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

Deep learning for compilers

Constructing compilers is hard. Optimising compilers are multi-million dollar projects spanning years of development, yet remain unable to fully exploit the available performance, and are prone to bugs. The rapid transition to heterogeneous parallelism and diverse architectures has raised demand for aggressively-optimising compilers to an all time high, leaving compiler developers struggling to keep up. What is needed are better tools to simplify compiler construction. This thesis presents new techniques that dramatically lower the cost of compiler construction, while improving robustness and performance. The enabling insight for this research is the leveraging of deep learning to model the correlations between source code and program behaviour, enabling tasks which previously required significant engineering effort to be automated. This is demonstrated in three domains: First, a generative model for compiler benchmarks is developed. The model requires no prior knowledge of programming languages, yet produces output of such quality that professional software developers cannot distinguish generated from hand-written programs. The efficacy of the generator is demonstrated by supplementing the training data of predictive models for compiler optimisations. The generator yields an automatic improvement in heuristic performance, and exposes weaknesses in state-of-the- art approaches which, when corrected, yield further performance improvements. Second, a compiler fuzzer is developed which is far simpler than prior techniques. By learning a generative model rather than engineering a generator from scratch, it is implemented in 100 fewer lines of code than the state-of-the-art, yet is capable of exposing bugs which prior techniques cannot. An extensive testing campaign reveals 67 new bugs in OpenCL compilers, many of which have now been fixed. Finally, this thesis addresses the challenge of feature design. A methodology for learning compiler heuristics is presented that, in contrast to prior approaches, learns directly over the raw textual representation of programs. The approach outperforms state-of-the-art models with hand-engineered features in two challenging optimisation domains, without requiring any expert guidance. Additionally, the methodology enables models trained in one task to be adapted to perform another, permitting the novel transfer of information between optimisation problem domains. The techniques developed in these three contrasting domains demonstrate the exciting potential of deep learning to simplify and improve compiler construction. The outcomes of this thesis enable new lines of research to equip compiler developers to keep up with the rapidly evolving landscape of heterogeneous architectures