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Implementing the lattice Boltzmann model on commodity graphics hardware

Arie E. Kaufman, Zhe Fan , Kaloian Petkov
Journal of Statistical Mechanics: Theory and Experiment, Volume 2009 - jun 2009
Download the publication : 4.pdf [2.4Mo]  

Abstract

Part of Focus on Recent Advances in Particle Methods for Fluid Dynamics Modern graphics processing units (GPUs) can perform general-purpose computations in addition to the native specialized graphics operations. Due to the highly parallel nature of graphics processing, the GPU has evolved into a many-core coprocessor that supports high data parallelism. Its performance has been growing at a rate of squared Moore's law, and its peak floating point performance exceeds that of the CPU by an order of magnitude. Therefore, it is a viable platform for time-sensitive and computationally intensive applications. The lattice Boltzmann model (LBM) computations are carried out via linear operations at discrete lattice sites, which can be implemented efficiently using a GPU-based architecture. Our simulations produce results comparable to the CPU version while improving performance by an order of magnitude. We have demonstrated that the GPU is well suited for interactive simulations in many applications, including simulating fire, smoke, lightweight objects in wind, jellyfish swimming in water, and heat shimmering and mirage (using the hybrid thermal LBM). We further advocate the use of a GPU cluster for large scale LBM simulations and for high performance computing. The Stony Brook Visual Computing Cluster has been the platform for several applications, including simulations of real-time plume dispersion in complex urban environments and thermal fluid dynamics in a pressurized water reactor. Major GPU vendors have been targeting the high performance computing market with GPU hardware implementations. Software toolkits such as NVIDIA CUDA provide a convenient development platform that abstracts the GPU and allows access to its underlying stream computing architecture. However, software programming for a GPU cluster remains a challenging task. We have therefore developed the Zippy framework to simplify GPU cluster programming. Zippy is based on global arrays combined with the stream programming model and it hides the low-level details of the underlying cluster architecture.

Images and movies

4.png [39Ko]
 

BibTex references

@Article {KFP09,
  author       = "Kaufman, Arie E. and Fan , Zhe and Petkov, Kaloian",
  title        = "Implementing the lattice Boltzmann model on commodity graphics hardware ",
  journal      = "Journal of Statistical Mechanics: Theory and Experiment",
  volume       = "2009",
  month        = "jun",
  year         = "2009",
  keywords     = "lattice Boltzmann methods ",
  url          = "http://cvc.cs.stonybrook.edu/Publications/2009/KFP09"
}

Other publications in the database

» Arie E. Kaufman
» Zhe Fan
» Kaloian Petkov
 
Department of Computer Science • Stony Brook University, Stony Brook, NY 11794-4400 • 631-632-8470 or 631-632-8471

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