Name
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 [1], [2] 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 article, 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 prameter 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. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1109/TVCG.2021.3059753 | IEEE Transactions on Visualization and Computer Graphics |
Keywords | DocType | Volume |
GPU optimization,parallel computing,fluid simulation,lattice Boltzmann method,immersed boundary method | Journal | 28 |
Issue | ISSN | Citations |
9 | 1077-2626 | 0 |
PageRank | References | Authors |
0.34 | 54 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yixin Chen | 1 | 4326 | 299.19 |
| Wei Li | 2 | 436 | 140.67 |
| Rui Fan | 3 | 258 | 28.91 |
| Xiaopei Liu | 4 | 8 | 4.53 |