Accelerating the Convex Hull Computation with a Parallel GPU Algorithm
| Autor: | Keith, Alan, Ferrada, Héctor, Navarro, Cristóbal A. |
|---|---|
| Jazyk: | Spanish; Castilian |
| Rok vydání: | 2022 |
| Předmět: | |
| Druh dokumentu: | Working Paper |
| Popis: | 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. Comment: 7 pages, in Spanish language |
| Databáze: | arXiv |
| Externí odkaz: |
|