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arXiv 2308.08085physics.comp-phphysics.flu-dyn

GPU原生的自适应网格细化及其在格子玻尔兹曼模拟中的应用

GPU-Native Adaptive Mesh Refinement with Application to Lattice Boltzmann Simulations

Khodr Jaber, Ebenezer Essel, Pierre Sullivan

AI总结:

本工作提出一种GPU原生的基于块状八叉树森林的自适应网格细化算法,消除CPU-GPU数据传输,并集成格子玻尔兹曼求解器,通过基准测试验证其在多种GPU上的有效性。

摘要:

自适应网格细化(AMR)通过在关键区域提供高分辨率,同时在不需要精细细节的区域允许粗化,从而实现对流动的高效计算。早期的AMR软件包仅依赖CPU并行化,而异构计算系统的广泛采用推动了GPU加速实现的发展。在这些混合方法中,模拟数据通常驻留在GPU上,而网格管理和自适应仅在CPU上执行,这导致两者之间需要频繁的数据传输。一种更高效的策略是仅在GPU上自适应并维护整个网格结构,从而消除这些传输。由于其固有的并行性,格子玻尔兹曼方法(LBM)已被广泛实现在混合AMR框架中。本工作提出了一种基于块状八叉树森林方法的GPU原生AMR算法,并以开源C++/CUDA代码形式在二维和三维中实现。该实现包含一个用于弱可压缩流动的格子玻尔兹曼求解器,尽管底层网格细化过程与任何在单元中心块状网格上运行的求解器兼容。顶盖驱动空腔和方柱绕流基准测试验证了该算法在单精度和双精度下多种速度集上的有效性。在消费级和数据中心级GPU上进行的测试展示了其在不同硬件平台上的通用性。仓库链接:https://github.com/KhodrJ/AGAL

英文摘要:

Adaptive Mesh Refinement (AMR) enables efficient computation of flows by providing high resolution in critical regions while allowing for coarsening in areas where fine detail is unnecessary. While early AMR software packages relied solely on CPU parallelization, the widespread adoption of heterogeneous computing systems has led to GPU-accelerated implementations. In these hybrid approaches, simulation data typically resides on the GPU, and mesh management and adaptation occur exclusively on the CPU, necessitating frequent data transfers between them. A more efficient strategy is to adapt and maintain the entire mesh structure exclusively on the GPU, eliminating these transfers. Because of its inherent parallelism, the Lattice Boltzmann Method (LBM) has been widely implemented in hybrid AMR frameworks. This work presents a GPU-native algorithm for AMR using a block-based forest of octrees approach, implemented in both two and three dimensions as open-source C++/CUDA code. The implementation includes a Lattice Boltzmann solver for weakly compressible flow, though the underlying grid refinement procedure is compatible with any solver operating on cell-centered block-based grids. The lid-driven cavity and flow past a square cylinder benchmarks validate the algorithm's effectiveness across multiple velocity sets in both single- and double-precision. Tests conducted on consumer and datacenter-grade GPUs demonstrate its versatility across different hardware platforms. Link to repository: https://github.com/KhodrJ/AGAL

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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