Alexandros Nikolaos Ziogas

ACM named an eight-member team drawn from US institutions as the winner of the 2025 ACM Gordon Bell Prize for their project,  “Real-time Bayesian inference at extreme scale: A digital twin for tsunami early warning applied to the Cascadia subduction zone.” The ACM Gordon Bell Prize tracks the progress of parallel computing and rewards innovation in applying high-performance computing to challenges in science, engineering, and large-scale data analytics.

Existing state-of-the art high-performance computing simulations for early tsunami warning are developed primarily through models which process seismic data. The drawbacks of these approaches include: 1) They do not allow for enough warning time, as destructive tsunami waves can arrive onshore in under ten minutes, and 2) They fail to capture the complexities of earthquake ruptures which cause the Tsunamis.

The Gordon Bell Prize-winning team created a far more predictive early warning framework by developing a full-physics Bayesian inversion framework—popularly called “digital twin.” A digital twin is a virtual simulation of a physical process (or object) that uses real-time data from sensors to match its physical counterpart. The digital twin developed by this year’s winning team enables real-time, data-driven tsunami forecasting with dynamic adaptivity to complex source behavior.

With this approach, they achieved the fastest time-to-solution of a partial differential equation (PDE)-based Bayesian inverse problem with 1 billion parameters in 0.2 seconds, a ten-billion-fold speedup over the existing state-of-the-art. This is the largest-to-date unstructured mesh finite element (FE) simulation with 55.5 trillion degrees of freedom (DOF) on 43,520 GPUs, with 92% weak and 79% strong parallel efficiencies in scaling over a 128× increase of GPUs on the full-scale El Capitan system—the world’s largest supercomputer.

The team simulated a Tsunami in an area in the Pacific Ocean called the Cascadia Subduction Zone, which stretches 1000 km from northern California to British Columbia. This area has been eerily quiet for over 300 years—but is considered overdue for a magnitude 8.0–9.0 megathrust earthquake.

The members of the ACM Gordon Bell Prize-Winning team are Stefan Henneking, Sreeram Venkat, Milinda Fernando, and Omar Ghattas (all of The University of Texas at Austin); Veselin Dobrev, John Camier, Tzanio Kolev (all of Lawrence Livermore National Laboratory); and Alice-Agnes Gabriel (University of California San Diego).

Honorable Mention

This year an Honorable Mention for the ACM Gordon Bell Prize was given to a 10-member team from ETH Zurich for their project “Ab-initio Quantum Transport with the GW Approximation, 42,240 Atoms, and Sustained Exascale Performance.” Team members include Nicolas Vetsch, Alexandros Nikolaos Ziogas, Alexander Maeder, Vincent Maillou, Anders Winka, Jiang Cao, Grzegorz Kwasniewski, Leonard Deutschle (also affiliated with NVIDIA), Torsten Hoefler, and Mathieu Luisier.

The ACM Gordon Bell Prize was presented during the International Conference for High-Performance Computing, Networking, Storage and Analysis (SC25) in St. Louis, Missouri.

ACM named a six-member team from the Swiss Federal Institute of Technology (ETH) Zurich recipients of the 2019 ACM Gordon Bell Prize for their project, “A Data-Centric Approach to Extreme-Scale Ab initio Dissipative Quantum Transport Simulations.”

The ETH Zurich team introduced DaCe OMEN, a new framework for simulating the transport of electrical signals through nanoscale materials (such as the silicon atoms used in transistors). To better understand the thermal properties of transistors, the team simulated how electricity would be transported through a two-dimensional slice of transistor consisting of 10,0000 atoms. The ETH Zurich researchers simulated the 10,000-atom system 14 times faster than an earlier framework that was used for a 1,000-atom system. The DaCe OMEN code they developed for the simulation has been run on two top-6 hybrid supercomputers, reaching a sustained performance of 85.45 Pflop/s on 4,560 nodes of Summit (42.55% of the peak) in double precision, and 90.89 Pflop/s in mixed precision.

The ACM Gordon Bell Prize tracks the progress of parallel computing and rewards innovation in applying high performance computing to challenges in science, engineering, and large-scale data analytics. The award was presented by ACM President Cherri M. Pancake and Arndt Bode, Chair of the 2019 Gordon Bell Prize Award Committee, during the International Conference for High Performance Computing, Networking, Storage and Analysis (SC19) in Denver, Colorado.

Today’s commercial microchips contain 100,000,000 transistors in the span of a single millimeter, and managing heat generation and dissipation is one of the central problems in computer architecture. As the transistors on each microchip have become smaller and more densely packed, the amount of heat they generate has steadily increased. The cooling systems needed to keep supercomputers and data centers from overheating have become increasingly expensive. They estimate that cooling can consume up to 40% of the total electricity needed for data centers, amounting to cumulative costs of many billions of dollars per year.

Today’s supercomputers, which can perform up to 200 quadrillion calculations per second, allow scientists in many disciplines to gain new insights by processing a staggering number of variables. The ETH Zurich team used their simulation to develop a map of where heat is produced in a single transistor, how it is generated and how it is evacuated. It is hoped that a deeper understanding of these thermal characteristics could inform the development of new semiconductors with optimal heat-evacuating properties.

In recent years, the OMEN framework has been a popular quantum transport simulator for modeling nanoscale materials, but has experienced scaling bottlenecks. The ETH Zurich Team wrote a variation of OMEN that is Data Centric (DaCe OMEN). “We show that the key to eliminating the scaling bottleneck is in formulating a communication-avoiding algorithm,” the team writes in their paper. The ETH Zurich team’s solver yields data movement characteristics that can be used for performance and communication modeling, communication avoidance, and dataflow transformations. They go on to note that the speedup made by the DaCe OMEN framework is two orders of magnitude faster per atom than the original OMEN code.

The ETH Zurich team also built a graphical interface for the DaCe OMEN framework that includes a visualization of dataflow in lieu of a simple textual description. Anyone running the code can use the image representation to interact with the data directly. The team believes this new innovation could be applied to numerous scientific disciplines beyond nanoelectronics.

Winning team members include Alexandros Nikolaos Ziogas, Tal Ben-Nun, Timo Schneider and Torsten Hoefler, from ETH Zurich’s Scalable Parallel Computing Laboratory, as well as Guillermo Indalecio Fernández and Mathieu Luisier from ETH Zurich’s Integrated Systems Laboratory.