Reader Jay Blair sent me a pointer to this story from TACC about an Android app that runs a reduced model locally on the cell phone based on results computed over a long series of runs on Ranger.
The team performed a series of expensive high-fidelity simulations on the Ranger supercomputer to generate a small “reduced model” which was transferred to a Google Android smart phone. They were then able to solve problems on the phone and visualize the results on the fly.
The project proved the potential for reduced order methods to perform real-time and reliable simulations for complicated problems on handheld devices.
This approach is already used operationally in a variety of civilian and defense scenarios to allow professionals ranging from bridge fatique assessment teams to rapid crisis response forces to tradeoff some accuracy for an answer right now. Typically these reduced models have run on laptops or larger portable computers, but today’s mobile devices are becoming quite powerful in their own right.
This is not the first time that model reduction algorithms have been used to ameliorate the complexities of large-scale physical simulations. The advantage of the system designed by Knezevic and his colleagues is its rigorous error bounds, which tell a user the range of possible solutions, and provide a metric of whether an answer is accurate or not. The error bounds are based on mathematical theory developed in Prof. Patera’s research group at MIT over a number of years.
“We have a bound on how much accuracy we’re losing with our reduced model, so we can say with rigor that we’re doing supercomputing on a phone,” Knezevic said.
The quantitative understanding of the error bounds is very important, and its a nice addition in this work.