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}
}
2015
Adhinarayanan, Vignesh
Performance, Power and Energy of In-situ and Post-processing Visualization: A Case Study in Climate Simulation Presentation
05.10.2015, (LA-UR-15-27749).
Abstract | Links | BibTeX | Tags: energy, in-situ, performance, post-processing, power
@misc{Adhinarayanan2015,
title = {Performance, Power and Energy of In-situ and Post-processing Visualization: A Case Study in Climate Simulation},
author = {Vignesh Adhinarayanan},
url = {http://datascience.dsscale.org/wp-content/uploads/2016/07/PerformancePowerAndEnergyOfInSituAndPostProcessingVisualization.pdf},
year = {2015},
date = {2015-10-05},
abstract = {This presentation summarizes a summer study of the performance, power, and energy trade-offs among traditional post-processing, modern post-processing, and in-situ visualization pipelines. It includes both detailed sub-component level power measurements within a node to gain detailed insights and measurements at scale to understand problems unique to big supercomputers.},
note = {LA-UR-15-27749},
keywords = {energy, in-situ, performance, post-processing, power},
pubstate = {published},
tppubtype = {presentation}
}
This presentation summarizes a summer study of the performance, power, and energy trade-offs among traditional post-processing, modern post-processing, and in-situ visualization pipelines. It includes both detailed sub-component level power measurements within a node to gain detailed insights and measurements at scale to understand problems unique to big supercomputers.
Adhinarayanan, Vignesh; Feng, Wu-chun; Woodring, Jonathan; Rogers, David; Ahrens, James
On the Greenness of In-Situ and Post-Processing Visualization Pipelines Proceedings Article
In: 11th workshop on High-Performance, Power-Aware Computing (HPPAC), Hyderabad, India, 2015, (LA-UR-15-21414).
Abstract | Links | BibTeX | Tags: greenness, in-situ, pipelines, post-processing, visualization
@inproceedings{vignesh-in-situ-hppac15,
title = {On the Greenness of In-Situ and Post-Processing Visualization Pipelines},
author = {Vignesh Adhinarayanan and Wu-chun Feng and Jonathan Woodring and David Rogers and James Ahrens},
url = {http://datascience.dsscale.org/wp-content/uploads/2016/06/OnTheGreenessOfIn-SituAndPost-ProcessingVisualizationPipelines.pdf},
year = {2015},
date = {2015-05-01},
booktitle = {11th workshop on High-Performance, Power-Aware Computing (HPPAC)},
address = {Hyderabad, India},
abstract = {Post-processing visualization pipelines are tradi- tionally used to gain insight from simulation data. However, changes to the system architecture for high-performance com- puting (HPC), dictated by the exascale goal, have limited the applicability of post-processing visualization. As an alternative, in-situ pipelines are proposed in order to enhance the knowl- edge discovery process via “real-time” visualization. Quantitative studies have already shown how in-situ visualization can improve performance and reduce storage needs at the cost of scientific exploration capabilities. However, to fully understand the trade- off space, a head-to-head comparison of power and energy (between the two types of visualization pipelines) is necessary. Thus, in this work, we study the greenness (i.e., power, energy, and energy efficiency) of the in-situ and the post-processing visualization pipelines, using a proxy heat-transfer simulation as an example. For a realistic I/O load, the in-situ pipeline consumes 43% less energy than the post-processing pipeline. Contrary to expectations, our findings also show that only 9% of the total energy is saved by reducing off-chip data movement, while the rest of the savings comes from reducing the system idle time. This suggests an alternative set of optimization techniques for reducing the power consumption of the traditional post- processing pipeline.},
note = {LA-UR-15-21414},
keywords = {greenness, in-situ, pipelines, post-processing, visualization},
pubstate = {published},
tppubtype = {inproceedings}
}
Post-processing visualization pipelines are tradi- tionally used to gain insight from simulation data. However, changes to the system architecture for high-performance com- puting (HPC), dictated by the exascale goal, have limited the applicability of post-processing visualization. As an alternative, in-situ pipelines are proposed in order to enhance the knowl- edge discovery process via “real-time” visualization. Quantitative studies have already shown how in-situ visualization can improve performance and reduce storage needs at the cost of scientific exploration capabilities. However, to fully understand the trade- off space, a head-to-head comparison of power and energy (between the two types of visualization pipelines) is necessary. Thus, in this work, we study the greenness (i.e., power, energy, and energy efficiency) of the in-situ and the post-processing visualization pipelines, using a proxy heat-transfer simulation as an example. For a realistic I/O load, the in-situ pipeline consumes 43% less energy than the post-processing pipeline. Contrary to expectations, our findings also show that only 9% of the total energy is saved by reducing off-chip data movement, while the rest of the savings comes from reducing the system idle time. This suggests an alternative set of optimization techniques for reducing the power consumption of the traditional post- processing pipeline.
