EuroGraphics Symposium on Parallel Graphics and Visualization (EGPGV) 2019
Acceleration structures are key to high performance parallel
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.