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20202026
most citedRaViTT: Random Vision Transformer Tokens

2 citations · 3 across the 8 of their papers we have counts for

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8 papers · 1 filter

cs.DC2026

Advancing RT Core-Accelerated Fixed-Radius Nearest Neighbor Search

Enzo Meneses, Hugo Bec, Cristóbal A. Navarro +5

In this work we introduce three ideas that can further improve particle FRNN physics simulations running on RT Cores; i) a real-time update/rebuild ratio optimizer for the bounding…

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.DC2022

A Scalable and Energy Efficient GPU Thread Map for m-Simplex Domains

Cristóbal A. Navarro, Felipe A. Quezada, Benjamin Bustos +2

This work proposes a new GPU thread map for -simplex domains, that scales its speedup with dimension and is energy efficient compared to other state of the art approaches. The m…

cs.DC2022★ 1 cited

Modeling GPU Dynamic Parallelism for Self Similar Density Workloads

Felipe A. Quezada, Cristóbal A. Navarro, Miguel Romero +1

Dynamic Parallelism (DP) is a runtime feature of the GPU programming model that allows GPU threads to execute additional GPU kernels, recursively. Apart from making the programming…

cs.DC2022

Squeeze: Efficient Compact Fractals for Tensor Core GPUs

Felipe A. Quezada, Cristóbal A. Navarro, Nancy Hitschfeld +1

This work presents Squeeze, an efficient compact fractal processing scheme for tensor core GPUs. By combining discrete-space transformations between compact and expanded forms, one…

cs.DC2021

Accelerating Compact Fractals with Tensor Core GPUs

Felipe A. Quezada, Cristóbal A. Navarro

This work presents a GPU thread mapping approach that allows doing fast parallel stencil-like computations on discrete fractals using their compact representation. The intuition be…

cs.DC2020

Efficient GPU Thread Mapping on Embedded 2D Fractals

Cristóbal A. Navarro, Felipe A. Quezada, Nancy Hitschfeld +2

This work proposes a new approach for mapping GPU threads onto a family of discrete embedded 2D fractals. A block-space map $λ: \mathbb{Z}_{\mathbb{E}}^{2} \mapsto \mathbb{Z}_{\mat…