Abstract
| Language | English |
|---|---|
| Article number | 106861 |
| Number of pages | 14 |
| Journal | Computer Physics Communications |
| Volume | 245 |
| Early online date | 14 Aug 2019 |
| DOIs | |
| Publication status | E-pub ahead of print - 14 Aug 2019 |
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Keywords
- GPU
- CUDA
- discrete velocity method
- gas-kinetic equation
- high performance computing
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GPU acceleration of an iterative scheme for gas-kinetic model equations with memory reduction techniques. / Zhu, Lianhua; Wang, Peng; Chen, Songze; Guo, Zhaoli; Zhang, Yonghao.
In: Computer Physics Communications, Vol. 245, 106861, 31.12.2019.Research output: Contribution to journal › Article
TY - JOUR
T1 - GPU acceleration of an iterative scheme for gas-kinetic model equations with memory reduction techniques
AU - Zhu, Lianhua
AU - Wang, Peng
AU - Chen, Songze
AU - Guo, Zhaoli
AU - Zhang, Yonghao
PY - 2019/8/14
Y1 - 2019/8/14
N2 - 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.
AB - 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.
KW - GPU
KW - CUDA
KW - discrete velocity method
KW - gas-kinetic equation
KW - high performance computing
U2 - 10.1016/j.cpc.2019.106861
DO - 10.1016/j.cpc.2019.106861
M3 - Article
VL - 245
JO - Computer Physics Communications
T2 - Computer Physics Communications
JF - Computer Physics Communications
SN - 0010-4655
M1 - 106861
ER -