2020
Lukasczyk, Jonas; Garth, Christoph; Larsen, Matthew; Engelke, Wito; Hotz, Ingrid; Rogers, David; Ahrens, James; Maciejewski, Ross
Cinema Darkroom: A Deferred Rendering Framework for Large-Scale Datasets Proceedings Article
In: 2020 IEEE 10th Symposium on Large Data Analysis and Visualization (LDAV), pp. 37–41, IEEE 2020.
Links | BibTeX | Tags: cinema, post-processing
@inproceedings{lukasczyk2020cinema,
title = {Cinema Darkroom: A Deferred Rendering Framework for Large-Scale Datasets},
author = {Jonas Lukasczyk and Christoph Garth and Matthew Larsen and Wito Engelke and Ingrid Hotz and David Rogers and James Ahrens and Ross Maciejewski},
url = {https://www.computer.org/csdl/proceedings-article/ldav/2020/846800a037/1pZ0U4aglxe},
doi = {10.1109/LDAV51489.2020.00011},
year = {2020},
date = {2020-01-01},
urldate = {2020-01-01},
booktitle = {2020 IEEE 10th Symposium on Large Data Analysis and Visualization (LDAV)},
pages = {37--41},
organization = {IEEE},
keywords = {cinema, post-processing},
pubstate = {published},
tppubtype = {inproceedings}
}
2018
Bujack, Roxana; Rogers, David; Ahrens, James
Reducing Occlusion in Cinema Databases through Feature-Centric Visualizations Proceedings Article
In: Leipzig Symposium on Visualization In Applications (LEVIA), 2018.
Abstract | Links | BibTeX | Tags: cinema, feature, image space, in situ, moment invariants, occlusion, pattern detection
@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}
}
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.
2015
Kares, Robert J.
In-Situ Visualization Experiments with ParaView Cinema in RAGE Technical Report
2015, (LA-UR-15-28026).
Abstract | Links | BibTeX | Tags: catalyst, cinema, ParaView
@techreport{Kares2015,
title = {In-Situ Visualization Experiments with ParaView Cinema in RAGE},
author = {Robert J. Kares},
url = {http://datascience.dsscale.org/wp-content/uploads/2016/06/In-SituVisualizationExperimentsWithParaViewCinemaInRAGE.pdf},
year = {2015},
date = {2015-10-15},
abstract = {In a previous paper Robert Kares described some numerical experiments performed using the ParaView/Catalyst in-situ visualization infrastructure deployed in the Los Alamos RAGE radiation-hydrodynamics code to produce images from a running large scale 3D ICF simulation. One challenge of the in-situ approach apparent in these experiments was the difficulty of choosing parameters likes isosurface values for the visualizations to be produced from the running simulation without the benefit of prior knowledge of the simulation results and the resultant cost of recomputing in-situ generated images when parameters are chosen sub- optimally. A proposed method of addressing this difficulty is to simply render multiple images at runtime with a range of possible parameter values to produce a large database of images and to provide the user with a tool for managing the resulting database of imagery. Recently, ParaView/Catalyst has been extended to include such a capability via the so-called Cinema framework. Here Kares describes some initial experiments with the first delivery of Cinema and make some recommendations for future extensions of Cinema’s capabilities.},
note = {LA-UR-15-28026},
keywords = {catalyst, cinema, ParaView},
pubstate = {published},
tppubtype = {techreport}
}
In a previous paper Robert Kares described some numerical experiments performed using the ParaView/Catalyst in-situ visualization infrastructure deployed in the Los Alamos RAGE radiation-hydrodynamics code to produce images from a running large scale 3D ICF simulation. One challenge of the in-situ approach apparent in these experiments was the difficulty of choosing parameters likes isosurface values for the visualizations to be produced from the running simulation without the benefit of prior knowledge of the simulation results and the resultant cost of recomputing in-situ generated images when parameters are chosen sub- optimally. A proposed method of addressing this difficulty is to simply render multiple images at runtime with a range of possible parameter values to produce a large database of images and to provide the user with a tool for managing the resulting database of imagery. Recently, ParaView/Catalyst has been extended to include such a capability via the so-called Cinema framework. Here Kares describes some initial experiments with the first delivery of Cinema and make some recommendations for future extensions of Cinema’s capabilities.
