• Paper: https://doi.org/10.1016/j.ascom.2017.11.003
• Wiki: https://github.com/miguelcarcamov/gpuvmem/wiki

If you use GPUVMEM for your research please do not forget to cite Cárcamo et al.

@article{CARCAMO201816,
title = "Multi-GPU maximum entropy image synthesis for radio astronomy",
journal = "Astronomy and Computing",
volume = "22",
pages = "16 - 27",
year = "2018",
issn = "2213-1337",
doi = "https://doi.org/10.1016/j.ascom.2017.11.003",
url = "http://www.sciencedirect.com/science/article/pii/S2213133717300094",
author = "M. Cárcamo and P.E. Román and S. Casassus and V. Moral and F.R. Rannou",
keywords = "Maximum entropy, GPU, ALMA, Inverse problem, Radio interferometry, Image synthesis"
}

1. Install git-lfs

   a. sudo apt-get install git-lfs

2. Install casacore latest stable version v3.2.1

   a. git clone --single-branch --branch v3.2.1 https://github.com/casacore/casacore.git

   b. sudo apt-get install -y build-essential cmake gfortran g++ libncurses5-dev libreadline-dev flex bison libblas-dev liblapacke-dev libcfitsio-dev wcslib-dev libhdf5-serial-dev libfftw3-dev python-numpy libboost-python-dev libpython2.7-dev

   b. cd casacore

   c. mkdir build

   d. cd build

   e. cmake -DUSE_FFTW3=ON -DUSE_OPENMP=ON -DUSE_HDF5=ON -DUSE_THREADS=ON ..

   f. make -j

   g. sudo make install

3. Install Boost

   a. sudo apt-get -y install libboost-all-dev

4. Install cfitsio

   a. sudo apt-get -y install libcfitsio-dev

5. Download or clone gpuvmem.

6. To compile GPUVMEM you will need:

   • cfitsio - Usually the package is called libcfitsio-dev.
   • cmake >= 3.8
   • git-lfs - git-lfs
   • casacore >= v3.1.2 (https://github.com/casacore/casacore - branch v3.1.2. please make sure you have installed the github version, Ubuntu package doesn't work well since doesn't have the put() function).
   • CUDA 9, 9.1, 9.2, 10.0 and 11.0. Remember to add binaries and libraries to the PATH and LD_LIBRARY_PATH environment variables, respectively.
   • OpenMP

7. To run the cmake tests you need to run git lfs install if not installled and then git-lfs pull to pull the measurement sets and model input FITS images.

docker pull ghcr.io/miguelcarcamov/gpuvmem:latest

cd gpuvmem
mkdir build
cd build
cmake ..
make -j

Create your FITS model input astrometry data on the header, typically we use the resulting dirty image from CASA's tclean.

Usage: ./bin/gpuvmem [options]

   -O --output_image [default: mod_out.fits]
       Name of the output visibility file/s (separated by a comma)
   -e --eta [default: -1]
       Variable that controls the minimum image value in the entropy prior
   -T --threshold [default: 0]
       Threshold to calculate the spectral index image above a certain number of
       sigmas in I_nu_0
   -p --path [default: mem/]
       Path to save FITS images. With last trail / included. (Example ./../mem/)
   -G --gpus [default: 0]
       Index of the GPU/s you are going to use separated by a comma
   -R --robust_parameter [default: 2]
       Robust weighting parameter when gridding. -2.0 for uniform weighting, 2.0
       for natural weighting and 0.0 for a tradeoff between these two.
   -X --blockSizeX [default: -1]
       GPU block X Size for image/Fourier plane (Needs to be pow of 2)
   -Y --blockSizeY [default: -1]
       GPU block Y Size for image/Fourier plane (Needs to be pow of 2)
   -V --blockSizeV [default: -1]
       GPU block V Size for visibilities (Needs to be pow of 2)
   -t --iterations [default: 500]
       Number of iterations for optimization
   -g --gridding [default: 0]
       Use gridded visibilities. This is done in CPU (Need to select the CPU thre
       ads that will grid the input visibilities)
   -z --initial_values [default: NULL]
       Initial values for image/s
   -Z --regularization_factors [default: NULL]
       Regularization factors for each regularization (separated by a comma)

 Flags:
   -v --verbose [default: (unset)]
       Shows information through all the execution
   -x --nopositivity [default: (unset)]
       Runs gpuvmem with no positivity restrictions on the images
   -a --apply-noise [default: (unset)]
       Applies random gaussian noise to visibilities
   -P --print-images [default: (unset)]
       Prints images per iteration
   -E --print-errors [default: (unset)]
       Prints final error maps
   -s --save_modelcolumn [default: (unset)]
       Saves the model visibilities on the model column of the input MS
   -M --use-radius-mask [default: (unset)]
       Use a mask based on a radius instead of the noise estimation

 Help:
   -h --help [default: (unset)]
       Shows this help
   -w --warranty [default: (unset)]
       Shows warranty details
   -c --copyright [default: (unset)]
       Shows copyright conditions

 Mandatory:
   -i --input [default: NULL]
       Name of the input visibility file/s (separated by a comma)
   -o --output [default: NULL]
       Name of the output visibility file/s (separated by a comma)
   -m --model_input [default: mod_in_0.fits]
       FITS file including a complete header for astrometry

 Optional:
   -n --noise [default: -1]
       Noise factor parameter
   -N --noise_cut [default: 10]
       Noise-cut Parameter
   -F --ref_frequency [default: -1]
       Reference frequency in Hz (if alpha is not zero). It will be calculated fr
       om the measurement set if not set
   -r --random_sampling [default: 1]
       Percentage of data used when random sampling
   -f --output_file [default: NULL]
       Output file where final objective function values are saved

• The normal flow of the program starts by creating a synthesizer, creating an optimizer, creating an objective function, and adding the terms to the objective function. It is also possible to add a convolution kernel for gridding and a weighting scheme.

• Objects can be created by their respective factory or by their constructors.

• The configuration of each objective function term is parameterized by the penalty factor (-Z), the index of the image from where data will be calculated and the index of the image where results are going to be applied.

• This will return a restored image: A convolution of the model image with the CLEAN beam + residuals (Jy/beam)
• Residuals (Jy/beam)
• The script file is on the scripts folder and it is named restore.py

Restoring usage:

python restore.py residual_folder.ms mem_model.fits restored_output 2.0

The last parameter, is the robust parameter that you want to use to clean the residuals.

• Miguel Cárcamo - The University of Manchester - [email protected]
• Nicolás Muñoz - Universidad de Santiago de Chile
• Fernando Rannou - Universidad de Santiago de Chile
• Pablo Román - Universidad de Santiago de Chile
• Simón Casassus - Universidad de Chile
• Axel Osses - Universidad de Chile
• Victor Moral - Universidad de Chile

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