Leveraging Google’s Tensor Processing Units for tsunami-risk mitigation planning in the Pacific Northwest and beyond
Abstract. Tsunami-risk and flood-risk mitigation planning has particular importance for communities like those of the Pacific Northwest, where coastlines are extremely dynamic and a seismically-active subduction zone looms large. The challenge does not stop here for risk managers: mitigation options have multiplied since communities have realized the viability and benefits of nature-based solutions. To identify suitable mitigation options for their community, risk managers need the ability to rapidly evaluate several different options through fast and accessible tsunami models, but may lack high-performance computing infrastructure. The goal of this work is to leverage the newly developed Google's Tensor Processing Unit (TPU), a high-performance hardware accessible via the Google Cloud framework, to enable the rapid evaluation of different tsunami-risk mitigation strategies available to all communities. We establish a starting point through a numerical solver of the nonlinear shallow-water equations that uses a fifth-order Weighted Essentially Non-Oscillatory method with the Lax-Friedrichs flux splitting, and a Total Variation Diminishing third-order Runge-Kutta method for time discretization. We verify numerical solutions through several analytical solutions and benchmarks, reproduce several findings about one particular tsunami-risk mitigation strategy, and model tsunami runup at Crescent City, California whose topography comes from a high-resolution Digital Elevation Model. The direct measurements of the simulations performance, energy usage, and ease of execution show that our code could be a first step towards a community-based, user-friendly virtual laboratory that can be run by a minimally trained user on the cloud thanks to the ease of use of the Google Cloud Platform.
Ian Madden et al.
Status: open (until 06 Apr 2023)
- RC1: 'Comment on egusphere-2023-116', Ilhan Özgen-Xian, 27 Feb 2023 reply
- RC2: 'Comment on egusphere-2023-116', Anonymous Referee #2, 28 Mar 2023 reply
Ian Madden et al.
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Ian Madden et al.
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The authors explore the use of Google's TPUs for hydrodynamic simulations with application to tsunami modelling. They present a case study of Crescent City, CA, USA. The performance of the model is convincing. The application on TPUs is novel and interesting. Another perceived novelty for me is the evaluation of ease of execution, which is often left out of the discussion when analysing research code.
I recommend moderate revision (small additional simulations requested). Please see my comments below.
Comment #1: In the equations 1–3, the non-linear advection term from page 4 seems to be contained in the term 0.5 (h2 - b2)? It would help the reader to point out this term in these equations.
Comment #2: Can the authors comment further on the trade-offs of using a high-order scheme with an arguably large stencil with regard to parallel performance, numerical accuracy, and memory? This could be added to the discussion on page 22.
Comment #3: Can the authors give a bit more detail on the numerical treatment at shocks and at wet/dry fronts?
Comment #4: In terms of validation, it would be nice to have an empirical proof of grid convergence and test of convergence rate for the analytical cases (Cases 2.1—2.4). The authors should run simulations with successively refined grids and report L-norms and convergence rates. Tables of L-norms could be provided as an Appendix.
Comment #5: I feel that the beginning of Section 3.1 discussing the benefits of TPUs for communities with no access to HPC facilities should be moved to the introduction, because it is a good motivation for the conducted research. In that context, Behrens et al. (2022) also suggested cloud computing as a possible alternative to HPC facilites. Perhaps it's interesting to the authors.
Behrens et al. (2022). doi: 10.3389/feart.2022.762768
Comment #6: Can the authors comment on the process of getting access to Google's TPUs? From the website, the cloud service seems to be a paid service. Is it similar to renting time on an AWS or Microsoft Azure?
Comment #7: In section 3.3, the authors should briefly report the formal accuracy of GeoClaw.
Comment #8: I suggest that some part of the discussion could be separated as conclusions. I think the part starting with "Though just a starting point ..." on about line 359 on page 22 marks the end of discussion of results and starts the conclusions and outlook. But the authors may disagree.