Graph-based features for machine learning driven code optimization
Anton Kindestam · KTH Publication Database DiVA (KTH Royal Institute of Technology) · 2017
In this paper we present a method of using the Shortest-Path Graph Kernel, on graph-based features of computer programs, to train a Support Vector Regression model which predicts execution time speedup over baseline given an unseen program and a point in optimization space, based on a method proposed in Using Graph-Based Program Characterization for Predictive Modeling by Park et al. The optimization space is represented by command-line parameters to the polyhedral C-to-C compiler PoCC, and PolyBench is used to generate the data set of speedups over baseline. The model is found to produce results reasonable by some metrics, but due to the large error and the pseudo-random behaviour of the output the method, in its current form, must reluctantly be rejected.