Accelerating Unstructured Mesh Point Location With RT Cores
Accelerating Unstructured Mesh Point Location With RT Cores
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DOI：
10.1109/tvcg.2020.3042930
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发表时间：
2020-12
影响因子：
5.2
通讯作者：
N. Morrical;I. Wald;W. Usher;Valerio Pascucci
中科院分区：
文献类型：
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作者：
N. Morrical;I. Wald;W. Usher;Valerio Pascucci
我们提出了一种利用射线追踪硬件的技术，在最近的NVIDIA RTX GPU中可以解决经典的射线追踪以外的其他问题，我们演示了如何使用这些单元加速由平面和双向面部的一般非结构化元素的位置加速这个非结构化的网点位置问题以前曾在GPU架构上加速，这些查询的性能对于许多非结构化的卷渲染和计算应用程序至关重要。这些点查询将算法复杂性映射到这些新的硬件射线跟踪单元。 - 三角形和每面元数据检测元素中的交集，尽管这些后来的变体在算法上更为复杂，但它们比使用我们的方法的参考方法要快得多。四面体网格的数量渲染器最高$ 4 \ $ 4×，最高$ 15 \ $ 15×$ 15×，用于一般双线性元素网格，匹配或表现不佳的先进解决方案，同时提高可靠性和便利性和便利性-执行。
We present a technique that leverages ray tracing hardware available in recent Nvidia RTX GPUs to solve a problem other than classical ray tracing. Specifically, we demonstrate how to use these units to accelerate the point location of general unstructured elements consisting of both planar and bilinear faces. This unstructured mesh point location problem has previously been challenging to accelerate on GPU architectures; yet, the performance of these queries is crucial to many unstructured volume rendering and compute applications. Starting with a CUDA reference method, we describe and evaluate three approaches that reformulate these point queries to incrementally map algorithmic complexity to these new hardware ray tracing units. Each variant replaces the simpler problem of point queries with a more complex one of ray queries. Initial variants exploit ray tracing cores for accelerated BVH traversal, and subsequent variants use ray-triangle intersections and per-face metadata to detect point-in-element intersections. Although these later variants are more algorithmically complex, they are significantly faster than the reference method thanks to hardware acceleration. Using our approach, we improve the performance of an unstructured volume renderer by up to $4\times$4× for tetrahedral meshes and up to $15\times$15× for general bilinear element meshes, matching, or out-performing state-of-the-art solutions while simultaneously improving on robustness and ease-of-implementation.