Real-Time Tsunami Forecasting Earns 2025 ACM Gordon Bell Prize
Breakthrough research enabling real-time tsunami forecasting has been awarded the prestigious 2025 ACM Gordon Bell Prize. A team from the University of Texas at Austin’s Oden Institute and Lawrence Livermore National Laboratory (LLNL) developed a digital twin framework that achieves a 10-billion-fold speedup over current state-of-the-art methods for predicting tsunami wave heights.
The system leverages earthquake motion as a source for computer simulations, rapidly forecasting wave heights at coastal locations. This speed is critical,as tsunamis can reach coastlines within as little as 15 minutes of an earthquake. The digital twin is a computational replica of the real geophysical system, combining physics-based models with real-time observations to estimate its current state and predict potential future scenarios.
“The biggest technical challenge in creating the cascadia digital twin was the enormous problem size combined with the need for providing forecasts in real time,” explained Stefan Henneking, Research Associate at the Oden Institute and the first author of the research. “Overcoming this challenge required novel parallel inversion algorithms, advanced numerical discretization libraries, and exploitation of leading-edge GPU supercomputers.”
The team utilized powerful supercomputers including LLNL’s El Capitan (currently top-ranked), the National Energy Research Scientific Computing Center’s Perlmutter, the Swiss National Supercomputing Center’s Alps, and the Texas Advanced Computing Center’s Frontera. They were able to solve problems with over 50 trillion unknowns on more than 40,000 GPUs, maintaining excellent parallel efficiency.
“By combining the power of El Capitan with advanced high-order finite element algorithms, we were able to achieve this,” said LLNL Computational Mathematician Tzanio Kolev. “this work builds on years of GPU algorithmic research by the MFEM team, and it was grate to see the resulting performance on the world’s most powerful HPC system.”
The research, titled ”Real-Time Bayesian Inference at Extreme Scale: A Digital Twin for Tsunami Early Warning Applied to the Cascadia Subduction Zone,” was published as part of the SC25 Conference Proceedings on November 15th.The team also received the Hyperion Research HPC Innovation Excellence Award and the HPCWire Reader’s Choice Award for Best Use of HPC in Physical Sciences at SC25.
The ACM Gordon Bell Prize, named after computer architect Gordon Bell, annually recognizes outstanding achievements in high-performance computing applications. The technology developed has potential applications beyond tsunami warnings, including early warning systems for wildfires, seismic ground motion, storm surge, space weather, and threat detection.
The research team includes Gabriel, Ghattas, Henneking, Sreeram Venkat, and Milinda Fernando of UT’s Oden Institute; and Veselin Dobrev, John Camier, and Tzanio Kolev of LLNL.
