@inproceedings{bujack2018reducing,

title = {Reducing Occlusion in Cinema Databases through Feature-Centric Visualizations},

author = {Roxana Bujack and David Rogers and James Ahrens},

url = {https://datascience.dsscale.org/wp-content/uploads/2019/01/ReducingOcclusioninCinemaDatabasesthroughFeature-CentricVisualizations.pdf},

year  = {2018},

date = {2018-01-01},

booktitle = {Leipzig Symposium on Visualization In Applications (LEVIA)},

abstract = {In modern supercomputer architectures, the I/O capabilities do not keep up with the computational speed. Image-based techniques are one very promising approach to a scalable output format for visual analysis, in which a reduced output that corresponds to the visible state of the simulation is rendered in-situ and stored to disk. These techniques can support interactive exploration of the data through image compositing and other methods, but automatic methods of highlighting data and reducing clutter can make these methods more effective. In this paper, we suggest a method of assisted exploration through the combination of feature-centric analysis with image space techniques and show how the reduction of the data to features of interest reduces occlusion in the output for a set of example applications.},

keywords = {cinema, feature, image space, in situ, moment invariants, occlusion, pattern detection},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{eurovisshort.20171134,

title = {In Situ and Post Processing Workflows for Asteroid Ablation Studies},

author = {John Patchett and Boonthanome Nouanesengsy and Galen Gisler and James Ahrens and Hans Hagen},

editor = {Barbora Kozlikova and Tobias Schreck and Thomas Wischgoll},

url = {http://datascience.dsscale.org/wp-content/uploads/2017/08/LA-UR-17-22699.pdf},

doi = {10.2312/eurovisshort.20171134},

isbn = {978-3-03868-043-7},

year  = {2017},

date = {2017-01-01},

booktitle = {EuroVis 2017 - Short Papers},

publisher = {The Eurographics Association},

abstract = {Simulation scientists need to make decisions about what and how much output to produce. They must balance their ability to efficiently ingest the analysis with their ability to get more analysis. We study this balance as a tradeoff between flexibility of saved data products and accessibility of saved data products. One end of the spectrum is raw data that comes directly from the simulation, making it highly flexible, but inaccessible due to its size and format. The other end of the spectrum is highly processed and comparatively small data, often in the form of imagery or single scalar values. This data is typically highly accessible, needing no special equipment or software, but lacks flexibility for deeper analysis than what is presented. We lay out a user driven model that considers the scientists' output needs in regards to flexibility and accessibility. This model allows us to analyze a real-world example of a large simulation lasting months of wall clock time on thousands of processing cores. Though the ensemble of simulation's original intent was to study asteroid generated tsunamis, the simulations are now being used beyond that scope to study the asteroid ablation as it moves through the atmosphere. With increasingly large supercomputers, designing workflows that support an intentional and understood balance of flexibility and accessibility is necessary. In this paper, we present a new strategy developed from a user driven perspective to support the collaborative capability between simulation developers, designers, users and analysts to effectively support science by wisely using both computer and human time.},

note = {LA-UR-17-22699},

keywords = {asteroid, in situ},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{hamilton2017extreme,

title = {Extreme Event Analysis in Next Generation Simulation Architectures},

author = {Stephen Hamilton and Randal Burns and Charles Meneveau and Perry Johnson and Peter Lindstrom and John Patchett and Alexander S. Szalay},

editor = {Julian M. Kunkel and Rio Yokota and Pavan Balaji and David Keyes},

url = {http://datascience.dsscale.org/wp-content/uploads/2017/08/ExtremeEventAnalysisinNextGenerationSimulationArchitectures.pdf},

doi = {10.1007/978-3-319-58667-0_15},

isbn = {978-3-319-58667-0},

year  = {2017},

date = {2017-01-01},

booktitle = {High Performance Computing: 32nd International Conference, ISC High Performance 2017, Frankfurt, Germany, June 18--22, 2017, Proceedings},

pages = {277--293},

publisher = {Springer International Publishing}},

organization = {Springer, Cham},

abstract = {Numerical simulations present challenges because they generate petabyte-scale data that must be extracted and reduced during the simulation. We demonstrate a seamless integration of feature extraction for a simulation of turbulent fluid dynamics. The simulation produces on the order of 6 terabytes per timestep. In order to analyze and store this data, we extract velocity data from a dilated volume of the strong vortical regions and also store a lossy compressed representation of the data. Both reduce data by one or more orders of magnitude. We extract data from user checkpoints in transit while they reside on temporary burst buffer SSD stores. In this way, analysis and compression algorithms are designed to meet specific time constraints so they do not interfere with simulation computations. Our results demonstrate that we can perform feature extraction on a world-class direct numerical simulation of turbulence while it is running and gather meaningful scientific data for archival and post analysis.},

keywords = {in situ, visualization},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{bujack2018reducing,

title = {Reducing Occlusion in Cinema Databases through Feature-Centric Visualizations},

author = {Roxana Bujack and David Rogers and James Ahrens},

url = {https://datascience.dsscale.org/wp-content/uploads/2019/01/ReducingOcclusioninCinemaDatabasesthroughFeature-CentricVisualizations.pdf},

year  = {2018},

date = {2018-01-01},

booktitle = {Leipzig Symposium on Visualization In Applications (LEVIA)},

abstract = {In modern supercomputer architectures, the I/O capabilities do not keep up with the computational speed. Image-based techniques are one very promising approach to a scalable output format for visual analysis, in which a reduced output that corresponds to the visible state of the simulation is rendered in-situ and stored to disk. These techniques can support interactive exploration of the data through image compositing and other methods, but automatic methods of highlighting data and reducing clutter can make these methods more effective. In this paper, we suggest a method of assisted exploration through the combination of feature-centric analysis with image space techniques and show how the reduction of the data to features of interest reduces occlusion in the output for a set of example applications.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{eurovisshort.20171134,

title = {In Situ and Post Processing Workflows for Asteroid Ablation Studies},

author = {John Patchett and Boonthanome Nouanesengsy and Galen Gisler and James Ahrens and Hans Hagen},

editor = {Barbora Kozlikova and Tobias Schreck and Thomas Wischgoll},

url = {http://datascience.dsscale.org/wp-content/uploads/2017/08/LA-UR-17-22699.pdf},

doi = {10.2312/eurovisshort.20171134},

isbn = {978-3-03868-043-7},

year  = {2017},

date = {2017-01-01},

booktitle = {EuroVis 2017 - Short Papers},

publisher = {The Eurographics Association},

abstract = {Simulation scientists need to make decisions about what and how much output to produce. They must balance their ability to efficiently ingest the analysis with their ability to get more analysis. We study this balance as a tradeoff between flexibility of saved data products and accessibility of saved data products. One end of the spectrum is raw data that comes directly from the simulation, making it highly flexible, but inaccessible due to its size and format. The other end of the spectrum is highly processed and comparatively small data, often in the form of imagery or single scalar values. This data is typically highly accessible, needing no special equipment or software, but lacks flexibility for deeper analysis than what is presented. We lay out a user driven model that considers the scientists' output needs in regards to flexibility and accessibility. This model allows us to analyze a real-world example of a large simulation lasting months of wall clock time on thousands of processing cores. Though the ensemble of simulation's original intent was to study asteroid generated tsunamis, the simulations are now being used beyond that scope to study the asteroid ablation as it moves through the atmosphere. With increasingly large supercomputers, designing workflows that support an intentional and understood balance of flexibility and accessibility is necessary. In this paper, we present a new strategy developed from a user driven perspective to support the collaborative capability between simulation developers, designers, users and analysts to effectively support science by wisely using both computer and human time.},

note = {LA-UR-17-22699},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{hamilton2017extreme,

title = {Extreme Event Analysis in Next Generation Simulation Architectures},

author = {Stephen Hamilton and Randal Burns and Charles Meneveau and Perry Johnson and Peter Lindstrom and John Patchett and Alexander S. Szalay},

editor = {Julian M. Kunkel and Rio Yokota and Pavan Balaji and David Keyes},

url = {http://datascience.dsscale.org/wp-content/uploads/2017/08/ExtremeEventAnalysisinNextGenerationSimulationArchitectures.pdf},

doi = {10.1007/978-3-319-58667-0_15},

isbn = {978-3-319-58667-0},

year  = {2017},

date = {2017-01-01},

booktitle = {High Performance Computing: 32nd International Conference, ISC High Performance 2017, Frankfurt, Germany, June 18--22, 2017, Proceedings},

pages = {277--293},

publisher = {Springer International Publishing}},

organization = {Springer, Cham},

abstract = {Numerical simulations present challenges because they generate petabyte-scale data that must be extracted and reduced during the simulation. We demonstrate a seamless integration of feature extraction for a simulation of turbulent fluid dynamics. The simulation produces on the order of 6 terabytes per timestep. In order to analyze and store this data, we extract velocity data from a dilated volume of the strong vortical regions and also store a lossy compressed representation of the data. Both reduce data by one or more orders of magnitude. We extract data from user checkpoints in transit while they reside on temporary burst buffer SSD stores. In this way, analysis and compression algorithms are designed to meet specific time constraints so they do not interfere with simulation computations. Our results demonstrate that we can perform feature extraction on a world-class direct numerical simulation of turbulence while it is running and gather meaningful scientific data for archival and post analysis.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}