ANT-MOC: Scalable Neutral Particle Transport Using 3D Method of Characteristics on Multi-GPU Systems

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 expe...

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Vydáno v:International Conference for High Performance Computing, Networking, Storage and Analysis (Online) s. 1 - 13
Hlavní autoři: Li, Shunde, Wang, Zongguo, Bu, Lingkun, Wang, Jue, Xin, Zhikuang, Li, Shigang, Wang, Yangang, Feng, Yangde, Shi, Peng, Hu, Yun, Chi, Xuebin
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: ACM 11.11.2023
Témata:
3D method of characteristic
Applied computing → Physics
Computing methodologies → Massively parallel algorithms
Graphics processing units
Load balancing
Memory management
Multi-GPUs
Neutron particle transport
Neutrons
Scalability
Software
Software algorithms
Three-dimensional displays
ISSN:2167-4337
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Shrnutí: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.
ISSN:2167-4337
DOI:10.1145/3581784.3607063