation and Adaptive Selection of Pareto-Optimal Compiler Optimization Sequences

TL;DR

This paper introduces MLComp, a machine learning-based methodology that uses reinforcement learning and analytical models to efficiently optimize compiler sequences for embedded systems, improving performance metrics with minimal profiling.

Contribution

The paper presents a novel MLComp framework that combines reinforcement learning and analytical models for multi-objective compiler optimization, reducing profiling time and achieving near-optimal sequences.

Findings

Performance Estimator achieves <2% error and 50x faster training.

Phase Selection Policy improves execution time by up to 12%.

Energy consumption is reduced by up to 6%.

Abstract

Embedded systems have proliferated in various consumer and industrial applications with the evolution of Cyber-Physical Systems and the Internet of Things. These systems are subjected to stringent constraints so that embedded software must be optimized for multiple objectives simultaneously, namely reduced energy consumption, execution time, and code size. Compilers offer optimization phases to improve these metrics. However, proper selection and ordering of them depends on multiple factors and typically requires expert knowledge. State-of-the-art optimizers facilitate different platforms and applications case by case, and they are limited by optimizing one metric at a time, as well as requiring a time-consuming adaptation for different targets through dynamic profiling. To address these problems, we propose the novel MLComp methodology, in which optimization phases are sequenced by a Reinforcement Learning-based policy. Training of the policy is supported by Machine Learning-based analytical models for quick performance estimation, thereby drastically reducing the time spent for dynamic profiling. In our framework, different Machine Learning models are automatically tested to choose the best-fitting one. The trained Performance Estimator model is leveraged to efficiently devise Reinforcement Learning-based multi-objective policies for creating quasi-optimal phase sequences. Compared to state-of-the-art estimation models, our Performance Estimator model achieves lower relative error (<2%) with up to 50x faster training time over multiple platforms and application domains. Our Phase Selection Policy improves execution time and energy consumption of a given code by up to 12% and 6%, respectively. The Performance Estimator and the Phase Selection Policy can be trained efficiently for any target platform and application domain.