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SW-DDFT: Parallel Optimization of the Dynamical Density Functional Theory Algorithm Based on Sunway Bluelight II Supercomputer

Xiaoguang Lv1,2, Tao Liu1,2,*, Han Qin1,2, Ying Guo1,2, Jingshan Pan1,2, Dawei Zhao1,2, Xiaoming Wu1,2, Meihong Yang1,2

1 Key Laboratory of Computing Power Network and Information Security, Ministry of Education, Shandong Computer Science Center (National Supercomputer Center in Jinan), Qilu University of Technology (Shandong Academy of Sciences), Jinan, 250014, China
2 Shandong Provincial Key Laboratory of Computing Power Internet and Service Computing, Shandong Fundamental Research Center for Computer Science, Jinan, 250014, China

* Corresponding Author: Tao Liu. Email: email

Computers, Materials & Continua 2025, 84(1), 1417-1436. https://doi.org/10.32604/cmc.2025.063852

Abstract

The Dynamical Density Functional Theory (DDFT) algorithm, derived by associating classical Density Functional Theory (DFT) with the fundamental Smoluchowski dynamical equation, describes the evolution of inhomogeneous fluid density distributions over time. It plays a significant role in studying the evolution of density distributions over time in inhomogeneous systems. The Sunway Bluelight II supercomputer, as a new generation of China’s developed supercomputer, possesses powerful computational capabilities. Porting and optimizing industrial software on this platform holds significant importance. For the optimization of the DDFT algorithm, based on the Sunway Bluelight II supercomputer and the unique hardware architecture of the SW39000 processor, this work proposes three acceleration strategies to enhance computational efficiency and performance, including direct parallel optimization, local-memory constrained optimization for CPEs, and multi-core groups collaboration and communication optimization. This method combines the characteristics of the program’s algorithm with the unique hardware architecture of the Sunway Bluelight II supercomputer, optimizing the storage and transmission structures to achieve a closer integration of software and hardware. For the first time, this paper presents Sunway-Dynamical Density Functional Theory (SW-DDFT). Experimental results show that SW-DDFT achieves a speedup of 6.67 times within a single-core group compared to the original DDFT implementation, with six core groups (a total of 384 CPEs), the maximum speedup can reach 28.64 times, and parallel efficiency can reach 71%, demonstrating excellent acceleration performance.

Keywords

Sunway supercomputer; high-performance computing; dynamical density functional theory; parallel optimization

Cite This Article

APA Style
Lv, X., Liu, T., Qin, H., Guo, Y., Pan, J. et al. (2025). SW-DDFT: Parallel Optimization of the Dynamical Density Functional Theory Algorithm Based on Sunway Bluelight II Supercomputer. Computers, Materials & Continua, 84(1), 1417–1436. https://doi.org/10.32604/cmc.2025.063852
Vancouver Style
Lv X, Liu T, Qin H, Guo Y, Pan J, Zhao D, et al. SW-DDFT: Parallel Optimization of the Dynamical Density Functional Theory Algorithm Based on Sunway Bluelight II Supercomputer. Comput Mater Contin. 2025;84(1):1417–1436. https://doi.org/10.32604/cmc.2025.063852
IEEE Style
X. Lv et al., “SW-DDFT: Parallel Optimization of the Dynamical Density Functional Theory Algorithm Based on Sunway Bluelight II Supercomputer,” Comput. Mater. Contin., vol. 84, no. 1, pp. 1417–1436, 2025. https://doi.org/10.32604/cmc.2025.063852



cc Copyright © 2025 The Author(s). Published by Tech Science Press.
This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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