A Peta-scale LES (Large-Eddy Simulation) for Turbulent Flows Based on Lattice Boltzmann Method
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1 GTC (GPU Technology Conference) 2013, San Jose, 2013, March 20 A Peta-scale LES (Large-Eddy Simulation) for Turbulent Flows Based on Lattice Boltzmann Method Takayuki Aoki Global Scientific Information and Computing Center (GSIC) Tokyo Institute of Technology Copyright Takayuki Aoki / Global Scientific Information and Computing Center, Tokyo Institute of Technology 1
2 TSUBAME 2.0 Compute Node (3 Tesla M2050 GPUs) Rack (30 nodes) Performance: 51.0 TFLOPS Memory: 2.03 TB System (58 racks) 1442 nodes: 2952 CPU sockets, 4264 GPUs Performance: TFLOPS (CPU) Turbo boost 2196 TFLOPS (GPU) Total: 2420 TFLOPS Performance: 1.7 TFLOPS Memory: 58.0GB(CPU) +9.7GB(GPU) Copyright Takayuki Aoki / Global Scientific Information and Computing Center, Tokyo Institute of Technology
3 TSUBAME Supercomputer 2013 Q3 or Q4 All the GPU will be replaced by new accelerators TSUBAME 2.5 will have PFlops In single precision Performance. Copyright Takayuki Aoki / Global Scientific Information and Computing Center, Tokyo Institute of Technology 3
4 Drop on dry floor
5 5
6 Industrial Appl. Steering Oil 6
7 Development of New Materials Mechanical Structure Microstructure Low-carbon society Improvement of fuel efficiency by reducing the weight of transportation and mechanical structures Developing lightweight strengthening material by controlling microstructure Copyright Global Scientific Information and Computing Center, Tokyo Institute of Technology
8 Copyright Global Scientific Information and Computing Center, Tokyo Institute of Technology
9 Weather News Copyright Global Scientific Information and Computing Center, Tokyo Institute of Technology
10 Full GPU Implementation: ASUCA Full GPU Approach CPU Initial condition GPU Dynamics Physics output J. Ishida, C. Muroi, K. Kawano, Y. Kitamura, Development of a new nonhydrostatic model ASUCA at JMA, CAS/JSC WGNE Reserch Activities in Atomospheric and Oceanic Modelling. Copyright Global Scientific Information and Computing Center, Tokyo Institute of Technology 10
11 ASUCA Typhoon Simulation 500m-horizontal resolution Using 437 GPUs Copyright Global Scientific Information and Computing Center, Tokyo Institute of Technology 11
12 Air Flow in a 10km x 10km Area of Tokyo 10km 10km TDM 3D 2012 Google, ZENRIN Copyright Takayuki Aoki / Global Scientific Information and Computing Center, Tokyo Institute of Technology 12
13 Lattice Boltzmann Method f t i e i f i 1 f i f eq i f eq i w i 1 3 c c 3 2 c 2 u e u u u e i 4 i 2 Strongly Memory Bound Problem: Collision step: Streaming step: i is the value in the direction of ith discrete velocity e i is the discrete velocity set; w i is the weighting factor c is the particle velocity u is the macroscopic velocity Copyright Global Scientific Information and Computing Center, Tokyo Institute of Technology 13
14 LES (Large-Eddy Simulation) Relaxation time for LES model Energy spectrum Molecular viscosity and Eddy viscosity GS SGS Copyright Global Scientific Information and Computing Center, Tokyo Institute of Technology
15 LES modeling Simple inaccurate for the flow with wall boundary emperical tuning for the constant model coefficient Copyright Global Scientific Information and Computing Center, Tokyo Institute of Technology applicable to wall boundary complicated calculation average process over the wide area not available for complex shaped body not suitable for large-scale problem *H.Kobayashi, Phys. Fluids.17, (2005). model coefficient determined by the second invariant of the velocity gradient tensor model coefficient applicable to wall boundary model coefficient is locally determined.
