A hardware pipeline for accelerating ray traversal algorithms on streaming processors

Michael Steffen, Joseph Zambreno
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引用次数: 4

Abstract

Ray Tracing is a graphics rendering method that uses rays to trace the path of light in a computer model. To accelerate the processing of rays, scenes are typically compiled into smaller spatial boxes using a tree structure and rays then traverse the tree structure to determine relevant spatial boxes. This allows computations involving rays and scene objects to be limited to only objects close to the ray and does not require processing all elements in the computer model. We present a ray traversal pipeline designed to accelerate ray tracing traversal algorithms using a combination of currently used programmable graphics processors and a new fixed hardware pipeline. Our fixed hardware pipeline performs an initial traversal operation that quickly identifies a smaller sized, fixed granularity spatial bounding box from the original scene. This spatial box can then be traversed further to identify subsequently smaller spatial bounding boxes using any user-defined acceleration algorithm. We show that our pipeline allows for an expected level of user programmability, including development of custom data structures, and can support a wide range of processor architectures. The performance of our pipeline is evaluated for ray traversal and intersection stages using a kd-tree ray tracing algorithm and a custom simulator modeling a generic streaming processor architecture. Experimental results show that our pipeline reduces the number of executed instructions on a graphics processor for the traversal operation by 2.15X for visible rays. The memory bandwidth required for traversal is also reduced by a factor of 1.3X for visible rays.
在流处理器上加速射线遍历算法的硬件管道
光线追踪是一种图形渲染方法,它使用光线在计算机模型中追踪光的路径。为了加速光线的处理,通常使用树形结构将场景编译成更小的空间框,然后光线遍历树形结构以确定相关的空间框。这允许涉及光线和场景对象的计算仅限于靠近光线的对象,并且不需要处理计算机模型中的所有元素。我们提出了一种射线遍历管道,旨在使用当前使用的可编程图形处理器和新的固定硬件管道的组合来加速射线跟踪遍历算法。我们的固定硬件管道执行初始遍历操作,快速识别来自原始场景的较小尺寸,固定粒度的空间边界框。然后可以使用任何用户定义的加速算法进一步遍历这个空间框,以识别随后更小的空间边界框。我们展示了我们的管道允许预期级别的用户可编程性,包括自定义数据结构的开发,并且可以支持广泛的处理器架构。我们的管道的性能是评估射线遍历和交叉阶段使用kd-tree射线跟踪算法和自定义模拟器建模通用流处理器架构。实验结果表明,我们的管道减少了图形处理器上用于遍历操作的指令数,减少了2.15倍。对于可见光,遍历所需的内存带宽也减少了1.3倍。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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