LBM based flow simulation using GPU computing processor

Kuznik, Frédéric; Obrecht, Christian; Rusaouën, Gilles; Roux, Jean-Jacques

doi:10.1016/j.camwa.2009.08.052

Cited by 179 publications

(79 citation statements)

References 12 publications

(12 reference statements)

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“…Simple in its design, the scenario is yet representative for a large class of problems regarding performance considerations for LBM simulations on regular Cartesian grids. It is further used as a benchmark in a variety of LBM performance evaluations and comparisons [7,9,10,14]. A relaxation time τ = 0.6152 ∈ (0.5, 2) is applied and all test runs take 1024 timesteps, thus, 512 α-and β-steps are executed.…”

Section: Resultsmentioning

confidence: 99%

A Holistic Scalable Implementation Approach of the Lattice Boltzmann Method for CPU/GPU Heterogeneous Clusters

et al. 2017

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Heterogeneous clusters are a widely utilized class of supercomputers assembled from different types of computing devices, for instance CPUs and GPUs, providing a huge computational potential. Programming them in a scalable way exploiting the maximal performance introduces numerous challenges such as optimizations for different computing devices, dealing with multiple levels of parallelism, the application of different programming models, work distribution, and hiding of communication with computation. We utilize the lattice Boltzmann method for fluid flow as a representative of a scientific computing application and develop a holistic implementation for large-scale CPU/GPU heterogeneous clusters. We review and combine a set of best practices and techniques ranging from optimizations for the particular computing devices to the orchestration of tens of thousands of CPU cores and thousands of GPUs. Eventually, we come up with an implementation using all the available computational resources for the lattice Boltzmann method operators. Our approach shows excellent scalability behavior making it future-proof for heterogeneous clusters of the upcoming architectures on the exaFLOPS scale. Parallel efficiencies of more than 90% are achieved leading to 2604.72 GLUPS utilizing 24,576 CPU cores and 2048 GPUs of the CPU/GPU heterogeneous cluster Piz Daint and computing more than 6.8 × 10 9 lattice cells.

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Section: Resultsmentioning

confidence: 99%

A Holistic Scalable Implementation Approach of the Lattice Boltzmann Method for CPU/GPU Heterogeneous Clusters

et al. 2017

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“…To take advantage of the massive parallelism of the GPU, CUDA implementations of the LBM usually assign a thread to each lattice node [9,22]. The layout of the execution grid needs therefore to reflect the geometry of the lattice.…”

Section: Gpu Implementation Of the Lbmmentioning

confidence: 99%

The TheLMA project: A thermal lattice Boltzmann solver for the GPU

et al. 2012

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In this paper, we consider the implementation of a thermal flow solver based on the lattice Boltzmann method (LBM) for graphics processing units (GPU). We first describe the hybrid thermal LBM model implemented, and give a concise review of the CUDA technology. The specific issues that arise with LBM on GPUs are outlined. We propose an approach for efficient handling of the thermal part. Performance is close to optimum and is significantly better than the one of comparable CPU solvers. We validate our code by simulating the differentially heated cubic cavity (DHC). The computed results for steady flow patterns are in good agreement with previously published ones. Finally, we use our solver to study the phenomenology of transitional flows in the DHC.

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“…They obtained a computational time speedup of 15.3 for GPU as compared with CPU. (Kuznik, Obrecht et al 2010) offered a method to implement various parts of the lattice Boltzmann method on GPU. (Bernaschi, Rossi et al 2009) could present a GPU implementation of the multicomponent of LB equation for soft-glassy materials successfully.…”

Section: Introductionmentioning

confidence: 99%

Lattice Boltzmann Simulation of Solid Particles Motion in a Three Dimensional Flow using Smoothed Profile Method

Khalili

Rahnama

Jafari

et al. 2017

JAFM

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Three-dimensional particulate flow has been simulated using Lattice Boltzmann Method (LBM). Solid-fluid interaction was modeled based on Smoothed Profile Method (SPM) (Jafari et. al, Lattice-Boltzmann method combined with smoothed-profile method for particulate suspensions, Phys. Rev. E, 2011). In this paper a GPU code based on three-dimensional lattice Boltzmann method and smoothed profile method has been prepared due to the ability of SPM-LBM to perform locally and in parallel mode. Results obtained for sedimentation of one and two spherical particles as well as their behavior in shear flow showed excellent correspondence with previous published works. Computations for a large number of particles sedimentation showed that combination of LBM and SPM on a GPU platform can be considered as an efficient and promising computational frame work in particulate flow simulations.

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LBM based flow simulation using GPU computing processor

Cited by 179 publications

References 12 publications

A Holistic Scalable Implementation Approach of the Lattice Boltzmann Method for CPU/GPU Heterogeneous Clusters

A Holistic Scalable Implementation Approach of the Lattice Boltzmann Method for CPU/GPU Heterogeneous Clusters

The TheLMA project: A thermal lattice Boltzmann solver for the GPU

Lattice Boltzmann Simulation of Solid Particles Motion in a Three Dimensional Flow using Smoothed Profile Method

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