A Fuzzy Neural Network Based Dynamic Data Allocation Model on Heterogeneous Multi-GPUs for Large-scale Computations

doi:10.1007/s11633-018-1120-4

CASIA OpenIR > 学术期刊 > Machine Intelligence Research

	A Fuzzy Neural Network Based Dynamic Data Allocation Model on Heterogeneous Multi-GPUs for Large-scale Computations
	Chao-Long Zhang 1,3; Yuan-Ping Xu 1; Zhi-Jie Xu 2,3; Jia He 2; Jing Wang4 ; Jian-Hua Adu 1
发表期刊	International Journal of Automation and Computing
ISSN	1476-8186
	2018
卷号	15 期号:2 页码:181-193
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.
关键词	Heterogeneous GPU cluster dynamic load balancing fuzzy neural network adaptive scheduler discrete wavelet transform.
DOI	10.1007/s11633-018-1120-4
引用统计	被引频次：8[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/42401
专题	学术期刊_Machine Intelligence Research
作者单位	1.School of Software Engineering, Chengdu University of Information Technology, Chengdu 610225, China 2.School of Computer Science, Chengdu University of Information Technology, Chengdu 610225, China 3.School of Computing & Engineering, University of Huddersfield, Queensgate, Huddersfield, HD1 3DH, UK 4.Department of Computing, Shefield Hallam University, Shefield, S1 2NT, UK
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.

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