Potential benefits of a block-space GPU approach for discrete tetrahedral domains

Cristobal A Navarro, Benjamín Bustos, Nancy Hitschfeld Kahler
October 2016
DOI

Abstract

This work analyzes combining tetrahedral data reorganization with an efficient block-space GPU mapping function. The approach can provide up to 2x improved performance over a linear data organization, and the mapping can be up to 6x more efficient than a bounding-box approach. The technique reduces unnecessary threads from O(n^3) to O(n^2 * rho) where rho is in O(1).

Type
Conference paper
Publication
XLII Latin American Computing Conference (CLEI 2016)
GPU Computing Thread Mapping Tetrahedral Domains
Cristobal A Navarro
Cristobal A Navarro
Professor at the Universidad Austral de Chile

Professor at the Universidad Austral de Chile

Nancy Hitschfeld Kahler
Nancy Hitschfeld Kahler
+Lab founder | Full Professor Universidad de Chile

Full Professor at the Department of Computer Science, University of Chile. Her main research interests include geometric modeling, geometric meshes, and parallel algorithms (GPU computing), focused in computational science, and engineering applications.