Online Autotuning for Interactive Raytracing

这个项目旨在结合和协同发展两个独立的领域，光线追踪和自动调谐。光线追踪是一种成熟且广泛使用的生成逼真图像的技术。光线追踪和自动调优的结合有着重要的前景，因为图像合成对性能至关重要，但也提供了大量的调优机会，例如加速结构和启发式的选择，CPU和GPU之间的工作划分，单个任务所使用的线程数量，或使用光线排序来实现连贯访问。自动调谐可以通过在众多可用的调谐参数中找到最佳工作点来显著加快光线跟踪速度。然而，最佳工作点可能随着时间的推移而变化，例如，由于视点、光源、几何形状或材料特性的变化。因此，高性能的光线追踪器必须不断地重新优化。该项目的目的是双重的。首先，我们希望开发自动调优的新功能，特别是动态或在线自动调优的功能。为此，我们计划探索合适的搜索程序、机器学习技术和动态建模，并在基于交互式光线追踪的方法上评估这些方法。其次，这些方法将被扩展，以适应当前的条件。为此，必须使它们具有适应性，并交付和聚合有关其运行时行为的重要信息。我们期望结果不仅包括在大范围的场景变化和用户交互中表现良好的自适应图像合成技术，而且还包括新的软件架构，例如自动调整框架，库和机器学习程序，这些程序大大简化了实现高性能所涉及的编程任务，适用于光线追踪之外。

This project aims to combine and synergistically develop two separate areas, raytracing and autotuning. Raytracing is an established and widely used technique for generating photorealistic images.The combination of raytracing and autotuning holds significant promise, because image synthesis is performance critical, but offers a large range of tuning opportunities, for example the choice of acceleration structures and heuristics, the partitioning of work among CPU and GPU, the number of threads employed for individual tasks, or the use of ray sorting to achieve coherent accesses. Autotuning can speed up raytracing significantly by finding the optimal operating point among the numerous tuning parameters available.However, the optimal operating point may shift over time, for example by changes in viewpoint, light source, geometry, or material properties. Thus, a performant raytracer must be re-optimized continually.The aim of the project is twofold. First, we wish to develop new capabilites for autotuning, in particular the capability of dynamic, or online, autotuning. For this, we plan to explore suitable search procedures, machine learning techniques, and dynamic modeling, and evaluate these approaches on interactive raytracing-based methods. Second, these methods will be extended to be tuneable to current conditions. For this, they must be made adaptable und deliver and aggregate important information about their runtime behavior.We expect results to include not only adaptive image synthesis techniques that perform well over a large range of scene variations and user interactions, but also new software architectures, for instance autotuning frameworks, libraries, and machine learning procedures that drastically simplify the programming tasks involved in achieving high performance, applicable beyond raytracing.