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Accelerating k-Shape Time Series Clustering Algorithm Using GPU

文献类型：期刊论文

作者Wang, Xun1,3; Song, Ruibao3; Xiao, Junmin1; Li, Tong; Li, Xueqi2
刊名IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS
出版日期2023-10-01
卷号34期号:10页码:2718-2734
ISSN号1045-9219
关键词Data space time series analysis time series clustering GPU architecture k-shape algorithm
DOI10.1109/TPDS.2023.3298148
英文摘要In the data space, time-series analysis has emerged in many fields, including biology, healthcare, and numerous large-scale scientific facilities like astronomy, climate science, particle physics, and genomics. Clustering is one of the most critical methods in time-series analysis. So far, the state-of-art time series clustering algorithm k-Shape has been widely used not only because of its high accuracy, but also because of its relatively low computation cost. However, due to the high heterogeneity of time series data, it can not be simply regarded as a high-dimensional vector. Two time series often need some alignment method in similarity comparison. The alignment between sequences is often a time-consuming process. For example, when using dynamic time warping as a sequence alignment algorithm and if the length of time series is greater than 1,000, a single iteration in the clustering process may take hundreds to tens of thousands of seconds, while the entire clustering cycle often requires dozens of iterations. In this article, we propose a set of novel parallel strategies suitable for GPU's computation model, called Times-C, which is an abbreviation for Time Series Clustering. We define three stages in the analysis process: aggregation, centroid, and class assignment. Times-C includes efficient parallel algorithms and corresponding implementations for these three stages. Overall, the experimental results show that the Times-C algorithm exhibits a performance improvement of one to two orders of magnitude compared to the multi-core CPU version of k-Shape. Furthermore, compared to the GPU version of the k-Shape algorithm, the Times-C algorithm achieves a maximum acceleration of up to 345 times.
资助项目National Key Ramp;D Program of China[2022YFB4500403] ; National Key Ramp;D Program of China[2021YFA1000103] ; National Key Ramp;D Program of China[2021YFA1000100] ; NSF of China[61972416] ; NSF of China[62202454] ; NSF of China[62272479] ; NSF of China[62202498] ; Taishan Scholarship[tsqn201812029] ; Foundation of Science and Technology Development of Jinan[201907116] ; Shandong Provincial Natural Science Foundation[ZR2021QF023] ; Fundamental Research Funds for the Central Universities[21CX06018A] ; Spanish project[PID2019-106960GB-I00] ; Juan de la Cierva[IJC2018-038539-I] ; China National Postdoctoral Program for Innovative Talents[BX2021320] ; Chinese Academy of Engineering Strategic Research and Consulting Program[2023-XBZD-16]
WOS研究方向Computer Science ; Engineering
语种英语
出版者IEEE COMPUTER SOC
WOS记录号WOS:001047237100004
源URL[http://119.78.100.204/handle/2XEOYT63/21367]  
专题中国科学院计算技术研究所期刊论文_英文
通讯作者Li, Xueqi
作者单位1.Chinese Acad Sci, Inst Comp Technol, High Performance Comp Res Ctr, Beijing 100190, Peoples R China
2.Chinese Acad Sci, Inst Comp Technol, State Key Lab Processors, Beijing 100190, Peoples R China
3.China Univ Petr East China, Dept Comp Sci & Technol, Qingdao 266580, Peoples R China
推荐引用方式
GB/T 7714		 Wang, Xun,Song, Ruibao,Xiao, Junmin,et al. Accelerating k-Shape Time Series Clustering Algorithm Using GPU[J]. IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS,2023,34(10):2718-2734.
APA Wang, Xun,Song, Ruibao,Xiao, Junmin,Li, Tong,&Li, Xueqi.(2023).Accelerating k-Shape Time Series Clustering Algorithm Using GPU.IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS,34(10),2718-2734.
MLA Wang, Xun,et al."Accelerating k-Shape Time Series Clustering Algorithm Using GPU".IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS 34.10(2023):2718-2734.

入库方式： OAI收割

来源：计算技术研究所

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