2018
Abram, Greg; Navrátil, Paul; Grossett, Pascal; Rogers, David; Ahrens, James
Galaxy: Asynchronous Ray Tracing for Large High-Fidelity Visualization Proceedings Article
In: 2018 IEEE 8th Symposium on Large Data Analysis and Visualization (LDAV), pp. 72-76, 2018, ISSN: null, (LA-UR-18-26088).
Abstract | Links | BibTeX | Tags: computer graphics, human-centered computing, ray tracing, rendering, visualization
@inproceedings{8739241,
title = {Galaxy: Asynchronous Ray Tracing for Large High-Fidelity Visualization},
author = {Greg Abram and Paul Navrátil and Pascal Grossett and David Rogers and James Ahrens},
doi = {10.1109/LDAV.2018.8739241},
issn = {null},
year = {2018},
date = {2018-10-01},
booktitle = {2018 IEEE 8th Symposium on Large Data Analysis and Visualization (LDAV)},
pages = {72-76},
abstract = {We present Galaxy, a fully asynchronous distributed parallel rendering engine geared towards using full global illumination for large-scale visualization. Galaxy provides performant distributed rendering of complex lighting and material models, particularly those that require ray traversal across nodes. Our design is favorable for tightly-coupled in situ scenarios, where data remains on simulation nodes. By employing asynchronous framebuffer updates and a novel subtractive lighting model, we achieve acceptable image quality from the first ray generation, and improve quality throughout the render epoch. On simulated in situ rendering tasks, Galaxy outperforms the current best-of-breed scientific ray tracer by over 3× for distributed geometric and particle data, while providing expanded rendering capability for global illumination and complex materials.},
note = {LA-UR-18-26088},
keywords = {computer graphics, human-centered computing, ray tracing, rendering, visualization},
pubstate = {published},
tppubtype = {inproceedings}
}
We present Galaxy, a fully asynchronous distributed parallel rendering engine geared towards using full global illumination for large-scale visualization. Galaxy provides performant distributed rendering of complex lighting and material models, particularly those that require ray traversal across nodes. Our design is favorable for tightly-coupled in situ scenarios, where data remains on simulation nodes. By employing asynchronous framebuffer updates and a novel subtractive lighting model, we achieve acceptable image quality from the first ray generation, and improve quality throughout the render epoch. On simulated in situ rendering tasks, Galaxy outperforms the current best-of-breed scientific ray tracer by over 3× for distributed geometric and particle data, while providing expanded rendering capability for global illumination and complex materials.
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1.
Abram, Greg; Navrátil, Paul; Grossett, Pascal; Rogers, David; Ahrens, James
Galaxy: Asynchronous Ray Tracing for Large High-Fidelity Visualization Proceedings Article
In: 2018 IEEE 8th Symposium on Large Data Analysis and Visualization (LDAV), pp. 72-76, 2018, ISSN: null, (LA-UR-18-26088).
@inproceedings{8739241,
title = {Galaxy: Asynchronous Ray Tracing for Large High-Fidelity Visualization},
author = {Greg Abram and Paul Navrátil and Pascal Grossett and David Rogers and James Ahrens},
doi = {10.1109/LDAV.2018.8739241},
issn = {null},
year = {2018},
date = {2018-10-01},
booktitle = {2018 IEEE 8th Symposium on Large Data Analysis and Visualization (LDAV)},
pages = {72-76},
abstract = {We present Galaxy, a fully asynchronous distributed parallel rendering engine geared towards using full global illumination for large-scale visualization. Galaxy provides performant distributed rendering of complex lighting and material models, particularly those that require ray traversal across nodes. Our design is favorable for tightly-coupled in situ scenarios, where data remains on simulation nodes. By employing asynchronous framebuffer updates and a novel subtractive lighting model, we achieve acceptable image quality from the first ray generation, and improve quality throughout the render epoch. On simulated in situ rendering tasks, Galaxy outperforms the current best-of-breed scientific ray tracer by over 3× for distributed geometric and particle data, while providing expanded rendering capability for global illumination and complex materials.},
note = {LA-UR-18-26088},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
We present Galaxy, a fully asynchronous distributed parallel rendering engine geared towards using full global illumination for large-scale visualization. Galaxy provides performant distributed rendering of complex lighting and material models, particularly those that require ray traversal across nodes. Our design is favorable for tightly-coupled in situ scenarios, where data remains on simulation nodes. By employing asynchronous framebuffer updates and a novel subtractive lighting model, we achieve acceptable image quality from the first ray generation, and improve quality throughout the render epoch. On simulated in situ rendering tasks, Galaxy outperforms the current best-of-breed scientific ray tracer by over 3× for distributed geometric and particle data, while providing expanded rendering capability for global illumination and complex materials.