Autopiler: An AI Based Framework for Program Autotuning and Options Recommendation

Abstract

Program autotuning has been proved to achieve great performance improvement in many compiler usage scenarios. Many autotuning frameworks have been provided to support fully-customizable configuration representations, a wide variety of representations for domain-specific tuning, and a user friendly interface for interaction between the program and the autotuner. However, tuning programs takes time, no matter it is autotuned or manually tuned. Oftentimes, programmers don’t have the time waiting for autotuners to finish and want to have rather good options to use instantly. This paper introduces Autopiler, a framework for building non-domain-specific program autotuners with machine learning based recommender systems for options prediction. This framework supports not only non-domain-specific tuning techniques, but also learns from previous tuning results and can make adequate good options recommendation before any tuning happens. We will illustrate the architecture of Autopiler and how to leverage recommender system for compiler options recommendation, in such way Autopiler can learn from the programs and becomes an AI boosted smart compiler. The experiment results show that Autopiler can deliver up to 19.46% performance improvement for in-house 4G LTE modem workloads.