Parallel faceted imaging in radio interferometry via proximal splitting (Faceted HyperSARA): when precision meets scalability. (arXiv:2003.07358v1 [astro-ph.IM])
<a href="http://arxiv.org/find/astro-ph/1/au:+Thouvenin_P/0/1/0/all/0/1">Pierre-Antoine Thouvenin</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Abdulaziz_A/0/1/0/all/0/1">Abdullah Abdulaziz</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Jiang_M/0/1/0/all/0/1">Ming Jiang</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Dabbech_A/0/1/0/all/0/1">Arwa Dabbech</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Repetti_A/0/1/0/all/0/1">Audrey Repetti</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Jackson_A/0/1/0/all/0/1">Adrian Jackson</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Thiran_J/0/1/0/all/0/1">Jean-Philippe Thiran</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Wiaux_Y/0/1/0/all/0/1">Yves Wiaux</a>
Upcoming radio interferometers are aiming to image the sky at new levels of
resolution and sensitivity, with wide-band image cubes reaching close to the
Petabyte scale for SKA. Modern proximal optimization algorithms have shown a
potential to significantly outperform CLEAN thanks to their ability to inject
complex image models to regularize the inverse problem for image formation from
visibility data. They were also shown to be scalable to large data volumes
thanks to a splitting functionality enabling the decomposition of data into
blocks, for parallel processing of block-specific data-fidelity terms of the
objective function. In this work, the splitting functionality is further
exploited to decompose the image cube into spatio-spectral facets, and enable
parallel processing of facet-specific regularization terms in the objective.
The resulting Faceted HyperSARA algorithm is implemented in MATLAB (code
available on GitHub). Simulation results on synthetic image cubes confirm that
faceting can provide a major increase in scalability at no cost in imaging
quality. A proof-of-concept reconstruction of a 15 GB image of Cyg A from 7.4
GB of VLA data, utilizing 496 CPU cores on a HPC system for 68 hours, confirms
both scalability and a quantum jump in imaging quality from CLEAN. Assuming
slow spectral slope of Cyg A, we also demonstrate that Faceted HyperSARA can be
combined with a dimensionality reduction technique, enabling utilizing only 31
CPU cores for 142 hours to form the Cyg A image from the same data, while
preserving reconstruction quality. Cyg A reconstructed cubes are available
online.
Upcoming radio interferometers are aiming to image the sky at new levels of
resolution and sensitivity, with wide-band image cubes reaching close to the
Petabyte scale for SKA. Modern proximal optimization algorithms have shown a
potential to significantly outperform CLEAN thanks to their ability to inject
complex image models to regularize the inverse problem for image formation from
visibility data. They were also shown to be scalable to large data volumes
thanks to a splitting functionality enabling the decomposition of data into
blocks, for parallel processing of block-specific data-fidelity terms of the
objective function. In this work, the splitting functionality is further
exploited to decompose the image cube into spatio-spectral facets, and enable
parallel processing of facet-specific regularization terms in the objective.
The resulting Faceted HyperSARA algorithm is implemented in MATLAB (code
available on GitHub). Simulation results on synthetic image cubes confirm that
faceting can provide a major increase in scalability at no cost in imaging
quality. A proof-of-concept reconstruction of a 15 GB image of Cyg A from 7.4
GB of VLA data, utilizing 496 CPU cores on a HPC system for 68 hours, confirms
both scalability and a quantum jump in imaging quality from CLEAN. Assuming
slow spectral slope of Cyg A, we also demonstrate that Faceted HyperSARA can be
combined with a dimensionality reduction technique, enabling utilizing only 31
CPU cores for 142 hours to form the Cyg A image from the same data, while
preserving reconstruction quality. Cyg A reconstructed cubes are available
online.
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