3DPipe: A Pipelined GPU Framework for Scalable Generalized Spatial Join over Polyhedral Objects

2026-04-21

AI Summary

This work addresses the inefficiency of existing spatial join methods, which are primarily designed for 2D data and struggle to scale to large collections of 3D polyhedral objects. To overcome this limitation, the paper introduces the first parallel framework that integrates pipelined execution, multi-level pruning, and out-of-core GPU processing. By leveraging a GPU-accelerated filter-and-refine pipeline, chunked streaming of data, and tight CPU–GPU collaboration, the proposed approach substantially enhances the scalability and throughput of 3D spatial joins. Experimental results demonstrate that the method achieves up to a 9.0× speedup over TDBase, the current state-of-the-art GPU-based solution, while exhibiting excellent scalability across varying dataset sizes.

Technology Category

• Computer Vision: 3D Computer Vision
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning
• Data Mining & Knowledge Management: Scalability, Parallel & Distributed Systems

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Data management and stream processing for Web, mobile and wireless applications
• Security and Privacy: Large-scale security measurements

Abstract

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.

Problem

• 3D spatial join
• polyhedral objects
• spatial databases
• scalability
• GPU computing

Innovation

• GPU pipelining
• 3D spatial join
• polyhedral objects
• multi-level pruning
• chunked streaming