In this work, we present a memory-efficient, high-performance GPU framework for moment-based lattice Boltzmann methods (LBM) with fluid-solid coupling. We introduce a split-kernel scheme that decouples fluid updates from solid boundary handling, substantially reducing warp divergence and improving utilization on GPUs. We further perform the first von Neumann stability analysis of the high-order moment-encoded LBM (HOME-LBM) formulation, characterizing its spectral behavior and deriving stability bounds for individual moment components. These theoretical insights directly guide a practical 16-bit moment quantization without compromising numerical stability. Our framework achieves up to 6x speedup and reduces GPU memory footprint by up to 50% in fluid-only scenarios and 25% in scenes with complex solid boundaries compared to the state-of-the-art LBM solver, while preserving physical fidelity across a range of large-scale benchmarks and real-time demonstrations. The proposed approach enables scalable, stable, and high-resolution LBM simulation on a single GPU, bridging theoretical stability analysis with practical GPU algorithm design.

翻译：本文提出了一种面向矩基格子玻尔兹曼方法（LBM）并包含流固耦合的内存高效、高性能GPU框架。我们引入了一种分离内核方案，将流体更新与固体边界处理解耦，显著减少了线程束发散并提升了GPU利用率。我们进一步对高阶矩编码LBM（HOME-LBM）公式进行了首次冯·诺依曼稳定性分析，刻画了其谱行为并推导了各矩分量的稳定性边界。这些理论洞见直接指导了一种实用的16位矩量化方案，且不损害数值稳定性。与最先进的LBM求解器相比，我们的框架在纯流体场景中实现了高达6倍的加速，GPU内存占用减少高达50%；在具有复杂固体边界的场景中，内存占用减少25%，同时在一系列大规模基准测试和实时演示中保持了物理保真度。所提出的方法使得在单块GPU上进行可扩展、稳定且高分辨率的LBM模拟成为可能，从而架起了理论稳定性分析与实用GPU算法设计之间的桥梁。