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https://doi.org/10.1109/TVCG.2021.3087863
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Título: | Omega-Test: A Predictive Early-Z Culling to Improve the Graphics Pipeline Energy-Efficiency |
Fecha de publicación: | 1-dic-2022 |
Editorial: | IEEE |
Cita bibliográfica: | IEEE Transactions on Visualization and Computer Graphics, vol. 28, issue 12, pp. 4375-4388, ISSN: 1077-2626, Diciembre 2022 |
ISSN: | 1077-2626 |
Palabras clave: | Graphics processors Mobile processors Portable devices Hardware architecture Processor architecture Energy-aware systems Low-power design Hidden line/surface removal Visibility determination |
Resumen: | The most common task of GPUs is to render images in real time. When rendering a 3D scene, a key step is to determine which parts of every object are visible in the final image. There are different approaches to solve the visibility problem, the Z-Test being the most common. A main factor that significantly penalizes the energy efficiency of a GPU, especially in the mobile arena, is the so-called overdraw , which happens when a portion of an object is shaded and rendered but finally occluded by another object. This useless work results in a waste of energy; however, a conventional Z-Test only avoids a fraction of it. In this article we present a novel microarchitectural technique, the Omega-Test, to drastically reduce the overdraw on a Tile-Based Rendering (TBR) architecture. Graphics applications have a great degree of inter-frame coherence, which makes the output of a frame very similar to the previous one. The proposed approach leverages the frame-to-frame coherence by using the resulting information of the Z-Test for a tile (a buffer containing all the calculated pixel depths for a tile), which is discarded by nowadays GPUs, to predict the visibility of the same tile in the next frame. As a result, the Omega-Test early identifies occluded parts of the scene and avoids the rendering of non-visible surfaces eliminating costly computations and off-chip memory accesses. Our experimental evaluation shows average EDP savings in the overall GPU/Memory system of 26.4 percent and an average speedup of 16.3 percent for the evaluated benchmarks. |
Autor/es principal/es: | Corbalán-Navarro, D. Aragón, J.L. Anglada, M. de Lucas, E. Parcerisa, J.M. González, A. |
Director/es: | Aragón, J.L. González, A. |
Facultad/Departamentos/Servicios: | Facultades, Departamentos, Servicios y Escuelas::Departamentos de la UMU::Ingeniería y Tecnología de Computadores |
URI: | http://hdl.handle.net/10201/138264 |
DOI: | https://doi.org/10.1109/TVCG.2021.3087863 |
Tipo de documento: | info:eu-repo/semantics/article |
Número páginas / Extensión: | 14 |
Derechos: | info:eu-repo/semantics/openAccess Attribution-NonCommercial-NoDerivatives 4.0 Internacional |
Descripción: | ©2022. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/ This document is the Accepted, version of a Published Work that appeared in final form in IEEE Transactions on Visualization and Computer Graphics (TVCG). To access the final edited and published work see https://doi.org/10.1109/TVCG.2021.3087863 |
Aparece en las colecciones: | Artículos: Ingeniería y Tecnología de Computadores |
Ficheros en este ítem:
Fichero | Descripción | Tamaño | Formato | |
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Omega-Test-TVCG2022-preprint.pdf | 4,22 MB | Adobe PDF | Visualizar/Abrir |
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