Papers and documentation

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

Citing

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"
}

Installation

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.

Installation using Docker

docker pull ghcr.io/miguelcarcamov/gpuvmem:latest

Compiling

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

Now antenna configurations are read directly from the MS file

Usage

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

Use GPUVMEM

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

Framework usage

• 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.

TO RESTORE YOUR IMAGE PLEASE SEE CARCAMO ET AL. 2018 FOR MORE INFORMATION

• 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.

CONTRIBUTORS

• Miguel Cárcamo - The University of Manchester - miguel.carcamo@postgrad.manchester.ac.uk
• 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

CONTRIBUTION AND BUG REPORTS

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