2013
Woodring, Jonathan; Ahrens, James; Tautges, Timothy J; Peterka, Tom; Vishwanath, Venkatram; Geveci, Berk
On-demand unstructured mesh translation for reducing memory pressure during in situ analysis Proceedings Article
In: Proceedings of the 8th International Workshop on Ultrascale Visualization, pp. 3, ACM 2013, (LA-UR-13-27909).
Abstract | Links | BibTeX | Tags: in-situ, memory pressure, mesh translation, on-demand, unstructured mesh
@inproceedings{woodring2013demand,
title = {On-demand unstructured mesh translation for reducing memory pressure during in situ analysis},
author = {Jonathan Woodring and James Ahrens and Timothy J Tautges and Tom Peterka and Venkatram Vishwanath and Berk Geveci},
url = {http://datascience.dsscale.org/wp-content/uploads/2016/06/On-DemandUnstructuredMeshTranslationForReducingMemoryPressureDuringInSituAnalysis.pdf},
year = {2013},
date = {2013-01-01},
booktitle = {Proceedings of the 8th International Workshop on Ultrascale Visualization},
pages = {3},
organization = {ACM},
abstract = {When coupling two different mesh-based codes, for example with in situ analytics, the typical strategy is to explicitly copy data (deep copy) from one implementation to another, doing translation in the process. This is necessary because codes usually do not share data model interfaces or imple- mentations. The drawback is that data duplication results in an increased memory footprint for the coupled code. An alternative strategy, which we study in this paper, is to share mesh data through on-demand, fine-grained, run-time data model translation. This saves memory, which is an increas- ingly scarce resource at exascale, for the increased use of in situ analysis and decreasing memory per core. We study the performance of our method compared against a deep copy with in situ analysis at scale.},
note = {LA-UR-13-27909},
keywords = {in-situ, memory pressure, mesh translation, on-demand, unstructured mesh},
pubstate = {published},
tppubtype = {inproceedings}
}
When coupling two different mesh-based codes, for example with in situ analytics, the typical strategy is to explicitly copy data (deep copy) from one implementation to another, doing translation in the process. This is necessary because codes usually do not share data model interfaces or imple- mentations. The drawback is that data duplication results in an increased memory footprint for the coupled code. An alternative strategy, which we study in this paper, is to share mesh data through on-demand, fine-grained, run-time data model translation. This saves memory, which is an increas- ingly scarce resource at exascale, for the increased use of in situ analysis and decreasing memory per core. We study the performance of our method compared against a deep copy with in situ analysis at scale.
: . .
1.
Woodring, Jonathan; Ahrens, James; Tautges, Timothy J; Peterka, Tom; Vishwanath, Venkatram; Geveci, Berk
On-demand unstructured mesh translation for reducing memory pressure during in situ analysis Proceedings Article
In: Proceedings of the 8th International Workshop on Ultrascale Visualization, pp. 3, ACM 2013, (LA-UR-13-27909).
@inproceedings{woodring2013demand,
title = {On-demand unstructured mesh translation for reducing memory pressure during in situ analysis},
author = {Jonathan Woodring and James Ahrens and Timothy J Tautges and Tom Peterka and Venkatram Vishwanath and Berk Geveci},
url = {http://datascience.dsscale.org/wp-content/uploads/2016/06/On-DemandUnstructuredMeshTranslationForReducingMemoryPressureDuringInSituAnalysis.pdf},
year = {2013},
date = {2013-01-01},
booktitle = {Proceedings of the 8th International Workshop on Ultrascale Visualization},
pages = {3},
organization = {ACM},
abstract = {When coupling two different mesh-based codes, for example with in situ analytics, the typical strategy is to explicitly copy data (deep copy) from one implementation to another, doing translation in the process. This is necessary because codes usually do not share data model interfaces or imple- mentations. The drawback is that data duplication results in an increased memory footprint for the coupled code. An alternative strategy, which we study in this paper, is to share mesh data through on-demand, fine-grained, run-time data model translation. This saves memory, which is an increas- ingly scarce resource at exascale, for the increased use of in situ analysis and decreasing memory per core. We study the performance of our method compared against a deep copy with in situ analysis at scale.},
note = {LA-UR-13-27909},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
When coupling two different mesh-based codes, for example with in situ analytics, the typical strategy is to explicitly copy data (deep copy) from one implementation to another, doing translation in the process. This is necessary because codes usually do not share data model interfaces or imple- mentations. The drawback is that data duplication results in an increased memory footprint for the coupled code. An alternative strategy, which we study in this paper, is to share mesh data through on-demand, fine-grained, run-time data model translation. This saves memory, which is an increas- ingly scarce resource at exascale, for the increased use of in situ analysis and decreasing memory per core. We study the performance of our method compared against a deep copy with in situ analysis at scale.