Direct Numerical Simulation and Large Eddy Simulation on a Turbulent Wall-Bounded Flow Using Lattice Boltzmann Method and Multiple GPUs

Xian, Wang; Shangguan, Yanqin; Onodera, Naoyuki; Kobayashi, Hiromichi; Aoki, Takayuki

doi:10.1155/2014/742432

Cited by 21 publications

(11 citation statements)

References 22 publications

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“…For the temporal discretization of formula (20), the method of lines is used, such that space and time integration can be handled separately. Space integration, which consists of the solution of convective fluxes and viscous fluxes, is introduced hereinbefore.…”

Section: Mathematical Problems In Engineeringmentioning

confidence: 99%

“…Khajeh-Saeed et al [19] accomplished direct numerical simulation (DNS) of turbulence using GPU accelerated supercomputers, which demonstrated that scientific problems could benefit significantly from advanced hardware. Wang et al [20] discussed DNS and Large Eddy Simulation (LES) on a turbulent wall-bounded flow using Lattice Boltzmann method and multiple GPUs, and the acceleration performance has been discussed. Emelyanov et al [21] discussed the popular CFD benchmark solution of the flow over a smooth flat plate on a GPU with various meshes, and the speedup reached more than 46 times.…”

Section: Introductionmentioning

confidence: 99%

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A Multi‐GPU Parallel Algorithm in Hypersonic Flow Computations

Lai

Tian

et al. 2019

Mathematical Problems in Engineering

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Computational fluid dynamics (CFD) plays an important role in the optimal design of aircraft and the analysis of complex flow mechanisms in the aerospace domain. The graphics processing unit (GPU) has a strong floating-point operation capability and a high memory bandwidth in data parallelism, which brings great opportunities for CFD. A cell-centred finite volume method is applied to solve three-dimensional compressible Navier–Stokes equations on structured meshes with an upwind AUSM+UP numerical scheme for space discretization, and four-stage Runge–Kutta method is used for time discretization. Compute unified device architecture (CUDA) is used as a parallel computing platform and programming model for GPUs, which reduces the complexity of programming. The main purpose of this paper is to design an extremely efficient multi-GPU parallel algorithm based on MPI+CUDA to study the hypersonic flow characteristics. Solutions of hypersonic flow over an aerospace plane model are provided at different Mach numbers. The agreement between numerical computations and experimental measurements is favourable. Acceleration performance of the parallel platform is studied with single GPU, two GPUs, and four GPUs. For single GPU implementation, the speedup reaches 63 for the coarser mesh and 78 for the finest mesh. GPUs are better suited for compute-intensive tasks than traditional CPUs. For multi-GPU parallelization, the speedup of four GPUs reaches 77 for the coarser mesh and 147 for the finest mesh; this is far greater than the acceleration achieved by single GPU and two GPUs. It is prospective to apply the multi-GPU parallel algorithm to hypersonic flow computations.

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Section: Mathematical Problems In Engineeringmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Multi‐GPU Parallel Algorithm in Hypersonic Flow Computations

Lai

Tian

et al. 2019

Mathematical Problems in Engineering

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“…Lai and Khan [9] adopted the message-passing interface (MPI) method to develop a parallel two-dimensional discontinuous Galerkin method for shallow-water flows. Since the multi-CPUs provided by supercomputers are relatively expensive, the more cost-effective computation platform based on a personal computer with a graphics processing unit (GPU) card could be used for fast simulation of large-scale floods, regarding the development that now one GPU card integrating thousands of computing cores can provide a powerful computational capability [10]. For example, Wang and Yang [11] adopted the GPU-based, high-performance-integrated hydrodynamic modelling system to simulate flood processes at the basin scale.…”

Section: Introductionmentioning

confidence: 99%

Fast Simulation of Large-Scale Floods Based on GPU Parallel Computing

Liu

Qin

2018

Water

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Abstract:Computing speed is a significant issue of large-scale flood simulations for real-time response to disaster prevention and mitigation. Even today, most of the large-scale flood simulations are generally run on supercomputers due to the massive amounts of data and computations necessary. In this work, a two-dimensional shallow water model based on an unstructured Godunov-type finite volume scheme was proposed for flood simulation. To realize a fast simulation of large-scale floods on a personal computer, a Graphics Processing Unit (GPU)-based, high-performance computing method using the OpenACC application was adopted to parallelize the shallow water model. An unstructured data management method was presented to control the data transportation between the GPU and CPU (Central Processing Unit) with minimum overhead, and then both computation and data were offloaded from the CPU to the GPU, which exploited the computational capability of the GPU as much as possible. The parallel model was validated using various benchmarks and real-world case studies. The results demonstrate that speed-ups of up to one order of magnitude can be achieved in comparison with the serial model. The proposed parallel model provides a fast and reliable tool with which to quickly assess flood hazards in large-scale areas and, thus, has a bright application prospect for dynamic inundation risk identification and disaster assessment.

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“…Among all the parallel algorithms, GPU models obtain the best acceleration [21][22][23]. The multistreaming processors and powerful computational capabilities of GPUs are vitally important for efficient parallel computation with large amounts of data and high-precision requirements [24][25][26][27][28]. Bradbrook et al [29] rewrote the twodimensional diffusive wave model JFLOW using generalpurpose GPUs and achieved substantially faster computation on single-accelerator processors.…”

Section: Introductionmentioning

confidence: 99%

Comparison of Three Different Parallel Computation Methods for a Two‐Dimensional Dam‐Break Model

Zhang

Jing

et al. 2017

Mathematical Problems in Engineering

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Three parallel methods (OpenMP, MPI, and OpenACC) are evaluated for the computation of a two-dimensional dam-break model using the explicit finite volume method. A dam-break event in the Pangtoupao flood storage area in China is selected as a case study to demonstrate the key technologies for implementing parallel computation. The subsequent acceleration of the methods is also evaluated. The simulation results show that the OpenMP and MPI parallel methods achieve a speedup factor of 9.8× and 5.1×, respectively, on a 32-core computer, whereas the OpenACC parallel method achieves a speedup factor of 20.7× on NVIDIA Tesla K20c graphics card. The results show that if the memory required by the dam-break simulation does not exceed the memory capacity of a single computer, the OpenMP parallel method is a good choice. Moreover, if GPU acceleration is used, the acceleration of the OpenACC parallel method is the best. Finally, the MPI parallel method is suitable for a model that requires little data exchange and large-scale calculation. This study compares the efficiency and methodology of accelerating algorithms for a dam-break model and can also be used as a reference for selecting the best acceleration method for a similar hydrodynamic model.

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Direct Numerical Simulation and Large Eddy Simulation on a Turbulent Wall-Bounded Flow Using Lattice Boltzmann Method and Multiple GPUs

Cited by 21 publications

References 22 publications

A Multi‐GPU Parallel Algorithm in Hypersonic Flow Computations

A Multi‐GPU Parallel Algorithm in Hypersonic Flow Computations

Fast Simulation of Large-Scale Floods Based on GPU Parallel Computing

Comparison of Three Different Parallel Computation Methods for a Two‐Dimensional Dam‐Break Model

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