DCGrid: An Adaptive Grid Structure for Memory-Constrained Fluid Simulation on the GPU
Herrera, Jorge Alejandro Amador
Banuti, Daniel T.
Michels, Dominik L.
KAUST DepartmentComputer Science Program
Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division
Visual Computing Center (VCC)
Permanent link to this recordhttp://hdl.handle.net/10754/676671
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AbstractWe introduce Dynamic Constrained Grid (DCGrid), a hierarchical and adaptive grid structure for fluid simulation combined with a scheme for effectively managing the grid adaptations. DCGrid is designed to be implemented on the GPU and used in high-performance simulations. Specifically, it allows us to efficiently vary and adjust the grid resolution across the spatial domain and to rapidly evaluate local stencils and individual cells in a GPU implementation. A special feature of DCGrid is that the control of the grid adaption is modeled as an optimization under a constraint on the maximum available memory, which addresses the memory limitations in GPU-based simulation. To further advance the use of DCGrid in high-performance simulations, we complement DCGrid with an efficient scheme for approximating collisions between fluids and static solids on cells with different resolutions. We demonstrate the effectiveness of DCGrid for smoke flows and complex cloud simulations in which terrain-atmosphere interaction requires working with cells of varying resolution and rapidly changing conditions. Finally, we compare the performance of DCGrid to that of alternative adaptive grid structures.
CitationRaateland, W., Hädrich, T., Herrera, J. A. A., Banuti, D. T., Pałubicki, W., Pirk, S., Hildebrandt, K., & Michels, D. L. (2022). DCGrid. Proceedings of the ACM on Computer Graphics and Interactive Techniques, 5(1), 1–14. https://doi.org/10.1145/3522608
SponsorsWe thank the authors of SPGrid and GVDB for partially making their code publicly available, and the anonymous reviewers for their constructive feedback and suggestions that improved the manuscript. The helpful discussions with Miłosz Makowski are gratefully acknowledged. This work has been supported by KAUST (individual baseline funding).
Except where otherwise noted, this item's license is described as This work is licensed under a Creative Commons Attribution International 4.0 License © 2022 Copyright held by the owner/author(s). 2577-6193/2022/5-ART3