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Accelerated regular grid traversals using extended anisotropic chessboard distance fields on a parallel stream processor

机译:在并行流处理器上使用扩展的各向异性棋board距离场来加速常规的网格遍历

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Modern graphics processing units (GPUs) are an implementation of parallel stream processors. In recent years, there have been a few studies on mapping ray tracing to the GPU. Since graphics processors are not designed to process complex data structures, it is crucial to explore data structures and algorithms for efficient stream processing. In particular ray traversal is one of the major bottlenecks in ray tracing and direct volume rendering methods. In this work we focus on the efficient regular grid based ray traversals on GPU. A new empty space skipping traversal method is introduced. Our method extends the anisotropic chessboard distance structure and employs a GPU friendly traversal algorithm with minimal dynamic branching. Additionally, several previous techniques have been redesigned and adapted to the stream processing model. We experimentally show that our traversal method is considerably faster and better suited to the parallel stream processing than the other grid based techniques.
机译:现代图形处理单元(GPU)是并行流处理器的一种实现。近年来,已经有一些关于将光线追踪映射到GPU的研究。由于图形处理器并非旨在处理复杂的数据结构,因此探索数据结构和算法以进行有效的流处理至关重要。尤其是,光线遍历是光线跟踪和直接体绘制方法中的主要瓶颈之一。在这项工作中,我们专注于在GPU上进行有效的基于规则网格的射线遍历。介绍了一种新的跳越空白空间的方法。我们的方法扩展了各向异性棋盘距离结构,并采用了GPU友好的遍历算法,并具有最小的动态分支。另外,已经重新设计了几种先前的技术并将其适应于流处理模型。我们通过实验证明,与其他基于网格的技术相比,我们的遍历方法相当快,并且更适合并行流处理。

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