2019
Dutta, Soumya; Brady, Riley; Maltrud, Mathew; Wolfram, Philip; Bujack, Roxana
Leveraging Lagrangian Analysis for Discriminating Nutrient Origins Proceedings Article
In: Bujack, Roxana; Feige, Kathrin; Rink, Karsten; Zeckzer, Dirk (Ed.): Workshop on Visualisation in Environmental Sciences (EnvirVis), pp. 17-24, The Eurographics Association, 2019, ISBN: 978-3-03868-086-4, (LA-UR-19-22455).
Links | BibTeX | Tags: human-centered computing, scientific visualization
@inproceedings{N20103:2019,
title = {Leveraging Lagrangian Analysis for Discriminating Nutrient Origins},
author = {Soumya Dutta and Riley Brady and Mathew Maltrud and Philip Wolfram and Roxana Bujack},
editor = {Roxana Bujack and Kathrin Feige and Karsten Rink and Dirk Zeckzer},
url = {https://dsscale.org/wp-content/uploads/2019/07/leveraging-lagrangian-analysis-for-discriminating-nutrient-origins.pdf},
doi = {10.2312/envirvis.20191100},
isbn = {978-3-03868-086-4},
year = {2019},
date = {2019-01-01},
booktitle = {Workshop on Visualisation in Environmental Sciences (EnvirVis)},
pages = {17-24},
publisher = {The Eurographics Association},
note = {LA-UR-19-22455},
keywords = {human-centered computing, scientific visualization},
pubstate = {published},
tppubtype = {inproceedings}
}
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.
Biswas, Ayan; Dutta, Soumya; Pulido, Jesus; Ahrens, James
In Situ Data-driven Adaptive Sampling for Large-scale Simulation Data Summarization Proceedings Article
In: Proceedings of the Workshop on In Situ Infrastructures for Enabling Extreme-Scale Analysis and Visualization, pp. 13–18, ACM, Dallas, Texas, 2018, ISBN: 978-1-4503-6579-6.
Abstract | Links | BibTeX | Tags: human-centered computing, Mathematics and Computing, scientific visualization, statistical paradigms
@inproceedings{Biswas:2018:SDA:3281464.3281467,
title = {In Situ Data-driven Adaptive Sampling for Large-scale Simulation Data Summarization},
author = {Ayan Biswas and Soumya Dutta and Jesus Pulido and James Ahrens},
url = {http://doi.acm.org/10.1145/3281464.3281467
https://datascience.dsscale.org/wp-content/uploads/2019/01/LA-UR-18-28035.pdf},
doi = {10.1145/3281464.3281467},
isbn = {978-1-4503-6579-6},
year = {2018},
date = {2018-01-01},
booktitle = {Proceedings of the Workshop on In Situ Infrastructures for Enabling Extreme-Scale Analysis and Visualization},
pages = {13--18},
publisher = {ACM},
address = {Dallas, Texas},
series = {ISAV '18},
abstract = {Recent advancements in high-performance computing have enabled scientists to model various scientific phenomena in great detail. However, the analysis and visualization of the output data from such large-scale simulations are posing significant challenges due to their excessive size and disk I/O bottlenecks. One viable solution to this problem is to create a sub-sampled dataset which is able to preserve the important information of the data and also is significantly smaller in size compared to the raw data. Creating an in situ workflow for generating such intelligently sub-sampled datasets is of prime importance for such simulations. In this work, we propose an information-driven data sampling technique and compare it with two well-known sampling methods to demonstrate the superiority of the proposed method. The in situ performance of the proposed method is evaluated by applying it to the Nyx Cosmology simulation. We compare and contrast the performance of these various sampling algorithms and provide a holistic view of all the methods so that the scientists can choose appropriate sampling schemes based on their analysis requirements.},
keywords = {human-centered computing, Mathematics and Computing, scientific visualization, statistical paradigms},
pubstate = {published},
tppubtype = {inproceedings}
}
Recent advancements in high-performance computing have enabled scientists to model various scientific phenomena in great detail. However, the analysis and visualization of the output data from such large-scale simulations are posing significant challenges due to their excessive size and disk I/O bottlenecks. One viable solution to this problem is to create a sub-sampled dataset which is able to preserve the important information of the data and also is significantly smaller in size compared to the raw data. Creating an in situ workflow for generating such intelligently sub-sampled datasets is of prime importance for such simulations. In this work, we propose an information-driven data sampling technique and compare it with two well-known sampling methods to demonstrate the superiority of the proposed method. The in situ performance of the proposed method is evaluated by applying it to the Nyx Cosmology simulation. We compare and contrast the performance of these various sampling algorithms and provide a holistic view of all the methods so that the scientists can choose appropriate sampling schemes based on their analysis requirements.
