Stuttgart’s supercomputer center has been cruising down the autobahn of high performance computing like a well-torqued coupe, and now it’s making a pitstop for some AI fuel.
Germany’s High-Performance Computing Center Stuttgart (HLRS), one of Europe’s largest supercomputing centers, has tripled the size of its staff and increased its revenues from industry collaborations 20x since Michael Resch became director in 2002. In the past year, much of the growth has come from interest in AI.
With demand for machine learning on the rise, HLRS signed a deal to add 192 NVIDIA Ampere architecture GPUs linked on NVIDIA Mellanox InfiniBand network to its Hawk supercomputer based on an Apollo system from Hewlett Packard Enterprise.
Hawk Flies to New Heights
The GPUs will propel what’s already ranked as the world’s 16th largest system to new heights. In preparation for the expansion, researchers are gearing up AI projects that range from predicting the path of the COVID-19 pandemic to the science behind building better cars and planes.
“Humans can create huge simulations, but we can’t always understand all the data — the big advantage of AI is it can work through the data and see its consequences,” said Resch, who also serves as a professor at the University of Stuttgart with a background in engineering, computer science and math.
The center made its first big leap into AI last year when it installed a Cray CS-Storm system with more than 60 NVIDIA GPUs. It is already running AI programs that analyze market data for Mercedes-Benz, investment portfolios for a large German bank and a music database for a local broadcaster.
“It turned out to be an extremely popular system because there’s a growing community of people who understand AI has a benefit for them,” Resch said of the system now running at near capacity. “By the middle of this year it was clear we had to expand to cover our growing AI requirements,” he added.
The New Math: HPC+AI
The future for the Stuttgart center, and the HPC community generally, is about hybrid computing where CPUs and GPUs work together, often to advance HPC simulations with AI.
“Combining the two is a golden bullet that propels us into a better future for understanding problems,” he said.
For example, one researcher at the University of Stuttgart will use data from as many as 2 billion simulations to train neural networks that can quickly and economically evaluate metal alloys. The AI model it spawns could run on a PC and help companies producing sheet metal choose the best alloys for, say, a car door.
“This is extremely helpful in situations where experimentation is difficult or costly,” he said.
And it’s an apropos app for the center situated in the same city that’s home to the headquarters of both Mercedes and Porsche.
In the Flow with Machine Learning
A separate project in fluid dynamics will take a similar approach.
A group from the university will train neural networks on data from highly accurate simulations to create an AI model that can improve analysis of turbulence. It’s a critical topic for companies such as Airbus that are collaborating with HLRS on efforts to mine the aerospace giant’s data on airflow.
The Stuttgart center also aims to use AI as part of a European research project to predict when hospital beds could fill up in intensive-care units amid the pandemic. The project started before the coronavirus hit, but it accelerated in the wake of COVID-19.
Tracking the Pandemic with AI
One of the project’s goals is to give policy makers a four-week window to respond before hospitals would reach their capacity.
“It’s a critical question with so many people dying — we’ve seen scenarios in places like Italy, New York and Wuhan where ICUs filled up in the first weeks of pandemic,” Resch said.
“So, we will conduct simulations and predictions of the outlook for the pandemic over the next weeks and months, and GPUs will be extremely helpful for that,” he added.
It’s perhaps the highest profile of many apps now in the pipeline for the GPU-enhanced engine that will propel Stuttgart’s researchers further down the road on their journey into AI.