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Saving Lives with Supercomputer Storm Simulations

227The University of Texas at Austin’s Institute for Computational Engineering and Sciences (ICES) has received a $3 million National Science Foundation (NSF) grant to upgrade computer codes used to carry out predictive simulations of storm surges and thus help emergency managers develop hurricane evacuation plans.

Speed is essential for disaster planning, and the new simulations will take advantage of developments in supercomputing to increase the speed of the simulation, so that converting large databases of weather and topographical data into storm surge predictions can be completed within an hour — half the current time. The project will also be a significant exercise in recasting legacy software for future generations of supercomputers.

Clint Dawson, director of the ICES Computational Hydraulics Group, has been predicting hurricane storm surge for the past 15 years, by feeding hurricane data into a computational program called ADCIRC. With the $3 million NSF grant, Dawson, a professor of aerospace engineering and engineering mechanics, and collaborators at Louisiana State University, The University of Notre Dame, and The University of North Carolina at Chapel Hill are overhauling ‘ADCIRC’ into a version called ‘STORM’.

The software is designed to perform more efficiently across a variety of computer hardware architectures. Dawson said: “The idea is how do we keep the program up to date and modernize it for the next generation.”

Using computational methods that detail the location and depth of surges, Dawson and collaborators have been helping Texas emergency managers develop hurricane evacuation plans and studied storm surges for every hurricane to strike the United States since the late 1990s.

Since first being developed in the mid-1990s, ADCIRC has been widely used by the National Oceanic and Atmospheric Administration, the US Army Corp of Engineers and academic researchers to simulate and predict water flow in coastal areas of the United States.

Storm surge prediction is a popular use for the program, but the governing equations describing fluid flow can be applied to investigate other research questions. During the Deep Water Horizon oil spill, for example, Dawson used ADCIRC to predict oil dispersal paths up to three days in advance.

Whatever the fluid flow problem being analyzed, the ADCIRC system works by analyzing the interaction between relatively static elements, such as coastal and undersea topography, and dynamic ones, such as how a hurricane influences water height and water velocity. ADCIRC’s computational algorithms produce a selection of potential scenarios. The most likely prediction is used by emergency response teams. In the case of storm surges, this information informs emergency response and evacuation plans and helps create maps.

The STORM program will maintain the same ADCIRC functionalities but will get a code upgrade with a completely new foundation for its algorithms, Dawson said.

The new code will be written in HPX — a C++ runtime system for parallel and distributed applications. It will be designed to be flexible, easy to integrate with other code types, and adaptable to diverse computer architectures. By rewriting the code using HPX, STORM will not only be able to run more efficiently on today’s super computing systems, but will be better equipped to handle new systems in the future.

Dawson said: “Where we hope to be in four years is to have a whole new code and a whole new piece of software. And it’s going to be a lot of work but it’s also necessary work if you want to keep your software useful for the next generation.”

At the same time, Dawson says converting ADCIRC into STORM will be an exercise in understanding the history and composition of the original code, which could help in constructing STORM, but also developing expertise but understanding the advantages and limitations of the previous system.

Dawson said: “If you don’t do these kinds of projects you lose all this memory of how you got to this point. We’re really fortunate to have this opportunity to take all the lessons that we learned and to put it into a new piece of software.”

This story appears here as part of a cross-publishing agreement with Scientific Computing World.

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