GPU-accelerated PostgreSQL for Scalable Management and Processing of Irregular Time-Series Data using SPI

As the demand for real-time signal processing increases in various fields, such as healthcare, artificial intelligence, machine learning, and scientific research, there is a need for more efficient methods to analyze large amounts of data. To address this challenge and explore the opportunities to accelerate different signal processing algorithms, this paper proposes the integration of graphics processing units (GPUs) with database management systems (DBMS) using the PostgreSQL server programming interface (SPI). The performance of the proposed method is evaluated by comparing central processing unit (CPU) and GPU approaches for feature extraction using a data processing pipeline for heart rate estimation from hydraulic bed sensor data. Furthermore, the paper analyzes timing metrics, usability, adaptability, and discusses precision differences between CPU and GPU code by performing different thread and block configurations.