Outlier Detection for Learning-Based Optimizing Compiler

Modern compilers use machine learning to find from their prior experience useful heuristics for new programs encountered in order to accelerate the optimization process. However, prior experience might not be applicable for outlier programs with unfamiliar code features. This paper presents a Reverse K-nearest neighbor (RKNN) algorithm based approach for outlier detection. The compiler can therefore launch a search within an optimization space when outlier programs are encountered, or directly apply its experience to non-outliers. Preliminary experimental results demonstrate the effectiveness of the approach.