RXMesh: a GPU mesh data structure
Ahmed H. Mahmoud, Serban D. Porumbescu, John D. Owens
Abstract
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 RXMesh : 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.
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Install the CLIlune papers fulltext 36cdbeb7-db01-4339-a2df-3e05af925ef1Cited by top-tier papers6
- Surface Simplification using Intrinsic Error MetricsHsueh-Ti Derek Liu, Mark Gillespie, Benjamin Chislett, Nicholas Sharp et al.SIGGRAPH 2023 · 26 citations
- Dynamic Mesh Processing on the GPUAhmed H. Mahmoud, Serban D. Porumbescu, John D. OwensSIGGRAPH 2025 · 4 citations
- GALE: Leveraging Heterogeneous Systems for Efficient Unstructured Mesh Data AnalysisGuoxi Liu, Thomas Randall, Rong Ge, Federico IuricichIEEE VIS 2025 · 1 citation
- Iskra: A System for Inverse Geometry ProcessingAna Dodik, Ahmed H. Mahmoud, Justin SolomonSIGGRAPH 2026
- Fast Sparse Matrix Permutation for Mesh-Based Direct SolversBehrooz Zarebavani, Ahmed H. Mahmoud, Ana Dodik, Changcheng Yuan et al.SIGGRAPH 2026
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