GPU Optimization for Data Analysis of Mario Schenberg Spherical Detector

AbstractThe Gravitational Wave (GW) detectors, advanced LIGO and advanced Virgo, are acquiring the potential for recording unprecedented astronomic data for astrophysical events. The Mario Schenberg detector (MSD) is a smaller scale experiment that could participate to this search. Previously, we developed a first data analysis pipeline (DAP) to transform the detector's signal into relevant GW information. This pipeline was extremely simplified in order to be executed in low-latency. In order to improve the analysis methods while keeping a low execution time, we propose three different parallel approaches using GPU/CUDA. We implemented the parallel models using cuBLAS library functions and enhance its capability with asynchronous processes in CUDA streams. Our novel model achieves performances that surpass the serial implementation within the data analysis pipeline by a speed up of 21% faster than the traditional model. This first result is part of a more comprehensive approach, in which all DAP modules that can be parallelized, are being re-written in GPGP/CUDA, and then tested and validated within the MSD context