A Dynamic Periodicity Detector: Application to Speedup Computation

We propose a dynamic periodicity detector (DPD) for the estimation of periodicities in data series obtained from the execution of applications. We analyze the algorithm used by the periodicity detector and its performance on a number of data streams. It is shown how the periodicity detector is used for segmentation and prediction of data streams. In an application case we describe how the periodicity detector is applied to the dynamic detection of iterations in parallel applications. By means of the periodicity detection we obtain the dynamic segmentation of a data stream during the execution of the application, where the detected segments are evaluated by a speedup computation tool. We test the performance of the periodicity detector on a number of parallelized benchmarks. The periodicity detector correctly identifies the iterations of parallel structures also in the case where the application has nested parallelism. In our implementation we measure only a negligible overhead produced by the periodicity detector. We find the DPD to be useful and suitable for the incorporation in dynamic optimization tools.