Seismologist Scripps Oceanography received an award for real-time tsunami forecasting

Scripps Oceanographic seismologist Alice Gabriel with the award

The Gordon Bell Prize, often referred to as the Nobel Prize for Supercomputing, will be awarded at the annual Supercomputing Conference (SC25) on November 20, to reward innovation in high-performance computing (HPC) applications in science, engineering, and large-scale data analytics.

“It is a great honor for our team to receive this highest recognition in the field of supercomputing,” said Omar Ghattas, professor of mechanical engineering and senior faculty at the UT Oden Institute for Computational Engineering and Science, and senior author of the paper.

The method uses seafloor pressure data in the subduction zone, combined with 3D coupled acoustic gravity-wave equations, to infer earthquake-induced seafloor motion using new Bayesian inversion algorithms. The movement then provides a resource for computers to simulate the movement of a tsunami towards the coast and produce forecasts of wave heights at specific locations. The sequence is executed in a fraction of a second, providing an actionable early warning to inform first responders before tsunamis occur, which can reach coastlines in less than 15 minutes after an earthquake. The team's digital dual framework achieves 10 billion times the speed compared to current state-of-the-art methods.

A digital twin is a computer replica of a real geophysical system that combines physics-based models with real-time observations to estimate its evolving state and explore plausible futures.

“The biggest technical challenge in creating the Cascadia digital twin was the sheer scale of the problem as well as the need to provide real-time predictions,” said Stefan Henking, a research associate at the Oden Institute and first author. “Overcoming this challenge requires new parallel inversion algorithms, advanced numerical discretization libraries, and exploiting industry-leading GPU supercomputers.”

In addition to Gabriel, Ghattas, and Henking, the research team includes Sairam Venkat and Melinda Fernando of the Auden Institute in Utah; Veselin Dobrev, John Kamer, and Tzanio Kilev of LLNL.

The team's work used some of the world's most powerful supercomputers, including LLNL's top-ranking El Capitan, the National Energy Research Center's Perlmutter, the Swiss National Supercomputing Center's Alpine, and the Texas Center for Advanced Computing's Frontera.

“By combining the power of El Capitan with advanced high-order finite element algorithms, we were able to solve problems with more than an unprecedented 50 trillion unknowns on 40,000 GPUs, while maintaining excellent parallel efficiency,” said Tzanio Kolev, a computational mathematician at LLNL. “This work builds on years of GPU algorithmic research by the MFEM team, and it was great to see the resulting performance on the world's most powerful HPC system.”

Applications could enable early warning systems in many other fields to anticipate events ranging from wildfires, seismic ground motion and storm surges to space weather and detect threats.

The Gordon Bell Award-winning research, titled “Large-Scale Real-Time Bayesian Inference: A Digital Twin of Tsunami Early Warning Applied to the Cascadia Subduction Zone,” was published on November 15 as part of the proceedings of the SC25 conference. In addition to the Gordon Bell Award, the team received two other awards at SC25: the Hyperion Research HPC Innovation Excellence Award and the HPCWire Reader's Choice Award for Best Use of HPC in the Physical Sciences.

The ACM Gordon Bell Award is awarded annually, honoring outstanding achievement in high-performance computing applications. The award is named after the pioneering computer engineer who founded the US National Science Foundation's Directorate of Computer and Information Science and Engineering.

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