3DPipe：一种用于可扩展多面体对象通用空间连接的流水线GPU框架
3DPipe: A Pipelined GPU Framework for Scalable Generalized Spatial Join over Polyhedral Objects
AI总结：
本文提出3DPipe，一种利用GPU并行性处理多面体对象空间连接的流水线框架，通过多级剪枝策略和分块流式处理提升效率，实验显示其比现有最佳GPU方案TDBase快9倍。
摘要：
空间连接是空间数据库中的基本操作。随着LiDAR基于对象检测和3D数字病理学等应用中3D数据的快速增长，支持3D数据集的空间连接需求日益增加。然而，现有技术大多针对2D数据，导致3D空间连接研究不足且计算成本高。本文提出3DPipe，一种用于可扩展多面体对象空间连接的流水线GPU框架。3DPipe利用GPU并行性在过滤和精炼阶段进行处理，采用多级剪枝策略实现高效候选减少，并采用分块流式处理来处理超过GPU内存的数据集。其流水线执行方式重叠CPU数据准备、主机-设备数据传输和GPU计算以提高吞吐量。实验表明，3DPipe在比现有最佳GPU解决方案TDBase快9.0倍的同时保持了良好的可扩展性。3DPipe在https://github.com/lyuheng/3dpipe上开源。
英文摘要：
Spatial join is a fundamental operation in spatial databases. With the rapid growth of 3D data in applications such as LiDAR-based object detection and 3D digital pathology, there is an increasing need to support spatial join over 3D datasets. However, existing techniques are largely designed for 2D data, leaving 3D spatial join underexplored and computationally expensive. We present 3DPipe, a pipelined GPU framework for scalable spatial join over polyhedral objects. 3DPipe exploits GPU parallelism across both filtering and refinement stages, incorporates a multi-level pruning strategy for efficient candidate reduction, and employs chunked streaming to handle datasets exceeding GPU memory. Its pipelined execution overlaps CPU data preparation, host-device data transfer, and GPU computation to improve throughput. Experiments show that 3DPipe achieves up to 9.0$\times$ speedup over the state-of-the-art GPU solution, TDBase, while maintaining excellent scalability. 3DPipe is open-sourced at https://github.com/lyuheng/3dpipe.
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