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Propagation pattern for moment representation of the lattice Boltzmann method
John Gounley1, Madhurima Vardhan2, Erik W Draeger3
1Computational Sciences and Engineering Division at Oak Ridge National Laboratory.
IEEE Transactions on Parallel and Distributed Systems : a Publication of the IEEE Computer Society
|May 2, 2022
Summary
This study introduces a moment representation for lattice Boltzmann method (LBM) simulations, significantly reducing storage and memory needs. The efficient pattern enhances performance on CPUs and GPUs, optimizing complex fluid dynamics modeling.
Area of Science:
- Computational fluid dynamics
- Numerical methods
Background:
- The lattice Boltzmann method (LBM) is a powerful tool for simulating fluid dynamics.
- Traditional LBM simulations require substantial computational resources, including storage and memory bandwidth.
- Representing the simulation state using moments of the distribution function offers potential for optimization.
Purpose of the Study:
- To present a novel propagation pattern for the moment representation of the regularized lattice Boltzmann method (LBM) in three dimensions.
- To demonstrate the effectiveness of this moment representation in reducing storage and memory bandwidth requirements.
- To extend performance analysis to CPU architectures and explore its application on GPU architectures.
Main Methods:
- Developed a propagation pattern for the moment representation of the regularized LBM.
- Utilized effectively lossless compression to store simulation state as moments instead of the full distribution function.
- Implemented cache-aware optimizations for the moment representation.
- Analyzed performance on central processing unit (CPU) architectures, including boundary condition implementations.
- Demonstrated the approach's effectiveness on graphics processing unit (GPU) architectures.
Main Results:
- The moment representation, with the proposed propagation pattern, substantially reduces storage requirements.
- Memory bandwidth demands for LBM simulations are significantly decreased.
- The method shows effectiveness and efficiency on both CPU and GPU architectures.
- Performance analysis was expanded, considering boundary condition implementations.
Conclusions:
- The moment representation offers a significant advantage for LBM simulations by reducing computational resource demands.
- The developed propagation pattern is efficient and applicable to modern hardware architectures (CPUs and GPUs).
- This approach paves the way for larger and more complex fluid dynamics simulations using LBM.
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