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ANT-MOC: Scalable Neutral Particle Transport Using 3D Method of Characteristics on Multi-GPU Systems
SessionExtreme-Scale Applications
DescriptionThe Method Of Characteristic (MOC) to solve the Neutron Transport Equation (NTE) is the core of full-core simulation for reactors. High resolution is enabled by discretizing the NTE through massive tracks to traverse the 3D reactor geometry. However, the 3D full-core simulation is prohibitively expensive because of the high memory consumption and the severe load imbalance. To deal with these challenges, we develop ANT-MOC. Specifically, we build a performance model for memory footprint, computation and communication, based on which a track management strategy is proposed to overcome the resolution bottlenecks caused by limited GPU memory. Furthermore, we implement a novel multi-level load mapping strategy to ensure load balancing among nodes, GPUs, and CUs. ANT-MOC enables a 3D full-core reactor simulation with 100 billion tracks on 16,000 GPUs, with 70.69% and 89.38% parallel efficiency for strong scalability and weak scalability, respectively.
Authors
Event Type
Paper
TimeTuesday, 14 November 202311:30am - 12pm MST
Location403-404
Accelerators
Applications
Modeling and Simulation
TP
Best Paper Finalist
Best Student Paper Finalist
