High-Performance Gridding For Radio Interferometric Image Synthesis

Convolutional Gridding is a technique (algorithm) extensively used in Radio Interferometric Image Synthesis for fast inversion of functions sampled with irregular intervals on the Fourier plane. In this thesis, we propose some modifications to the technique to execute faster on a GPU. These modifications give rise to \textit{Hybrid Gridding} and \textit{Pruned NN Interpolation}, which take advantage of the oversampling of the Gridding Convolutional Function in Convolutional Gridding to try to make gridding faster with no reduction in the quality of the output. Our experiments showed that given the right conditions, Hybrid Gridding executes up to $6.8\times$ faster than Convolutional Gridding, and Pruned NN Interpolation is generally slower than Hybrid Gridding. The two new techniques feature the downsampling of an oversampled grid through convolution to accelerate the Fourier inversion. It is a well-known approximate technique which suffers from aliasing. In this thesis, we are re-proposing the technique as a \textit{Convolution-Based FFT Pruning} algorithm able to suppress aliasing below arithmetic noise. The algorithm uses the recently discovered least-misfit gridding functions, which through our experiments gave promising results, although not as good as expected from the related published work on the stated gridding functions. Nevertheless, our experiments showed that, given the right conditions, Convolutional-Based Pruning reduces the Fourier inversion execution time on a GPU by approximately a factor of $8\times$.