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Parallel kd-Tree Construction on the GPU with an Adaptive Split and Sort Strategy

机译:具有自适应拆分和排序策略的GPU上并行kd树构建

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We introduce a parallel kd-tree construction method for 3-dimensional points on a GPU which employs a sorting algorithm that maintains high parallelism throughout construction. Typically, large arrays in the upper levels of a kd-tree do not yield high performance when computing each node in one thread. Conversely, small arrays in the lower levels of the tree do not benefit from typical parallel sorts. To address these issues, the proposed sorting approach uses a modified parallel sort on the upper levels before switching to basic parallelization on the lower levels. Our work focuses on 3D point registration and our results indicate that a speed gain by a factor of 100 can be achieved in comparison to a naive parallel algorithm for a typical scene.
机译:我们介绍了一种在GPU上针对3维点的并行kd树构造方法,该方法采用了在整个构造过程中保持高并行度的排序算法。通常,在计算一个线程中的每个节点时,kd树的高层中的大型阵列不会产生高性能。相反,树的较低层中的小数组无法从典型的并行排序中受益。为了解决这些问题,建议的排序方法在切换到较低级别的基本并行化之前,在较高级别上使用了改进的并行排序。我们的工作集中在3D点注册上,我们的结果表明,与典型场景的朴素并行算法相比,速度提高了100倍。

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