Co-design of a Particle-in-Cell Plasma Simulation Code for Intel Xeon Phi: A First Look at Knights Landing

  • Igor Surmin
  • Sergey Bastrakov
  • Zakhar Matveev
  • Evgeny Efimenko
  • Arkady Gonoskov
  • Iosif MeyerovEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10049)


Three dimensional particle-in-cell laser-plasma simulation is an important area of computational physics. Solving state-of-the-art problems requires large-scale simulation on a supercomputer using specialized codes. A growing demand in computational resources inspires research in improving efficiency and co-design for supercomputers based on many-core architectures. This paper presents first performance results of the particle-in-cell plasma simulation code PICADOR on the recently introduced Knights Landing generation of Intel Xeon Phi. A straightforward rebuilding of the code yields a 2.43 x speedup compared to the previous Knights Corner generation. Further code optimization results in an additional 1.89 x speedup. The optimization performed is beneficial not only for Knights Landing, but also for high-end CPUs and Knights Corner. The optimized version achieves 100 GFLOPS double precision performance on a Knights Landing device with the speedups of 2.35 x compared to a 14-core Haswell CPU and 3.47 x compared to a 61-core Knights Corner Xeon Phi.


Current Deposition Plasma Simulation Baseline Version OpenMP Thread Supercell Size 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. 1.
    Kunkel, J.M., Balaji, P., Dongarra, J. (eds.): ISC High Performance 2016. LNCS, vol. 9697. Springer, Heidelberg (2016)Google Scholar
  2. 2.
    Jeffers, J., Reinders, J., Sodani, A.: Intel Xeon Phi Processor High Performance Programming: Knights Landing Edition. Morgan Kaufmann, New York (2016)Google Scholar
  3. 3.
    Fonseca, R.A., Vieira, J., Fiuza, F., Davidson, A., Tsung, F.S., Mori, W.B., Silva, L.O.: Exploiting multi-scale parallelism for large scale numerical modelling of laser wakefield accelerators. Plasma Phys. Control. Fusion. 55(12), 124011 (2013)CrossRefGoogle Scholar
  4. 4.
    Bowers, K.J., Albright, B.J., Yin, L., Bergen, B., Kwan, T.J.T.: Ultrahigh performance three-dimensional electromagnetic relativistic kinetic plasma simulation. Phys. Plasmas 15(5), 055703 (2008)CrossRefGoogle Scholar
  5. 5.
    Pukhov, A.: Three-dimensional electromagnetic relativistic particle-in-cell code VLPL (Virtual Laser Plasma Lab). J. Plasma Phys. 61(3), 425–433 (1999)CrossRefGoogle Scholar
  6. 6.
    Vay, J.-L., Bruhwiler, D.L., Geddes, C.G.R., Fawley, W.M., Martins, S.F., Cary, J.R., Cormier-Michel, E., Cowan, B., Fonseca, R.A., Furman, M.A., Lu, W., Mori, W.B., Silva, L.O.: Simulating relativistic beam and plasma systems using an optimal boosted frame. J. Phys. Conf. Ser. 180(1), 012006 (2009)CrossRefGoogle Scholar
  7. 7.
    Burau, H., Widera, R., Honig, W., Juckeland, G., Debus, A., Kluge, T., Schramm, U., Cowan, T.E., Sauerbrey, R., Bussmann, M.: PIConGPU: a fully relativistic particle-in-cell code for a GPU cluster. IEEE Trans. Plasma Sci. 38(10), 2831–2839 (2010)CrossRefGoogle Scholar
  8. 8.
    Kong, X., Huang, M.C., Ren, C., Decyk, V.K.: Particle-in-cell simulations with charge-conserving current deposition on graphic processing units. J. Comput. Phys. 230(4), 1676–1685 (2011)CrossRefzbMATHGoogle Scholar
  9. 9.
    Decyk, V.K., Singh, T.V.: Particle-in-cell algorithms for emerging computer architectures. Comput. Phys. Commun. 185(3), 708–719 (2014)MathSciNetCrossRefGoogle Scholar
  10. 10.
    Glinsky, B.M., Kulikov, I.M., Snytnikov, A.V., Romanenko, A.A., Chernykh, I.G., Vshivkov, V.A.: Co-design of parallel numerical methods for plasma physics and astrophysics. Supercomput. Front. Innov. 1(3), 88–98 (2014)Google Scholar
