FULL GPU Implementation of Lattice-Boltzmann Methods with Immersed Boundary Conditions for Fast Fluid Simulations

Boroni, Gustavo; Dottori, Javier Alejandro; Rinaldi, Pablo R.

doi:10.21152/1750-9548.11.1.1

Cited by 3 publications

(1 citation statement)

References 12 publications

(15 reference statements)

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“…Since the beginning of the era of general-purpose computing on graphics accelerators, various algorithms of computational physics have been ported to NVIDIA GPUs and thoroughly optimized for this architecture [15,28]. Mesh methods and particularly cubic lattice methods allow researchers to utilize the massive parallelizm of GPUs for the two following reasons [6,23,42]. First, a parallelization method is natively embedded into the lattice decomposition.…”

Section: Formulation Of the Problemmentioning

confidence: 99%

Efficient Implementation of Liquid Crystal Simulation Software on Modern HPC Platforms

Afanasyev

Lichmanov

Rudyak

et al. 2021

JSFI

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In this paper we demonstrate the process of efficient porting a software package for Markov chain Monte Carlo (MCMC) simulations on a finite cubic lattice on multiple modern architectures: Pascal, Volta and Turing NVIDIA GPUs, NEC SX-Aurora TSUBASA vector engines and Intel Xeon Gold processors. In the studied software, MCMC methodology is used for simulations of liquid crystal structures, but it can be as well employed in a wide range of problems of mathematical physics and numerical methods. The main goals of this work are to determine the best software optimization strategy for this class of algorithms and to examine the speed and the efficiency of such simulations on modern HPC platforms. We evaluate the effects of various optimizations, such as using more suitable memory access patterns, multitasking for efficient utilization of massive parallelism on the target architectures, improved cache hit-rates, parallel workload balancing, etc. We perform a detailed performance analysis for each target platform using software tools such as nvprof, Ftrace and VTune. On this basis, we evaluate and compare the efficiency of the developed computational kernels on different platforms and subsequently rank these platforms by their performance. The results show that NVIDIA GPU and NEC SX-Aurora TSUBASA platforms, although at first glance seem very different, require similar optimization approaches in many cases due to similarities in data processing principles.

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Section: Formulation Of the Problemmentioning

confidence: 99%

Efficient Implementation of Liquid Crystal Simulation Software on Modern HPC Platforms

Afanasyev

Lichmanov

Rudyak

et al. 2021

JSFI

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The implementation of the three-dimensional unified gas-kinetic wave-particle method on multiple graphics processing units

et al. 2023

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To further improve the efficiency of the unified gas-kinetic wave-particle (UGKWP) method in hypersonic rarefied non-equilibrium flows, particularly the particle simulation process, we presented the first application of the three-dimensional UGKWP method to multiple graphics processing unit (GPU) devices in this study. The wave and particle evolution components of the method are addressed using cell and particle paralleling strategies, respectively, enabling the primary loop of the GPU-based UPKWP (GPU-UGKWP) to be executed entirely by the compute unified device architecture threads on GPU devices. Concurrently, communication issues between central processing unit (CPU) nodes are resolved by employing the message passing interface model. Additionally, we introduce a tailored memory management scheme for the GPU-UGKWP method, facilitating efficient access to the particle array. Performance comparisons reveal that, relative to a single Intel Xeon Gold 6148 CPU core, the Nvidia Tesla P100 achieves a total speedup of 34 using one GPU device, and 226 with eight GPU devices, and a single Nvidia Titan V GPU device attains a speedup of 62. The speedup outcomes on multiple CPU cores and GPU devices demonstrate that the GPU-based algorithm is better suited for computationally demanding tasks, particularly in particle-dominated simulations. As evidenced by the reduced calculation time for a hypersonic technology vehicle simulation performed on the P100 cluster, GPU devices significantly outperform their CPU counterparts.

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Cross-Platform GPU-Based Implementation of Lattice Boltzmann Method Solver Using ArrayFire Library

Takac

Petráš

2021

Mathematics

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This paper deals with the design and implementation of cross-platform, D2Q9-BGK and D3Q27-MRT, lattice Boltzmann method solver for 2D and 3D flows developed with ArrayFire library for high-performance computing. The solver leverages ArrayFire’s just-in-time compilation engine for compiling high-level code into optimized kernels for both CUDA and OpenCL GPU backends. We also provide C++ and Rust implementations and show that it is possible to produce fast cross-platform lattice Boltzmann method simulations with minimal code, effectively less than 90 lines of code. An illustrative benchmarks (lid-driven cavity and Kármán vortex street) for single and double precision floating-point simulations on 4 different GPUs are provided.

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FULL GPU Implementation of Lattice-Boltzmann Methods with Immersed Boundary Conditions for Fast Fluid Simulations

Cited by 3 publications

References 12 publications

Efficient Implementation of Liquid Crystal Simulation Software on Modern HPC Platforms

Efficient Implementation of Liquid Crystal Simulation Software on Modern HPC Platforms

The implementation of the three-dimensional unified gas-kinetic wave-particle method on multiple graphics processing units

Cross-Platform GPU-Based Implementation of Lattice Boltzmann Method Solver Using ArrayFire Library

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