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Performance benchmarks for a next generation numerical dynamo model

  • Author(s): Matsui, H
  • Heien, E
  • Aubert, J
  • Aurnou, JM
  • Avery, M
  • Brown, B
  • Buffett, BA
  • Busse, F
  • Christensen, UR
  • Davies, CJ
  • Featherstone, N
  • Gastine, T
  • Glatzmaier, GA
  • Gubbins, D
  • Guermond, JL
  • Hayashi, YY
  • Hollerbach, R
  • Hwang, LJ
  • Jackson, A
  • Jones, CA
  • Jiang, W
  • Kellogg, LH
  • Kuang, W
  • Landeau, M
  • Marti, P
  • Olson, P
  • Ribeiro, A
  • Sasaki, Y
  • Schaeffer, N
  • Simitev, RD
  • Sheyko, A
  • Silva, L
  • Stanley, S
  • Takahashi, F
  • Takehiro, SI
  • Wicht, J
  • Willis, AP
  • et al.
Abstract

© 2016. American Geophysical Union. All Rights Reserved. Numerical simulations of the geodynamo have successfully represented many observable characteristics of the geomagnetic field, yielding insight into the fundamental processes that generate magnetic fields in the Earth's core. Because of limited spatial resolution, however, the diffusivities in numerical dynamo models are much larger than those in the Earth's core, and consequently, questions remain about how realistic these models are. The typical strategy used to address this issue has been to continue to increase the resolution of these quasi-laminar models with increasing computational resources, thus pushing them toward more realistic parameter regimes. We assess which methods are most promising for the next generation of supercomputers, which will offer access to O(106) processor cores for large problems. Here we report performance and accuracy benchmarks from 15 dynamo codes that employ a range of numerical and parallelization methods. Computational performance is assessed on the basis of weak and strong scaling behavior up to 16,384 processor cores. Extrapolations of our weak-scaling results indicate that dynamo codes that employ two-dimensional or three-dimensional domain decompositions can perform efficiently on up to ∼106processor cores, paving the way for more realistic simulations in the next model generation.

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