A Survey on Compiler Autotuning using Machine Learning

Since the mid-1990s, researchers have been trying to use machine-learning based approaches to solve a number of different compiler optimization problems. These techniques primarily enhance the quality of the obtained results and, more importantly, make it feasible to tackle two main compiler optimization problems: optimization selection (choosing which optimizations to apply) and phase-ordering (choosing the order of applying optimizations). The compiler optimization space continues to grow due to the advancement of applications, increasing number of compiler optimizations, and new target architectures. Generic optimization passes in compilers cannot fully leverage newly introduced optimizations and, therefore, cannot keep up with the pace of increasing options. This survey summarizes and classifies the recent advances in using machine learning for the compiler optimization field, particularly on the two major problems of (1) selecting the best optimizations and (2) the phase-ordering of optimizations. The survey highlights the approaches taken so far, the obtained results, the fine-grain classification among different approaches and finally, the influential papers of the field.Comment: version 5.0 (updated on September 2018)- Preprint Version For our Accepted Journal @ ACM CSUR 2018 (42 pages) - This survey will be updated quarterly here (Send me your new published papers to be added in the subsequent version) History: Received November 2016; Revised August 2017; Revised February 2018; Accepted March 2018

Model-Driven Engineering and Optimizing Compilers: A bridge too far?

International audienceA primary goal of Model Driven Engineering (MDE) is to reduce the cost and effort of developing complex software systems using techniques for transforming abstract views of software to concrete implementations. The rich set of tools that have been developed, especially the growing maturity of model transformation technologies, opens the possibility of applying MDE technologies to transformation-based problems in other domains. In this paper, we present our experience with using MDE technologies to build and evolve compiler infrastructures in the optimizing compiler domain.We illustrate, through our two ongoing research compiler projects for C and a functional language, the challenging aspects of optimizing compiler research and show how mature MDE technologies can be used to address them.We also identify some of the pitfalls that arise from unrealistic expectations of what can be accomplished using MDE and discuss how they can lead to unsuccessful and frustrating application of MDE technologies