Abstract
To determine the best method for solving a numerical problem modeled by a partial differential equation, one should consider the discretization of the problem, the computational hardware used and the implementation of the software solution. In solving a scientific computing problem, the level of accuracy can also be important, with some numerical methods being efficient for low accuracy simulations, but others more efficient for high accuracy simulations. Very few high performance benchmarking efforts allow the computational scientist to easily measure such tradeoffs in order to obtain an accurate enough numerical solution at a low computational cost. These tradeoffs are examined in the numerical solution of the one dimensional Klein Gordon equation on single cores of an ARM CPU, an AMD x86-64 CPU, two Intel x86-64 CPUs and a NEC SX-ACE vector processor. The work focuses on comparing the speed and accuracy of several high order finite difference spatial discretizations using a conjugate gradient linear solver and a fast Fourier transform based spatial discretization. In addition implementations using second and fourth order timestepping are also included in the comparison. The work uses accuracy-efficiency frontiers to compare the effectiveness of five hardware platforms
BKM was partially supported by HPC Europa 3 (INFRAIA-2016-1-730897). Compute time on Isamabard was partially supported by ESPRC grant EP/P020224/1.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Abdulkadir, Y.A.: Comparison of finite difference schemes for the wave equation based on dispersion. J. Appl. Math. Phys. 3, 1544–1562 (2015). https://doi.org/10.4236/jamp.2015.311179
Adams, M.F., Brown, J., Shalf, J., Van Straalen, B., Strohmaier, E., Williams, S.: HPGMG 1.0: A Benchmark for Ranking High Performance Computing Systems, Lawrence Berkely National Laboratory Preprint (2014). https://escholarship.org/uc/item/00r9w79m. Accessed 16 July 2019
Afanasyev, I.V., et al.: Developing efficient implementations of Bellman-Ford and Forward-Backward Graph Algorithms for NEC SX-ACE. Supercomput. Front. Innov. 5(3), 65–69 (2018). https://doi.org/10.14529/jsfi180311
Arm Performance Library. https://www.arm.com/products/development-tools/server-and-hpc/allinea-studio/performance-libraries. Accessed 16 Nov 2019
Aseeri, S., et al.: Solving the Klein-Gordon equation using Fourier spectral methods: a benchmark test for computer performance. In: HPC 2015 Proceedings of the Symposium on High Performance Computing, pp. 182–191. Society for Computer Simulation International (2015)
Aseeri, S., Muite, B.K., Takahashi, D.: Reproducibility in benchmarking parallel fast Fourier transform based applications. In: Companion of the 2019 ACM/SPEC International Conference on Performance Engineering - ICPE 2019, pp. 5–8 (2019). https://doi.org/10.1145/3302541.3313105
Auzinger, W., Br̆ezinová, I., Hofstätter, H., Koch, O., Quell, M.: Practical splitting methods for the adaptive integration of nonlinear evolution equations. Part II: comparisons of local error estimation and step-selection strategies for nonlinear Schrödinger and Wave equations. Comput. Phys. Commun. 234, 55–71 (2018). https://doi.org/10.1016/j.cpc.2018.08.003
Bailey, D.H., et al.: The NAS parallel benchmarks. Int. J. High Perform. Comput. Appl. 5(3), 63–73 (1991). https://doi.org/10.1177/109434209100500306
Balakrishnan, S., et al.: Parallel Spectral Numerical Methods. http://en.wikibooks.org/wiki/Parallel_Spectral_Numerical_Methods. Accessed 24 June 2019
