论文标题
使用Exabricks射线跟踪结构化AMR数据
Ray Tracing Structured AMR Data Using ExaBricks
论文作者
论文摘要
结构化的自适应网格细化(结构化AMR)使模拟能够适应域分辨率以节省计算和存储,并已成为科学模拟使用的主要数据表示之一。但是,有效渲染此类数据仍然是一个挑战。我们使用两种不同的数据结构的组合提出了一种在配备GPU的工作站上的结构化AMR数据的体积和ISO表面射线跟踪的有效方法。总之,这些数据结构允许基于射线跟踪的渲染器快速确定需要集成沿射线的哪些段,以及以何种频率进行集成,同时还可以快速访问流畅的样品重建内核所需的所有数据值。我们的方法利用RTX射线跟踪硬件进行表面渲染,射线行进,跳过空间和自适应采样;并允许对传输函数和隐式ISO曲面阈值进行交互式更改。我们证明,我们的方法在很少的内存开销中实现了高性能,从而可以对单个GPU工作站的复杂AMR数据集进行交互式高质量渲染。
Structured Adaptive Mesh Refinement (Structured AMR) enables simulations to adapt the domain resolution to save computation and storage, and has become one of the dominant data representations used by scientific simulations; however, efficiently rendering such data remains a challenge. We present an efficient approach for volume- and iso-surface ray tracing of Structured AMR data on GPU-equipped workstations, using a combination of two different data structures. Together, these data structures allow a ray tracing based renderer to quickly determine which segments along the ray need to be integrated and at what frequency, while also providing quick access to all data values required for a smooth sample reconstruction kernel. Our method makes use of the RTX ray tracing hardware for surface rendering, ray marching, space skipping, and adaptive sampling; and allows for interactive changes to the transfer function and implicit iso-surfacing thresholds. We demonstrate that our method achieves high performance with little memory overhead, enabling interactive high quality rendering of complex AMR data sets on individual GPU workstations.