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Next generation radio interferometric telescopes are entering an era of bigdata with extremely large data sets. While these telescopes can observe the skyin higher sensitivity and resolution than before, computational challenges inimage reconstruction need to be overcome to realize the potential offorthcoming telescopes. New methods in sparse image reconstruction and convexoptimization techniques (cf. compressive sensing) have shown to produce higherfidelity reconstructions of simulations and real observations than traditionalmethods. This article presents distributed and parallel algorithms andimplementations to perform sparse image reconstruction, with significantpractical considerations that are important for implementing these algorithmsfor Big Data. We benchmark the algorithms presented, showing that they areconsiderably faster than their serial equivalents. We then pre-sample griddingkernels to scale the distributed algorithms to larger data sizes, showingapplication times for 1 Gb to 2.4 Tb data sets over 25 to 100 nodes for up to50 billion visibilities, and find that the run-times for the distributedalgorithms range from 100 milliseconds to 3 minutes per iteration. This workpresents an important step in working towards computationally scalable andefficient algorithms and implementations that are needed to image observationsof both extended and compact sources from next generation radio interferometerssuch as the SKA. The algorithms are implemented in the latest versions of theSOPT (https://github.com/astro-informatics/sopt) and PURIFY(https://github.com/astro-informatics/purify) software packages {(Versions3.1.0)}, which have been released alongside of this article.

Distributed and parallel sparse convex optimization for radio interferometry with PURIFY