Buttari, A., Dongarra, J., Kurzak, J., Luszczek, P., Tomov, S.: Using mixed precision for sparse matrix computations to enhance performance while achieving 64-bit accuracy. ACM Trans. Math. Softw. 34(4), 17 (2008). https://doi.org/10.1145/1377596.1377597
Canuto, C., Hussaini, M.Y., Quarteroni, A., Zang, T.A.: Spectral Methods Fundamentals in Single Domains. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-540-30726-6
Cloutier, B., Muite, B.K., Parsani, M.: Fully implicit time stepping can be efficient on parallel computers. Supercomput. Front. Innov. 6(2), 75–85 (2019). https://doi.org/10.14529/jsfi190206
Cloutier, B., Muite, B.K., Rigge, P.: A comparison of CPU and GPU performance for Fourier Pseudospectral Simulations of the Navier-Stokes, Cubic Nonlinear Schrödinger and Sine Gordon Equations. In: Proceedings of the 2012 Symposium on Application Accelerators in High Performance Computing, pp. 145–148 (2012). https://doi.org/10.1109/SAAHPC.2012.24
Chang, J., Nakshatrala, K.B., Knepley, M.G., Johnsson, L.: A performance spectrum for parallel computational frameworks that solve PDEs. Concurr. Comput. Pract. Exp. 30, e4401 (2018). https://doi.org/10.1002/cpe.4401
Deconinck, W., et al.: Accelerating extreme-scale numerical weather prediction. In: Wyrzykowski, R., Deelman, E., Dongarra, J., Karczewski, K., Kitowski, J., Wiatr, K. (eds.) PPAM 2015. LNCS, vol. 9574, pp. 583–593. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-32152-3_54
Dongarra, J., Heroux, M.A., Luszcek, P.: A new metric for ranking high-performance computing systems. Int. J. High Perform. Comput. Appl. 30(1), 3–10 (2016). https://doi.org/10.1177/1094342015593158
Frigo, M., Johnson, S.G.: The design and implementation of FFTW. Proc. IEEE 93(2), 216–231 (2005). https://doi.org/10.1109/JPROC.2004.840301
Fornberg, B.: A Practical Guide to Pseudospectral Methods. Cambridge University Press (1996). https://doi.org/10.1017/CBO9780511626357
Fornberg, B.: Generation of finite difference formulas on arbitrarily spaced grids. Math. Comput. 51, 699–706 (1988). https://doi.org/10.1090/S0025-5718-1988-0935077-0
Gholami, A., Malhotra, D., Sundar, H., Biros, G.: FFT, FMM, or Multigrid? A comparative study of state-of-the-art poisson solvers for uniform and nonuniform grids in the unit cube. SIAM J. Sci. Comput. 38(3), C280–C306 (2016). https://doi.org/10.1137/15M1010798
GW4: Isambard. https://gw4.ac.uk/isambard/. Accessed 9 Nov 2019
Hairer, E., Wanner, G.: Solving Ordinary Differential Equations I. Springer, Heidelberg (1993). https://doi.org/10.1007/978-3-540-78862-1
Hairer, E., Wanner, G.: Solving Ordinary Differential Equations II. Springer, Heidelberg (1996). https://doi.org/10.1007/978-3-642-05221-7
Höchstleistungsrechenzentrum Stuttgart (HLRS): Hazelhen. https://www.hlrs.de/systems/cray-xc40-hazel-hen/. Accessed 15 July 2019
Höchstleistungsrechenzentrum Stuttgart (HLRS): Kabuki. https://kb.hlrs.de/platforms/index.php/NEC_SX-ACE. Accessed 15 July 2019
Hutchinson, M., Heinecke, A., Pabst, H., Henry, G., Parsani, M., Keyes, D.: Efficiency of high order spectral element methods on petascale architectures. In: Kunkel, J.M., Balaji, P., Dongarra, J. (eds.) ISC High Performance 2016. LNCS, vol. 9697, pp. 449–466. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-41321-1_23
Ibeid, H., Olson, L., Gropp, W.: FFT, FMM, and Multigrid on the Road to Exascale: Performance Challenges and Opportunities, arXiv:1810.11883v1 (2018)