Eatmon, Arnold
Generating Cinema Databases for In Situ Visualization of Ocean Modeling Simulations Presentation
05.10.2015, (LA-UR-15-27748).
Abstract | Links | BibTeX | Tags: cinema, oceanography simulation and modeling
@misc{Eatmon2015,
title = {Generating Cinema Databases for In Situ Visualization of Ocean Modeling Simulations},
author = {Arnold Eatmon},
url = {http://datascience.dsscale.org/wp-content/uploads/2016/12/Eatmon2015.pdf},
year = {2015},
date = {2015-10-05},
abstract = {Science is changing. In the last few years science has seen a dramatic shift towards data intensive discovery, a combination of past paradigms of discovery integrated with computational power and an ample supply of data from which we can derive information.
One notable problem with this is that while data and computational power are increasing, storage is decreasing. Storage in this day and age is a resource, and resources are inherently limited. Due to being a resource decisions must be made on how to wisely utilize storage to tackle scientific challenges.
Cinema databases allow for in situ processing and visualization, eliminating the need to write large amounts of data to disk. Cinema databases allow for scientists to not only view the data but also to interact with the data in meaningful ways. Simultaneously, cinema databases drastically reduce the amount of storage utilized in simulation.
In this project, I applied cinema database technology to a climate simulation model, MPAS-Ocean.},
note = {LA-UR-15-27748},
keywords = {cinema, oceanography simulation and modeling},
pubstate = {published},
tppubtype = {presentation}
}
Science is changing. In the last few years science has seen a dramatic shift towards data intensive discovery, a combination of past paradigms of discovery integrated with computational power and an ample supply of data from which we can derive information.
One notable problem with this is that while data and computational power are increasing, storage is decreasing. Storage in this day and age is a resource, and resources are inherently limited. Due to being a resource decisions must be made on how to wisely utilize storage to tackle scientific challenges.
Cinema databases allow for in situ processing and visualization, eliminating the need to write large amounts of data to disk. Cinema databases allow for scientists to not only view the data but also to interact with the data in meaningful ways. Simultaneously, cinema databases drastically reduce the amount of storage utilized in simulation.
In this project, I applied cinema database technology to a climate simulation model, MPAS-Ocean.
One notable problem with this is that while data and computational power are increasing, storage is decreasing. Storage in this day and age is a resource, and resources are inherently limited. Due to being a resource decisions must be made on how to wisely utilize storage to tackle scientific challenges.
Cinema databases allow for in situ processing and visualization, eliminating the need to write large amounts of data to disk. Cinema databases allow for scientists to not only view the data but also to interact with the data in meaningful ways. Simultaneously, cinema databases drastically reduce the amount of storage utilized in simulation.
In this project, I applied cinema database technology to a climate simulation model, MPAS-Ocean.
Shaikh, Uzma
Summer of Storyboards Presentation
05.10.2015, (LA-UR-15-27737).
Abstract | Links | BibTeX | Tags: cinema, storyboards
@misc{Shaikh2015,
title = {Summer of Storyboards},
author = {Uzma Shaikh},
url = {http://datascience.dsscale.org/wp-content/uploads/2016/07/Summer_of_Storyboards.pdf},
year = {2015},
date = {2015-10-05},
abstract = {This presentation summarizes a summer project to create a Cinema web interface which provides both a generalized or holistic view of the system in a global view and a specified view of the system in a web interface.},
note = {LA-UR-15-27737},
keywords = {cinema, storyboards},
pubstate = {published},
tppubtype = {presentation}
}
This presentation summarizes a summer project to create a Cinema web interface which provides both a generalized or holistic view of the system in a global view and a specified view of the system in a web interface.
Barnes, David C.
Image Clustering of Scientific Databases Presentation
05.10.2015, (LA-UR-15-27725).
