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

TL;DR

This paper introduces a scalable gravitational-wave search pipeline that leverages PyTorch for flexible CPU and GPU execution, significantly accelerating data analysis for compact binary merger detection.

Contribution

The core filtering engine of the GstLAL pipeline is adapted to PyTorch, enabling high-performance GPU acceleration and maintaining comparable search effectiveness.

Findings

GPU float16 configuration achieves up to 169x speedup over CPU-based GstLAL

Search performance remains comparable between original and PyTorch-adapted pipelines

The pipeline effectively handles large-scale gravitational-wave data analysis

Abstract

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 which 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 GPU float16 configuration of the PyTorch adaptation executed on an A100 GPU can achieve a speedup factor of up to 169 times compared to GstLAL's performance on a single CPU core.