Parallelizing Accelerographic Records Processing

Strong-motion processing holds paramount importance in earthquake engineering and disaster risk management systems. By leveraging parallel loops and task-parallelism techniques, we address computational challenges posed by large-scale accelerographic datasets. Through experimentation with more than one million data points from six real-world seismic events, our approach achieved speedups of up to 2.9x, demonstrating the effectiveness of parallel programming in accelerating seismic data processing. Our findings highlight the significance of parallel programming techniques in advancing seismological research and enhancing earthquake mitigation strategies.