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GPU Ray-traced Collision Detection Fine Pipeline Reorganization

机译：GPU射线追踪碰撞检测精细管道重组

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Ray-tracing algorithms can be used to render a virtual scene and to detect collisions between objects. Numerous ray-tracing algorithms have been proposed which use data structures optimized for specific cases (rigid objects, deformable objects, etc.). Some solutions try to optimize performance by combining several algorithms to use the most efficient algorithm for each ray. This paper presents a ray-traced collision detection pipeline that improves the performance on a graphicd processing unit (GPU) when several ray-tracing algorithms are used. When combining several ray-tracing algorithms on a GPU, a well-known drawback is thread divergence among work-groups that can cause loss of performance by causing idle threads. In this paper we avoid branch divergence by dividing the ray tracing into three steps with append buffers in between. We also show that prediction can be used to avoid unnecessary synchronizations between the CPU and GPU. Applied to a narrow-phase collision detection algorithm, results show an improvement of performance up to 2.7 times.
机译：射线跟踪算法可用于呈现虚拟场景并检测对象之间的碰撞。已经提出了许多光线跟踪算法，其使用针对特定情况优化的数据结构（刚性物体，可变形物体等）。一些解决方案尝试通过组合多种算法来优化性能，以便为每个光线使用最有效的算法。本文介绍了一种光线跟踪碰撞检测流水线，当使用几种射线跟踪算法时，提高了图形处理单元（GPU）上的性能。当在GPU上结合几种射线跟踪算法时，众所周知的缺点是通过导致空闲线程导致性能损失的工作组之间的线程分歧。在本文中，我们通过将射线描绘分成三个步骤，避免分支发散，其中介于两者之间的附加缓冲区。我们还表明，可以使用预测来避免CPU和GPU之间不必要的同步。应用于窄相碰撞检测算法，结果显示出高达2.7倍的性能。

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