From Knights Corner to Landing: A Case Study Based on a Hodgkin-Huxley Neuron Simulator

  • George ChatzikonstantisEmail author
  • Diego Jiménez
  • Esteban Meneses
  • Christos Strydis
  • Harry Sidiropoulos
  • Dimitrios Soudris
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10524)


Brain modeling has been presenting significant challenges to the world of high-performance computing (HPC) over the years. The field of computational neuroscience has been developing a demand for physiologically plausible neuron models, that feature increased complexity and thus, require greater computational power. We explore Intel’s newest generation of Xeon Phi computing platforms, named Knights Landing (KNL), as a way to match the need for processing power and as an upgrade over the previous generation of Xeon Phi models, the Knights Corner (KNC). Our neuron simulator of choice features a Hodgkin-Huxley-based (HH) model which has been ported on both generations of Xeon Phi platforms and aggressively draws on both platforms’ computational assets. The application uses the OpenMP interface for efficient parallelization and the Xeon Phi’s vectorization buffers for Single-Instruction Multiple Data (SIMD) processing. In this study we offer insight into the efficiency with which the application utilizes the assets of the two Xeon Phi generations and we evaluate the merits of utilizing the KNL over its predecessor. In our case, an out-of-the-box transition on Knights Landing, offers on average 2.4\(\times \) speed up while consuming 48% less energy than KNC.


Intel Xeon Phi Knights landing Computational neuroscience 



This work is partially supported by European Commission project H2020–687628–VINEYARD.


