Automatic Selection of Machine Learning Models for WCET-aware Compiler Heuristic Generation
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Machine learning has shown its capabilities for an automatic generation 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 outperform 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 parameters 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 evolutionary 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.
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