Effective static bin patterns for sort-middle rendering

Effective static bin patterns for sort-middle rendering
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DOI:
10.1145/3105762.3105777
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发表时间:
2017-07
期刊:
Proceedings of High Performance Graphics
影响因子:
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通讯作者:
B. Kerbl;Michael Kenzel;D. Schmalstieg;M. Steinberger
B. Kerbl;Michael Kenzel;D. Schmalstieg;M. Steinberger
中科院分区:
其他
文献类型:
--
作者:
B. Kerbl;Michael Kenzel;D. Schmalstieg;M. Steinberger

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为了在并行渲染期间有效地利用不断增加的处理器数量,硬件和软件设计人员依赖于复杂的负载平衡策略。虽然动态负载平衡是一个强大的解决方案,但它需要复杂的工作分配和同步机制。图形硬件制造商选择采用静态负载平衡策略。具体地,三角形数据基于其与以固定图案布置的屏幕空间图块的重叠而被分发到处理器。虽然目前的策略,使用简单的模式,为少数快速光栅实现强大的性能,这是值得怀疑的,这种方法将如何规模的处理器的数量进一步增加。为了解决这个问题,我们分析了现实世界的渲染工作量,得出有效的模式的要求,并提出了十个不同的模式设计策略,根据这些要求。除了这些设计策略的理论评估,我们比较选择模式的性能在一个并行的排序中间的软件渲染流水线上的一组广泛的三角形数据从最近的八个视频游戏。因此,我们能够识别出一组可扩展性良好的模式,并表现出比简单方法显着提高的性能。
To effectively utilize an ever increasing number of processors during parallel rendering, hardware and software designers rely on sophisticated load balancing strategies. While dynamic load balancing is a powerful solution, it requires complex work distribution and synchronization mechanisms. Graphics hardware manufacturers have opted to employ static load balancing strategies instead. Specifically, triangle data is distributed to processors based on its overlap with screenspace tiles arranged in a fixed pattern. While the current strategy of using simple patterns for a small number of fast rasterizers achieves formidable performance, it is questionable how this approach will scale as the number of processors increases further. To address this issue, we analyze real-world rendering workloads, derive requirements for effective patterns, and present ten different pattern design strategies based on these requirements. In addition to a theoretical evaluation of these design strategies, we compare the performance of select patterns in a parallel sort-middle software rendering pipeline on an extensive set of triangle data captured from eight recent video games. As a result, we are able to identify a set of patterns that scale well and exhibit significantly improved performance over naïve approaches.