2017
Adhinarayanan, Vignesh; Feng, Wu-chun; Rogers, David; Ahrens, James; Pakin, Scott
Characterizing and Modeling Power and Energy for Extreme-Scale In-Situ Visualization Proceedings Article
In: 2017 IEEE International Parallel and Distributed Processing Symposium (IPDPS), pp. 978-987, 2017, (LA-UR-16-22435).
Abstract | Links | BibTeX | Tags: energy efficiency, in-situ visualization
@inproceedings{7967188,
title = {Characterizing and Modeling Power and Energy for Extreme-Scale In-Situ Visualization},
author = {Vignesh Adhinarayanan and Wu-chun Feng and David Rogers and James Ahrens and Scott Pakin},
url = {http://datascience.dsscale.org/wp-content/uploads/2017/08/CharacterizingandModelingPowerandEnergyforExtreme-ScaleIn-SituVisualization.pdf},
doi = {10.1109/IPDPS.2017.113},
year = {2017},
date = {2017-05-01},
booktitle = {2017 IEEE International Parallel and Distributed Processing Symposium (IPDPS)},
pages = {978-987},
abstract = {Plans for exascale computing have identified power and energy as looming problems for simulations running at that scale. In particular, writing to disk all the data generated by these simulations is becoming prohibitively expensive due to the energy consumption of the supercomputer while it idles waiting for data to be written to permanent storage. In addition, the power cost of data movement is also steadily increasing. A solution to this problem is to write only a small fraction of the data generated while still maintaining the cognitive fidelity of the visualization. With domain scientists increasingly amenable towards adopting an in-situ framework that can identify and extract valuable data from extremely large simulation results and write them to permanent storage as compact images, a large-scale simulation will commit to disk a reduced dataset of data extracts that will be much smaller than the raw results, resulting in a savings in both power and energy. The goal of this paper is two-fold: (i) to understand the role of in-situ techniques in combating power and energy issues of extreme-scale visualization and (ii) to create a model for performance, power, energy, and storage to facilitate what-if analysis. Our experiments on a specially instrumented, dedicated 150-node cluster show that while it is difficult to achieve power savings in practice using in-situ techniques, applications can achieve significant energy savings due to shorter write times for in-situ visualization. We present a characterization of power and energy for in-situ visualization; an application-aware, architecture-specific methodology for modeling and analysis of such in-situ workflows; and results that uncover indirect power savings in visualization workflows for high-performance computing (HPC).},
note = {LA-UR-16-22435},
keywords = {energy efficiency, in-situ visualization},
pubstate = {published},
tppubtype = {inproceedings}
}
Plans for exascale computing have identified power and energy as looming problems for simulations running at that scale. In particular, writing to disk all the data generated by these simulations is becoming prohibitively expensive due to the energy consumption of the supercomputer while it idles waiting for data to be written to permanent storage. In addition, the power cost of data movement is also steadily increasing. A solution to this problem is to write only a small fraction of the data generated while still maintaining the cognitive fidelity of the visualization. With domain scientists increasingly amenable towards adopting an in-situ framework that can identify and extract valuable data from extremely large simulation results and write them to permanent storage as compact images, a large-scale simulation will commit to disk a reduced dataset of data extracts that will be much smaller than the raw results, resulting in a savings in both power and energy. The goal of this paper is two-fold: (i) to understand the role of in-situ techniques in combating power and energy issues of extreme-scale visualization and (ii) to create a model for performance, power, energy, and storage to facilitate what-if analysis. Our experiments on a specially instrumented, dedicated 150-node cluster show that while it is difficult to achieve power savings in practice using in-situ techniques, applications can achieve significant energy savings due to shorter write times for in-situ visualization. We present a characterization of power and energy for in-situ visualization; an application-aware, architecture-specific methodology for modeling and analysis of such in-situ workflows; and results that uncover indirect power savings in visualization workflows for high-performance computing (HPC).
2015
Adhinarayanan, Vignesh; Pakin, Scott; Rogers, David; Feng, Wu-chun; Ahrens, James
Performance, Power, and Energy of In-Situ and Post-Processing Visualization: A Case Study in Climate Simulation Proceedings Article
In: 2015, (Best Research Poster Finalist, LA-UR-15-26284).
Links | BibTeX | Tags: in-situ visualization
@inproceedings{Adhinarayanan2015climate,
title = {Performance, Power, and Energy of In-Situ and Post-Processing Visualization: A Case Study in Climate Simulation},
author = {Vignesh Adhinarayanan and Scott Pakin and David Rogers and Wu-chun Feng and James Ahrens},
url = {http://datascience.dsscale.org/wp-content/uploads/2017/08/PerformancePowerandEnergyofIn-SituandPost-ProcessingVisualization-ACaseStudyinClimateSimulation.pdf},
year = {2015},
date = {2015-01-01},
journal = {2015 ACM/IEEE International Conference for High Performance Computing, Networking, Storage, and Analysis (SC)},
note = {Best Research Poster Finalist, LA-UR-15-26284},
keywords = {in-situ visualization},
pubstate = {published},
tppubtype = {inproceedings}
}
: . .
