GPU Optimization for High-Quality Kinetic Fluid Simulation

TL;DR

This paper introduces GPU optimization techniques for high-quality kinetic fluid simulations using the ACM-MRT model, enabling efficient large-scale simulations with complex solids and demonstrating superior performance and visual quality.

Contribution

The paper presents novel GPU optimization strategies for ACM-MRT-based kinetic fluid simulation, including data layout, load balancing, multi-kernel launch, and multi-GPU extension, with an automated parameter tuning framework.

Findings

Achieved high performance in large-scale simulations on GPUs.

Demonstrated superior visual quality compared to traditional solvers.

Extended the method to multi-GPU systems for scalability.

Abstract

Fluid simulations are often performed using the incompressible Navier-Stokes equations (INSE), leading to sparse linear systems which are difficult to solve efficiently in parallel. Recently, kinetic methods based on the adaptive-central-moment multiple-relaxation-time (ACM-MRT) model have demonstrated impressive capabilities to simulate both laminar and turbulent flows, with quality matching or surpassing that of state-of-the-art INSE solvers. Furthermore, due to its local formulation, this method presents the opportunity for highly scalable implementations on parallel systems such as GPUs. However, an efficient ACM-MRT-based kinetic solver needs to overcome a number of computational challenges, especially when dealing with complex solids inside the fluid domain. In this paper, we present multiple novel GPU optimization techniques to efficiently implement high-quality ACM-MRT-based kinetic fluid simulations in domains containing complex solids. Our techniques include a new communication-efficient data layout, a load-balanced immersed-boundary method, a multi-kernel launch method using a simplified formulation of ACM-MRT calculations to enable greater parallelism, and the integration of these techniques into a parametric cost model to enable automated parameter search to achieve optimal execution performance. We also extended our method to multi-GPU systems to enable large-scale simulations. To demonstrate the state-of-the-art performance and high visual quality of our solver, we present extensive experimental results and comparisons to other solvers.