数据与计算发展前沿2025，Vol.7Issue(3)：67-80,14.DOI:10.11871/jfdc.issn.2096-742X.2025.03.006
极端尺度相场模拟中的原位特征提取
Enabling In-situ Feature Extraction in Extreme Scale Phase Field Simulations
摘要
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 simu-lation 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 as-pect 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 reconstruc-tion.The operator achieves up to 91%of the theoretical peak performance on the new generation Sunway proces-sor.[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 mil-lion 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 to-ward a better understanding of the process-structure-property paradigm for alloys.For example,data-driven mod-eling 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 supercom-puting.关键词
合金材料/相场/原位特征提取Key words
alloy/Phase Field/In-situ feature extraction引用本文复制引用
冯志宸,李佳霖,高雅倩,田少博,叶煌,张鉴..极端尺度相场模拟中的原位特征提取[J].数据与计算发展前沿,2025,7(3):67-80,14.基金项目
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) （2021YFB0300203）
