Geoscientific Model Development
Geoscientific Model Development
GMD
Articles | Volume 16, issue 12
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.
https://doi.org/10.5194/gmd-16-3479-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 16, issue 12
Development and technical paper
 | 
27 Jun 2023
Development and technical paper |  | 27 Jun 2023

Leveraging Google's Tensor Processing Units for tsunami-risk mitigation planning in the Pacific Northwest and beyond

Ian Madden, Simone Marras, and Jenny Suckale

Viewed

Total article views: 4,670 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
3,379 1,146 145 4,670 184 288
  • HTML: 3,379
  • PDF: 1,146
  • XML: 145
  • Total: 4,670
  • BibTeX: 184
  • EndNote: 288
Views and downloads (calculated since 08 Feb 2023)
Cumulative views and downloads (calculated since 08 Feb 2023)

Viewed (geographical distribution)

Total article views: 4,670 (including HTML, PDF, and XML) Thereof 4,634 with geography defined and 36 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Saved (final revised paper)

Latest update: 07 Jun 2026
Download
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.
Read more
Share