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In: Translational Recurrences – From Mathematical Theory to Real-World Applications, 103, Eds.: N. Marwan and M. A. Riley and A. Giuliani and C. L. Webber, Jr., Springer, Cham, 17–29 (2014) DOI:10.1007/978-3-319-09531-8_2
Fast Computation of Recurrences in Long Time Series
T. Rawald, M. Sips, N. Marwan, D. DranschWe present an approach to recurrence quantification analysis (RQA) that allows to process very long time series fast. To do so, it utilizes the paradigm Divide and Recombine. We divide the underlying matrix of a recurrence plot (RP) into submatrices. The processing of the sub matrices is distributed across multiple graphics processing unit (GPU) devices. GPU devices perform RQA computations very fast since they match the problem very well. The individual results of the sub matrices are recombined into a global RQA solution. To address the specific challenges of subdividing the recurrence matrix, we introduce means of synchronization as well as additional data structures. Outperforming existing implementations dramatically, our GPU implementation of RQA processes time series consisting of N ∼ 1,000,000 data points in about 5 min.
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