With the fast digitalization of our society, mining patterns from large time series data is increasingly becoming a critical problem for a wide range of big data applications. Motif and discord discovery algorithms, which offer effective solutions to identify repeatedly appearing and abnormal patterns, respectively, are fundamental building blocks for time series processing. Both approaches, however, can be time extremely consuming when handling large time series due to the subsequence-based computations of distance similarity metrics. In this article, we show that the highly involved subsequence-based computations can actually be decomposed into a few fine-grained computing patterns for efficient data parallel computing. By developing highly efficient GPU algorithms for such basic patterns and effectively composing such patterns, we are able to solve both motif and discord discovery problems under euclidean and DTW distance metrics in a unified GPU acceleration framework. Extensive experiments prove that the proposed framework outperforms pruned CPU algorithms by up to three orders of magnitude. Our work paves the foundation of building GPU acceleration frameworks for large-scale time series datasets.

中文翻译：

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基于Motif和Discord的模式挖掘的GPU加速框架

随着我们社会的快速数字化，从大的时间序列数据中挖掘模式已日益成为各种大数据应用中的关键问题。Motif和Discord发现算法分别提供有效的解决方案，以分别识别重复出现和异常的模式，它们是时间序列处理的基本组成部分。然而，由于距离相似性度量的基于子序列的计算，因此在处理大的时间序列时，这两种方法都可能非常耗时。在本文中，我们表明，实际上可以将高度参与的基于子序列的计算分解为一些细粒度的计算模式，以进行有效的数据并行计算。通过针对此类基本模式开发高效的GPU算法并有效地组成此类模式，我们能够在统一的GPU加速框架下解决欧几里德距离和DTW距离指标下的主题和不和谐发现问题。大量的实验证明，所提出的框架比修剪过的CPU算法的性能高出三个数量级。我们的工作为构建大规模时间序列数据集的GPU加速框架奠定了基础。