数据与计算发展前沿 ›› 2025, Vol. 7 ›› Issue (3): 67-80.

CSTR: 32002.14.jfdc.CN10-1649/TP.2025.03.006

doi: 10.11871/jfdc.issn.2096-742X.2025.03.006

• 专刊：中国科学院计算机网络信息中心成立30周年 • 上一篇    下一篇

极端尺度相场模拟中的原位特征提取

冯志宸(),李佳霖,高雅倩,田少博,叶煌,张鉴*()   

  1. 中国科学院计算机网络信息中心，北京 100083
  • 收稿日期:2025-05-14 出版日期:2025-06-20 发布日期:2025-06-25
  • 通讯作者: *张鉴（E-mail: zhangjian@sccas.cn）
  • 作者简介:冯志宸，中国科学院计算机网络信息中心，博士研究生，主要研究方向为高性能计算。
    本文承担工作为：特征提取算法设计和实现。
    FENG Zhichen is a Ph.D. candidate at the Computer Network Information Center of the Chinese Academy of Sciences. His main research field is High Performance Computing.
    In this paper, he is mainly responsible for the design and implementation of the feature extraction algorithm.
    E-mail: fengzhichen@cnic.cn|张鉴，中国科学院计算机网络信息中心，博士，研究员，主要研究方向为高性能计算。
    本文承担工作为：指导算法设计和实现。
    ZHANG Jian, Ph.D., is a research fellow and PhD supervisor at the Computer Network Information Center of the Chinese Academy of Sciences. His main research field is High Performance Computing.
    In this paper, he is mainly responsible for the overall framework design of the parallel algorithm and providing research guidance.
    E-mail: zhangjian@sccas.cn
  • 基金资助:
    国家重点研发计划高性能计算重点专项“大规模数值模拟应用移植优化与平台集成”(2021YFB0300203)

Enabling In-situ Feature Extraction in Extreme Scale Phase Field Simulations

FENG Zhichen(),LI Jialin,GAO Yaqian,TIAN Shaobo,YE Huang,ZHANG Jian*()   

  1. Computer Network Information Center, Chinese Academy of Sciences, Beijing 100083, China
  • Received:2025-05-14 Online:2025-06-20 Published:2025-06-25

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摘要：

【目的】相场模拟是研究合金微观结构演化的一个有力工具。由于演化过程具有内在的时空异质性，因此需要进行极大规模的模拟。由于原始模拟数据产生速度快且数量巨大因此原位数据处理变得十分必要。【方法】通过对新型特征提取和三维卷积算法的系统设计，以及针对新一代神威超级计算机的精心优化，我们在具有33.6 万亿自由度的真实合金模拟中实现了对每个晶粒特征的实时提取。【结果】在新一代神威超级计算机上使用超过3,900 万个核心，实现了混合双精度和单精度下637 Pflops的持续性能。同时，以不到总模拟时间10%的代价提取并保存了超过400 万个晶粒的演化过程。【结论】这些数据可作为演化过程的详细记录，为采用数据驱动的新方法更好地理解合金的过程-结构-性能范式打开了大门。从更宏观的角度来看，此实践为高性能计算成为模拟数据的强大生产者探索了道路，促进超级计算与大数据融合。

关键词: 合金材料, 相场, 原位特征提取

Abstract:

[Objective] Phase Field simulation is a powerful tool for studying microstructure evolution in alloys. The intrinsic spatiotemporal heterogeneity of the evolution process requires extreme scale simulation. In-situ data processing becomes necessary in these scenarios for the raw simulation data being produced in vast amounts at high speed. [Methods] Through the systematic design of novel feature extraction and 3D convolution algorithms and careful optimization for adapting the new generation Sunway supercomputer, we present here a novel feature extraction framework that enables us to extract characteristic features of each grain on the fly in a real-world alloy simulation with 33.6 trillion dofs. For the feature extraction, we propose a fully 3D convolutional network structure which is more conducive to performing optimization. In the aspect of pose estimation, which we are particularly interested in, it surpassed the performance of the current state-of-the-art methods. For the 3D convolution optimization, we propose a three-level blocking scheme with a novel scatter communication strategy to make full use of the on-chip network bandwidth. It allows 3D convolution to be accelerated with the SIMD vector unit of SW26010Pro without explicit matrix reconstruction. The operator achieves up to 91% of the theoretical peak performance on the new generation Sunway processor. [Results] Using over 39 million cores on the new generation Sunway supercomputer, sustained performance of 637 PFlops in mixed double and single precision is reached. Meanwhile, the evolution process of over four million grains is extracted and saved at the cost of less than 10% of the overall simulation time. [Conclusions] This data can be considered a detailed record of the evolution process and open the gates to various new approaches toward a better understanding of the process-structure-property paradigm for alloys. For example, data-driven modeling for grain precipitation and growth mechanisms. In a bigger picture, our practice here also shed light on the path that HPC becomes a powerful producer of simulation data and facilitates big data association with supercomputing.

Key words: alloy, Phase Field, In-situ feature extraction