A performance portable implementation of the semi-Lagrangian algorithm in six dimensions
Computer Physics Communications, ISSN: 0010-4655, Vol: 295, Page: 108973
2024
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Most Recent News
Recent Findings from Max-Planck-Institute for Plasma Physics Provides New Insights into Mathematics (A Performance Portable Implementation of the Semi-lagrangian Algorithm In Six Dimensions)
2024 APR 02 (NewsRx) -- By a News Reporter-Staff News Editor at Computer News Today -- Fresh data on Mathematics are presented in a new
Article Description
This paper describes our approach to developing a simulation software application for the fully kinetic 6D-Vlasov equation, which will be used to explore physics beyond the reduced gyrokinetic model. Simulating the fully kinetic Vlasov equation requires efficient utilization of compute and storage capabilities due to the high dimensionality of the problem. In addition, the implementation needs to be extensible regarding the physical model and flexible regarding the hardware for production runs. We start on the algorithmic background to simulate the 6-D Vlasov equation using a semi-Lagrangian algorithm. The performance portable software stack, which enables production runs on pure CPU as well as AMD or Nvidia GPU accelerated nodes, is presented. The extensibility of our implementation is guaranteed through the described software architecture of the main kernel, which achieves a memory bandwidth of almost 500 GB/s on a V100 Nvidia GPU and around 100 GB/s on an Intel Xeon Gold CPU using a single code base. We provide performance data on multiple node-level architectures discussing utilized and further available hardware capabilities. Finally, the network communication bottleneck of 6-D grid-based algorithms is quantified. A verification of physics beyond gyrokinetic theory, for the example of ion Bernstein waves, concludes the work.
Bibliographic Details
http://www.sciencedirect.com/science/article/pii/S0010465523003181; http://dx.doi.org/10.1016/j.cpc.2023.108973; http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85174830924&origin=inward; https://linkinghub.elsevier.com/retrieve/pii/S0010465523003181; https://dx.doi.org/10.1016/j.cpc.2023.108973
Elsevier BV
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