Articles | Volume 16, issue 12
https://doi.org/10.5194/gmd-16-3479-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-16-3479-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Development and technical paper
| 
27 Jun 2023
Development and technical paper |
| 27 Jun 2023
| 27 Jun 2023
Leveraging Google's Tensor Processing Units for tsunami-risk mitigation planning in the Pacific Northwest and beyond
Ian Madden
Institute for Computational and Mathematical Engineering, Stanford University, Stanford, CA, USA
Simone Marras
CORRESPONDING AUTHOR
Department of Mechanical Engineering & Center for Applied Mathematics and Statistics, New Jersey Institute of Technology, Newark, NJ, USA
Jenny Suckale
Institute for Computational and Mathematical Engineering, Stanford University, Stanford, CA, USA
Department of Geophysics, Doerr School of Sustainability, Stanford University, USA
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Short summary
To aid risk managers who may wish to rapidly assess tsunami risk but may lack high-performance computing infrastructure, we provide an accessible software package able to rapidly model tsunami inundation over real topography by leveraging Google's Tensor Processing Unit, a high-performance hardware. Minimally trained users can take advantage of the rapid modeling abilities provided by this package via a web browser thanks to the ease of use of Google Cloud Platform.
To aid risk managers who may wish to rapidly assess tsunami risk but may lack high-performance...
