• DOI:10.1109/SCCC57464.2022.10000307
• Corpus ID: 252531561

Accelerating the Convex Hull Computation with a Parallel GPU Algorithm

@article{Keith2022AcceleratingTC,
  title={Accelerating the Convex Hull Computation with a Parallel GPU Algorithm},
  author={Alan Keith and H{\'e}ctor Ferrada and Crist{\'o}bal A. Navarro},
  journal={2022 41st International Conference of the Chilean Computer Science Society (SCCC)},
  year={2022},
  pages={1-7}
}

• Alan Keith, Héctor Ferrada, C. Navarro
• Published 25 September 2022
• Computer Science
• 2022 41st International Conference of the Chilean Computer Science Society (SCCC)

The convex hull is a fundamental geometrical structure for many applications where groups of points must be enclosed or represented by a convex polygon. Although efficient sequential convex hull algorithms exist, and are constantly being used in applications, their computation time is often considered an issue for time-sensitive tasks such as real-time collision detection, clustering or image processing for virtual reality, among others, where fast response times are required. In this work we propose a parallel GPU-based adaptation of heaphull, which is a state of the art CPU algorithm that computes the convex hull by first doing a efficient filtering stage followed by the actual convex hull computation. More specifically, this work parallelizes the filtering stage, adapting it to the GPU programming model as a series of parallel reductions. Experimental evaluation shows that the proposed implementation significantly improves the performance of the convex hull computation, reaching up to $4\times$ of speedup over the sequential CPU-based heaphull and between $3\times\sim 4\times$ over existing GPU based approaches.