  11. 11.
    Bastrakov, S., Meyerov, I., Surmin, I., Efimenko, E., Gonoskov, A., Malyshev, A., Shiryaev, M.: Particle-in-cell plasma simulation on CPUs, GPUs and Xeon Phi coprocessors. In: Kunkel, J.M., Ludwig, T., Meuer, H.W. (eds.) ISC 2014. LNCS, vol. 8488, pp. 513–514. Springer, Heidelberg (2014)Google Scholar
  12. 12.
    Surmin, I.A., Bastrakov, S.I., Efimenko, E.S., Gonoskov, A.A., Korzhimanov, A.V., Meyerov, I.B.: Particle-in-cell laser-plasma simulation on xeon phi coprocessors. Comput. Phys. Commun. 202, 204–210 (2016)CrossRefGoogle Scholar
  13. 13.
    Nakashima, H.: Manycore challenge in particle-in-cell simulation: how to exploit 1 TFlops peak performance for simulation codes with irregular computation. Comput. Electr. Eng. 46, 81–94 (2015)CrossRefGoogle Scholar
  14. 14.
    Bastrakov, S., Donchenko, R., Gonoskov, A., Efimenko, E., Malyshev, A., Meyerov, I., Surmin, I.: Particle-in-cell plasma simulation on heterogeneous cluster systems. J. Comput. Sci. 3, 474–479 (2013)CrossRefGoogle Scholar
  15. 15.
    Gonoskov, A., Bastrakov, S., Efimenko, E., Ilderton, A., Marklund, M., Meyerov, I., Muraviev, A., Sergeev, A., Surmin, I., Wallin, E.: Extended particle-in-cell schemes for physics in ultrastrong laser fields: review and developments. Phys. Rev. E 92(2), 023305 (2015)CrossRefGoogle Scholar
  16. 16.
    Hockney, R.W., Eastwood, J.W.: Computer Simulation Using Particles. McGraw-Hill, New York (1981)zbMATHGoogle Scholar
  17. 17.
    Dawson, J.M.: Particle simulation of plasmas. Rev. Modern Phys. 55(2), 403–447 (1983)CrossRefGoogle Scholar
  18. 18.
    Birdsal, C.K.: Plasma Physics via Computer Simulation. CRC Press, Boca Raton (2004)Google Scholar
  19. 19.
    Muraviev, A.A., Bastrakov, S.I., Bashinov, A.V., Gonoskov, A.A., Efimenko, E.S., Kim, A.V., Meyerov, I.B., Sergeev, A.M.: Generation of current sheets and giant quasistatic magnetic fields at the ionization of vacuum in extremely strong light fields. JETP Lett. 102(3), 148–153 (2015)CrossRefGoogle Scholar
  20. 20.
    Mackenroth, F., Gonoskov, A., Marklund, M.: Chirped-standing-wave acceleration of ions with intense lasers. Phys. Rev. Lett. 117(10), 104801 (2016)CrossRefGoogle Scholar
  21. 21.
    Mackenroth, F., Gonoskov, A., Marklund, M.: Theoretical benchmarking of laser-accelerated ion fluxes by 2D-PIC simulations (to appear).
  22. 22.
    Surmin, I., Bashinov, A., Bastrakov, S., Efimenko, E., Gonoskov, A., Meyerov, I.: Dynamic load balancing based on rectilinear partitioning in particle-in-cell plasma simulation. In: Malyshkin, V. (ed.) PaCT 2015. LNCS, vol. 9251, pp. 107–119. Springer, Heidelberg (2015). doi: 10.1007/978-3-319-21909-7_12 CrossRefGoogle Scholar
  23. 23.
    Villasenor, J., Buneman, O.: Rigorous charge conservation for local electromagnetic field solvers. Comput. Phys. Commun. 69, 306–316 (1992)CrossRefGoogle Scholar
  24. 24.
    Vincenti, H., Lehe, R., Sasanka, R., Vay, J.-L.: An efficient and portable SIMD algorithm for charge/current deposition in Particle-In-Cell codes (to appear).
  25. 25.
    Williams, S., Waterman, A., Patterson, D.: Roofline: an insightful visual performance model for multicore architectures. Commun. ACM 52(4), 65–76 (2009)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Igor Surmin
    • 1
  • Sergey Bastrakov
    • 1
  • Zakhar Matveev
    • 2
  • Evgeny Efimenko
    • 1
    • 3
  • Arkady Gonoskov
    • 1
    • 3
    • 4
  • Iosif Meyerov
    • 1
    Email author
  1. 1.Lobachevsky State University of Nizhni NovgorodNizhni NovgorodRussia
  2. 2.Intel CorporationNizhni NovgorodRussia
  3. 3.Institute of Applied Physics of the Russian Academy of SciencesNizhni NovgorodRussia
  4. 4.Chalmers University of TechnologyGothenburgSweden

Personalised recommendations