Development of production-ready GPU data processing pipeline software for AstroAccelerate

TL;DR

This paper details transforming AstroAccelerate, a GPU-based radio astronomy data analysis software, into a stable, production-ready library with improved interfaces, enabling faster processing of large telescope data.

Contribution

It introduces a production-ready, GPU-accelerated software library for radio astronomy data analysis with enhanced interfaces and maintained legacy compatibility.

Findings

Substantially faster than realtime data processing.

Successful transition to a stable, maintainable software library.

Enhanced software interfaces for broader usability.

Abstract

Upcoming large scale telescope projects such as the Square Kilometre Array (SKA) will see high data rates and large data volumes; requiring tools that can analyse telescope event data quickly and accurately. In modern radio telescopes, analysis software forms a core part of the data read out, and long-term software stability and maintainability are essential. AstroAccelerate is a many core accelerated software package that uses NVIDIA(R) GPUs to perform realtime analysis of radio telescope data, and it has been shown to be substantially faster than realtime at processing simulated SKA-like data. AstroAccelerate contains optimised GPU implementations of signal processing tools used in radio astronomy including dedispersion, Fourier domain acceleration search, single pulse detection, and others. This article describes the transformation of AstroAccelerate from a C-like prototype code to a production-ready software library with a C++ API and a Python interface; while preserving compatibility with legacy software that is implemented in C. The design of the software library interfaces, refactoring aspects, and coding techniques are discussed.