학술논문
ANT-MOC: Scalable Neutral Particle Transport Using 3D Method of Characteristics on Multi-GPU Systems
Document Type
Conference
Author
Source
SC23: International Conference for High Performance Computing, Networking, Storage and Analysis High Performance Computing, Networking, Storage and Analysis, SC23: International Conference for. :1-13 Nov, 2023
Subject
Language
ISSN
2167-4337
Abstract
The 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 1 1 The name “ANT-MOC” is inspired by the cooperative transport behavior of ants, which allows them to efficiently exploit resources from their environment.. 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.