Scalable matched-filtering pipeline for gravitational-wave searches of compact binary mergers

As gravitational-wave observations expand in scope and detection rate, the data analysis infrastructure must be modernized to accommodate rising computational demands and ensure sustainability. We present a scalable gravitational-wave search pipeline that modernizes the GstLAL pipeline by adapting the core filtering engine to the PyTorch framework, enabling flexible execution on both central processing units (CPUs) and graphics processing units (GPUs). Offline search results on the same 8.8 day stretch of public gravitational-wave data indicate that the GstLAL and the PyTorch adaptation demonstrate comparable search performance, even with float16 precision. Lastly, computational benchmarking results show that the PyTorch adaptation executed on an A100 GPU achieves speedups of up to 169 times in the GPU float16 configuration and 123 times in the GPU float32 configuration, compared to GstLAL's performance on a single CPU core.