Kassam, A.-K., Trefethen, L.N.: Fourth-order time-stepping for stiff PDEs. SIAM J. Sci. Comput. 26(4), 1214–1233 (2005). https://doi.org/10.1137/S1064827502410633
Ketcheson, D.I., Mortensen, M., Parsani, M., Schilling, N.: More efficient time integration for Fourier pseudo-spectral DNS of incompressible turbulence. arXiv:1810.10197v1
King Abdullah University of Science and Technology Supercomputing Laboratory: Ibex. https://www.hpc.kaust.edu.sa/ibex. Accessed 9 Nov 2019
Komatitsch, D., et al.: SPECFEM3D Cartesian [software], GITHASH8 (1999). https://geodynamics.org/cig/software/specfem3d/. Accessed 16 July 2019
Leimkuhler, B., Reich, S.: Simulating Hamiltonian Dynamics. Cambridge University Press (2009). https://doi.org/10.1017/CBO9780511614118
McIntosh-Smith, S., Price, J., Poenaru, A., Deakin, T.: Scaling results from the first generation of ARM-based supercomputers. In: Proceedings of the Cray User Group 2019. http://uob-hpc.github.io/assets/cug-2019.pdf. Accessed 9 Nov 2019
Muite, B.K.: https://github.com/bkmgit/KleinGordon1D [software]. Accessed 16 July 2019
Müller, E.H., Scheichl, R., Vainikko, E.: Petascale solvers for anisotropic PDEs in atmospheric modelling on GPU clusters. Parallel Comput. 50, 53–69 (2015). https://doi.org/10.1016/j.parco.2015.10.007
NEC. http://mathkeisan.com/ [software]. Accessed 16 July 2019
Pershin, I.S., Levchenko, V.D., Perepelkina, A.Y.: Performance limits study of stencil codes on modern GPGPUs. Supercomput. Front. Innov. 6(2), 86–101 (2019). https://doi.org/10.14529/jsfi190207
Shen, J., Tang, T., Wang, L.-L.: Spectral Methods: Algorithms, Analysis and Applications. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-540-71041-7
Top500. https://www.top500.org/. Accessed 10 Nov 2019
Trefethen, L.: Spectral methods in MATLAB. SIAM 10(1137/1) (2000). https://doi.org/10.1137/1.9780898719598
Treibig, J., Hager, G., Wellein, G.: LIKWID: a lightweight performance-oriented tool suite for x86 multicore environments. In: Proceedings of the First International Workshop on Parallel Software Tools and Tool Infrastructures. https://doi.org/10.1109/ICPPW.2010.38
Williams, S., Waterman, A., Patterson, D.: Roofline: an insightful visual performance model for multicore architectures. Commun. ACM 52(4), 65–76 (2009). https://doi.org/10.1145/1498765.1498785
Yang, C., et al.: 10M-core scalable fully-implicit solver for nonhydrostatic atmospheric dynamics. In: SC 2016: Proceedings of the International Conference on High Performance Computing, Networking, Storage and Analysis, pp. 57–68 (2016). https://doi.org/10.1109/SC.2016.5
Acknowledgements
We thank Holger Berger, José Gracia, John Linford and Simon McIntosh-Smith for helpful conversations. We thank Höchstleistungsrechenzentrum Stuttgart (HLRS), the KAUST Supercomputing Laboratory, the University of Tartu High Performance Computing Center and the GW4 Isamabard project for access to supercomputing resources used in development and testing.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Muite, B.K., Aseeri, S. (2020). Benchmarking Solvers for the One Dimensional Cubic Nonlinear Klein Gordon Equation on a Single Core. In: Gao, W., Zhan, J., Fox, G., Lu, X., Stanzione, D. (eds) Benchmarking, Measuring, and Optimizing. Bench 2019. Lecture Notes in Computer Science(), vol 12093. Springer, Cham. https://doi.org/10.1007/978-3-030-49556-5_18
Download citation
DOI: https://doi.org/10.1007/978-3-030-49556-5_18
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-49555-8
Online ISBN: 978-3-030-49556-5
eBook Packages: Computer ScienceComputer Science (R0)