A Processing Pipeline for High Volume Pulsar Data Streams

TL;DR

This paper introduces a scalable, open-source on-line pulsar data processing pipeline designed to handle high-volume data streams, addressing limitations of traditional off-line analysis for modern radio telescopes like the SKA.

Contribution

It characterizes modern pulsar search pipelines, assesses on-line implementation feasibility, identifies transition challenges, and proposes a new prototype pipeline using COTS components.

Findings

Prototype pipeline is scalable and cost-effective.

System performs well under simulated SKA data loads.

Open source design facilitates widespread adoption.

Abstract

Pulsar data analysis pipelines have historically been comprised of bespoke software systems, supporting the off-line analysis of data. However modern data acquisition systems are making off-line analyses impractical. They often output multiple simultaneous high volume data streams, significantly increasing data capture rates. This leads to the accumulation of large data volumes, which are prohibitively expensive to retain. To maintain processing capabilities when off-line analysis becomes infeasible due to cost, requires a shift to on-line data processing. This paper makes four contributions facilitating this shift with respect to the search for radio pulsars: i) it characterises for the modern era, the key components of a pulsar search science (not signal processing) pipeline, ii) it examines the feasibility of implementing on-line pulsar search via existing tools, iii) problems preventing an easy transition to on-line search are identified and explained, and finally iv) it provides the design for a new prototype pipeline capable of overcoming such problems. Realised using Commercial off-the-shelf (COTS) software components, the deployable system is open source, simple, scalable, and cheap to produce. It has the potential to achieve pulsar search design requirements for the Square Kilometre Array (SKA), illustrated via testing under simulated SKA loads.