Flow simulation with locally-refined LBM

Zhao, Ye; Qiu, Feng; Fan, Zhe; Kaufman, Arie

doi:10.1145/1230100.1230132

Cited by 19 publications

(5 citation statements)

References 34 publications

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“…The advent of DirectX 9 hardware in 2003 with floating point support, combined with early work on high level language support such as BrookGPU, Cg, and Sh, led to a rapid expansion of the field. [14][15][16][17][18][19] Brandvik and Pullan 20 show the implementation of 2D and 3D Euler solver on a single GPU, showing 29× speedup for the 2D solver and 16× speedup for the 3D solver. One unique feature of their paper is the implementation of the solvers in both BrookGPU and CUDA.…”

Section: Related Workmentioning

confidence: 99%

An MPI-CUDA Implementation for Massively Parallel Incompressible Flow Computations on Multi-GPU Clusters

Jacobsen

Thibault

Şenocak

2010

48th AIAA Aerospace Sciences Meeting Including the New Horizons Forum and Aerospace Exposition

147

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Modern graphics processing units (GPUs) with many-core architectures have emerged as general-purpose parallel computing platforms that can accelerate simulation science applications tremendously. While multi-GPU workstations with several TeraFLOPS of peak computing power are available to accelerate computational problems, larger problems require even more resources. Conventional clusters of central processing units (CPU) are now being augmented with multiple GPUs in each compute-node to tackle large problems. The heterogeneous architecture of a multi-GPU cluster with a deep memory hierarchy creates unique challenges in developing scalable and efficient simulation codes. In this study, we pursue mixed MPI-CUDA implementations and investigate three strategies to probe the efficiency and scalability of incompressible flow computations on the Lincoln Tesla cluster at the National Center for Supercomputing Applications (NCSA). We exploit some of the advanced features of MPI and CUDA programming to overlap both GPU data transfer and MPI communications with computations on the GPU. We sustain approximately 2.4 TeraFLOPS on the 64 nodes of the NCSA Lincoln Tesla cluster using 128 GPUs with a total of 30,720 processing elements. Our results demonstrate that multi-GPU clusters can substantially accelerate computational fluid dynamics (CFD) simulations.

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Section: Related Workmentioning

confidence: 99%

An MPI-CUDA Implementation for Massively Parallel Incompressible Flow Computations on Multi-GPU Clusters

Jacobsen

Thibault

Şenocak

2010

48th AIAA Aerospace Sciences Meeting Including the New Horizons Forum and Aerospace Exposition

147

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“…In terms of computational accuracy, the 32-bit single-precision GPU pipeline offers sufficient accuracy for many LBM flow simulations in interactive applications. A GPU-based LBM implementation has been used for interactive modeling of 3D natural phenomena, such as soap bubbles and feathers in the wind [23], fire [26], smoke propagation [25], flow on curved surfaces [4] and heat shimmering and mirage [24].…”

Section: Resultsmentioning

confidence: 99%

Implementing the lattice Boltzmann model on commodity graphics hardware

Kaufman¹,

Fan²,

Petkov³

2009

J. Stat. Mech.

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Modern graphics processing units (GPUs) can perform generalpurpose 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

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“…While INSE solvers are inherently difficult to parallelize, kinetic solvers use a local formulation and thus are naturally scalable and well suited for implementation on highly parallel GPUs. Similar to earlier parallelization methods for INSE solvers on the GPU, texture and render buffers were initially used to accelerate kinetic solvers [85], [86]. Using CUDA, GPU implementations of kinetic solvers became much simpler.…”

Section: Gpu Optimizations For Parallel Kinetic Solversmentioning

confidence: 99%

GPU Optimization for High-Quality Kinetic Fluid Simulation

Chen

Fan

et al. 2021

Preprint

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Fluid simulations are often performed using the incompressible Navier-Stokes equations (INSE), leading to sparse linear systems which are difficult to solve efficiently in parallel. Recently, kinetic methods based on the adaptive-central-moment multiple-relaxation-time (ACM-MRT) model [1], [2] have demonstrated impressive capabilities to simulate both laminar and turbulent flows, with quality matching or surpassing that of state-of-the-art INSE solvers. Furthermore, due to its local formulation, this method presents the opportunity for highly scalable implementations on parallel systems such as GPUs. However, an efficient ACM-MRT-based kinetic solver needs to overcome a number of computational challenges, especially when dealing with complex solids inside the fluid domain. In this paper, we present multiple novel GPU optimization techniques to efficiently implement high-quality ACM-MRT-based kinetic fluid simulations in domains containing complex solids. Our techniques include a new communication-efficient data layout, a load-balanced immersed-boundary method, a multi-kernel launch method using a simplified formulation of ACM-MRT calculations to enable greater parallelism, and the integration of these techniques into a parametric cost model to enable automated parameter search to achieve optimal execution performance. We also extended our method to multi-GPU systems to enable large-scale simulations. To demonstrate the state-of-the-art performance and high visual quality of our solver, we present extensive experimental results and comparisons to other solvers.

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Flow simulation with locally-refined LBM

Cited by 19 publications

References 34 publications

An MPI-CUDA Implementation for Massively Parallel Incompressible Flow Computations on Multi-GPU Clusters

An MPI-CUDA Implementation for Massively Parallel Incompressible Flow Computations on Multi-GPU Clusters

Implementing the lattice Boltzmann model on commodity graphics hardware

GPU Optimization for High-Quality Kinetic Fluid Simulation

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