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Domain-Decomposition Parallelization for Molecular Dynamics Algorithm with Short-Ranged Potentials on Epiphany Architecture

  • Part 1. Special issue “High Performance Data Intensive Computing” Editors: V. V. Voevodin, A. S. Simonov, and A. V. Lapin
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Abstract

Many-core processor architecture is a promising paradigm for the development of modern supercomputers. In this paper, we consider the parallel implementation of the generic molecular dynamics algorithm for the many-core Epiphany architecture. This architecture implements a new type of many-core processor composed of 16 simple cores connected by a network on chip with mesh topology. New approaches to parallel programming are required to deploy this processor. We use LAMMPS running on one 64-bit ARMv8 Cortex-A53 CPU core for comparing the accuracy of the results of the presented variant of the molecular dynamics algorithm for Epiphany and its computational efficiency.

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References

  1. J. A. Ang, R. F. Barrett, R. E. Benner, D. Burke, C. Chan, J. Cook, D. Donofrio, S. D. Hammond, K. S. Hemmert, and S. M. Kelly, “Abstract machine models and proxy architectures for exascale computing,” in Proceedings of the 2014 Hardware-Software Co-Design for High Performance Computing (2014), pp. 25–32.

    Chapter  Google Scholar 

  2. W. D. Gropp, “MPI + X for extreme scale computing,” in Proceedings of the 12th International Conference on Parallel Processing and Applied Mathematics (2017).

    Google Scholar 

  3. B. Glinsky, I. Kulikov, I. Chernykh, D. Weins, A. Snytnikov, V. Nenashev, A. Andreev, V. Egunov, and E. Kharkov, “The co-design of astrophysical code for massively parallel supercomputers,” in Algorithms and Architectures for Parallel Processing, Ed. by J. Carretero et al. (Springer International, Cham, 2016), pp. 342–353.

    Chapter  Google Scholar 

  4. D. D. Pruitt and E. A. Freudenthal, “Preliminary investigation of mobile system features potentially relevant toHPC,” in Proceedings of the 4th InternationalWorkshop on Energy Efficient Supercomputing (IEEE, Piscataway, NJ, USA, 2016).

    Google Scholar 

  5. F. Mantovani and E. Calore, “Performance and power analysis of HPC workloads on heterogeneous multinode clusters,” J. Low Power Electron. Appl. 8 (2), 1–14 (2018).

    Article  Google Scholar 

  6. Y. Lee, R. Avizienis, A. Bishara, R. Xia, D. Lockhart, C. Batten, and K. Asanović, “Exploring the tradeoffs between programmability and efficiency in data-parallel accelerators,” SIGARCH Comput. Archit. New. 39, 129 (2011).

    Article  Google Scholar 

  7. U. Lopez-Novoa, A. Mendiburu, and J. Miguel-Alonso, “A survey of performance modeling and simulation techniques for accelerator-based computing,” IEEE Trans. Parallel Distrib. Syst. 26, 272 (2015).

    Article  Google Scholar 

  8. Q. Wu, C. Yang, T. Tang, and L. Xiao, “MIC acceleration of short-range molecular dynamics simulations,” in Proceedings of the 1st International Workshop on Code OptimiSation for Multi and Many Cores (ACM, New York, NY, USA, 2013).

    Google Scholar 

  9. M. Tasende, “Generation of the single precision BLAS Library for the Parallella platform, with Epiphany coprocessor acceleration, using the BLIS framework,” in Proceedings of the 2016 IEEE 14th International Conference on Dependable, Autonomic and Secure Computing, 14th International Conference on Pervasive Intelligence and Computing, and 2nd International Conference on Big Data Intelligence and Computing and Cyber Science and Technology Congress(DASC/PiCom/DataCom/CyberSciTech) (2016), pp. 894–897.

    Google Scholar 

  10. J. A. Ross, D. A. Richie, S. J. Park, and D. R. Shires, “Parallel programming model for the Epiphany manycore coprocessor using threaded MPI,” Microprocess. Microsyst. 43, 95 (2016).

    Article  Google Scholar 

  11. S. N. Agathos, A. Papadogiannakis, and V. V. Dimakopoulos, “Targeting the Parallella,” in Euro-Par 2015: Parallel Processing, Proceedings of the 21st International Conference on Parallel and Distributed Computing, Vienna, Austria, August 24–28, 2015, Ed. by J. L. Träff, S. Hunold, and F. Versaci (Springer, Berlin, Heidelberg, 2015), pp. 662–674.

