Analysis of GPU Data Access Patterns on Complex Geometries for the D3Q19 Lattice Boltzmann Algorithm

Accepted

Journal Article (Journal Article)

GPU performance of the lattice Boltzmann method (LBM) depends heavily on memory access patterns. When implemented with GPUs on complex domains, typically, geometric data is accessed indirectly and lattice data is accessed lexicographically. Although there are a variety of other options, no study has examined the relative efficacy between them. Here, we examine a suite of memory access schemes via empirical testing and performance modeling. We find strong evidence that semi-direct is often better suited than the more common indirect addressing, providing increased computational speed and reducing memory consumption. For the layout, we find that the Collected Structure of Arrays (CSoA) and bundling layouts outperform the common Structure of Array layout; on V100 and P100 devices, CSoA consistently outperforms bundling, however the relationship is more complicated on K40 devices. When compared to state-of-the-art practices, our recommendations lead to speedups of 10-40 percent and reduce memory consumption up to 17 percent. Using performance modeling and computational experimentation, we determine the mechanisms behind the accelerations. We demonstrate that our results hold across multiple GPUs on two leadership class systems, and present the first near-optimal strong results for LBM with arterial geometries run on GPUs.

Full Text

Duke Authors

Cited Authors

  • Herschlag, G; Lee, S; Vetter, JS; Randles, A

Published Date

  • October 1, 2021

Published In

Volume / Issue

  • 32 / 10

Start / End Page

  • 2400 - 2414

Electronic International Standard Serial Number (EISSN)

  • 1558-2183

International Standard Serial Number (ISSN)

  • 1045-9219

Digital Object Identifier (DOI)

  • 10.1109/TPDS.2021.3061895

Citation Source

  • Scopus