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20222026
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cs.DC2026

Leveraging Mathematical Reasoning of LLMs for Efficient GPU Thread Mapping

Jose Maureira, Cristóbal A. Navarro, Hector Ferrada +1

Mapping parallel threads onto non-box-shaped domains is a known challenge in GPU computing; efficient mapping prevents performance penalties from unnecessary resource allocation. C…

cs.DC2026

Ray Tracing Cores for General-Purpose Computing: A Literature Review

Enzo Meneses, Cristóbal A. Navarro, Héctor Ferrada +2

Recent research on ray tracing cores has explored repurposing these cores to solve non-graphical problems by reformulating them as geometric queries, leveraging the inherent parall…

cs.DC2024

CAT: Cellular Automata on Tensor cores

Cristóbal A. Navarro, Felipe A. Quezada, Enzo Meneses +2

Cellular automata (CA) are simulation models that can produce complex emergent behaviors from simple local rules. Although state-of-the-art GPU solutions are already fast due to th…

cs.DC2023

Accelerating Range Minimum Queries with Ray Tracing Cores

Enzo Meneses, Cristóbal A. Navarro, Héctor Ferrada +1

During the last decade GPU technology has shifted from pure general purpose computation to the inclusion of application specific integrated circuits (ASICs), such as Tensor Cores a…

cs.DC2023

An Evaluation of GPU Filters for Accelerating the 2D Convex Hull

Roberto Carrasco, Héctor Ferrada, Cristóbal A. Navarro +1

The Convex Hull algorithm is one of the most important algorithms in computational geometry, with many applications such as in computer graphics, robotics, and data mining. Despite…

cs.DC2022

Accelerating the Convex Hull Computation with a Parallel GPU Algorithm

Alan Keith, Héctor Ferrada, Cristóbal A. Navarro

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 sequenti…