Name
Abstract | ||
|---|---|---|
PPMLR-MHD is a new magnetohydrodynamics (MHD) model used to simulate the interactions of the solar wind with the magnetosphere, which has been proved to be the key element of the space weather cause-and-effect chain process from the Sun to Earth. Compared to existing MHD methods, PPMLR-MHD achieves the advantage of high order spatial accuracy and low numerical dissipation. However, the accuracy comes at a cost. On one hand, this method requires more intensive computation. On the other hand, more boundary data is subject to be transferred during the process of simulation. In this work, we present a parallel hybrid solution of the PPMLR-MHD model implemented using the computing capabilities of both CPUs and GPUs. We demonstrate that our optimized implementation alleviates the data transfer overhead by using GPU Direct technology and can scale up to 151 processes and achieve significant performance gains by distributing the workload among the CPUs and GPUs on Titan at Oak Ridge National Laboratory. The performance results show that our implementation is fast enough to carry out highly accurate MHD simulations in real time. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1109/CCGrid.2016.68 | 2016 16th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGrid) |
Keywords | DocType | Volume |
CUDA,Space Weather Forecast,PPMLR-MHD,CUDA-aware MPI | Conference | abs/1607.02214 |
ISSN | ISBN | Citations |
2376-4414 | 978-1-5090-2454-4 | 0 |
PageRank | References | Authors |
0.34 | 1 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| XiangYu Guo | 1 | 2 | 5.71 |
| Binbin Tang | 2 | 0 | 0.34 |
| Jian Tao | 3 | 51 | 7.96 |
| PengLiuZhihui Du | 4 | 383 | 48.74 |
| PengLiuZhihui Du | 5 | 383 | 48.74 |