Highly Parallel Sequential Pattern Mining on a Heterogeneous Platform

Yu Heng Hsieh, Chun Chieh Chen, Hong-Han Shuai, Ming Syan Chen

研究成果: Conference contribution同行評審

2 引文 斯高帕斯(Scopus)

摘要

Sequential pattern mining can be applied to various fields such as disease prediction and stock analysis. Many algorithms have been proposed for sequential pattern mining, together with acceleration methods. In this paper, we show that a heterogeneous platform with CPU and GPU is more suitable for sequential pattern mining than traditional CPU-based approaches since the support counting process is inherently succinct and repetitive. Therefore, we propose the PArallel SequenTial pAttern mining algorithm, referred to as PASTA, to accelerate sequential pattern mining by combining the merits of CPU and GPU computing. Explicitly, PASTA adopts the vertical bitmap representation of database to exploits the GPU parallelism. In addition, a pipeline strategy is proposed to ensure that both CPU and GPU on the heterogeneous platform operate concurrently to fully utilize the computing power of the platform. Furthermore, we develop a swapping scheme to mitigate the limited memory problem of the GPU hardware without decreasing the performance. Finally, comprehensive experiments are conducted to analyze PASTA with different baselines. The experiments show that PASTA outperforms the state-of-the-art algorithms by orders of magnitude on both real and synthetic datasets.

原文English
主出版物標題2018 IEEE International Conference on Data Mining, ICDM 2018
發行者Institute of Electrical and Electronics Engineers Inc.
頁面1037-1042
頁數6
ISBN(電子)9781538691588
DOIs
出版狀態Published - 27 十二月 2018
事件18th IEEE International Conference on Data Mining, ICDM 2018 - Singapore, Singapore
持續時間: 17 十一月 201820 十一月 2018

出版系列

名字Proceedings - IEEE International Conference on Data Mining, ICDM
2018-November
ISSN(列印)1550-4786

Conference

Conference18th IEEE International Conference on Data Mining, ICDM 2018
國家/地區Singapore
城市Singapore
期間17/11/1820/11/18

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