16 LES modeling on LBM Turbulence model : Molecular viscosity + eddy viscosity Smagorinsky model subgrid closure C S = 0.22 Copyright Global Scientific Information and Computing Center, Tokyo Institute of Technology
17 Coherent-structure SGS model Dynamic Smagorinsky model (DSM) DSM requires to take an average operation for a wide area to determine the model parameter. Automatically determine model coefficient Turbulent flow around a complex object Computational efficiency is poor : average operation Coherent-structure Smagorinsky model The model parameter is locally determined by the second invariant of the velocity gradient tensor. Second invariant of the velocity gradient tensor(q) and Energy dissipation(ε) Turbulent flow around a complex object Large-scale parallel computation Copyright Global Scientific Information and Computing Center, Tokyo Institute of Technology
18 Computational Area Major part of Tokyo Including Shnjuku-ku, Chiyoda-ku, Minato-ku, Meguro-ku, Chuou-ku, 10km 10km Building Data: Pasco Co. Ltd. TDM 3D Shinjyuku Shibuya Shinagawa Tokyo Copyright Global Scientific Information and Computing Center, Tokyo Institute of Technology Map 2012 Google, ZENRIN 18
19 Copyright Takayuki Aoki / Global Scientific Information and Computing Center, Tokyo Institute of Technology
20 Copyright Takayuki Aoki / Global Scientific Information and Computing Center, Tokyo Institute of Technology
21 Area Around Metropolitan Government Building Wind Flow profile at the 25m height on the ground 960 m 640 m 2012 Google, ZENRIN Copyright Takayuki Aoki / Global Scientific Information and Computing Center, Tokyo Institute of Technology 21
22 Copyright Takayuki Aoki / Global Scientific Information and Computing Center, Tokyo Institute of Technology 22
23 Copyright Takayuki Aoki / Global Scientific Information and Computing Center, Tokyo Institute of Technology
24 Copyright Takayuki Aoki / Global Scientific Information and Computing Center, Tokyo Institute of Technology 24
25 Copyright Takayuki Aoki / Global Scientific Information and Computing Center, Tokyo Institute of Technology 25
26 Copyright Takayuki Aoki / Global Scientific Information and Computing Center, Tokyo Institute of Technology 26
27 Performance of the GPU code Performance estimation by using Improved Roofline Model CUDA Programing Tuning Using SFU (Special Function Unit) and single precision computation Kernel fusion of the collision step and streaming step Loop unrolling to save resister usage Reduction of the address calculation by use of a 32-bit compile option 32bit compile 198 GFlops(efficiency 92%) 310 MLUPS (Mega Lattice site Updates /sec) 64bit compile 183 GFlops(efficiency 88%) Copyright Takayuki Aoki / Global Scientific Information and Computing Center, Tokyo Institute of Technology
28 Performance (Strong Scalability) For the fixed problem size, the performances are shown with increasing the number of GPUs. By introducing the overlapping technique, the performance is improved up to 30%. It is found that the elapsed time is shorted by increasing GPUs. Copyright Takayuki Aoki / Global Scientific Information and Computing Center, Tokyo Institute of Technology 28
29 Performance (Weak Scalability) 600 TFLOPS on 4000 GPUs 15 % of the peak performance Copyright Takayuki Aoki / Global Scientific Information and Computing Center, Tokyo Institute of Technology 29
30 Turbulent Flow behind football Re = 100,000 Mesh: 2000x1000x1000 Copyright Takayuki Aoki / Global Scientific Information and Computing Center, Tokyo Institute of Technology 30
31 DriVar: BMW-Audi 3,000x1,500x1,500 Re = 1,000,000
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35 SUMMARY Lattice Boltzmann LES turbulent simulation has been successfully conducted with 1-m resolution for 10km x 10km area by using the whole TSUBAME 2.0 resource. Coherent-Structure Smagorinsky model works well in association with LBM. The performance of 15% has been achieved on TSUBAME 2.0. Copyright Global Scientific Information and Computing Center, Tokyo Institute of Technology 35
36 Thank you for your kind attention Copyright Global Scientific Information and Computing Center, Tokyo Institute of Technology 36
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