    Google Scholar 

  12. A. Sukhinov and G. Ostrobrod, “Efficient face detection on Epiphany multicore processor,” in Parallel Computational Technologies (PCT’2014), Vestn. YuUr Univ., Ser. Vychisl. Mat. Inform. 3, 5–19 (2014),.

    Google Scholar 

  13. A. Sukhinov and G. Ostrobrod, “Efficient face detection on Epiphany multicore processor,” Comput. Math. Inform. Technol. 1, 113 (2017).

    Article  Google Scholar 

  14. S. Raase and T. Nordström, “On the use of a many-core processor for computational fluid dynamics simulations,” Proc. Comput. Sci. 51, 1403 (2015).

    Article  Google Scholar 

  15. A. Olofsson, T. Nordström, and Z. Ul-Abdin, “Kickstarting high-performance energy-efficient manycore architectures with Epiphany,” in Proceedings of the 48th Asilomar Conference on Signals, Systems and Computers, 2014.

    Google Scholar 

  16. A. Olofsson, R. Trogan, and O. Raikhman, “A 1024-core 70 GFLOP/W floating point manycore microprocessor,” in Proceedings of the 15th Annual Workshop on High Performance Embedded Computing, 2011.

    Google Scholar 

  17. T. Vocke, “An evaluation of the Adapteva Epiphany Many-core architecture,” Master’s Thesis (Univ. of Twente/Thales, 2015).

    Google Scholar 

  18. J. A. Ross and D. A. Richie, “Implementing OpenSHMEM for the Adapteva Epiphany RISC array processor,” Proc. Comput. Sci. 80, 2353 (2016).

    Article  Google Scholar 

  19. J. Ross and D. Richie, “An OpenSHMEM implementation for the Adapteva Epiphany coprocessor,” in OpenSHMEM and Related Technologies. Enhancing OpenSHMEM for Hybrid Environments, Ed. by M. Gorentla Venkata, N. Imam, S. Pophale, and T. M. Mintz (Springer International, Cham, 2016), pp. 146–159.

    Chapter  Google Scholar 

  20. M. López-Marcos, J. Sanz-Serna, and J. Diáz, “Are Gauss-Legendre methods useful in molecular dynamics?,” J. Comput. Appl. Math. 67, 173 (1996).

    Article  MathSciNet  MATH  Google Scholar 

  21. M. A. López-Marcos, J. M. Sanz-Serna, and R. D. Skeel, “Explicit symplectic integrators using Hessianvector products,” SIAM J. Sci. Comput. 18, 223 (1997).

    Article  MathSciNet  MATH  Google Scholar 

  22. S. Plimpton, “Fast parallel algorithms for short-range molecular dynamics,” J. Comp. Phys. 117, 1 (1995).

    Article  MATH  Google Scholar 

  23. G. E. Norman and V. V. Stegailov, “Stochastic theory of the classical molecular dynamics method,” Math. Models Comput. Simul. 5, 305 (2013).

    Article  MathSciNet  Google Scholar 

  24. S. Stoddard and J. Ford, “Numerical experiments on the stochastic behavior of a Lennard–Jones gas system,” Phys. Rev. A 8, 1504 (1973).

    Article  Google Scholar 

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Authors and Affiliations

  1. National Research University Higher School of Economics, ul. Myasnitskaya 20, Moscow, 101000, Russia

    V. Nikolskii & V. Stegailov

  2. Joint Institute for High Temperatures, Russian Academy of Sciences, ul. Izhorskaya 13, str. 2, Moscow, 125412, Russia

    V. Nikolskii & V. Stegailov

Authors
  1. V. Nikolskii
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  2. V. Stegailov
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Correspondence to V. Nikolskii.

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(Submitted by A. V. Lapin)

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Nikolskii, V., Stegailov, V. Domain-Decomposition Parallelization for Molecular Dynamics Algorithm with Short-Ranged Potentials on Epiphany Architecture. Lobachevskii J Math 39, 1228–1238 (2018). https://doi.org/10.1134/S1995080218090159

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  • DOI: https://doi.org/10.1134/S1995080218090159

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