2008
McCormick, Patrick; Anderson, Erik; Martin, Steven; Brownlee, Carson; Inman, Jeff; Maltrud, Mathew; Kim, Mark; Ahrens, James; Nau, Lee
Quantitatively driven visualization and analysis on emerging architectures Proceedings Article
In: Journal of Physics: Conference Series, pp. 012095, IOP Publishing 2008, (LA-UR-10-02239).
Abstract | Links | BibTeX | Tags: emerging architectures, quantitatively driven visualization, visualization
@inproceedings{mccormick2008quantitatively,
title = {Quantitatively driven visualization and analysis on emerging architectures},
author = {Patrick McCormick and Erik Anderson and Steven Martin and Carson Brownlee and Jeff Inman and Mathew Maltrud and Mark Kim and James Ahrens and Lee Nau},
url = {http://datascience.dsscale.org/wp-content/uploads/2016/06/QuantitativelyDrivenVisualizationAndAnalysisOnEmergingArchitectures.pdf},
year = {2008},
date = {2008-01-01},
booktitle = {Journal of Physics: Conference Series},
volume = {125},
number = {1},
pages = {012095},
organization = {IOP Publishing},
abstract = {We live in a world of ever-increasing amounts of information that is not only dynamically changing but also dramatically changing in complexity. This trend of “information overload” has quickly overwhelmed our capabilities to explore, hypothesize, and thus fully interpret the underlying details in these data. To further complicate matters, the computer architectures that have traditionally provided improved performance are undergoing a revolutionary change as manufacturers transition to building multi- and many-core processors. While these trends have the potential to lead to new scientific breakthroughs via simulation and modeling, they will do so in a disruptive manner, potentially placing a significant strain on software development activities including the overall data analysis process. In this paper we explore an approach that exploits these emerging architectures to provide an integrated environment for high-performance data analysis and visualization.},
note = {LA-UR-10-02239},
keywords = {emerging architectures, quantitatively driven visualization, visualization},
pubstate = {published},
tppubtype = {inproceedings}
}
We live in a world of ever-increasing amounts of information that is not only dynamically changing but also dramatically changing in complexity. This trend of “information overload” has quickly overwhelmed our capabilities to explore, hypothesize, and thus fully interpret the underlying details in these data. To further complicate matters, the computer architectures that have traditionally provided improved performance are undergoing a revolutionary change as manufacturers transition to building multi- and many-core processors. While these trends have the potential to lead to new scientific breakthroughs via simulation and modeling, they will do so in a disruptive manner, potentially placing a significant strain on software development activities including the overall data analysis process. In this paper we explore an approach that exploits these emerging architectures to provide an integrated environment for high-performance data analysis and visualization.
2004
McCormick, Patrick; Inman, Jeff; Ahrens, James; Hansen, Charles; Roth, Greg
Scout: A hardware-accelerated system for quantitatively driven visualization and analysis Proceedings Article
In: Visualization, 2004. IEEE, pp. 171–178, IEEE 2004, (LA-UR-04-7045).
Abstract | Links | BibTeX | Tags: hardware-accelerated, quantitatively driven visualization, scout
@inproceedings{mccormick2004scout,
title = {Scout: A hardware-accelerated system for quantitatively driven visualization and analysis},
author = {Patrick McCormick and Jeff Inman and James Ahrens and Charles Hansen and Greg Roth},
url = {http://datascience.dsscale.org/wp-content/uploads/2016/06/ScoutAHardware-AcceleratedSystemForQuantitativelyDrivenVisualizationAndAnalysis.pdf},
year = {2004},
date = {2004-01-01},
booktitle = {Visualization, 2004. IEEE},
pages = {171--178},
organization = {IEEE},
abstract = {Quantitative techniques for visualization are critical to the successful analysis of both acquired and simulated scientific data. Many visualization techniques rely on indirect mappings, such as transfer functions, to produce the final imagery. In many situations, it is preferable and more powerful to express these mappings as mathematical expressions, or queries, that can then be directly applied to the data. In this paper, we present a hardware-accelerated system that provides such capabilities and exploits current graphics hardware for portions of the computational tasks that would otherwise be executed on the CPU. In our approach, the direct programming of the graphics processor using a concise data parallel language, gives scientists the capability to efficiently explore and visualize data sets.},
note = {LA-UR-04-7045},
keywords = {hardware-accelerated, quantitatively driven visualization, scout},
pubstate = {published},
tppubtype = {inproceedings}
}
Quantitative techniques for visualization are critical to the successful analysis of both acquired and simulated scientific data. Many visualization techniques rely on indirect mappings, such as transfer functions, to produce the final imagery. In many situations, it is preferable and more powerful to express these mappings as mathematical expressions, or queries, that can then be directly applied to the data. In this paper, we present a hardware-accelerated system that provides such capabilities and exploits current graphics hardware for portions of the computational tasks that would otherwise be executed on the CPU. In our approach, the direct programming of the graphics processor using a concise data parallel language, gives scientists the capability to efficiently explore and visualize data sets.
