Skip to main content
SpringerLink
Log in

Performance and Portability of State-of-Art Molecular Dynamics Software on Modern GPUs

  • Conference paper
  • First Online:
Parallel Processing and Applied Mathematics (PPAM 2019)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 12043))

Abstract

Classical molecular dynamics (MD) calculations represent a significant part of utilization time of high performance computing systems. As usual, efficiency of such calculations is based on an interplay of software and hardware that are nowadays moving to hybrid GPU-based technologies. Several well-developed MD packages focused on GPUs differ both in their data management capabilities and in performance. In this paper, we present our results for the porting of the CUDA backend of LAMMPS to ROCm HIP that shows considerable benefits for AMD GPUs comparatively to the existing OpenCL backend. We consider the efficiency of solving the same physical models using different software and hardware combinations. We analyze the performance of LAMMPS, HOOMD, GROMACS and OpenMM MD packages with different GPU back-ends on modern Nvidia Volta and AMD Vega20 GPUs.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 69.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 89.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Tchipev, N., et al.: TweTriS: twenty trillion-atom simulation. Int. J. High Perform. Comput. Appl. 33(5), 838–854 (2019). https://doi.org/10.1177/1094342018819741

    Article  Google Scholar 

  2. Morozov, I., Kazennov, A., Bystryi, R., Norman, G., Pisarev, V., Stegailov, V.: Molecular dynamics simulations of the relaxation processes in the condensed matter on GPUs. Comput. Phys. Commun. 182(9), 1974–1978 (2011). https://doi.org/10.1016/j.cpc.2010.12.026. Computer Physics Communications Special Edition for Conference on Computational Physics Trondheim, Norway, 23–26 June 2010

  3. Dong, W., et al.: Implementing molecular dynamics simulation on Sunway TaihuLight system. In: 2016 IEEE 18th International Conference on High Performance Computing and Communications, IEEE 14th International Conference on Smart City, IEEE 2nd International Conference on Data Science and Systems (HPCC/SmartCity/DSS), pp. 443–450, December 2016. https://doi.org/10.1109/HPCC-SmartCity-DSS.2016.0070

  4. Dong, W., Li, K., Kang, L., Quan, Z., Li, K.: Implementing molecular dynamics simulation on the Sunway TaihuLight system with heterogeneous many-core processors. Concurr. Comput. Pract. Experience 30(16), e4468 (2018). https://doi.org/10.1002/cpe.4468

    Article  Google Scholar 

  5. Yu, Y., An, H., Chen, J., Liang, W., Xu, Q., Chen, Y.: Pipelining computation and optimization strategies for scaling GROMACS on the sunway many-core processor. In: Ibrahim, S., Choo, K.-K.R., Yan, Z., Pedrycz, W. (eds.) ICA3PP 2017. LNCS, vol. 10393, pp. 18–32. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-65482-9_2

    Chapter  Google Scholar 

  6. Duan, X., et al.: Redesigning LAMMPS for peta-scale and hundred-billion-atom simulation on Sunway TaihuLight. In: SC18: International Conference for High Performance Computing, Networking, Storage and Analysis, pp. 148–159, November 2018. https://doi.org/10.1109/SC.2018.00015

  7. Nikolskii, V., Stegailov, V.: Domain-decomposition parallelization for molecular dynamics algorithm with short-ranged potentials on Epiphany architecture. Lobachevskii J. Math. 39(9), 1228–1238 (2018). https://doi.org/10.1134/S1995080218090159

    Article  MathSciNet  MATH  Google Scholar 

  8. Kondratyuk, N.D., Pisarev, V.V.: Calculation of viscosities of branched alkanes from 0.1 to 1000 MPa by molecular dynamics methods using COMPASS force field. Fluid Phase Equilib. 498, 151–159 (2019). https://doi.org/10.1016/j.fluid.2019.06.023

    Article  Google Scholar 

  9. Pisarev, V., Kondratyuk, N.: Prediction of viscosity-density dependence of liquid methane+n-butane+n-pentane mixtures using the molecular dynamics method and empirical correlations. Fluid Phase Equilib. 501, 112273 (2019). https://doi.org/10.1016/j.fluid.2019.112273

