high performance computing on graphics processing units: hgpu.org

Shared memory many-core processors such as GPUs have been extensively used in accelerating computation-intensive algorithms and applications. When porting existing algorithms from sequential or other parallel architecture models to shared memory many-core architectures, non-trivial modifications are often needed in order to match the execution patterns of the target algorithms with the characteristics of many-core architectures. 3D curve traversal is a fundamental process in many applications, and is commonly accelerated by spatial decomposition schemes captured in hierarchical data structures (e.g., kd-trees). However, curve traversal using hierarchical data structures needs to conduct repeated hierarchical searches. Such search process is time-consuming on shared memory many-core architectures since it incurs considerable amounts of expensive memory accesses and execution divergence. In this paper, we propose a novel spatial decomposition based data structure, called Shell, which completely avoids hierarchical search for 3D curve traversal. In Shell, a structure is built on the boundary of each region in the decomposed space, which allows any curve traversing in a region to find the next neighboring region to traverse using table lookup schemes, without any hierarchical search. While our 3D curve traversal approach works for other spatial decomposition paradigms and many-core processors, we illustrate it using kd-tree decomposition on GPU and compare with the fastest known kd-tree searching algorithms for ray traversal. Analysis and experimental results show that our approach improves ray traversal performance considerably over the kd-tree searching approaches.