Abstract
Large-scale atomistic simulation of low-dimensional silicon nanostructures has been implemented on a heterogeneous supercomputer equipped with a large number of GPU-like accelerators (GLA). In the simulation, an innovative parallel algorithm was developed for the combined utilization of the dynamic neighbor and static neighbor list algorithms aiming at the different regions of the nanostructures. Furthermore, some optimization techniques were performed for the computationally intensive many-body force evaluation between atoms, such as SIMD vectorization, manual loop unrolling, pre-calculation of memory addresses and reordering of data structure etc. Finally, the simulation achieved the excellent weak and strong scalabilities in the parallel implementation, where up to 805.3 billion silicon atoms were simulated. This development suggests an exciting future of predicting the thermodynamic properties of low-dimensional nanostructures.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Allen, M.P., Tidesley, D.J.: Computer simulation of liquids. Oxford University Press, Oxford (1987)
Bao, H., Chen, J., Gu, X., Cao, B.: A review of simulation methods in micro/nanoscale heat conduction. ES Energy Environ. 1, 16–55 (2018)
Boukai, A.I., et al.: Silicon nanowires as efficient thermoelectric materials. Nature 451, 168–171 (2008)
Chantrenne, P., Barrat, J.: Finite size effects in determination of thermal conductivities: comparing molecular dynamics results with simple models. J. Heat Trans. 126, 577–585 (2004)
Cruz, C., Termentzidis, K., Chantrenne, P., Kleber, X.: Molecular dynamics simulation for the prediction of thermal conductivity of bulk silicon and silicon nanowires: influence of interatomic potentials and boundary conditions. J. Appl. Phys. 110, 034309 (2011)
Duan, X., Gao, P., Zhang, T., Zhang, M., Liu, W., Zhang, W., Xue, W., Fu, H., Gan, L., Chen, D., Meng, X., Yang, G.: Redesigning LAMMPS for peta-scale and hundred-billion-atom simulation on Sunway TaihuLight, SC18: International Conference for High Performance Computing, Networking, Storage and Analysis. IEEE, 148–159 (2018)
Fu, H., Liao, J., Yang, J., Wang, L., Song, Z., Huang, X., Yang, C., Xue, W., Liu, F., Qiao, F., Zhao, W., Yin, X., Hou, C., Zhang, C., Ge, W., Zhang, J., Wang, Y., Zhou, C., Yang, G.: The Sunway TaihuLight supercomputer: system and applications. Sci. China 59, 072001 (2016)
Glaser, J., Nguyen, T.D., Anderson, J.A., Lui, P., Spiga, F., Millan, J.A., Morse, D.C., Glotzer, S.C.: Strong scaling of general-purpose molecular dynamics simulations on GPUs. Comput. Phys. Commun. 192, 97–107 (2015)
Green, M.A.: Solar cells: operating principles, technology, and system applications. Prentice Hall, Englewood Cliffs (1982)
He, Y., Galli, G.: Microscopic origin of the reduced thermal conductivity of silicon nanowires. Phys. Rev. Lett. 108, 215901 (2012)
Hess, B., Kutzner, C., van der Spoel, D., Lindahl, E.: GROMACS 4: algorithms for highly efficient, load-balanced, and scalable molecular simulation. J. Chem. Theory Comput. 4, 435–447 (2008)
Hou, C.F., Xu, J., Wang, P., Huang, W.L., Wang, X.W.: Efficient GPU-accelerated molecular dynamics simulation of solid covalent crystals. Comput. Phys. Commun. 184(5), 1364–1371 (2013a)
Hou, C.F., Xu, J., Wang, P., Huang, W.L., Wang, X.W., Ge, W., He, X.F., Guo, L., Li, J.H.: Petascale molecular dynamics simulation of crystalline silicon on Tianhe-1A. Int. J. High Perform. C. 27(3), 307–317 (2013b)
Hou, C.F., Xu, J., Ge, W., Li, J.H.: Molecular dynamics simulation overcoming the finite size effects of thyermal conductivity of bulk silicon and silicon nanowires. Model. Simul. Mater. Sci. Eng. 24(4), 45005–45013 (2016)
Hou, C.F., Zhang, C., Ge, W., Wang, L., Han, L., Pang, J.: Record Atomistic simulation of crystalline silicon: bridging microscale structures and macroscale properties. J. Comput. Chem. 41, 731–738 (2020)
Hou, C., Zhu, A., Zhang, S., Zhao, M., Ye, Y., Xu, J., Ge, W.: Atomistic simulation toward real-scale microprocessor circuits. Chem. Phys. Lett. 791, 139389 (2022)
https://docs.nvidia.com/cuda/profiler-users-guide/index.html. Accessed 27 Oct 2021
