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20112022
most citedA Convolutional Architecture for 3D Model Embedding

1 citations · 1 across the 4 of their papers we have counts for

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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

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.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…

cs.DC2017

Block-space GPU Mapping for Embedded Sierpiński Gasket Fractals

Cristóbal A. Navarro, Benjamín Bustos, Raimundo Vega +1

This work studies the problem of GPU thread mapping for a Sierpiński gasket fractal embedded in a discrete Euclidean space of . A block-space map $λ: \mathbb{Z}_{\mathb…

cs.DC2016

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

Cristóbal A. Navarro, Benjamín Bustos, Nancy Hitschfeld

The study of data-parallel domain re-organization and thread-mapping techniques are relevant topics as they can increase the efficiency of GPU computations when working on spatial…