Benchmarking solvers for the one dimensional cubic nonlinear klein gordon equation on a single core
Type
Conference PaperAuthors
Muite, B. K.Aseeri, Samar
KAUST Department
Extreme Computing Research CenterComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Date
2020-06-09Online Publication Date
2020-06-09Print Publication Date
2020Permanent link to this record
http://hdl.handle.net/10754/664492
Metadata
Show full item recordAbstract
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 platformsCitation
Muite, B. K., & Aseeri, S. (2020). Benchmarking Solvers for the One Dimensional Cubic Nonlinear Klein Gordon Equation on a Single Core. Lecture Notes in Computer Science, 172–184. doi:10.1007/978-3-030-49556-5_18Sponsors
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.. 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.Publisher
Springer NatureConference/Event name
2nd International Symposium on Benchmarking, Measuring, and Optimization, Bench 2019ISBN
9783030495558Additional Links
http://link.springer.com/10.1007/978-3-030-49556-5_18ae974a485f413a2113503eed53cd6c53
10.1007/978-3-030-49556-5_18