  1. 1.
    CUDA C Programming Guide. Technical report, NVIDIA CorporationGoogle Scholar
  2. 2.
    Bhuiyan, M., Nallamuthu, A., Smith, M., Pallipuram, V.: Optimization and performance study of large-scale biological networks for reconfigurable computing. In: Fourth International Workshop on High-Performance Reconfigurable Computing Technology and Applications (HPRCTA), pp. 1–9, November 2010Google Scholar
  3. 3.
    Bhuiyan, M., et al.: Acceleration of spiking neural networks in emerging multi-core and GPU architectures. In: IPDPSW (2010)Google Scholar
  4. 4.
    Birrittella, M.S., Debbage, M., Huggahalli, R., Kunz, J., Lovett, T., Rimmer, T., Underwood, K.D., Zak, R.C.: Intel® omni-path architecture: enabling scalable, high performance fabrics. In: 2015 IEEE 23rd Annual Symposium on High-Performance Interconnects (HOTI), pp. 1–9. IEEE (2015)Google Scholar
  5. 5.
    Chatzikonstantis, G., Rodopoulos, D., Nomikou, S., Strydis, C., De Zeeuw, C.I., Soudris, D.: First impressions from detailed brain model simulations on a Xeon/Xeon-Phi Node. In: Proceedings of the ACM International Conference on Computing Frontiers, CF 2016, NY, USA, pp. 361–364 (2016). doi: 10.1145/2903150.2903477
  6. 6.
    Chatzikonstantis, G., Rodopoulos, D., Strydis, C., De Zeeuw, C.I., Soudris, D.: Optimizing extended Hodgkin-Huxley neuron model simulations for a Xeon/Xeon Phi node. IEEE Trans. Parallel Distrib. Syst. (2017)Google Scholar
  7. 7.
    Dagum, L., Enon, R.: OpenMP: an industry standard API for shared-memory programming. IEEE CSE 5(1), 46–55 (1998)Google Scholar
  8. 8.
    De Zeeuw, C.I., Hoebeek, F.E., Bosman, L.W., Schonewille, M., Witter, L., Koekkoek, S.K.: Spatiotemporal firing patterns in the cerebellum. Nat. Rev. Neurosci. 12(6), 327–344 (2011)CrossRefGoogle Scholar
  9. 9.
    De Zeeuw, C.I., et al.: Microcircuitry and function of the inferior olive. Trends Neurosci. 21(9), 391–400 (1998)CrossRefGoogle Scholar
  10. 10.
    Fang, J., et al.: Test-driving Intel Xeon Phi. In: ICPE (2014)Google Scholar
  11. 11.
    Glackin, B., Wall, J.A., McGinnity, T.M., Maguire, L.P., McDaid, L.: A spiking neural network model of the medial superior olive using spike timing dependent plasticity for sound localization. Front. Comput. Neurosci. 4(18) (2010)Google Scholar
  12. 12.
    Goodman, D.F., Brette, R.: The brian simulator. Front. Neurosci. 3, 26 (2009)CrossRefGoogle Scholar
  13. 13.
    Hines, M.L., Carnevale, N.T.: The NEURON simulation environment. Neural Comput. 9(6), 1245–1249 (1997)CrossRefGoogle Scholar
  14. 14.
    Hodgkin, A.L., Huxley, A.F.: Propagation of electrical signals along giant nerve fibres. Proc. R. Soc. Lond. Ser. B Biol. Sci. 140(899), 177–183 (1952)CrossRefGoogle Scholar
  15. 15.
    Jeffers, J., Reinders, J.: Intel Xeon Phi Coprocessor High-Performance Programming. Elsevier, Waltham (2013)Google Scholar
  16. 16.
    Jeffers, J., Reinders, J., Sodani, A.: Intel Xeon Phi Processor High Performance Programming: Knights Landing Edition. Morgan Kaufmann, Boston (2016)Google Scholar
  17. 17.
    Kaufman, G.J., et al.: System and method for application programming interface for extended intelligent platform management. US Patent 7,966,389, 21 Jun 2011Google Scholar
  18. 18.
    Nguyen, H.D., Al-Ars, Z., Smaragdos, G., Strydis, C.: Accelerating complex brain-model simulations on GPU platforms. In: Design, Automation, and Test in Europe, DATE 2015, March 2015Google Scholar
  19. 19.
    Du Nguyen, H.A., et al.: Accelerating complex brain-model simulations on GPU platforms. In: DATE, pp. 974–979 (2015)Google Scholar
  20. 20.
    Press, W.H., Teukolsky, S.A., Vetterling, W.T., Flannery, B.P.: Numerical Recipes in C, vol. 2. Cambridge University Press, Cambridge (1996)zbMATHGoogle Scholar
  21. 21.
    Rosales, C., James, D., Gómez-Iglesias, A., Cazes, J., Huang, L., Liu, H., Liu, S., Barth, W.: TACC Technical Report TR-16-03 KNL Utilization Guidelines. Technical report, University of Texas at Austin, Texas Advanced Computing Center, November 2016.
  22. 22.
    Smaragdos, G., Isaza, S., Eijk, M.V., Sourdis, I., Strydis, C.: FPGA-based biophysically-meaningful modeling of olivocerebellar neurons. In: 22nd ACM/SIGDA International Symposium on Field-Programmable Gate Arrays (FPGA), February 2014Google Scholar
  23. 23.
    Snir, M.: MPI-the Complete Reference: The MPI Core. MIT, Cambridge (1998)Google Scholar
  24. 24.
    Wallisch, P., Lusignan, M.E., Benayoun, M.D., Baker, T.I., Dickey, A.S., Hatsopoulos, N.G.: MATLAB for Neuroscientists: An Introduction to Scientific Computing in MATLAB. Academic Press, San Diego (2014)zbMATHGoogle Scholar
  25. 25.
    Yamazaki, T., Igarashi, J.: Realtime cerebellum: a large-scale spiking network model of the cerebellum that runs in realtime using a graphics processing unit. Neural Netw. 47, 103–111 (2013). Computation in the CerebellumCrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • George Chatzikonstantis
    • 1
    Email author
  • Diego Jiménez
    • 2
  • Esteban Meneses
    • 2
    • 3
  • Christos Strydis
    • 4
  • Harry Sidiropoulos
    • 1
  • Dimitrios Soudris
    • 1
  1. 1.Microprocessors and Digital Systems LabNational Technical University of AthensAthensGreece
  2. 2.Advanced Computing LaboratoryCosta Rica National High Technology CenterSan JoséCosta Rica
  3. 3.School of ComputingCosta Rica Institute of TechnologyCartagoCosta Rica
  4. 4.Neuroscience DepartmentErasmus Medical Center RotterdamRotterdamNetherlands

Personalised recommendations