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What is GPUVMEM?

GPUVMEM is a framework that allows radio astronomers to do a process called image synthesis. This process takes Fourier space data taken by radio-interferometer like ALMA, ATCA, VLA, or a set of radio-interferometers as EHT, and returns an image from a certain a radio source.

Radio interferometers are an array of antennas capable of sampling the sky collecting radio signals from specific sources. Each antenna's signal is correlated with every other signal to produce sample of the sky image but on the Fourier domain. As it can be seen these irregular and sparse samples are a set of complex numbers in the uv plane called _visibilities_.

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What are GPUVMEM's current features?

1. Object oriented Framework: Object orientation makes easier the creation and building of a feature that you are interested in and plug that to the program.
2. Multi-GPU: Radio datasets and memory are distributed between different GPU using the P2P approach.
3. Multi-Field/Mosaic support: Mosaic images in interferometry allow the study of large scale objects in the sky. In this Bayesian approach we fit a single model image to the ensemble of all pointings.
4. Parameterized reconstruction: Although GPUVMEM requires a few parameters, it does not require user assistance during algorithm iteration. In this sense, we say GPUVMEM is an unsupervised image synthesis algorithm.
5. Regular grid-to-irregular grid approach: An interpolation step is done to compute model visibilities from the 2D Fast Fourier transform of the image estimate to compare them directly with observed visibilities.
6. Gridding to speed up the reconstructions: Gridding is the process of resampling visibility data into a regular grid. Usually this is done convolving the data with a kernel to avoid aliasing. Although convolutional kernels are not yet implemented in GPUVMEM, the gridding process reduce the amount of data and speeds up the algorithm reducing computation time.
7. Multi-frequency support: Spectral dependency can introduce strong effects into image synthesis. In this implementation we use the following image representation for multi-frequency synthesis:

where Inu0 is the image at reference frequency and alpha is the spectral index image.

How do I install it?