PHASTA Science Gateway for High Performance Computational Fluid Dynamics

PHASTA Science Gateway for High Performance Computational Fluid Dynamics. Smith, C., W., Abeysinghe, E., Marru, S., & Jansen, K., E. In Proceedings of the Practice and Experience on Advanced Research Computing - PEARC '18, pages 1-4, 7, 2018. ACM Press.

Paper

PHASTA Science Gateway for High Performance Computational Fluid Dynamics [link]

Website doi abstract bibtex

The Parallel Hierarchic Adaptive Stabilized Transient Analysis (PHASTA) software supports modeling compressible or incompressible, laminar or turbulent, steady or unsteady flows in 3D using unstructured grids. PHASTA has been applied to industrial and academic flows on complex, as-designed geometric models with over one billion mesh elements using upwards of one million compute cores. The PHASTA Science Gateway (phasta.scigap.org) brings these increasingly critical technologies to a larger user base by providing a central hub for simulation execution, simulation data management, and documentation. Researchers and engineers using the gateway can easily define and execute simulations on the TACC Stampede2 Skylake and Knights Landing nodes without being burdened by the details of remote access, the job scheduler, and filesystem configuration. In addition to simplifying the simulation execution process, the gateway creates a searchable archive of past jobs that can be shared with other users to support reproducibility and increase productivity. Our poster presents the construction of the gateway with Apache Airavata, the simulation definition process, applications it currently supports, and our ongoing efforts to expand functionality, the user base, and the community.

@inproceedings{
 title = {PHASTA Science Gateway for High Performance Computational Fluid Dynamics},
 type = {inproceedings},
 year = {2018},
 keywords = {Apache Airavata,CFD,Paralllel Unstructured Mesh,Pervasive Technology Institute,Science Gateway,Science Gateways Research Center},
 pages = {1-4},
 websites = {http://dl.acm.org/citation.cfm?doid=3219104.3229243},
 month = {7},
 publisher = {ACM Press},
 day = {22},
 city = {New York, New York, USA},
 id = {fecc18a6-bca9-30e3-9ee9-16d417d0ec33},
 created = {2019-09-12T15:18:41.894Z},
 accessed = {2019-09-12},
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 profile_id = {42d295c0-0737-38d6-8b43-508cab6ea85d},
 group_id = {0e433c5b-85c4-32aa-851c-c145aac9f80f},
 last_modified = {2019-09-12T17:32:38.330Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 private_publication = {false},
 abstract = {The Parallel Hierarchic Adaptive Stabilized Transient Analysis (PHASTA) software supports modeling compressible or incompressible, laminar or turbulent, steady or unsteady flows in 3D using unstructured grids. PHASTA has been applied to industrial and academic flows on complex, as-designed geometric models with over one billion mesh elements using upwards of one million compute cores. The PHASTA Science Gateway (phasta.scigap.org) brings these increasingly critical technologies to a larger user base by providing a central hub for simulation execution, simulation data management, and documentation. Researchers and engineers using the gateway can easily define and execute simulations on the TACC Stampede2 Skylake and Knights Landing nodes without being burdened by the details of remote access, the job scheduler, and filesystem configuration. In addition to simplifying the simulation execution process, the gateway creates a searchable archive of past jobs that can be shared with other users to support reproducibility and increase productivity. Our poster presents the construction of the gateway with Apache Airavata, the simulation definition process, applications it currently supports, and our ongoing efforts to expand functionality, the user base, and the community.},
 bibtype = {inproceedings},
 author = {Smith, Cameron W. and Abeysinghe, Eroma and Marru, Suresh and Jansen, Kenneth E.},
 doi = {10.1145/3219104.3229243},
 booktitle = {Proceedings of the Practice and Experience on Advanced Research Computing - PEARC '18}
}

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