Massive parallelism for non-linear and non-stationary data analysis with GPGPU

In recent years, a large volume of natural signal data has become available for scientists because of the maturity of sensor techniques. However, the sensor data can form huge data streams that are non-linear and non-stationary. Existing methods cannot process such a large volume of data efficiently with a single CPU because of the high complexity of the algorithms. In this paper, we present Massive Parallelism GPU-Optimized Adaptive Data Analysis (MG-ADA), a new parallel signal data analysis algorithm that utilizes General-Purpose Graphics Programming Unit (GPGPU) to improve data scalability and reduce computation time for large non-linear and non-stationary datasets. We propose effective strategies to significantly improve the efficiency and scalability of MG-ADA. Our experimental results show that MG-ADA provides high scalability and significantly reduces the processing time in large datasets compared to other baseline algorithms.