Abstract | Links | BibTeX | Tags: cinema, clustering
@misc{Barnes2015,
title = {Image Clustering of Scientific Databases},
author = {David C. Barnes},
url = {http://datascience.dsscale.org/wp-content/uploads/2016/08/Image_Clustering_of_Scientific_Databases.pdf
http://datascience.dsscale.org/wp-content/uploads/2016/08/Data_Science_Cinema_Poster_Full.png},
year = {2015},
date = {2015-10-05},
abstract = {This presentation summarizes summer work to provide image clustering of scientific databases. },
note = {LA-UR-15-27725},
keywords = {cinema, clustering},
pubstate = {published},
tppubtype = {presentation}
}
This presentation summarizes summer work to provide image clustering of scientific databases.
Rogers, David; Ahrens, James; Patchett, John; DeMarle, David
Exploring Cinema with the Cinema Virtual Machine Proceedings Article
In: 2015, (Documentation/instructions. LA-UR-15-21934).
Abstract | Links | BibTeX | Tags: cinema, in-situ data analysis
@inproceedings{rogers2015exploring,
title = {Exploring Cinema with the Cinema Virtual Machine},
author = {David Rogers and James Ahrens and John Patchett and David DeMarle},
url = {http://datascience.dsscale.org/wp-content/uploads/2017/08/LA-UR-15-21934.pdf},
year = {2015},
date = {2015-05-27},
abstract = {Extreme scale scientific simulations are pushing the limits of scientific computation, and are stressing the limits of the data that we can store, explore, and understand. Options for extreme scale data analysis are often presented as a stark contrast: save massive data files to disk for interactive, exploratory visualization, or perform in situ analysis to save detailed data about phenomena a scientist knows about in advance. We propose that there is an alternative approach—a highly interactive, image-based approach that promotes exploration of simulation results, and is easily accessed through extensions to widely used open source tools. This new approach supports interactve exploration of a wide
range of results, while still significantly reducing data movement and storage.},
note = {Documentation/instructions. LA-UR-15-21934},
keywords = {cinema, in-situ data analysis},
pubstate = {published},
tppubtype = {inproceedings}
}
Extreme scale scientific simulations are pushing the limits of scientific computation, and are stressing the limits of the data that we can store, explore, and understand. Options for extreme scale data analysis are often presented as a stark contrast: save massive data files to disk for interactive, exploratory visualization, or perform in situ analysis to save detailed data about phenomena a scientist knows about in advance. We propose that there is an alternative approach—a highly interactive, image-based approach that promotes exploration of simulation results, and is easily accessed through extensions to widely used open source tools. This new approach supports interactve exploration of a wide
range of results, while still significantly reducing data movement and storage.
range of results, while still significantly reducing data movement and storage.
2014
Ahrens, James; Jourdain, Sebastien; O'Leary, Patrick; Patchett, John; Rogers, David; Petersen, Mark
An Image-based Approach to Extreme Scale In Situ Visualization and Analysis Presentation
22.11.2014, (LA-UR-14-26864).
Abstract | Links | BibTeX | Tags: cinema, in situ
@misc{Ahrens2014,
title = {An Image-based Approach to Extreme Scale In Situ Visualization and Analysis},
author = {James Ahrens and Sebastien Jourdain and Patrick O'Leary and John Patchett and David Rogers and Mark Petersen},
url = {http://datascience.dsscale.org/wp-content/uploads/2016/08/ImageBasedApproachSC2014v3.pdf
},
year = {2014},
date = {2014-11-22},
abstract = {This presentation given at SC14 by Los Alamos and Kitware scientists describes a new image based approach to extreme scale in-situ visualization and ayalysis.},
note = {LA-UR-14-26864},
keywords = {cinema, in situ},
pubstate = {published},
tppubtype = {presentation}
}
This presentation given at SC14 by Los Alamos and Kitware scientists describes a new image based approach to extreme scale in-situ visualization and ayalysis.
