A motif is a pair of subsequences of a longer time series, which are very similar to each other. Motif discovery is applied in a wide range of subject areas involving time series: medicine, biology, entertainment, weather prediction, and others. In this paper, we propose a novel parallel algorithm for motif discovery using Intel MIC (Many Integrated Core) accelerators in the case when time series fit in the main memory. We perform parallelization through thread-level parallelism and OpenMP technology. The algorithm employs a set of matrix data structures to store and index the subsequences of a time series and to provide an efficient vectorization of computations on the Intel MIC platform. The experimental evaluation shows the high scalability of the proposed algorithm.

中文翻译：

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在英特尔多核系统上发现时间序列主题

主题是一对较长时间序列的子序列，它们彼此非常相似。主题发现已应用于涉及时间序列的广泛主题领域：医学，生物学，娱乐，天气预报等。在本文中，我们提出了一种新颖的并行算法，用于在时间序列适合主存储器的情况下使用Intel MIC（许多集成核心）加速器进行主题发现。我们通过线程级并行性和OpenMP技术执行并行化。该算法采用一组矩阵数据结构来存储和索引时间序列的子序列，并在Intel MIC平台上提供有效的计算矢量化。实验评估表明该算法具有较高的可扩展性。