Publication | ACM Transactions on Graphics (SIGGRAPH Proceedings) 2021

RXMesh: A GPU Mesh Data Structure

Accelerating mesh processing is essential for many applications in computer-aided design, computer graphics, physical simulation, and visualization. In this paper, we present a data structure and programming model to accelerate triangle mesh processing using the GPU. The data structure is carefully designed for the GPU and outperforms alternative GPU and CPU data structures in the analyzed applications.


RXMesh: A GPU Mesh Data Structure

Ahmed H. Mahmoud, Serban D. Porumbescu, and John D. Owens

ACM Transactions on Graphics (SIGGRAPH Proceedings) 2021

We propose a new static high-performance mesh data structure for triangle surface meshes on the GPU. Our data structure is carefully designed for parallel execution while capturing mesh locality and confining data access, as much as possible, within the GPU’s fast shared memory. We achieve this by subdividing the mesh into patches and representing these patches compactly using a matrix-based representation. Our patching technique is decorated with ribbons, thin mesh strips around patches that eliminate the need to communicate between different computation thread blocks, resulting in consistent high throughput. We call our data structure EXMesh: Ribbon-matriX Mesh. We hide the complexity of our data structure behind a flexible but powerful programming model that helps deliver high performance by inducing load balance even in highly irregular input meshes. We show the efficacy of our programming model on common geometry processing applications—mesh smoothing and filtering, geodesic distance, and vertex normal computation. For evaluation, we benchmark our data structure against well-optimized GPU and (single and multi-core) CPU data structures and show significant speedups.

Associated Researchers

Serban D. Porumbescu

University of California, Davis

John D. Owens

University of California, Davis

View all researchers

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