    Article  Google Scholar 

  10. Stegailov, V.V., Orekhov, N.D., Smirnov, G.S.: HPC hardware efficiency for quantum and classical molecular dynamics. In: Malyshkin, V. (ed.) PaCT 2015. LNCS, vol. 9251, pp. 469–473. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-21909-7_45

    Chapter  Google Scholar 

  11. Vermaas, J.V., Hardy, D.J., Stone, J.E., Tajkhorshid, E., Kohlmeyer, A.: TopoGromacs: automated topology conversion from CHARMM to GROMACS within VMD. J. Chem. Inf. Model. 56(6), 1112–1116 (2016). https://doi.org/10.1021/acs.jcim.6b00103

    Article  Google Scholar 

  12. Lee, J., et al.: CHARMM-GUI input generator for NAMD, GROMACS, AMBER, OpenMM, and CHARMM/OpenMM simulations using the CHARMM36 additive force field. J. Chem. Theory Comput. 12(1), 405–413 (2016). https://doi.org/10.1021/acs.jctc.5b00935

    Article  Google Scholar 

  13. Merz, P.T., Shirts, M.R.: Testing for physical validity in molecular simulations. PLOS ONE 13(9), 1–22 (2018). https://doi.org/10.1371/journal.pone.0202764

    Article  Google Scholar 

  14. Mesnard, O., Barba, L.A.: Reproducible and replicable computational fluid dynamics: it’s harder than you think. Comput. Sci. Eng. 19(4), 44–55 (2017). https://doi.org/10.1109/MCSE.2017.3151254

    Article  Google Scholar 

  15. Humphrey, W., Dalke, A., Schulten, K.: VMD - visual molecular dynamics. J. Mol. Graph. 14, 33–38 (1996)

    Article  Google Scholar 

  16. Sun, Y., et al.: Evaluating performance tradeoffs on the radeon open compute platform. In: 2018 IEEE International Symposium on Performance Analysis of Systems and Software (ISPASS), pp. 209–218, April 2018. https://doi.org/10.1109/ISPASS.2018.00034

  17. Stegailov, V., et al.: Angara interconnect makes GPU-based desmos supercomputer an efficient tool for molecular dynamics calculations. Int. J. High Perform. Comput. Appl. 33(3), 507–521 (2019). https://doi.org/10.1177/1094342019826667

    Article  Google Scholar 

  18. Norman, G.E., Stegailov, V.V.: Stochastic theory of the classical molecular dynamics method. Math. Models Comput. Simul. 5(4), 305–333 (2013). https://doi.org/10.1134/S2070048213040108

    Article  MathSciNet  Google Scholar 

  19. Eastman, P., et al.: OpenMM 7: rapid development of high performance algorithms for molecular dynamics. PLOS Comput. Biol. 13, 1–17 (2017). https://doi.org/10.1371/journal.pcbi.1005659

    Article  Google Scholar 

  20. Plimpton, S.: Fast parallel algorithms for short-range molecular dynamics. J. Comput. Phys. 117(1), 1–19 (1995). https://doi.org/10.1006/jcph.1995.1039

    Article  MATH  Google Scholar 

  21. Berendsen, H., van der Spoel, D., van Drunen, R.: GROMACS: a message-passing parallel molecular dynamics implementation. Comput. Phys. Commun. 91(1), 43–56 (1995). https://doi.org/10.1016/0010-4655(95)00042-E

    Article  Google Scholar 

  22. Brown, W.M., Wang, P., Plimpton, S.J., Tharrington, A.N.: Implementing molecular dynamics on hybrid high performance computers – short range forces. Comput. Phys. Commun. 182(4), 898–911 (2011). https://doi.org/10.1016/j.cpc.2010.12.021

    Article  MATH  Google Scholar 

  23. Brown, W.M., Kohlmeyer, A., Plimpton, S.J., Tharrington, A.N.: Implementing molecular dynamics on hybrid high performance computers – particle-particle particle-mesh. Comput. Phys. Commun. 183(3), 449–459 (2012). https://doi.org/10.1016/j.cpc.2011.10.012