Ahrens, James; Jourdain, Sebastien; O'Leary, Patrick; Patchett, John; Rogers, David; Petersen, Mark
An Image-based Approach to Extreme Scale in Situ Visualization and Analysis Proceedings Article
In: Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis, pp. 424–434, IEEE Press, New Orleans, Louisana, 2014, ISBN: 978-1-4799-5500-8, (LA-UR-14-26864).
Abstract | Links | BibTeX | Tags: analysis, cinema, cinemascience, image-based, in-situ, visualization
@inproceedings{Ahrens:2014:IAE:2683593.2683640,
title = {An Image-based Approach to Extreme Scale in Situ Visualization and Analysis},
author = {James Ahrens and Sebastien Jourdain and Patrick O'Leary and John Patchett and David Rogers and Mark Petersen},
url = {http://datascience.dsscale.org/wp-content/uploads/2016/06/AnImage-basedApproachToExtremeScaleInSituvisualizationAndAnalysis.pdf
http://dx.doi.org/10.1109/SC.2014.40},
doi = {10.1109/SC.2014.40},
isbn = {978-1-4799-5500-8},
year = {2014},
date = {2014-01-01},
booktitle = {Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis},
pages = {424--434},
publisher = {IEEE Press},
address = {New Orleans, Louisana},
series = {SC '14},
abstract = {Extreme scale scientific simulations are leading a charge to exascale computation, and data analytics runs the risk of being a bottleneck to scientific discovery. Due to power and I/O constraints, we expect in situ visualization and analysis will be a critical component of these workflows. Options for extreme scale data analysis are often presented as a stark contrast: write large files to disk for interactive, exploratory analysis, or perform in situ analysis to save detailed data about phenomena that a scientists knows about in advance. We present a novel framework for a third option - a highly interactive, image-based approach that promotes exploration of simulation results, and is easily accessed through extensions to widely used open source tools. This in situ approach supports interactive exploration of a wide range of results, while still significantly reducing data movement and storage.},
note = {LA-UR-14-26864},
keywords = {analysis, cinema, cinemascience, image-based, in-situ, visualization},
pubstate = {published},
tppubtype = {inproceedings}
}
Extreme scale scientific simulations are leading a charge to exascale computation, and data analytics runs the risk of being a bottleneck to scientific discovery. Due to power and I/O constraints, we expect in situ visualization and analysis will be a critical component of these workflows. Options for extreme scale data analysis are often presented as a stark contrast: write large files to disk for interactive, exploratory analysis, or perform in situ analysis to save detailed data about phenomena that a scientists knows about in advance. We present a novel framework for a third option - a highly interactive, image-based approach that promotes exploration of simulation results, and is easily accessed through extensions to widely used open source tools. This in situ approach supports interactive exploration of a wide range of results, while still significantly reducing data movement and storage.
: . .
1.
Lukasczyk, Jonas; Garth, Christoph; Larsen, Matthew; Engelke, Wito; Hotz, Ingrid; Rogers, David; Ahrens, James; Maciejewski, Ross
Cinema Darkroom: A Deferred Rendering Framework for Large-Scale Datasets Proceedings Article
In: 2020 IEEE 10th Symposium on Large Data Analysis and Visualization (LDAV), pp. 37–41, IEEE 2020.
@inproceedings{lukasczyk2020cinema,
title = {Cinema Darkroom: A Deferred Rendering Framework for Large-Scale Datasets},
author = {Jonas Lukasczyk and Christoph Garth and Matthew Larsen and Wito Engelke and Ingrid Hotz and David Rogers and James Ahrens and Ross Maciejewski},
url = {https://www.computer.org/csdl/proceedings-article/ldav/2020/846800a037/1pZ0U4aglxe},
doi = {10.1109/LDAV51489.2020.00011},
year = {2020},
date = {2020-01-01},
urldate = {2020-01-01},
booktitle = {2020 IEEE 10th Symposium on Large Data Analysis and Visualization (LDAV)},
pages = {37--41},
organization = {IEEE},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
2.
Bujack, Roxana; Rogers, David; Ahrens, James
Reducing Occlusion in Cinema Databases through Feature-Centric Visualizations Proceedings Article
In: Leipzig Symposium on Visualization In Applications (LEVIA), 2018.
