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Annals of Computer Science and Information Systems, Volume 19

Position Papers of the 2019 Federated Conference on Computer Science and Information Systems

Performance and Energy Evaluation of Parallel Particle Simulation Algorithms for Different Input Particle Data

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DOI: http://dx.doi.org/10.15439/2019F344

Citation: Position Papers of the 2019 Federated Conference on Computer Science and Information Systems, M. Ganzha, L. Maciaszek, M. Paprzycki (eds). ACSIS, Vol. 19, pages 31–37 ()

Full text

Abstract. Particle simulations are popular methods for the simulation of applications from a wide range of sciences, including astrophysics, biology or chemistry. Usually, these applications require a large number of simulation steps, each of which computes a change of the entire particle system. Depending on the number of simulation steps and also the size and structure of the specific particle system, the computation time can be quite large and the exploitation of parallel architectures is usually necessary. In this article, we investigate the performance and energy consumption for different particle simulation methods and distinguish different input particle data. The investigations are done for the particle simulation methods from the ScaFaCoS library and use the various input data of homogeneous or in-homogeneous nature. Experiments are performed on multicore systems.

References

  1. L. Greengard and V. Rokhlin, “A fast algorithm for particle simulations,” J. of Computational Physics, vol. 73, pp. 325–348, 1987.
  2. M. Pippig and D. Potts, “Parallel three-dimensional nonequispaced fast fourier transforms and their application to particle simulation,” SIAM J. on Scientific Computing, vol. 35, no. 4, pp. C411–C437, 2013. http://dx.doi.org/10.1137/120888478
  3. M. Bolten, F. Fahrenberger, R. Halver, F. Heber, M. Hofmann, I. Kabadshow, O. Lenz, M. Pippig, and G. Sutmann, “ScaFaCoS, C subroutine library,” http://scafacos.github.com/. [Online]. Available: http://scafacos.github.com
  4. M. Hofmann, R. Kiesel, D. Leichsenring, and G. Rünger, “A hybrid cpu/gpu implementation of computationally intensive particle simulations using opencl,” in 2018 17th International Symposium on Parallel and Distributed Computing (ISPDC), June 2018. http://dx.doi.org/10.1109/IS- PDC2018.2018.00011 pp. 9–16.
  5. A. Arnold, F. Fahrenberger, C. Holm, O. Lenz, M. Bolten, H. Dachsel, R. Halver, I. Kabadshow, F. Gähler, F. Heber, J. Iseringhausen, M. Hofmann, M. Pippig, D. Potts, and G. Sutmann, “Comparison of scalable fast methods for long-range interactions,” Physical Review E, vol. 88, p. 063308, 2013.
  6. J. M. Hammersley, “Monte carlo methods for solving multivariable problems,” Annals of the New York Academy of Sciences, vol. 86, no. 3, pp. 844–874, 1960. http://dx.doi.org/10.1111/j.1749-6632.1960.tb42846.x. [Online]. Available: https://nyaspubs.onlinelibrary.wiley.com/doi/abs/10.1111/j.1749-6632.1960.tb42846.x
  7. M. Pippig and D. Potts, “Particle simulation based on nonequispaced fast fourier transforms,” 01 2011, pp. 131–158.
  8. T. Rauber, G. Rünger, and C. Scholtes, “Execution Behavior Analysis and Performance Prediction for a Shared-Memory Implementation of an Irregular Particle Simulation Method,” Simulation: Practice and Theory, vol. 6, no. 7, pp. 665–687, 1998. http://dx.doi.org/10.1016/S0928-4869(98)00006-8
  9. H. Dachsel, M. Hofmann, and G. Rünger, “Library Support for Parallel Sorting in Scientific Computations,” in Proceedings of the 13th International Euro-Par Conference, ser. LNCS, vol. 4641. Springer, August 2007. http://dx.doi.org/10.1007/978-3-540-74466-5 73. ISBN 978-3-540-74465-8 pp. 695–704.
  10. H. Dachsel, M. Hofmann, J. Lang, and G. Rünger, “Automatic Tuning of the Fast Multipole Method Based on Integrated Performance Prediction,” in Proceedings of the 14th IEEE International Conference on High Performance Computing and Communications (HPCC-2012). IEEE, Juni 2012. http://dx.doi.org/10.1109/HPCC.2012.88. ISBN 978-1-4673-2164-8 pp. 617–624.
  11. N. Kalinnik, R. Kiesel, T. Rauber, M. Richter, and G. Rünger, “On the Autotuning Potential of Time-stepping methods from Scientific Computing,” in Proceedings of the 2018 Federated Conference on Computer Science and Information Systems (FedCSIS 2018), 11th Workshop on Computer Aspects of Numerical Algorithms (CANA’18), vol. 15. ACSIS, September 2018. http://dx.doi.org/10.15439/2018F169. ISSN 2300-596 pp. 329–338.
  12. M. Hofmann, R. Kiesel, and G. Rünger, “Energy and Performance Analysis of Parallel Particle Solvers from the ScaFaCoS Library,” in Proceedings of the 2018 ACM/SPEC International Conference on Performance Engineering (ICPE 2018). ACM, April 2018. doi: 10.1145/3184407.3184409. ISBN 978-1-4503-5095-2 pp. 88–95.
  13. R. Yokota and L. Barba, “Hierarchical N-body Simulations with Autotuning for Heterogeneous Systems,” Computing in Science Engineering, vol. 14, no. 3, pp. 30–39, May 2012. http://dx.doi.org/10.1109/MCSE.2012.1
  14. M. Holm, S. Engblom, A. Goude, and S. Holmgren, “Dynamic Autotuning of Adaptive Fast Multipole Methods on Hybrid Multicore CPU and GPU Systems,” SIAM Journal on Scientific Computing, vol. 36, no. 4, pp. C376–C399, Jan. 2014. http://dx.doi.org/10.1137/130943595. [Online]. Available: https://epubs.siam.org/doi/abs/10.1137/130943595