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

Authors:

Xuebin ChiAuthors Info & Claims

SC '23: Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis

Article No.: 12, Pages 1 - 13

https://doi.org/10.1145/3581784.3607063

Published: 11 November 2023 Publication History

Editorial Notes

The authors have requested minor, non-substantive changes to the VoR and, in accordance with ACM policies, a Corrected VoR was published on November 14, 2023.

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-MOC¹. 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.

Supplemental Material

MP4 File - SC23 video presentation for "ANT-MOC: Scalable Neutral Particle Transport Using 3D Method of Characteristics on Multi-GPU Systems"

SC23 video presentation for the main program paper "ANT-MOC: Scalable Neutral Particle Transport Using 3D Method of Characteristics on Multi-GPU Systems" by Shunde Li, Zongguo Wang, Lingkun Bu, Jue Wang, Zhikuang Xin, Shigang Li, Yangang Wang, Yangde Feng, Peng Shi, Yun Hu and Xuebin Chi

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Cited By

You XXuan ZYang HLuan ZLiu YQian D(2024)GVARP: Detecting Performance Variance on Large-Scale Heterogeneous SystemsProceedings of the International Conference for High Performance Computing, Networking, Storage, and Analysis10.1109/SC41406.2024.00063(1-16)Online publication date: 17-Nov-2024
https://dl.acm.org/doi/10.1109/SC41406.2024.00063

Index Terms

ANT-MOC: Scalable Neutral Particle Transport Using 3D Method of Characteristics on Multi-GPU Systems
1. Applied computing
  1. Physical sciences and engineering
    1. Physics
2. Computing methodologies
  1. Parallel computing methodologies
    1. Parallel algorithms
      1. Massively parallel algorithms

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Published In

cover image ACM Conferences

SC '23: Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis

November 2023

1428 pages

ISBN:9798400701092

DOI:10.1145/3581784

Chair:
Dorian Arnold,
Program Chair:
Rosa M Badia,
Program Co-chair:
Kathryn Mohror

Copyright © 2023 Owner/Author(s).

This work is licensed under a Creative Commons Attribution International 4.0 License.

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Association for Computing Machinery

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Published: 11 November 2023

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the National Key Research and Development Program of China
the Strategic Priority Research Program of Chinese Academy of Sciences
the Fundamental Reasearch Funds for the Central Universities
GuangHe Fund B

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SC '23

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SIGHPC

SC '23: International Conference for High Performance Computing, Networking, Storage and Analysis

November 12 - 17, 2023

CO, Denver, USA

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Cited By

You XXuan ZYang HLuan ZLiu YQian D(2024)GVARP: Detecting Performance Variance on Large-Scale Heterogeneous SystemsProceedings of the International Conference for High Performance Computing, Networking, Storage, and Analysis10.1109/SC41406.2024.00063(1-16)Online publication date: 17-Nov-2024
https://dl.acm.org/doi/10.1109/SC41406.2024.00063

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