Pulscan: Binary pulsar detection using unmatched filters on NVIDIA GPUs

TL;DR

Pulscan is a novel unmatched filtering method that significantly accelerates binary pulsar detection in radio astronomy data, enabling real-time processing on GPU systems and improving detection efficiency over traditional matched filtering techniques.

Contribution

The paper introduces Pulscan, an unmatched filtering approach that outperforms existing methods like FDAS in speed, supporting real-time binary pulsar detection on GPU hardware.

Findings

Pulscan achieves an order-of-magnitude speedup over FDAS.

It can detect both accelerated and some jerked binary pulsars.

Pulscan effectively reduces data for further analysis, optimizing resource use.

Abstract

The Fourier Domain Acceleration Search (FDAS) and Fourier Domain Jerk Search (FDJS) are proven matched filtering techniques for detecting binary pulsar signatures in time-domain radio astronomy datasets. Next generation radio telescopes such as the SPOTLIGHT project at the GMRT produce data at rates that mandate real-time processing, as storage of the entire captured dataset for subsequent offline processing is infeasible. The computational demands of FDAS and FDJS make them challenging to implement in real-time detection pipelines, requiring costly high performance computing facilities. To address this we propose Pulscan, an unmatched filtering approach which achieves order-of-magnitude improvements in runtime performance compared to FDAS whilst being able to detect both accelerated and some jerked binary pulsars. We profile the sensitivity of Pulscan using a distribution (N = 10,955) of synthetic binary pulsars and compare its performance with FDAS and FDJS. Our implementation of Pulscan includes an OpenMP version for multicore CPU acceleration, a version for heterogeneous CPU/GPU environments such as NVIDIA Grace Hopper, and a fully optimized NVIDIA GPU implementation for integration into an AstroAccelerate pipeline, which will be deployed in the SPOTLIGHT project at the GMRT. Our results demonstrate that unmatched filtering in Pulscan can serve as an efficient data reduction step, prioritizing datasets for further analysis and focusing human and subsequent computational resources on likely binary pulsar signatures.