Optimization and large scale computation of an entropy-based moment closure

Hauck, Cory D.; Hill, Judith C.; Garrett, C. Kristopher

doi:10.1016/j.jcp.2015.09.008

Optimization and large scale computation of an entropy-based moment closure

Journal Article · Thu Sep 10 00:00:00 EDT 2015 · Journal of Computational Physics

DOI:https://doi.org/10.1016/j.jcp.2015.09.008· OSTI ID:1261246

Hauck, Cory D. ^[1]; Hill, Judith C. ^[2]; Garrett, C. Kristopher ^[2]

Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States); Univ. of Tennessee, Knoxville, TN (United States)
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)

We present computational advances and results in the implementation of an entropy-based moment closure, M_N, in the context of linear kinetic equations, with an emphasis on heterogeneous and large-scale computing platforms. Entropy-based closures are known in several cases to yield more accurate results than closures based on standard spectral approximations, such as P_N, but the computational cost is generally much higher and often prohibitive. Several optimizations are introduced to improve the performance of entropy-based algorithms over previous implementations. These optimizations include the use of GPU acceleration and the exploitation of the mathematical properties of spherical harmonics, which are used as test functions in the moment formulation. To test the emerging high-performance computing paradigm of communication bound simulations, we present timing results at the largest computational scales currently available. Lastly, these results show, in particular, load balancing issues in scaling the M_N algorithm that do not appear for the P_N algorithm. We also observe that in weak scaling tests, the ratio in time to solution of M_N to P_N decreases.

View Accepted Manuscript (DOE)

Research Organization:: Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States). Oak Ridge Leadership Computing Facility (OLCF)

Sponsoring Organization:: ORNL LDRD Director's R&D; USDOE Office of Science (SC)

Grant/Contract Number:: AC05-00OR22725

OSTI ID:: 1261246

Alternate ID(s):: OSTI ID: 1247046

Journal Information:: Journal of Computational Physics, Journal Name: Journal of Computational Physics Journal Issue: C Vol. 302; ISSN 0021-9991

Publisher:: ElsevierCopyright Statement

Country of Publication:: United States

Language:: English

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A structure-preserving surrogate model for the closure of the moment system of the Boltzmann equation using convex deep neural networks Schotthöfer, Steffen; Xiao, Tianbai; Frank, Martin Karlsruher Institut für Technologie (KIT) https://doi.org/10.5445/ir/1000144563	text	January 2021

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97 MATHEMATICS AND COMPUTING
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Optimization and large scale computation of an entropy-based moment closure

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