GPolylla: Fully GPU-accelerated polygonal mesh generator
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arXiv
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| Hauptverfasser: | , , |
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| Format: | Preprint |
| Veröffentlicht: |
2024
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| _version_ | 1866913277027549184 |
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| author | Salinas-Fernández, Sergio Hitschfeld-Kahler, Nancy Carrasco, Roberto |
| author_facet | Salinas-Fernández, Sergio Hitschfeld-Kahler, Nancy Carrasco, Roberto |
| contents | This work presents a fully GPU-accelerated algorithm for the polygonal mesh generator known as Polylla. Polylla is a tri-to-polygon mesh generator, which benefits from the half-edge data structure to manage any polygonal shape. The proposed parallel algorithm introduces a novel approach to modify triangulations to get polygonal meshes using the half-edge data structure in parallel on the GPU. By changing the adjacency values of each half-edge, the algorithm accomplish to unlink half-edges that are not used in the new polygonal mesh without the need neither removing nor allocating new memory in the GPU. The experimental results show a speedup, reaching up to $\times 83.2$ when compared to the CPU sequential implementation. Additionally, the speedup is $\times 746.8$ when the cost of copying the data structure from the host device and back is not included. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_14723 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | GPolylla: Fully GPU-accelerated polygonal mesh generator Salinas-Fernández, Sergio Hitschfeld-Kahler, Nancy Carrasco, Roberto Graphics Computational Geometry This work presents a fully GPU-accelerated algorithm for the polygonal mesh generator known as Polylla. Polylla is a tri-to-polygon mesh generator, which benefits from the half-edge data structure to manage any polygonal shape. The proposed parallel algorithm introduces a novel approach to modify triangulations to get polygonal meshes using the half-edge data structure in parallel on the GPU. By changing the adjacency values of each half-edge, the algorithm accomplish to unlink half-edges that are not used in the new polygonal mesh without the need neither removing nor allocating new memory in the GPU. The experimental results show a speedup, reaching up to $\times 83.2$ when compared to the CPU sequential implementation. Additionally, the speedup is $\times 746.8$ when the cost of copying the data structure from the host device and back is not included. |
| title | GPolylla: Fully GPU-accelerated polygonal mesh generator |
| topic | Graphics Computational Geometry |
| url | https://arxiv.org/abs/2403.14723 |