Articles | Volume 16, issue 12
https://doi.org/10.5194/gmd-16-3479-2023
https://doi.org/10.5194/gmd-16-3479-2023
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

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Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on egusphere-2023-116', Ilhan Özgen-Xian, 27 Feb 2023
    • AC1: 'Comment on egusphere-2023-116', Ian Madden, 05 May 2023
  • RC2: 'Comment on egusphere-2023-116', Anonymous Referee #2, 28 Mar 2023
    • AC1: 'Comment on egusphere-2023-116', Ian Madden, 05 May 2023
  • RC3: 'Comment on egusphere-2023-116', Anonymous Referee #3, 13 Apr 2023
    • AC1: 'Comment on egusphere-2023-116', Ian Madden, 05 May 2023
  • AC1: 'Comment on egusphere-2023-116', Ian Madden, 05 May 2023

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision | EF: Editorial file upload
AR by Ian Madden on behalf of the Authors (05 May 2023)  Author's response   Author's tracked changes   Manuscript 
ED: Publish as is (12 May 2023) by Deepak Subramani
AR by Ian Madden on behalf of the Authors (12 May 2023)
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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.