GBEES-GPU: An efficient parallel GPU algorithm for high-dimensional nonlinear uncertainty propagation

[EN] Eulerian nonlinear uncertainty propagation methods often suffer from finite domain limitations and computational inefficiencies. A recent approach to this class of algorithm, Grid-based Bayesian Estimation Exploiting Sparsity, addresses the first challenge by dynamically allocating a discretize...

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Detalhes bibliográficos
Autores: Hanson, Benjamin L., Rubio Sierra, Carlos, García Gutiérrez, Adrián, Bewley, Thomas
Tipo de documento: artigo
Estado:Versión enviada para evaluación y publicación
Data de publicação:2025
País:España
Recursos:Universidad de León
Repositório:BULERIA. Repositorio Institucional de la Universidad de León
OAI Identifier:oai:buleria.unileon.es:10612/25734
Acesso em linha:https://www.sciencedirect.com/science/article/pii/S0010465525003212?via%3Dihub
https://hdl.handle.net/10612/25734
https://doi.org/10.1016/j.cpc.2025.109819
Access Level:Acceso aberto
Palavra-chave:Aeronáutica
CUDA
Eulerian uncertainty propagation
Corner transport upwind
Dynamic gridding
3301 Ingeniería y Tecnología Aeronáuticas
Descrição
Resumo:[EN] Eulerian nonlinear uncertainty propagation methods often suffer from finite domain limitations and computational inefficiencies. A recent approach to this class of algorithm, Grid-based Bayesian Estimation Exploiting Sparsity, addresses the first challenge by dynamically allocating a discretized grid in regions of phase space where probability is non-negligible. However, the design of the original algorithm causes the second challenge to persist in high-dimensional systems. This paper presents an architectural optimization of the algorithm for CPU implementation, followed by its adaptation to the CUDA framework for single GPU execution. The algorithm is validated for accuracy and convergence, with performance evaluated across distinct GPUs. Tests include propagating a three-dimensional probability distribution subject to the Lorenz ’63 model and a six-dimensional probability distribution subject to the Lorenz ’96 model. The results imply that the improvements made result in a speedup of over 1000 times compared to the original implementation.