Free-Surface Lattice-Boltzmann Simulation on Many-Core Architectures

Schreiber, Martin; Neumann, Philipp; Zimmer, Stefan; Bungartz, Hans‐Joachim

doi:10.1016/j.procs.2011.04.104

Cited by 17 publications

(10 citation statements)

References 14 publications

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“…Analogously to the CPU kernels, there are also an α-and a β-kernel for the GPU which implement the same functionality and which are based on [20,53]. In contrast to [12,16] in which a thread deals with an entire row of lattice cells, we assign one thread to each lattice cell [9,18].…”

Section: Lattice Boltzmann Methods Kernels For the Gpumentioning

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: Lattice Boltzmann Methods Kernels For the Gpumentioning

confidence: 99%

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

et al. 2017

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“…Bailey et al [2] gave a 3D parallel implementation of bulk LBM. Schreiber et al [19] describe an OpenCL implementation for multicore architectures to obtain realtime simulations of free surfaces. Januszewski [15] present an LBM method on multiple GPUs for Newtonian fluids.…”

Section: Related Workmentioning

confidence: 99%

Interactive Simulation of Generalised Newtonian Fluids using GPUs

Jain

Tripathi

Narayanan

2014

Proceedings of the 2014 Indian Conference on Computer Vision Graphics and Image Processing

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We present a method to interactively simulate and visualise Generalised Newtonian Fluids (GNF) using GPUs. GNFs include regular constant viscosity fluids as well as other fluids such as blood, which display variable viscosity due to variable shear rate. We use a statistical approach called Lattice Boltzmann Method (LBM) for the simulation. LBM is easy to understand and implement and does not include discretisation of differential equations. We exploit the inherent parallelism of LBM coupled with its memory access pattern to create a fast GPU implementation that gives scientifically accurate and fast results such as interactive real time simulations for reasonable domain size. MultiGPU implementations provide the potential to scale to larger problem sizes.

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“…Among the applications for PLIC are Volume-of-Fluid simulation codes such as Flu-idX3D [6][7][8] and others [9][10][11][12][13][14][15][16][17][18][19], often in conjunction with GPU implementations [6][7][8][20][21][22][23][24][25][26] of the lattice Boltzmann method [27][28][29], used for simulating free surface fluid flows. In particular, these simulations work on a cubic lattice, with every lattice point having a fill level assigned to it, and PLIC is used in the process of surface reconstruction during curvature calculation for calculating physical surface tension effects [6][7][8][9].…”

Section: Introductionmentioning

confidence: 99%

Analytic Solution to the Piecewise Linear Interface Construction Problem and Its Application in Curvature Calculation for Volume-of-Fluid Simulation Codes

Lehmann

Gekle

2022

Computation

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The plane–cube intersection problem has been discussed in the literature since 1984 and iterative solutions to it have been used as part of piecewise linear interface construction (PLIC) in computational fluid dynamics simulation codes ever since. In many cases, PLIC is the bottleneck of these simulations regarding computing time, so a faster analytic solution to the plane–cube intersection would greatly reduce the computing time for such simulations. We derive an analytic solution for all intersection cases and compare it to the previous solution from Scardovelli and Zaleski (Scardovelli, R.; Zaleski, S. Analytical relations connecting linear interfaces and volume fractions in rectangular grids. J. Comput. Phys. 2000, 164, 228–237), which we further improve to include edge cases and micro-optimize to reduce arithmetic operations and branching. We then extend our comparison regarding computing time and accuracy to include two different iterative solutions as well. We find that the best choice depends on the employed hardware platform: on the CPU, Newton–Raphson is fastest with compiler optimization enabled, while analytic solutions perform better than iterative solutions without. On the GPU, the fastest method is our optimized version of the analytic SZ solution. We finally provide details on one of the applications of PLIC—curvature calculation for the Volume-of-Fluid model used for free surface fluid simulations in combination with the lattice Boltzmann method.

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Free-Surface Lattice-Boltzmann Simulation on Many-Core Architectures

Cited by 17 publications

References 14 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

Interactive Simulation of Generalised Newtonian Fluids using GPUs

Analytic Solution to the Piecewise Linear Interface Construction Problem and Its Application in Curvature Calculation for Volume-of-Fluid Simulation Codes

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