https://top500.org/system/176899/. Accessed 27 Oct 2021
https://www.top500.org/lists/2016/06/. Accessed 27 Oct 2021
https://www.top500.org/lists/top500/2010/11/highlights/. Accessed 27 Oct 2021
Lee, J., et al.: Thermal transport in silicon nanowires at high temperature up to 700 K. Nano Lett. 16, 4133–4140 (2016)
Li, D., Wu, Y., Kim, P., Shi, L., Yang, P., Majumdar, A.: Thermal conductivity of individual silicon nanowire. Appl. Phys. Lett. 83(14), 2934–2936 (2003)
Narumi, T., Ohno, Y., Okimoto, N., Koishi, T., Suenaga, A., Futatsugi, N., Yanai, R., Himeno, R., Fujikawa, S., Taiji, M., Ikei, M.: A 55 TFLOPS simulation of amyloid-forming peptides from yeast prion Sup35 with the special-purpose computer system MDGRAPE-3, SC'06: Proceedings of the 2006 ACM/IEEE conference on Supercomputing. IEEE, (2006)
Neogi, S., Donadio, D.: Thermal transport in free-standing silicon membrane: influence of dimensional reduction and surface nanostructures. Eur. Phys. J. B 88, 73 (2015)
Pascual-Gutierrez, J.A., Murthy, J.Y., Viskanta, R.: Limits of size confinement in silicon thin films and wires. J. Appl. Phys. 102, 8 (2007)
Phillips, J. C., Zheng, G., Kumar, S., Kale, L. V.: NAMD: Biomolecular simulation on thousands of processors, SC'02: Proceedings of the 2002 ACM/IEEE conference on Supercomputing. IEEE, 36–36 (2002)
Plimpton, S.: Fast parallel algorithms for short-range molecular dynamics. J. Comput. Phys. 117, 1–19 (1995)
Rapaport, A.: The art of molecular dynamics simulation. Cambridge University Press, Cambridge (1995)
Sellan, D.P., et al.: Size effects in molecular dynamics thermal conductivity predictions. Phys. Rev. B 81, 214305 (2010)
Shaw, D. E. et al.: Anton 2: raising the bar for performance and programmability in a specialpurpose molecular dynamics supercomputer. In Supercopmuting Conference’14, 41–53 (2014)
Stillinger, F.H., Weber, T.A.: Computer simulation of local order in condensed phases of silicon. Phys. Rev. B 31, 5262–5270 (1985)
Streitz, FH., Glosli, JN., Patel, MV., Chan, B., Yates, RK., de Supinski, BR.: 100+ TFlop solidification simulations on BlueGene/L, SC'05: Proceedings of the 2006 ACM/IEEE conference on Supercomputing. IEEE, (2005)
Sun, L., Murthy, J.Y.: Domain size effects in molecular dynamics simulation of phonon transport in silicon. Appl. Phys. Lett. 89, 171919 (2006)
Swaminarayan, S., Kadau, K., Germann, T. C., Fossum, G. C.: 369 Tflop/s molecular dynamics simulations on the Roadrunner general-purpose heterogeneous supercomputer, SC'08: Proceedings of the 2008 ACM/IEEE Conference on Supercomputing. IEEE, 1–10 (2008)
Tersoff, J.: New empirical approach for the structure and energy of covalent systems. Phys. Rev. B 37(14), 6991–7000 (1988a)
Tersoff, J.: Empirical interatomic potential for silicon with improved elastic properties. Phys. Rev. B 38(14), 9902–9905 (1988b)
Wang, Z., Ruan, X.: On the domain size effect of thermal conductivities from equilibrium and nonequilibrium molecular dynamics simulation. J. Appl. Phys. 121, 044301 (2017)
Acknowledgements
This study is financially supported by the Beijing Natural Science Foundation under grant No. JQ21034, the Major Research Program of Henan Province under grant No. 201400211300, the National Natural Science Foundation of China (NSFC) under grant Nos. 21776280, 22073103 and 91934302, and the Strategic Priority Research Program of Chinese Academy of Sciences under grant No. XDC01040100. The authors are also grateful to the Computer Network and Information Center of Chinese Academy of Sciences and the National Supercomputing Center of China at Zhengzhou for their valuable help in computational resource.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
On behalf of all authors, the corresponding author states that there is no conflict of interest.
Rights and permissions
About this article
Cite this article
Hou, C., Zhu, A., Zhang, S. et al. Atomistic simulation of low-dimensional nanostructures toward extreme-scale supercomputing. CCF Trans. HPC 5, 3–11 (2023). https://doi.org/10.1007/s42514-022-00115-x
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s42514-022-00115-x