A Fuzzy Neural Network Based Dynamic Data Allocation Model on Heterogeneous Multi-GPUs for Large-scale Computations
文献类型：期刊论文
| 作者 | Chao-Long Zhang2,4; Yuan-Ping Xu4; Zhi-Jie Xu2,3; Jia He3; Jing Wang1 ; Jian-Hua Adu4
|
| 刊名 | International Journal of Automation and Computing
 |
| 出版日期 | 2018 |
| 卷号 | 15期号:2页码:181-193 |
| 关键词 | Heterogeneous GPU cluster dynamic load balancing fuzzy neural network adaptive scheduler discrete wavelet transform. |
| ISSN号 | 1476-8186 |
| DOI | 10.1007/s11633-018-1120-4 |
| 英文摘要 | The parallel computation capabilities of modern graphics processing units (GPUs) have attracted increasing attention from researchers and engineers who have been conducting high computational throughput studies. However, current single GPU based engineering solutions are often struggling to fulfill their real-time requirements. Thus, the multi-GPU-based approach has become a popular and cost-effective choice for tackling the demands. In those cases, the computational load balancing over multiple GPU "nodes"is often the key and bottleneck that affect the quality and performance of the real-time system. The existing load balancing approaches are mainly based on the assumption that all GPU nodes in the same computer framework are of equal computational performance, which is often not the case due to cluster design and other legacy issues. This paper presents a novel dynamic load balancing (DLB) model for rapid data division and allocation on heterogeneous GPU nodes based on an innovative fuzzy neural network (FNN). In this research, a 5-state parameter feedback mechanism de¯ning the overall cluster and node performance is proposed. The corresponding FNN-based DLB model will be capable of monitoring and predicting individual node performance under different workload scenarios. A real-time adaptive scheduler has been devised to reorganize the data inputs to each node when necessary to maintain their runtime computational performance. The devised model has been implemented on two dimensional (2D) discrete wavelet transform (DWT) applications for evaluation. Experiment results show that this DLB model enables a high computational throughput while ensuring real-time and precision requirements from complex computational tasks. |
| 源URL | [http://ir.ia.ac.cn/handle/173211/42401]  |
| 专题 | 自动化研究所_学术期刊_International Journal of Automation and Computing |
| 作者单位 | 1.Department of Computing, Shefield Hallam University, Shefield, S1 2NT, UK 2.School of Computing & Engineering, University of Huddersfield, Queensgate, Huddersfield, HD1 3DH, UK 3.School of Computer Science, Chengdu University of Information Technology, Chengdu 610225, China 4.School of Software Engineering, Chengdu University of Information Technology, Chengdu 610225, China |
| 推荐引用方式 GB/T 7714 | Chao-Long Zhang,Yuan-Ping Xu,Zhi-Jie Xu,et al. A Fuzzy Neural Network Based Dynamic Data Allocation Model on Heterogeneous Multi-GPUs for Large-scale Computations[J]. International Journal of Automation and Computing,2018,15(2):181-193. |
| APA | Chao-Long Zhang,Yuan-Ping Xu,Zhi-Jie Xu,Jia He,Jing Wang,&Jian-Hua Adu.(2018).A Fuzzy Neural Network Based Dynamic Data Allocation Model on Heterogeneous Multi-GPUs for Large-scale Computations.International Journal of Automation and Computing,15(2),181-193. |
| MLA | Chao-Long Zhang,et al."A Fuzzy Neural Network Based Dynamic Data Allocation Model on Heterogeneous Multi-GPUs for Large-scale Computations".International Journal of Automation and Computing 15.2(2018):181-193. |
入库方式： OAI收割
来源：自动化研究所
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