GPU acceleration of an iterative scheme for gas-kinetic model equations with memory reduction techniques

Lianhua Zhu, Peng Wang, Songze Chen, Zhaoli Guo, Yonghao Zhang

Research output: Contribution to journalArticlepeer-review


This paper presents a Graphics Processing Unit (GPU) acceleration of an iteration-based discrete velocity method (DVM) for gas-kinetic model equations. Unlike the previous GPU parallelization of explicit kinetic schemes, this work is based on a fast converging iterative scheme. The memory reduction techniques previously proposed for DVM are applied for GPU computing, enabling full three-dimensional (3D) solutions of kinetic model equations in the contemporary GPUs usually with a limited memory capacity that otherwise would need terabytes of memory. The GPU algorithm is validated against the direct simulation Monte Carlo (DSMC) simulation of the 3D lid-driven cavity flow and the supersonic rarefied gas flow past a cube with the phase-space grid points up to 0.7 trillion. The computing performance profiling on three models of GPUs shows that the two main kernel functions can utilize 56% ~ 79% of the GPU computing and memory resources. The performance of the GPU algorithm is compared with a typical parallel CPU implementation of the same algorithm using the Message Passing Interface (MPI). The comparison shows that the GPU program on K40 and K80 achieves 1.2 ~ 2.8 and 1.2 ~ 2.4 speedups for the 3D lid-driven cavity flow, respectively, compared with the MPI parallelized CPU program running on 96 CPU cores.
Original languageEnglish
Article number106861
JournalComputer Physics Communications
Early online date14 Aug 2019
Publication statusPublished - 31 Dec 2019


  • GPU
  • CUDA
  • discrete velocity method
  • gas-kinetic equation
  • high performance computing


Dive into the research topics of 'GPU acceleration of an iterative scheme for gas-kinetic model equations with memory reduction techniques'. Together they form a unique fingerprint.

Cite this