  We present a scalable distributed imaging framework for next-generation radio
telescopes, managing the Fourier transformation from apertures to sky (or
vice-versa) with a focus on minimising memory load, data transfers and compute
work. We use smooth window functions to isolate the influence between specific
regions of spatial-frequency and image space. This allows distribution of image
data between nodes and constructing 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 (baseline length / field of view), reducing the
risk involved in growing radio astronomy processing to large telescopes like
the Square Kilometre Array.

Instrumentation and Methods for Astrophysics