CUDA-Accelerated Alignment of Subsequences in Streamed Time Series Data

Abstract

Euclidean Distance (ED) and Dynamic Time Warping (DTW) are cornerstones in the field of time series data mining. Many high-level algorithms like kNN-classification, clustering or anomaly detection make excessive use of these distance measures as subroutines. Furthermore, the vast growth of recorded data produced by automated monitoring systems or integrated sensors establishes the need for efficient implementations. In this paper, we introduce linear memory parallelization schemes for the alignment of a given query Q in a stream of time series data S for both ED and DTW using CUDA-enabled accelerators. The ED parallelization features a log-linear calculation scheme in contrast to the naive implementation with quadratic time complexity which allows for more efficient processing of long queries. The DTW implementation makes extensive use of a lower-bound cascade to avoid expensive calculations for unpromising candidates. Our CUDA-parallelizations for both ED and DTW outperform state-of-the-art algorithms, namely the UCR-Suite. The gained speedups range from one to two orders-of-magnitude which allows for significantly faster processing of exceedingly bigger data streams.