HLRS (High Performance Computing Center Stuttgart) sits at the intersection of classic high-performance computing and applied research, with one foot in engineering workflows and the other in “global challenges” like climate modeling and risk analysis. In this conversation, Managing Director Basan Kola frames supercomputing as practical infrastructure: accelerate design loops, validate manufacturing ideas earlier, and make large simulations usable by teams that do not live in MPI code every day. https://www.hlrs.de/
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A nice part of the booth story is how HLRS turns raw compute into something people can feel and understand: a VR bicycle ride where head motion drives the viewpoint, and the workload “pushes back” as the track climbs, blending real-time visualization, interaction latency constraints, and human factors into one demo. It’s a small example of a bigger theme in scientific visualization: tight coupling between simulation output, rendering pipelines, tracking, and user input so decisions can happen in the moment rather than after a batch job is done there.
The drone demo points at a more operational pipeline: capture reality with 3D scanning (photogrammetry and/or LiDAR), build a high-fidelity model, then run scenario simulation on top—flooding, terrain evolution, and climate-condition what-ifs. That workflow depends on data fusion, meshing, uncertainty handling, and repeatable compute environments, because the value is not the pretty point cloud but the ability to test how a system behaves under stress in the future.
For robotics, the focus is “virtual commissioning” and modular digital twins: training and validating a robot arm against a virtual cell before the physical module is even assembled, then translating that to real hardware with fewer integration surprises. Recorded on the Supercomputing SC25 show floor in St. Louis, the booth walk-through ties this to industrial partners (including TRUMPF-style laser/manufacturing contexts), where physics-based simulation, collision checking, and control-loop tuning benefit from HPC throughput and clean toolchains on the floor.
The forward-looking thread is convergence: HLRS is betting on HPC + AI as a single stack (and keeping quantum on the horizon), where simulation produces data, AI improves surrogate modeling and parameter search, and both share the same expensive infrastructure efficiently. The HammerHAI “AI Factory” angle adds the missing glue—secure and accessible AI compute for industry and academia, plus practical support for datasets, training, and deployment patterns that make supercomputing useful beyond a single lab, right up to production edge.
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