: . .
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.
Adhinarayanan, Vignesh
Performance, Power and Energy of In-situ and Post-processing Visualization: A Case Study in Climate Simulation Presentation
05.10.2015, (LA-UR-15-27749).
@misc{Adhinarayanan2015,
title = {Performance, Power and Energy of In-situ and Post-processing Visualization: A Case Study in Climate Simulation},
author = {Vignesh Adhinarayanan},
url = {http://datascience.dsscale.org/wp-content/uploads/2016/07/PerformancePowerAndEnergyOfInSituAndPostProcessingVisualization.pdf},
year = {2015},
date = {2015-10-05},
abstract = {This presentation summarizes a summer study of the performance, power, and energy trade-offs among traditional post-processing, modern post-processing, and in-situ visualization pipelines. It includes both detailed sub-component level power measurements within a node to gain detailed insights and measurements at scale to understand problems unique to big supercomputers.},
note = {LA-UR-15-27749},
keywords = {},
pubstate = {published},
tppubtype = {presentation}
}
This presentation summarizes a summer study of the performance, power, and energy trade-offs among traditional post-processing, modern post-processing, and in-situ visualization pipelines. It includes both detailed sub-component level power measurements within a node to gain detailed insights and measurements at scale to understand problems unique to big supercomputers.
3.
Adhinarayanan, Vignesh; Feng, Wu-chun; Woodring, Jonathan; Rogers, David; Ahrens, James
On the Greenness of In-Situ and Post-Processing Visualization Pipelines Proceedings Article
In: 11th workshop on High-Performance, Power-Aware Computing (HPPAC), Hyderabad, India, 2015, (LA-UR-15-21414).
@inproceedings{vignesh-in-situ-hppac15,
title = {On the Greenness of In-Situ and Post-Processing Visualization Pipelines},
author = {Vignesh Adhinarayanan and Wu-chun Feng and Jonathan Woodring and David Rogers and James Ahrens},
url = {http://datascience.dsscale.org/wp-content/uploads/2016/06/OnTheGreenessOfIn-SituAndPost-ProcessingVisualizationPipelines.pdf},
year = {2015},
date = {2015-05-01},
booktitle = {11th workshop on High-Performance, Power-Aware Computing (HPPAC)},
address = {Hyderabad, India},
abstract = {Post-processing visualization pipelines are tradi- tionally used to gain insight from simulation data. However, changes to the system architecture for high-performance com- puting (HPC), dictated by the exascale goal, have limited the applicability of post-processing visualization. As an alternative, in-situ pipelines are proposed in order to enhance the knowl- edge discovery process via “real-time” visualization. Quantitative studies have already shown how in-situ visualization can improve performance and reduce storage needs at the cost of scientific exploration capabilities. However, to fully understand the trade- off space, a head-to-head comparison of power and energy (between the two types of visualization pipelines) is necessary. Thus, in this work, we study the greenness (i.e., power, energy, and energy efficiency) of the in-situ and the post-processing visualization pipelines, using a proxy heat-transfer simulation as an example. For a realistic I/O load, the in-situ pipeline consumes 43% less energy than the post-processing pipeline. Contrary to expectations, our findings also show that only 9% of the total energy is saved by reducing off-chip data movement, while the rest of the savings comes from reducing the system idle time. This suggests an alternative set of optimization techniques for reducing the power consumption of the traditional post- processing pipeline.},
note = {LA-UR-15-21414},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Post-processing visualization pipelines are tradi- tionally used to gain insight from simulation data. However, changes to the system architecture for high-performance com- puting (HPC), dictated by the exascale goal, have limited the applicability of post-processing visualization. As an alternative, in-situ pipelines are proposed in order to enhance the knowl- edge discovery process via “real-time” visualization. Quantitative studies have already shown how in-situ visualization can improve performance and reduce storage needs at the cost of scientific exploration capabilities. However, to fully understand the trade- off space, a head-to-head comparison of power and energy (between the two types of visualization pipelines) is necessary. Thus, in this work, we study the greenness (i.e., power, energy, and energy efficiency) of the in-situ and the post-processing visualization pipelines, using a proxy heat-transfer simulation as an example. For a realistic I/O load, the in-situ pipeline consumes 43% less energy than the post-processing pipeline. Contrary to expectations, our findings also show that only 9% of the total energy is saved by reducing off-chip data movement, while the rest of the savings comes from reducing the system idle time. This suggests an alternative set of optimization techniques for reducing the power consumption of the traditional post- processing pipeline.