A Combined Eulerian-Lagrangian Data Representation for Large-Scale Applications
The Eulerian and Lagrangian reference frames each provide a unique perspective when studying and visualizing results from scientific systems. As a result, many large-scale simulations produce data in both formats, and analysis tasks that simultaneously utilize information from both representations a...
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| Vydáno v: | IEEE transactions on visualization and computer graphics Ročník 23; číslo 10; s. 2248 - 2261 |
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| Hlavní autoři: | , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
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United States
IEEE
01.10.2017
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
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| ISSN: | 1077-2626, 1941-0506 |
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| Abstract | The Eulerian and Lagrangian reference frames each provide a unique perspective when studying and visualizing results from scientific systems. As a result, many large-scale simulations produce data in both formats, and analysis tasks that simultaneously utilize information from both representations are becoming increasingly popular. However, due to their fundamentally different nature, drawing correlations between these data formats is a computationally difficult task, especially in a large-scale setting. In this work, we present a new data representation which combines both reference frames into a joint Eulerian-Lagrangian format. By reorganizing Lagrangian information according to the Eulerian simulation grid into a "unit cell" based approach, we can provide an efficient out-of-core means of sampling, querying, and operating with both representations simultaneously. We also extend this design to generate multi-resolution subsets of the full data to suit the viewer's needs and provide a fast flow-aware trajectory construction scheme. We demonstrate the effectiveness of our method using three large-scale real world scientific datasets and provide insight into the types of performance gains that can be achieved. |
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| AbstractList | The Eulerian and Lagrangian reference frames each provide a unique perspective when studying and visualizing results from scientific systems. As a result, many large-scale simulations produce data in both formats, and analysis tasks that simultaneously utilize information from both representations are becoming increasingly popular. However, due to their fundamentally different nature, drawing correlations between these data formats is a computationally difficult task, especially in a large-scale setting. In this work, we present a new data representation which combines both reference frames into a joint Eulerian-Lagrangian format. By reorganizing Lagrangian information according to the Eulerian simulation grid into a "unit cell" based approach, we can provide an efficient out-of-core means of sampling, querying, and operating with both representations simultaneously. We also extend this design to generate multi-resolution subsets of the full data to suit the viewer's needs and provide a fast flow-aware trajectory construction scheme. We demonstrate the effectiveness of our method using three large-scale real world scientific datasets and provide insight into the types of performance gains that can be achieved. Not provided. |
| Author | Jinrong Xie Kwan-Liu Ma Sauer, Franz |
| Author_xml | – sequence: 1 givenname: Franz surname: Sauer fullname: Sauer, Franz email: fasauer@ucdavis.edu organization: Dept. of Comput. Sci., Univ. of California at Davis, Davis, CA, USA – sequence: 2 surname: Jinrong Xie fullname: Jinrong Xie email: jrxie@ucdavis.edu organization: Dept. of Comput. Sci., Univ. of California at Davis, Davis, CA, USA – sequence: 3 surname: Kwan-Liu Ma fullname: Kwan-Liu Ma email: ma@cs.ucdavis.edu organization: Dept. of Comput. Sci., Univ. of California at Davis, Davis, CA, USA |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/28113769$$D View this record in MEDLINE/PubMed https://www.osti.gov/biblio/1541564$$D View this record in Osti.gov |
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| SubjectTerms | Analytical models Computational modeling Computer Science Computer simulation Correlation Data models data structures Data visualization Flow visualization large-scale data multi-resolution Octrees particle data Representations volume data |
| Title | A Combined Eulerian-Lagrangian Data Representation for Large-Scale Applications |
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