Imaging swiFTly: streaming widefield Fourier Transforms for large-scale interferometry

TL;DR

This paper introduces a scalable distributed Fourier transform algorithm for large-scale radio interferometry, reducing memory, data transfer, and computation loads, enabling efficient processing for next-generation telescopes.

Contribution

It presents a novel distributed imaging framework using smooth window functions, allowing efficient segmentation and distribution of data for large-scale interferometric imaging.

Findings

Distributed prototype handles terabytes of data efficiently.

Achieves throughput and accuracy comparable to existing software.

Scaling better than cubic with problem size, suitable for large telescopes.

Abstract

We describe a scalable distributed imaging algorithm framework for next-generation radio telescopes, managing the Fourier transform from apertures to sky (or vice versa) with a focus on minimising memory load, data transfers, and computation. Our algorithm uses smooth window functions to isolate the influence between specific regions of spatial-frequency and image space. This allows the distribution of image data between nodes and the construction of segments of frequency space exactly when and where needed. The developed prototype distributes terabytes of image data across many nodes, while generating visibilities at throughput and accuracy competitive with existing software. Scaling is demonstrated to be better than cubic in problem complexity (for baseline length and field of view), reducing the risk involved in growing radio astronomy processing to large telescopes like the Square Kilometre Array.