GPU-accelerated Large-Eddy Simulation of Turbulent Channel Flows

DeLeon, Rey; Şenocak, İnanç

doi:10.2514/6.2012-722

Cited by 4 publications

(3 citation statements)

References 23 publications

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“…DeLeon and coworkers [28,29] recently demonstrated another GPU-based solver for LES of turbulent incompressible flows. The subgrid-scale terms were modeled using the Lagrangian dynamic Smagorinsky model.…”

Section: Turbulent Flowmentioning

confidence: 99%

Recent progress and challenges in exploiting graphics processors in computational fluid dynamics

Niemeyer

Sung

2013

J Supercomput

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The progress made in accelerating simulations of fluid flow using GPUs, and the challenges that remain, are surveyed. The review first provides an introduction to GPU computing and programming, and discusses various considerations for improved performance. Case studies comparing the performance of CPU-and GPU-based solvers for the Laplace and incompressible Navier-Stokes equations are performed in order to demonstrate the potential improvement even with simple codes. Recent efforts to accelerate CFD simulations using GPUs are reviewed for laminar, turbulent, and reactive flow solvers. Also, GPU implementations of the lattice Boltzmann method are reviewed. Finally, recommendations for implementing CFD codes on GPUs are given and remaining challenges are discussed, such as the need to develop new strategies and redesign algorithms to enable GPU acceleration.

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Section: Turbulent Flowmentioning

confidence: 99%

Recent progress and challenges in exploiting graphics processors in computational fluid dynamics

Niemeyer

Sung

2013

J Supercomput

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“…However, considering that many scientific computational tools had been previously developed in Fortran from a joint work of NVIDIA and PGI (The Port-land Group), a CUDA Fortran Compiler was made available in 2010, which is essentially a conventional FORTRAN compiler with CUDA extensions. After that, many works (Griebel and Zaspel 2010;DeLeon and Senocak 2012;Markesteijn, Semiletov, and Karabasov 2015;Zhu, Phillips, Spandan, Donners, Ruetsch, Romero, Ostilla-M´onico, Yang, Lohse, Verzicco, et al 2018;Kumar, Abdel-Majeed, and Annavaram 2019) have been developed in order to adapt and redesign the existing codes to the architecture of the GPU.…”

Section: Introductionmentioning

confidence: 99%

PMLES: A Hybrid Open MP CUDA Source Code for LES of Turbulent Flows

Pinho¹,

Muniz²

2020

JAFM

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The occurrence of turbulent flows is quite common in nature and several industrial applications. The accurate simulation of these complex flows is still a great challenge in science. Large Eddy Simulation (LES) is an efficient technique based on the elimination of all scales of a flow smaller than a characteristic length ∆, considering that the flow pattern in small scales is homogeneous and isotropic. Therefore, modeling of turbulence in such scales is universal and independent of the flow type. This work present PMLES, a new OpenMP CUDA Fortran solver for complex turbulent flows at high Reynolds numbers and large computational domains (about 1 × 10 8 cells), using a single GPU card. This was possible by using an economical numerical scheme associated with a robust and efficient solution method that requires little variable storage. Theoretical and numerical aspects are firstly discussed, and then details of the computational implementation are given. Finally, the developed code is tested and validated by simulating a turbulent jet, and comparing the results with experimental and computational data from the literature. An analysis of performance gain is also carried out, demonstrating the code's ability to solve this class of problems with a considerable reduction in computational time.

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“…Researchers have made the technology of extension mature from single to several GPUs and even clusters [6][7][8], including the different speedups between explicit and implicit schemes [9], the variance among structured, unstructured and hybrid grids [10,11], the influence of single and double precision [12], as well as high-order schemes and high-fidelity methods attracting increasing attention [13][14][15][16][17][18]. Contributed by hardware's development, GPU has possessed the power of simulating more complicated problems, such as turbulence, where LES was studies earlier [19,20] but DNS was still in the infancy [21][22][23][24]. All the work involved the optimization of program performance, critical to accelerate codes on GPU.…”

Section: Introductionmentioning

confidence: 99%

Optimization and acceleration of flow simulations for CFD on CPU/GPU architecture

Jiang

Zhou

et al. 2019

J Braz. Soc. Mech. Sci. Eng.

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With the increasing requirement of high computational power in computational fluid dynamics (CFD) field, the graphic processing units (GPUs) with great floating-point computing capability play more important roles. This work explores the porting of an Euler solver from central processing units (CPUs) to three different CPU/GPU heterogeneous hardware platforms using MUSCL and NND schemes, and then the computational acceleration of one-dimensional (1D) Riemann problem and two-dimensional (2D) flow past a forward-facing step is investigated. Based on hardware structures, memory models and programming methods, the working manner of heterogeneous systems was firstly introduced in this paper. Subsequently, three different heterogeneous methods employed in the current study were presented in detail, while porting all parts of the solver loop to GPU possessed the best performance among them. Several optimization strategies suitable for the solver were adopted to achieve substantial execution speedups, while using shared memory on GPU was relatively rarely reported in CFD literature. Finally, the simulation of 1D Riemann verified the reliability of the modified codes on GPU, demonstrating strong ability in capturing discontinuities of both schemes. The two cases with their 1D computational domains discretized into 10,000 cells both realized a speedup exceeding 25, compared to that executed on a single-core CPU. In simulation of the 2D step flow, we came to the highest speedups of 260 for MUSCL scheme with 800 × 400 mesh size and 144 for NND scheme with 400 × 200 computational domain, respectively.

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GPU-accelerated Large-Eddy Simulation of Turbulent Channel Flows

Cited by 4 publications

References 23 publications

Recent progress and challenges in exploiting graphics processors in computational fluid dynamics

Recent progress and challenges in exploiting graphics processors in computational fluid dynamics

PMLES: A Hybrid Open MP CUDA Source Code for LES of Turbulent Flows

Optimization and acceleration of flow simulations for CFD on CPU/GPU architecture

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