1.
Adhinarayanan, Vignesh; Feng, Wu-chun; Rogers, David; Ahrens, James; Pakin, Scott
Characterizing and Modeling Power and Energy for Extreme-Scale In-Situ Visualization Proceedings Article
In: 2017 IEEE International Parallel and Distributed Processing Symposium (IPDPS), pp. 978-987, 2017, (LA-UR-16-22435).
@inproceedings{7967188,
title = {Characterizing and Modeling Power and Energy for Extreme-Scale In-Situ Visualization},
author = {Vignesh Adhinarayanan and Wu-chun Feng and David Rogers and James Ahrens and Scott Pakin},
url = {http://datascience.dsscale.org/wp-content/uploads/2017/08/CharacterizingandModelingPowerandEnergyforExtreme-ScaleIn-SituVisualization.pdf},
doi = {10.1109/IPDPS.2017.113},
year = {2017},
date = {2017-05-01},
booktitle = {2017 IEEE International Parallel and Distributed Processing Symposium (IPDPS)},
pages = {978-987},
abstract = {Plans for exascale computing have identified power and energy as looming problems for simulations running at that scale. In particular, writing to disk all the data generated by these simulations is becoming prohibitively expensive due to the energy consumption of the supercomputer while it idles waiting for data to be written to permanent storage. In addition, the power cost of data movement is also steadily increasing. A solution to this problem is to write only a small fraction of the data generated while still maintaining the cognitive fidelity of the visualization. With domain scientists increasingly amenable towards adopting an in-situ framework that can identify and extract valuable data from extremely large simulation results and write them to permanent storage as compact images, a large-scale simulation will commit to disk a reduced dataset of data extracts that will be much smaller than the raw results, resulting in a savings in both power and energy. The goal of this paper is two-fold: (i) to understand the role of in-situ techniques in combating power and energy issues of extreme-scale visualization and (ii) to create a model for performance, power, energy, and storage to facilitate what-if analysis. Our experiments on a specially instrumented, dedicated 150-node cluster show that while it is difficult to achieve power savings in practice using in-situ techniques, applications can achieve significant energy savings due to shorter write times for in-situ visualization. We present a characterization of power and energy for in-situ visualization; an application-aware, architecture-specific methodology for modeling and analysis of such in-situ workflows; and results that uncover indirect power savings in visualization workflows for high-performance computing (HPC).},
note = {LA-UR-16-22435},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Plans for exascale computing have identified power and energy as looming problems for simulations running at that scale. In particular, writing to disk all the data generated by these simulations is becoming prohibitively expensive due to the energy consumption of the supercomputer while it idles waiting for data to be written to permanent storage. In addition, the power cost of data movement is also steadily increasing. A solution to this problem is to write only a small fraction of the data generated while still maintaining the cognitive fidelity of the visualization. With domain scientists increasingly amenable towards adopting an in-situ framework that can identify and extract valuable data from extremely large simulation results and write them to permanent storage as compact images, a large-scale simulation will commit to disk a reduced dataset of data extracts that will be much smaller than the raw results, resulting in a savings in both power and energy. The goal of this paper is two-fold: (i) to understand the role of in-situ techniques in combating power and energy issues of extreme-scale visualization and (ii) to create a model for performance, power, energy, and storage to facilitate what-if analysis. Our experiments on a specially instrumented, dedicated 150-node cluster show that while it is difficult to achieve power savings in practice using in-situ techniques, applications can achieve significant energy savings due to shorter write times for in-situ visualization. We present a characterization of power and energy for in-situ visualization; an application-aware, architecture-specific methodology for modeling and analysis of such in-situ workflows; and results that uncover indirect power savings in visualization workflows for high-performance computing (HPC).
2.
Adhinarayanan, Vignesh; Pakin, Scott; Rogers, David; Feng, Wu-chun; Ahrens, James
Performance, Power, and Energy of In-Situ and Post-Processing Visualization: A Case Study in Climate Simulation Proceedings Article
In: 2015, (Best Research Poster Finalist, LA-UR-15-26284).
@inproceedings{Adhinarayanan2015climate,
title = {Performance, Power, and Energy of In-Situ and Post-Processing Visualization: A Case Study in Climate Simulation},
author = {Vignesh Adhinarayanan and Scott Pakin and David Rogers and Wu-chun Feng and James Ahrens},
url = {http://datascience.dsscale.org/wp-content/uploads/2017/08/PerformancePowerandEnergyofIn-SituandPost-ProcessingVisualization-ACaseStudyinClimateSimulation.pdf},
year = {2015},
date = {2015-01-01},
journal = {2015 ACM/IEEE International Conference for High Performance Computing, Networking, Storage, and Analysis (SC)},
note = {Best Research Poster Finalist, LA-UR-15-26284},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}