Faster GPU-based convolutional gridding via thread coarsening

Abstract

Convolutional gridding is a processor-intensive step in interferometric imaging. While it is possible to use graphics processing units (GPUs) to accelerate this operation, existing methods use only a fraction of the available flops. We apply thread coarsening to improve the efficiency of an existing algorithm, and observe performance gains of up to $3.2\times$ for single-polarization gridding and $1.9\times$ for quad-polarization gridding on a GeForce GTX 980, and smaller but still significant gains on a Radeon R9 290X.

Accepted by Astronomy and Computing. ©2016. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/