Accelerating Dynamic Time Warping Subsequence Search with GPUs and FPGAs

Many time series data mining problems require subsequence similarity search as a subroutine. While this can be performed with any distance measure, and dozens of distance measures have been proposed in the last decade, there is increasing evidence that Dynamic Time Warping (DTW) is the best measure across a wide range of domains. Given DTW's usefulness and ubiquity, there has been a large community-wide effort to mitigate its relative lethargy. Proposed speedup techniques include early abandoning strategies, lower-bound based pruning, indexing and embedding. In this work we argue that we are now close to exhausting all possible speedup from software, and that we must turn to hardware-based solutions if we are to tackle the many problems that are currently untenable even with stateof-the-art algorithms running on high-end desktops. With this motivation, we investigate both GPU (Graphics Processing Unit) and FPGA (Field Programmable Gate Array) based acceleration of subsequence simi...

• Type: Journal
• Year: 2010
• Where: ICDM
• Authors: Doruk Sart, Abdullah Mueen, Walid A. Najjar, Eamonn J. Keogh, Vit Niennattrakul
• Keywords: Data Mining, ICDM 2010, Magnitude Speedup, Similarity Search, Speedup