標題: Highly Parallel Sequential Pattern Mining on a Heterogeneous Platform
作者: Hsieh, Yu-Heng
Chen, Chun-Chieh
Shuai, Hong-Han
Chen, Ming-Syan
電子工程學系及電子研究所
Department of Electronics Engineering and Institute of Electronics
關鍵字: Data Mining;Frequent Sequential Pattern;GPGPU;Parallel Computing;Heterogeneous Platform
公開日期: 1-一月-2018
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.
URI: http://dx.doi.org/10.1109/ICDM.2018.00131
http://hdl.handle.net/11536/151753
ISBN: 978-1-5386-9159-5
ISSN: 1550-4786
DOI: 10.1109/ICDM.2018.00131
期刊: 2018 IEEE INTERNATIONAL CONFERENCE ON DATA MINING (ICDM)
起始頁: 1037
結束頁: 1042
顯示於類別:會議論文