Automatic Selection of Machine Learning Models for WCET-aware Compiler Heuristic Generation!

Abstract. Machine learning has shown its capabilities for an automatic genera-tion of heuristics used by optimizing compilers. The advantages of these heuristics are that they can be easily adopted to a new environment and in some cases out-perform hand-crafted compiler optimizations. However, this approach shifts the effort from manual heuristic tuning to the model selection problem of machine learning – i. e., selecting learning algorithms and their respective parameters – which is a tedious task in its own right. In this paper, we tackle the model selection problem in a systematic way. As our experiments show, the right choice of a learning algorithm and its parame-ters can significantly affect the quality of the generated heuristics. We present a generic framework integrating machine learning into a compiler to enable an automatic search for the best learning algorithm. To find good settings for the learner parameters within the large search space, optimizations based on evolu-tionary algorithms are applied. In contrast to the majority of other approaches aiming at a reduction of the average-case execution time (ACET), our goal is the minimization of the worst-case execution time (WCET) which is a key parameter for embedded systems acting as real-time systems. A careful case study on the heuristic generation for the well-known optimization loop invariant code motion shows the challenges and benefits of our methods.