Exploit every cycle: Vectorized time series algorithms on modern commodity CPUs
Tang, Bo1; Yiu, Man Lung1; Li, Yuhong2; U, Leong Hou2
Conference Name7th International Workshop on Accelerating Data Analysis and Data Management Systems Using Modern Processor and Storage Architectures, ADMS 2016 and 4th International Workshop on In-Memory Data Management and Analytics, IMDM 2016
Source PublicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10195 LNCS
Conference Date9 1, 2016 - 9 1, 2016
Conference PlaceNew Delhi, India
Author of SourceSpringer Verlag

Many time series algorithms reduce the computation cost by pruning unpromising candidates with lower-bound distance functions. In this paper, we focus on an orthogonal research direction that further boosts the performance by unlocking the potentials of modern commodity CPUs. First, we conduct a performance profiling on existing algorithms to understand where does time go. Second, we design vectorized implementations for lower-bound and distance functions that can enjoy characteristics (e.g., data parallelism, caching, branch prediction) provided by CPU. Third, our vectorized methods are general and applicable to many time series problems such as subsequence search, motif discovery and kNN classification. Our experimental study on real datasets shows that our proposal can achieve up to 6 times of speedup. © Springer International Publishing AG 2017.

Indexed BySCI
WOS Research AreaComputer Science
WOS SubjectComputer Science, Hardware & Architecture ; Computer Science, Information Systems ; Computer Science, Theory & Methods
WOS IDWOS:000425841900002
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Document TypeConference paper
Affiliation1.Hong Kong Polytechnic University, Hung Hom, Hong Kong;
2.University of Macau, Av. Padre Tomás Pereira, Taipa, China
Recommended Citation
GB/T 7714
Tang, Bo,Yiu, Man Lung,Li, Yuhong,et al. Exploit every cycle: Vectorized time series algorithms on modern commodity CPUs[C]//Springer Verlag,2017:18-39.
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