@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}
}
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.
3.
Kares, Robert J.
In-Situ Visualization Experiments with ParaView Cinema in RAGE Technical Report
2015, (LA-UR-15-28026).
@techreport{Kares2015,
title = {In-Situ Visualization Experiments with ParaView Cinema in RAGE},
author = {Robert J. Kares},
url = {http://datascience.dsscale.org/wp-content/uploads/2016/06/In-SituVisualizationExperimentsWithParaViewCinemaInRAGE.pdf},
year = {2015},
date = {2015-10-15},
abstract = {In a previous paper Robert Kares described some numerical experiments performed using the ParaView/Catalyst in-situ visualization infrastructure deployed in the Los Alamos RAGE radiation-hydrodynamics code to produce images from a running large scale 3D ICF simulation. One challenge of the in-situ approach apparent in these experiments was the difficulty of choosing parameters likes isosurface values for the visualizations to be produced from the running simulation without the benefit of prior knowledge of the simulation results and the resultant cost of recomputing in-situ generated images when parameters are chosen sub- optimally. A proposed method of addressing this difficulty is to simply render multiple images at runtime with a range of possible parameter values to produce a large database of images and to provide the user with a tool for managing the resulting database of imagery. Recently, ParaView/Catalyst has been extended to include such a capability via the so-called Cinema framework. Here Kares describes some initial experiments with the first delivery of Cinema and make some recommendations for future extensions of Cinema’s capabilities.},
note = {LA-UR-15-28026},
keywords = {},
pubstate = {published},
tppubtype = {techreport}
}
In a previous paper Robert Kares described some numerical experiments performed using the ParaView/Catalyst in-situ visualization infrastructure deployed in the Los Alamos RAGE radiation-hydrodynamics code to produce images from a running large scale 3D ICF simulation. One challenge of the in-situ approach apparent in these experiments was the difficulty of choosing parameters likes isosurface values for the visualizations to be produced from the running simulation without the benefit of prior knowledge of the simulation results and the resultant cost of recomputing in-situ generated images when parameters are chosen sub- optimally. A proposed method of addressing this difficulty is to simply render multiple images at runtime with a range of possible parameter values to produce a large database of images and to provide the user with a tool for managing the resulting database of imagery. Recently, ParaView/Catalyst has been extended to include such a capability via the so-called Cinema framework. Here Kares describes some initial experiments with the first delivery of Cinema and make some recommendations for future extensions of Cinema’s capabilities.
4.
Eatmon, Arnold
Generating Cinema Databases for In Situ Visualization of Ocean Modeling Simulations Presentation
05.10.2015, (LA-UR-15-27748).
@misc{Eatmon2015,
title = {Generating Cinema Databases for In Situ Visualization of Ocean Modeling Simulations},
author = {Arnold Eatmon},
url = {http://datascience.dsscale.org/wp-content/uploads/2016/12/Eatmon2015.pdf},
year = {2015},
date = {2015-10-05},
abstract = {Science is changing. In the last few years science has seen a dramatic shift towards data intensive discovery, a combination of past paradigms of discovery integrated with computational power and an ample supply of data from which we can derive information.
One notable problem with this is that while data and computational power are increasing, storage is decreasing. Storage in this day and age is a resource, and resources are inherently limited. Due to being a resource decisions must be made on how to wisely utilize storage to tackle scientific challenges.
Cinema databases allow for in situ processing and visualization, eliminating the need to write large amounts of data to disk. Cinema databases allow for scientists to not only view the data but also to interact with the data in meaningful ways. Simultaneously, cinema databases drastically reduce the amount of storage utilized in simulation.
In this project, I applied cinema database technology to a climate simulation model, MPAS-Ocean.},
note = {LA-UR-15-27748},
keywords = {},
pubstate = {published},
tppubtype = {presentation}
}
Science is changing. In the last few years science has seen a dramatic shift towards data intensive discovery, a combination of past paradigms of discovery integrated with computational power and an ample supply of data from which we can derive information.
