Presentation
Memory-Efficient CFD Based on MPS: Effective One-Billion-Cell Resolution on a Single Node
DescriptionWe investigate matrix product states (MPS), a tensor-network compression method, as a memory-efficient representation of flow variables. A three-dimensional incompressible Navier-Stokes solver is implemented entirely in MPS form and is applied to canonical flow problems. Results show substantial memory savings and the ability to perform a $1024^3$ simulation on a single GPU. Performance analysis revealed new bottlenecks, particularly bond-dimension growth during nonlinear operations,
suggesting novel optimization strategies are needed to fully realize MPS-based CFD at extreme scales.
suggesting novel optimization strategies are needed to fully realize MPS-based CFD at extreme scales.
Event Type
Research and ACM SRC Posters
TimeTuesday, 18 November 20258:00am - 5:00pm CST
LocationSecond Floor Atrium
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