High-Performance Moment-Encoded Lattice Boltzmann Method with Stability-Guided Quantization

2026-02-05

AI Summary

This work addresses the high memory consumption, low computational efficiency, and poor numerical stability of the High-Order Moment-Encoding Lattice Boltzmann Method (HOME-LBM) on GPUs, particularly in simulations involving complex solid-wall boundaries. The authors propose an efficient GPU-oriented implementation framework featuring two key innovations: a von Neumann stability analysis—performed for the first time on HOME-LBM—to reveal the spectral properties of individual moment components and derive precise stability bounds, enabling physically faithful 16-bit floating-point quantization; and a decoupled kernel architecture that separates fluid updates from boundary handling, substantially reducing thread divergence. Experimental results demonstrate up to a 6× speedup on a single GPU, with memory usage reduced by 50% for pure-fluid cases and 25% for scenarios with complex boundaries, while maintaining high accuracy in both large-scale benchmarks and real-time simulations.

Technology Category

• Machine Learning: Hardware-aware ML
• Cognitive Modeling & Cognitive Systems: Neural Spike Coding
• Computer Vision: Low Level & Physics-based Vision

Application Category

• Economics, Online Markets and Human Computation: Architectures and workflows that use LLMs for crowd work
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments

Abstract

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.

Problem

• lattice Boltzmann method
• moment encoding
• GPU performance
• numerical stability
• memory efficiency

Innovation

• moment-encoded LBM
• stability-guided quantization
• split-kernel GPU optimization
• von Neumann stability analysis
• memory-efficient simulation