A Parallelizable Framework for Segmenting Piecewise Signals

Piecewise signals appear in many application fields. Here, we propose a framework for segmenting such signals based on the modeling of each piece using a parametric probability distribution. The proposed framework first models the segmentation as an optimization problem with sparsity regularization. Then, an algorithm based on dynamic programming is utilized for finding the optimal solution. However, dynamic programming often suffers from a heavy computational burden. Therefore, we further show that the proposed framework is parallelizable and propose using GPU-based parallel computing to accelerate the computation. This approach is highly desirable for the analysis of large volumes of data that are ubiquitous. The experiments on both the simulated and real genomic datasets from the next-generation sequencing demonstrate an improved performance in terms of both segmentation quality and computational speed.