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.
2006
Ahrens, James; Moreland, Kenneth; Geveci, Berk; Cedilnik, Andy; Favre, Jean
Remote large data visualization in the paraview framework Proceedings Article
In: Proceedings of the 6th Eurographics conference on Parallel Graphics and Visualization, pp. 163–170, Eurographics Association 2006, (LA-UR-10-02236).
Abstract | Links | BibTeX | Tags: computer graphics, parallel processing
@inproceedings{cedilnik2006remote,
title = {Remote large data visualization in the paraview framework},
author = {James Ahrens and Kenneth Moreland and Berk Geveci and Andy Cedilnik and Jean Favre},
url = {http://datascience.dsscale.org/wp-content/uploads/2016/06/RemoteLargeDataVisualizationInTheParaViewFramework.pdf},
year = {2006},
date = {2006-01-01},
booktitle = {Proceedings of the 6th Eurographics conference on Parallel Graphics and Visualization},
pages = {163--170},
organization = {Eurographics Association},
abstract = {Scientists are using remote parallel computing resources to run scientific simulations to model a range of scientific problems. Visualization tools are used to understand the massive datasets that result from these simulations. A number of problems need to be overcome in order to create a visualization tool that effectively visualizes these datasets under this scenario. Problems include how to effectively process and display massive datasets and how to effectively communicate data and control information between the geographically distributed computing and visualization resources. We believe a solution that incorporates a data parallel data server, a data parallel rendering server and client controller is key. Using this data server, render server, client model as a basis, this paper describes in detail a set of integrated solutions to remote/distributed visualization problems including presenting an efficient M to N parallel algorithm for transferring geometry data, an effective server interface abstraction and parallel rendering techniques for a range of rendering modalities including tiled display walls and CAVEs.},
note = {LA-UR-10-02236},
keywords = {computer graphics, parallel processing},
pubstate = {published},
tppubtype = {inproceedings}
}
Scientists are using remote parallel computing resources to run scientific simulations to model a range of scientific problems. Visualization tools are used to understand the massive datasets that result from these simulations. A number of problems need to be overcome in order to create a visualization tool that effectively visualizes these datasets under this scenario. Problems include how to effectively process and display massive datasets and how to effectively communicate data and control information between the geographically distributed computing and visualization resources. We believe a solution that incorporates a data parallel data server, a data parallel rendering server and client controller is key. Using this data server, render server, client model as a basis, this paper describes in detail a set of integrated solutions to remote/distributed visualization problems including presenting an efficient M to N parallel algorithm for transferring geometry data, an effective server interface abstraction and parallel rendering techniques for a range of rendering modalities including tiled display walls and CAVEs.
: . .
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.
2.
Ahrens, James; Moreland, Kenneth; Geveci, Berk; Cedilnik, Andy; Favre, Jean
Remote large data visualization in the paraview framework Proceedings Article
In: Proceedings of the 6th Eurographics conference on Parallel Graphics and Visualization, pp. 163–170, Eurographics Association 2006, (LA-UR-10-02236).
@inproceedings{cedilnik2006remote,
title = {Remote large data visualization in the paraview framework},
author = {James Ahrens and Kenneth Moreland and Berk Geveci and Andy Cedilnik and Jean Favre},
url = {http://datascience.dsscale.org/wp-content/uploads/2016/06/RemoteLargeDataVisualizationInTheParaViewFramework.pdf},
year = {2006},
date = {2006-01-01},
booktitle = {Proceedings of the 6th Eurographics conference on Parallel Graphics and Visualization},
pages = {163--170},
organization = {Eurographics Association},
abstract = {Scientists are using remote parallel computing resources to run scientific simulations to model a range of scientific problems. Visualization tools are used to understand the massive datasets that result from these simulations. A number of problems need to be overcome in order to create a visualization tool that effectively visualizes these datasets under this scenario. Problems include how to effectively process and display massive datasets and how to effectively communicate data and control information between the geographically distributed computing and visualization resources. We believe a solution that incorporates a data parallel data server, a data parallel rendering server and client controller is key. Using this data server, render server, client model as a basis, this paper describes in detail a set of integrated solutions to remote/distributed visualization problems including presenting an efficient M to N parallel algorithm for transferring geometry data, an effective server interface abstraction and parallel rendering techniques for a range of rendering modalities including tiled display walls and CAVEs.},
note = {LA-UR-10-02236},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Scientists are using remote parallel computing resources to run scientific simulations to model a range of scientific problems. Visualization tools are used to understand the massive datasets that result from these simulations. A number of problems need to be overcome in order to create a visualization tool that effectively visualizes these datasets under this scenario. Problems include how to effectively process and display massive datasets and how to effectively communicate data and control information between the geographically distributed computing and visualization resources. We believe a solution that incorporates a data parallel data server, a data parallel rendering server and client controller is key. Using this data server, render server, client model as a basis, this paper describes in detail a set of integrated solutions to remote/distributed visualization problems including presenting an efficient M to N parallel algorithm for transferring geometry data, an effective server interface abstraction and parallel rendering techniques for a range of rendering modalities including tiled display walls and CAVEs.