GPU Accelerated Dispersion Simulation for Urban Security

1. GPU Accelerated Dispersion Simulation for Urban Security Feng Qiu, Ye Zhao, Zhe Fan, Xiaomin Wei, Haik Lorenz, Jianning Wang, Suzanne Yoakum-Stover, Arie Kaufman, Klaus Mueller Center for Visual Computing and Department of Computer Science, Stony Brook University http://www.cs.sunysb.edu/~vislab/projects/urbansecurity • Overview • Simulating and visualizing the propagation of dispersive contaminants • Open environments characterized by sky-scrapers and deep urban canyons • Multiple Relaxation Time Lattice Boltzmann Model for flow simulation • GPU accelerated computation and visualization • Visualization • Rendering of building: • Building textures from real images • Reserve texture memory for LBM simulation • Shader program adds weathering and cracks to textures • Rendering of smoke: • Splatting of smoke particles • Splats distorted for correct projection • Half angle slicing for self-shadowing • Single GPU Results • West Village area of New York City (10 blocks) • Lattice size: 90x30x60 with grid unit 3.8m • Speedup (GPU/CPU): 7 • Lattice Boltzmann Model (LBM) • LBM models Boltzmann particle dynamics on a regular lattice • Streaming and collision in discrete time steps • 2nd order space-time accurate CFD method • Advantages: GPU accelerated, complex boundary, easy to implement, multi-resolution, sensor feedback • GPU Acceleration • Local operations in LBM are accelerated by GPU • Data layout: • One state variable stored in one volume • Each volume packed into a series of 2D textures • Boundary link information packed into small 2D textures • Computation: • LBM operations mapped to fragment program • Results stored in pixel buffers and copied back to textures for next step • Simulation and visualization on same GPU, reducing data transfer Snapshots of simulation with smoke and flow streamlines Original façade Façade variation • Multi-GPU Results • Time Square area of New York City (110 blocks) • Lattice size: 320x80x320 with grid unit 4.5m • Speedup on 30 nodes (GPU cluster/CPU cluster): 4-5 • Acknowledgement • NSF CCR-0306438 • Department of Homeland Security, Environment Measurement Laboratory • Sensor Feedback • Two methods: • Incorporate sensor data as body force • Modify boundary nodes affecting sensor readings Smoke in city model Streamlines in Time Square