An Analytical Framework for Estimating Scale-Out and Scale-Up Power Efficiency of Heterogeneous Manycores

doi:10.1109/TC.2015.2419655

	An Analytical Framework for Estimating Scale-Out and Scale-Up Power Efficiency of Heterogeneous Manycores
	Ma, Jun 1,2; Yan, Guihai 1,2; Han, Yinhe 1,2; Li, Xiaowei 1,2
	2016-02-01
发表期刊	IEEE TRANSACTIONS ON COMPUTERS
ISSN	0018-9340
卷号	65 期号:2 页码:367-381
摘要	Heterogeneous manycore architectures have shown to be highly promising to boost power efficiency through two independent ways: 1) enabling massive thread-level parallelism, called "scale-out" approach, and 2) enabling thread migration between heterogeneous cores, called "scale-up" approach. How to accurately model the profitability of power efficiency of the two ways, particularly in an analytical and computational-effective manner, is essential to reap the power efficiency of such architectures. We propose a comprehensive analytical model to predict the power efficiency from the two independent ways. Given power efficiency is measured by performance per watt, this model is composed of a performance and a power model. The performance model is built by two orthogonal functions alpha and beta. Function alpha describes the scale-out speedup from multithreading; function beta presents the scale-up speedup from core heterogeneity. Thus, the performance model can clearly capture the overall speedup of any multithreading and thread-to-core mapping strategies. The power model predicts the power of corresponding scale-out and scale-up configurations. It simultaneously captures the power variations caused by thread synchronization and thread migration between heterogeneous cores. We build both performance and power model in an analytical way and keep the computational complexity in mind. This merit leads to a suit of comprehensive and low-complexity models for runtime management. These models are validated on large-scale heterogeneous manycore architecture with full-system simulations. For performance prediction, the average error is below 12 percent, lower than that of the state-of-the-art methods. For power prediction, the average error is 7.74 percent. On top of the models, we introduce two heuristic scheduling algorithms, performance-oriented MAX-P and power efficiency-oriented MAX-E, to demonstrate the usage of these models. The results show that MAX-P outperforms the state-of-the-art methods by 18 percent in performance averagely; MAX-E outperforms the baseline by 70 percent in power efficiency on average.
关键词	Heterogeneous manycores scale-out scale-up analytical model power efficiency runtime management
DOI	10.1109/TC.2015.2419655
收录类别	SCI
语种	英语
资助项目	National Basic Research Program of China (973)[2011CB302503] ; National Natural Science Foundation of China[61100016] ; National Natural Science Foundation of China[61376043] ; National Natural Science Foundation of China[61221062]
WOS研究方向	Computer Science ; Engineering
WOS类目	Computer Science, Hardware & Architecture ; Engineering, Electrical & Electronic
WOS记录号	WOS:000372753500003
出版者	IEEE COMPUTER SOC
引用统计	被引频次：14[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/8448
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Ma, Jun; Yan, Guihai; Han, Yinhe; Li, Xiaowei
作者单位	1.Chinese Acad Sci, Inst Comp Technol, State Key Lab Comp Architecture, Beijing 100190, Peoples R China 2.Univ Chinese Acad Sci, Beijing 100190, Peoples R China
推荐引用方式 GB/T 7714	Ma, Jun,Yan, Guihai,Han, Yinhe,et al. An Analytical Framework for Estimating Scale-Out and Scale-Up Power Efficiency of Heterogeneous Manycores[J]. IEEE TRANSACTIONS ON COMPUTERS,2016,65(2):367-381.
APA	Ma, Jun,Yan, Guihai,Han, Yinhe,&Li, Xiaowei.(2016).An Analytical Framework for Estimating Scale-Out and Scale-Up Power Efficiency of Heterogeneous Manycores.IEEE TRANSACTIONS ON COMPUTERS,65(2),367-381.
MLA	Ma, Jun,et al."An Analytical Framework for Estimating Scale-Out and Scale-Up Power Efficiency of Heterogeneous Manycores".IEEE TRANSACTIONS ON COMPUTERS 65.2(2016):367-381.