CSPTH：基于天河二号的晶体结构预测软件框架
CSPTH: A Crystal Structure Prediction Framework on Tianhe-2 Supercomputer
DOI: 10.12677/CSA.2022.124088, PDF,  被引量    科研立项经费支持
作者: 刘瑾瑜：中山大学，计算机学院，广东 广州；陈 品, 卢宇彤*：中山大学，计算机学院，广东 广州；中山大学国家超级计算中心，广东 广州
关键词: 晶体结构预测；遗传算法；高性能计算；Crystal Structure Prediction； Genetic Algorithm； High-Performance Computing
摘要: 晶体结构是深入理解材料的物理及化学性质的重要信息，发展可以从理论上预测晶体结构的方法具有重要意义。通过高性能计算集群甚至利用超级计算机来加速晶体结构预测已逐步成为趋势。本文中，我们基于天河二号超级计算机开发了一套开源的晶体结构预测软件框架，命名为CSPTH。在算法层面，我们基于当前效率最高的遗传算法进行晶体结构预测，并采用了多种技术提升结构预测效率，包括并行化生成种群结构；引入空间群限定，减少自由度搜索，提升结构多样性；引入晶体指纹进行相似性算法，排除相似结构干扰，避免“基因漂变”的问题。特别地，我们针对晶体结构预测算法的应用特点以及天河二号的系统环境，从任务以及数据管理两个方面做了优化。在任务管理上，我们设计了多层任务调度管理模块，根据计算任务的规模大小的分发细粒度作业(节点内)以及粗粒度作业(跨节点)，提升计算资源的高效使用；在数据管理上，我们将每个计算任务的数据都临时储存于计算节点的RAMDISK，提取有效信息后再存储于MongoDB数据库，避免大量小文件存储于公共存储。CSPTH已在15种已知一元、二元以及三元体系上进行了结构预测，实验结果表明CSPTH能根据给定的组分及外部压力条件下全部预测出相应的稳定结构。
Abstract: Crystal structure is the critical information for understanding the physical and chemical properties of materials. Therefore, theoretical prediction of crystal structures only with chemical composition and external conditions is significant. It has become a trend to design new materials through high-performance computing clusters or even using supercomputers. In this paper, we developed an open source framework for crystal structure prediction (CSP) based on Tianhe-2 supercomputer, named CSPTH. When designing the algorithm in CSP, we chose the most efficient genetic algorithm in our framework and adopted numerous technologies to improve prediction accuracy. Specifically, we used a multi-process parallel method to generate the trying structures. The space group restriction is introduced to reduce the searching space and improve the structural diversity in population. We utilized a crystal fingerprint to eliminate the similar structures, which can avoid the problem of “gene drift”. In particular, considering the characteristics of crystal structure algorithm and the system environment of Tianhe-2, we optimize the crystal structure prediction algorithm from two aspects: task management and data management. In terms of task management, we designed a multi-layer task scheduling management module to distribute fine-grained tasks (within a node) and coarse-grained tasks (multi-nodes) according to the system size of tasks to improve the efficiency of employing resources. In our data management module, the data of each computing task is temporarily cached in the RAMDISK of the computing node, and the useful information is extracted and later stored in the MongoDB database, which can avoid a large number of small files stored in the public storage. CSPTH has been used to predict the structures of 15 known element, binary and ternary systems. Experimental results show that CSPTH can predict all the correspond-ing stable structures with the only known of chemical composition and external pressure.
文章引用：刘瑾瑜, 陈品, 卢宇彤. CSPTH：基于天河二号的晶体结构预测软件框架[J]. 计算机科学与应用, 2022, 12(4): 866-878. https://doi.org/10.12677/CSA.2022.124088

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