TSUNAMI WARNINGSLLNL Scientists Explore Real-Time Tsunami Warning System on World’s Fastest Supercomputer

Scientists at Lawrence Livermore National Laboratory(LLNL) have helped develop an advanced, real-time tsunami forecasting system — powered by El Capitan, the world’s fastest supercomputer — that could dramatically improve early warning capabilities for coastal communities near earthquake zones.

The exascale El Capitan, which has a theoretical peak performance of 2.79 quintillion calculations per second, was developed with funding from the Advanced Simulation and Computing (ASC) program at the National Nuclear Security Administration (NNSA). As described in a preprint paper selected as a finalist for the 2025 ACM Gordon Bell Prize, researchers at LLNL harnessed the machine’s full computing power in a one-time, offline precomputation step, prior to the system’s transition to classified national-security work. The goal: to generate an immense library of physics-based simulations, linking earthquake-induced seafloor motion to resulting tsunami waves.

The project used more than 43,500 AMD Instinct MI300A Accelerated Processing Units (APUs) to solve extreme-scale acoustic-gravity wave propagation problems, producing a rich dataset that enables real-time tsunami forecasting on much smaller systems. By front-loading the intensive computation work on El Capitan, the team was able to solve an extremely high-fidelity Bayesian inverse problem that makes it possible to generate rapid predictions in seconds — during an actual tsunami — using modest GPU clusters. Researchers said this capability could fundamentally transform the future of early warning systems and save lives.

Developed in partnership with the Oden Institute at the University of Texas at Austin (UT Austin) and the Scripps Institution of Oceanography at the University of California, San Diego (UC San Diego), the resulting tsunami “digital twin” models the effects of seafloor earthquake motion using real-time pressure sensor data and advanced physics-based simulations. This dynamic, data-driven system can infer the earthquake’s impact on the ocean floor and forecast the tsunami’s behavior in real time — complete with uncertainty quantification.

“This is the first digital twin with this level of complexity that runs in real time,” said LLNL computational mathematician Tzanio Kolev, co-author on the paper. “It combines extreme-scale forward simulation with advanced statistical methods to extract physics-based predictions from sensor data at unprecedented speed.”