: . .
1.
McCormick, Patrick; Anderson, Erik; Martin, Steven; Brownlee, Carson; Inman, Jeff; Maltrud, Mathew; Kim, Mark; Ahrens, James; Nau, Lee
Quantitatively driven visualization and analysis on emerging architectures Proceedings Article
In: Journal of Physics: Conference Series, pp. 012095, IOP Publishing 2008, (LA-UR-10-02239).
@inproceedings{mccormick2008quantitatively,
title = {Quantitatively driven visualization and analysis on emerging architectures},
author = {Patrick McCormick and Erik Anderson and Steven Martin and Carson Brownlee and Jeff Inman and Mathew Maltrud and Mark Kim and James Ahrens and Lee Nau},
url = {http://datascience.dsscale.org/wp-content/uploads/2016/06/QuantitativelyDrivenVisualizationAndAnalysisOnEmergingArchitectures.pdf},
year = {2008},
date = {2008-01-01},
booktitle = {Journal of Physics: Conference Series},
volume = {125},
number = {1},
pages = {012095},
organization = {IOP Publishing},
abstract = {We live in a world of ever-increasing amounts of information that is not only dynamically changing but also dramatically changing in complexity. This trend of “information overload” has quickly overwhelmed our capabilities to explore, hypothesize, and thus fully interpret the underlying details in these data. To further complicate matters, the computer architectures that have traditionally provided improved performance are undergoing a revolutionary change as manufacturers transition to building multi- and many-core processors. While these trends have the potential to lead to new scientific breakthroughs via simulation and modeling, they will do so in a disruptive manner, potentially placing a significant strain on software development activities including the overall data analysis process. In this paper we explore an approach that exploits these emerging architectures to provide an integrated environment for high-performance data analysis and visualization.},
note = {LA-UR-10-02239},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
We live in a world of ever-increasing amounts of information that is not only dynamically changing but also dramatically changing in complexity. This trend of “information overload” has quickly overwhelmed our capabilities to explore, hypothesize, and thus fully interpret the underlying details in these data. To further complicate matters, the computer architectures that have traditionally provided improved performance are undergoing a revolutionary change as manufacturers transition to building multi- and many-core processors. While these trends have the potential to lead to new scientific breakthroughs via simulation and modeling, they will do so in a disruptive manner, potentially placing a significant strain on software development activities including the overall data analysis process. In this paper we explore an approach that exploits these emerging architectures to provide an integrated environment for high-performance data analysis and visualization.
2.
McCormick, Patrick; Inman, Jeff; Ahrens, James; Hansen, Charles; Roth, Greg
Scout: A hardware-accelerated system for quantitatively driven visualization and analysis Proceedings Article
In: Visualization, 2004. IEEE, pp. 171–178, IEEE 2004, (LA-UR-04-7045).
@inproceedings{mccormick2004scout,
title = {Scout: A hardware-accelerated system for quantitatively driven visualization and analysis},
author = {Patrick McCormick and Jeff Inman and James Ahrens and Charles Hansen and Greg Roth},
url = {http://datascience.dsscale.org/wp-content/uploads/2016/06/ScoutAHardware-AcceleratedSystemForQuantitativelyDrivenVisualizationAndAnalysis.pdf},
year = {2004},
date = {2004-01-01},
booktitle = {Visualization, 2004. IEEE},
pages = {171--178},
organization = {IEEE},
abstract = {Quantitative techniques for visualization are critical to the successful analysis of both acquired and simulated scientific data. Many visualization techniques rely on indirect mappings, such as transfer functions, to produce the final imagery. In many situations, it is preferable and more powerful to express these mappings as mathematical expressions, or queries, that can then be directly applied to the data. In this paper, we present a hardware-accelerated system that provides such capabilities and exploits current graphics hardware for portions of the computational tasks that would otherwise be executed on the CPU. In our approach, the direct programming of the graphics processor using a concise data parallel language, gives scientists the capability to efficiently explore and visualize data sets.},
note = {LA-UR-04-7045},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Quantitative techniques for visualization are critical to the successful analysis of both acquired and simulated scientific data. Many visualization techniques rely on indirect mappings, such as transfer functions, to produce the final imagery. In many situations, it is preferable and more powerful to express these mappings as mathematical expressions, or queries, that can then be directly applied to the data. In this paper, we present a hardware-accelerated system that provides such capabilities and exploits current graphics hardware for portions of the computational tasks that would otherwise be executed on the CPU. In our approach, the direct programming of the graphics processor using a concise data parallel language, gives scientists the capability to efficiently explore and visualize data sets.