: . .
1.
Dutta, Soumya; Brady, Riley; Maltrud, Mathew; Wolfram, Philip; Bujack, Roxana
Leveraging Lagrangian Analysis for Discriminating Nutrient Origins Proceedings Article
In: Bujack, Roxana; Feige, Kathrin; Rink, Karsten; Zeckzer, Dirk (Ed.): Workshop on Visualisation in Environmental Sciences (EnvirVis), pp. 17-24, The Eurographics Association, 2019, ISBN: 978-3-03868-086-4, (LA-UR-19-22455).
@inproceedings{N20103:2019,
title = {Leveraging Lagrangian Analysis for Discriminating Nutrient Origins},
author = {Soumya Dutta and Riley Brady and Mathew Maltrud and Philip Wolfram and Roxana Bujack},
editor = {Roxana Bujack and Kathrin Feige and Karsten Rink and Dirk Zeckzer},
url = {https://dsscale.org/wp-content/uploads/2019/07/leveraging-lagrangian-analysis-for-discriminating-nutrient-origins.pdf},
doi = {10.2312/envirvis.20191100},
isbn = {978-3-03868-086-4},
year = {2019},
date = {2019-01-01},
booktitle = {Workshop on Visualisation in Environmental Sciences (EnvirVis)},
pages = {17-24},
publisher = {The Eurographics Association},
note = {LA-UR-19-22455},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
2.
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.
3.
Biswas, Ayan; Dutta, Soumya; Pulido, Jesus; Ahrens, James
In Situ Data-driven Adaptive Sampling for Large-scale Simulation Data Summarization Proceedings Article
In: Proceedings of the Workshop on In Situ Infrastructures for Enabling Extreme-Scale Analysis and Visualization, pp. 13–18, ACM, Dallas, Texas, 2018, ISBN: 978-1-4503-6579-6.
@inproceedings{Biswas:2018:SDA:3281464.3281467,
title = {In Situ Data-driven Adaptive Sampling for Large-scale Simulation Data Summarization},
author = {Ayan Biswas and Soumya Dutta and Jesus Pulido and James Ahrens},
url = {http://doi.acm.org/10.1145/3281464.3281467
https://datascience.dsscale.org/wp-content/uploads/2019/01/LA-UR-18-28035.pdf},
doi = {10.1145/3281464.3281467},
isbn = {978-1-4503-6579-6},
year = {2018},
date = {2018-01-01},
booktitle = {Proceedings of the Workshop on In Situ Infrastructures for Enabling Extreme-Scale Analysis and Visualization},
pages = {13--18},
publisher = {ACM},
address = {Dallas, Texas},
series = {ISAV '18},
abstract = {Recent advancements in high-performance computing have enabled scientists to model various scientific phenomena in great detail. However, the analysis and visualization of the output data from such large-scale simulations are posing significant challenges due to their excessive size and disk I/O bottlenecks. One viable solution to this problem is to create a sub-sampled dataset which is able to preserve the important information of the data and also is significantly smaller in size compared to the raw data. Creating an in situ workflow for generating such intelligently sub-sampled datasets is of prime importance for such simulations. In this work, we propose an information-driven data sampling technique and compare it with two well-known sampling methods to demonstrate the superiority of the proposed method. The in situ performance of the proposed method is evaluated by applying it to the Nyx Cosmology simulation. We compare and contrast the performance of these various sampling algorithms and provide a holistic view of all the methods so that the scientists can choose appropriate sampling schemes based on their analysis requirements.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Recent advancements in high-performance computing have enabled scientists to model various scientific phenomena in great detail. However, the analysis and visualization of the output data from such large-scale simulations are posing significant challenges due to their excessive size and disk I/O bottlenecks. One viable solution to this problem is to create a sub-sampled dataset which is able to preserve the important information of the data and also is significantly smaller in size compared to the raw data. Creating an in situ workflow for generating such intelligently sub-sampled datasets is of prime importance for such simulations. In this work, we propose an information-driven data sampling technique and compare it with two well-known sampling methods to demonstrate the superiority of the proposed method. The in situ performance of the proposed method is evaluated by applying it to the Nyx Cosmology simulation. We compare and contrast the performance of these various sampling algorithms and provide a holistic view of all the methods so that the scientists can choose appropriate sampling schemes based on their analysis requirements.