One notable problem with this is that while data and computational power are increasing, storage is decreasing. Storage in this day and age is a resource, and resources are inherently limited. Due to being a resource decisions must be made on how to wisely utilize storage to tackle scientific challenges.
Cinema databases allow for in situ processing and visualization, eliminating the need to write large amounts of data to disk. Cinema databases allow for scientists to not only view the data but also to interact with the data in meaningful ways. Simultaneously, cinema databases drastically reduce the amount of storage utilized in simulation.
In this project, I applied cinema database technology to a climate simulation model, MPAS-Ocean.
One notable problem with this is that while data and computational power are increasing, storage is decreasing. Storage in this day and age is a resource, and resources are inherently limited. Due to being a resource decisions must be made on how to wisely utilize storage to tackle scientific challenges.
Cinema databases allow for in situ processing and visualization, eliminating the need to write large amounts of data to disk. Cinema databases allow for scientists to not only view the data but also to interact with the data in meaningful ways. Simultaneously, cinema databases drastically reduce the amount of storage utilized in simulation.
In this project, I applied cinema database technology to a climate simulation model, MPAS-Ocean.
5.
Shaikh, Uzma
Summer of Storyboards Presentation
05.10.2015, (LA-UR-15-27737).
@misc{Shaikh2015,
title = {Summer of Storyboards},
author = {Uzma Shaikh},
url = {http://datascience.dsscale.org/wp-content/uploads/2016/07/Summer_of_Storyboards.pdf},
year = {2015},
date = {2015-10-05},
abstract = {This presentation summarizes a summer project to create a Cinema web interface which provides both a generalized or holistic view of the system in a global view and a specified view of the system in a web interface.},
note = {LA-UR-15-27737},
keywords = {},
pubstate = {published},
tppubtype = {presentation}
}
This presentation summarizes a summer project to create a Cinema web interface which provides both a generalized or holistic view of the system in a global view and a specified view of the system in a web interface.
6.
Barnes, David C.
Image Clustering of Scientific Databases Presentation
05.10.2015, (LA-UR-15-27725).
@misc{Barnes2015,
title = {Image Clustering of Scientific Databases},
author = {David C. Barnes},
url = {http://datascience.dsscale.org/wp-content/uploads/2016/08/Image_Clustering_of_Scientific_Databases.pdf
http://datascience.dsscale.org/wp-content/uploads/2016/08/Data_Science_Cinema_Poster_Full.png},
year = {2015},
date = {2015-10-05},
abstract = {This presentation summarizes summer work to provide image clustering of scientific databases. },
note = {LA-UR-15-27725},
keywords = {},
pubstate = {published},
tppubtype = {presentation}
}
This presentation summarizes summer work to provide image clustering of scientific databases.
7.
Rogers, David; Ahrens, James; Patchett, John; DeMarle, David
Exploring Cinema with the Cinema Virtual Machine Proceedings Article
In: 2015, (Documentation/instructions. LA-UR-15-21934).
@inproceedings{rogers2015exploring,
title = {Exploring Cinema with the Cinema Virtual Machine},
author = {David Rogers and James Ahrens and John Patchett and David DeMarle},
url = {http://datascience.dsscale.org/wp-content/uploads/2017/08/LA-UR-15-21934.pdf},
year = {2015},
date = {2015-05-27},
abstract = {Extreme scale scientific simulations are pushing the limits of scientific computation, and are stressing the limits of the data that we can store, explore, and understand. Options for extreme scale data analysis are often presented as a stark contrast: save massive data files to disk for interactive, exploratory visualization, or perform in situ analysis to save detailed data about phenomena a scientist knows about in advance. We propose that there is an alternative approach—a highly interactive, image-based approach that promotes exploration of simulation results, and is easily accessed through extensions to widely used open source tools. This new approach supports interactve exploration of a wide
range of results, while still significantly reducing data movement and storage.},
note = {Documentation/instructions. LA-UR-15-21934},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Extreme scale scientific simulations are pushing the limits of scientific computation, and are stressing the limits of the data that we can store, explore, and understand. Options for extreme scale data analysis are often presented as a stark contrast: save massive data files to disk for interactive, exploratory visualization, or perform in situ analysis to save detailed data about phenomena a scientist knows about in advance. We propose that there is an alternative approach—a highly interactive, image-based approach that promotes exploration of simulation results, and is easily accessed through extensions to widely used open source tools. This new approach supports interactve exploration of a wide
range of results, while still significantly reducing data movement and storage.
range of results, while still significantly reducing data movement and storage.
