Hybrid Online Autotuning for Parallel Ray Tracing
Hybrid Online Autotuning for Parallel Ray Tracing
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DOI：
10.2312/pgv.20191110
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发表时间：
2019
期刊：
影响因子：
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通讯作者：
K. Herveau;Philip Pfaffe;Martin Tillmann;W. Tichy;C. Dachsbacher
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文献类型：
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作者：
K. Herveau;Philip Pfaffe;Martin Tillmann;W. Tichy;C. Dachsbacher
加速结构是实现高性能平行光线追踪的关键。最大化性能需要配置这些数据结构暴露的自由度（例如，构造参数）。参数设置是否最佳取决于输入（例如，场景和视图参数）和硬件。手动选择是乏味的、容易出错的，而且不便于移植。为了实现参数选择任务的自动化，我们使用了基于模型的预测和在线自动调整的混合方法。这种组合受益于两方面的优势：当输入已知或相似时进行一次配置选择，否则对配置空间进行有效探索。在线调优还可以在真实输入上训练模型，而不需要先验的训练样本。在线自动调优优于文献中推荐的最佳实践配置，平均高出11%。模型预测实现了95%的在线自动调谐性能，同时减少了90%的自动调谐器开销。因此，混合在线自动调谐使并行光线跟踪的调谐始终开启。
Acceleration 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.