In Situ Visualization and Production of Extract Databases
- Intelligent Light, Rutherford, NJ (United States)
Simulations running at high concurrency on HPC systems generate large volumes of data that are impractical to write to disk due to time and storage constraints. Applications often adapt by saving data infrequently, resulting in datasets with poor temporal resolution. This can make datasets difficult to interpret during post hoc visualization and analysis, or worse, it can lead to lost science. In Situ visualization and analysis can enable efficient production of small data products such as rendered images or surface extracts that consist of polygonal geometry plus fields. These data products are far smaller than their source data and can be processed much more economically in a traditional post hoc workflow using far fewer computational resources. We used the SENSEI and Libsim in situ infrastructures to implement rendering workflow and surface data extraction workflows in the AVF-LESLIE combustion code. Finally, these workflows were then demonstrated at high levels of concurrency and showed significant data reductions and limited impact on the simulation runtime.
- Research Organization:
- Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States). Oak Ridge Leadership Computing Facility (OLCF)
- Sponsoring Organization:
- USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR) (SC-21)
- Grant/Contract Number:
- SC0007548; SC0012449
- OSTI ID:
- 1567661
- Journal Information:
- Supercomputing frontiers and innovations, Journal Name: Supercomputing frontiers and innovations Journal Issue: 4 Vol. 3; ISSN 2313-8734
- Publisher:
- South Ural State UniversityCopyright Statement
- Country of Publication:
- United States
- Language:
- English
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