8.
Ahrens, James; Jourdain, Sebastien; O'Leary, Patrick; Patchett, John; Rogers, David; Petersen, Mark
An Image-based Approach to Extreme Scale In Situ Visualization and Analysis Presentation
22.11.2014, (LA-UR-14-26864).
@misc{Ahrens2014,
title = {An Image-based Approach to Extreme Scale In Situ Visualization and Analysis},
author = {James Ahrens and Sebastien Jourdain and Patrick O'Leary and John Patchett and David Rogers and Mark Petersen},
url = {http://datascience.dsscale.org/wp-content/uploads/2016/08/ImageBasedApproachSC2014v3.pdf
},
year = {2014},
date = {2014-11-22},
abstract = {This presentation given at SC14 by Los Alamos and Kitware scientists describes a new image based approach to extreme scale in-situ visualization and ayalysis.},
note = {LA-UR-14-26864},
keywords = {},
pubstate = {published},
tppubtype = {presentation}
}
This presentation given at SC14 by Los Alamos and Kitware scientists describes a new image based approach to extreme scale in-situ visualization and ayalysis.
9.
Ahrens, James; Jourdain, Sebastien; O'Leary, Patrick; Patchett, John; Rogers, David; Petersen, Mark
An Image-based Approach to Extreme Scale in Situ Visualization and Analysis Proceedings Article
In: Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis, pp. 424–434, IEEE Press, New Orleans, Louisana, 2014, ISBN: 978-1-4799-5500-8, (LA-UR-14-26864).
@inproceedings{Ahrens:2014:IAE:2683593.2683640,
title = {An Image-based Approach to Extreme Scale in Situ Visualization and Analysis},
author = {James Ahrens and Sebastien Jourdain and Patrick O'Leary and John Patchett and David Rogers and Mark Petersen},
url = {http://datascience.dsscale.org/wp-content/uploads/2016/06/AnImage-basedApproachToExtremeScaleInSituvisualizationAndAnalysis.pdf
http://dx.doi.org/10.1109/SC.2014.40},
doi = {10.1109/SC.2014.40},
isbn = {978-1-4799-5500-8},
year = {2014},
date = {2014-01-01},
booktitle = {Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis},
pages = {424--434},
publisher = {IEEE Press},
address = {New Orleans, Louisana},
series = {SC '14},
abstract = {Extreme scale scientific simulations are leading a charge to exascale computation, and data analytics runs the risk of being a bottleneck to scientific discovery. Due to power and I/O constraints, we expect in situ visualization and analysis will be a critical component of these workflows. Options for extreme scale data analysis are often presented as a stark contrast: write large files to disk for interactive, exploratory analysis, or perform in situ analysis to save detailed data about phenomena that a scientists knows about in advance. We present a novel framework for a third option - a highly interactive, image-based approach that promotes exploration of simulation results, and is easily accessed through extensions to widely used open source tools. This in situ approach supports interactive exploration of a wide range of results, while still significantly reducing data movement and storage.},
note = {LA-UR-14-26864},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Extreme scale scientific simulations are leading a charge to exascale computation, and data analytics runs the risk of being a bottleneck to scientific discovery. Due to power and I/O constraints, we expect in situ visualization and analysis will be a critical component of these workflows. Options for extreme scale data analysis are often presented as a stark contrast: write large files to disk for interactive, exploratory analysis, or perform in situ analysis to save detailed data about phenomena that a scientists knows about in advance. We present a novel framework for a third option - a highly interactive, image-based approach that promotes exploration of simulation results, and is easily accessed through extensions to widely used open source tools. This in situ approach supports interactive exploration of a wide range of results, while still significantly reducing data movement and storage.