Herveau, KillianPfaffe, PhilipTillmann, Martin PeterTichy, Walter F.Dachsbacher, CarstenChilds, Hank and Frey, Steffen2019-06-022019-06-022019978-3-03868-079-61727-348Xhttps://doi.org/10.2312/pgv.20191110https://diglib.eg.org:443/handle/10.2312/pgv20191110Acceleration structures are key to high performance parallel ray tracing. Maximizing performance requires configuring the degrees of freedom (e.g., construction parameters) these data structures expose. Whether a parameter setting is optimal depends on the input (e.g., the scene and view parameters) and hardware. Manual selection is tedious, error prone, and is not portable. To automate the parameter selection task we use a hybrid of model-based prediction and online autotuning. The combination benefits from the best of both worlds: one-shot configuration selection when inputs are known or similar, effective exploration of the configuration space otherwise. Online tuning additionally serves to train the model on real inputs without requiring a-priori training samples. Online autotuning outperforms best-practice configurations recommended by the literature, by up to 11% median. The model predictions achieve 95% of the online autotuning performance while reducing 90% of the autotuner overhead. Hybrid online autotuning thus enables always-on tuning of parallel ray tracing.I.3.6 [Computer Graphics]Methodology and TechniquesGraphics data structures and data types10.2312/pgv.2019111059-68