Bio-ESMD: A Data Centric Implementation for Large-Scale Biological System Simulation on Sunway TaihuLight Supercomputer

• Published in: IEEE transactions on parallel and distributed systems Vol. 34; no. 3; pp. 1 - 12
• Main Authors: Duan, Xiaohui, Shao, Qi, Weng, Junben, Schmidt, Bertil, Gan, Lin, Li, Guohui, Fu, Haohuan, Xue, Wei, Liu, Weiguo, Yang, Guangwen
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.03.2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Biological system modeling; cell-list method; Computational modeling; Data structures; data-centric algorithm; Force; Mathematical models; Molecular dynamics; Processors; Program processors; Simulation; Software; Springs; supercomputing; Tags
• ISSN: 1045-9219; 1558-2183
• DOI: 10.1109/TPDS.2022.3220559

Molecular dynamics (MD) simulations of biological systems are playing an increasingly important role in the research of pathogens and drugs. Most MD methods for biological simulations rely on the listed bonds which interact among specific groups of atoms identified by atom tags (unique atom tags regardless the storage location). However, efficient mapping of tags to atom locations is often challenging on modern many-core processors because data locality can not always be guaranteed for large-scale systems. In this paper, we present Bio-ESMD, a new MD implementation supporting listed bonds. Bio-ESMD is designed and developed based on our previously designed ESMD framework for many-core processors. In Bio-ESMD, we have introduced a data-centric approach for refactoring MD algorithms by reorganizing the cell list data structure to adopt bond lists with guaranteed data locality. Our implementation achieves speedups of over two compared to SW_GROMACS on Sunway TaihuLight. Furthermore, Bio-ESMD can simulate a system of 308.8 million atoms at 1.33 ns/day or 14.44 million atoms at 17.28 ns/day with linear weak scaling efficiency.