    Article  Google Scholar 

  24. Brown, W.M., Yamada, M.: Implementing molecular dynamics on hybrid high performance computers—three-body potentials. Comput. Phys. Commun. 184(12), 2785–2793 (2013). https://doi.org/10.1016/j.cpc.2013.08.002

    Article  Google Scholar 

  25. Abraham, M.J., et al.: GROMACS: high performance molecular simulations through multi-level parallelism from laptops to supercomputers. SoftwareX 1–2, 19–25 (2015). https://doi.org/10.1016/j.softx.2015.06.001

    Article  Google Scholar 

  26. Anderson, J.A., Lorenz, C.D., Travesset, A.: General purpose molecular dynamics simulations fully implemented on graphics processing units. J. Comput. Phys. 227(10), 5342–5359 (2008). https://doi.org/10.1016/j.jcp.2008.01.047

    Article  MATH  Google Scholar 

  27. Glaser, J., et al.: Strong scaling of general-purpose molecular dynamics simulations on GPUs. Comput. Phys. Commun. 192, 97–107 (2015). https://doi.org/10.1016/j.cpc.2015.02.028

    Article  Google Scholar 

  28. Eastman, P., et al.: OpenMM 4: a reusable, extensible, hardware independent library for high performance molecular simulation. J. Chem. Theory Comput. 9(1), 461–469 (2013). https://doi.org/10.1021/ct300857j

    Article  Google Scholar 

  29. Kutzner, C., Páll, S., Fechner, M., Esztermann, A., de Groot, B.L., Grubmüller, H.: Best bang for your buck: GPU nodes for GROMACS biomolecular simulations. J. Comput. Chem. 36(26), 1990–2008 (2015)

    Article  Google Scholar 

  30. Kutzner, C., Páll, S., Fechner, M., Esztermann, A., de Groot, B.L., Grubmüller, H.: More bang for your buck: improved use of GPU nodes for GROMACS 2018. CoRR abs/1903.05918 (2019). http://arxiv.org/abs/1903.05918

  31. https://github.com/Vsevak/lammps

Download references

Acknowledgments

The authors gratefully acknowledge financial support of the President grant NS-5922.2018.8.

Author information

Authors and Affiliations

  1. National Research University Higher School of Economics, Moscow, Russia

    Evgeny Kuznetsov, Nikolay Kondratyuk, Vsevolod Nikolskiy & Vladimir Stegailov

  2. Joint Institute for High Temperatures of RAS, Moscow, Russia

    Nikolay Kondratyuk, Mikhail Logunov, Vsevolod Nikolskiy & Vladimir Stegailov

  3. Moscow Institute of Physics and Technology, Dolgoprudny, Russia

    Nikolay Kondratyuk, Mikhail Logunov & Vladimir Stegailov

Authors
  1. Evgeny Kuznetsov
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Nikolay Kondratyuk
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Mikhail Logunov
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Vsevolod Nikolskiy
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Vladimir Stegailov
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Vladimir Stegailov .

Editor information

Editors and Affiliations

  1. Czestochowa University of Technology, Czestochowa, Poland

    Roman Wyrzykowski

  2. University of Southern California, Marina del Rey, CA, USA

    Ewa Deelman

  3. University of Tennessee, Knoxville, TN, USA

    Jack Dongarra

  4. Czestochowa University of Technology, Czestochowa, Poland

    Konrad Karczewski

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Kuznetsov, E., Kondratyuk, N., Logunov, M., Nikolskiy, V., Stegailov, V. (2020). Performance and Portability of State-of-Art Molecular Dynamics Software on Modern GPUs. In: Wyrzykowski, R., Deelman, E., Dongarra, J., Karczewski, K. (eds) Parallel Processing and Applied Mathematics. PPAM 2019. Lecture Notes in Computer Science(), vol 12043. Springer, Cham. https://doi.org/10.1007/978-3-030-43229-4_28

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-43229-4_28

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-43228-7

  • Online ISBN: 978-3-030-43229-4

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation