Ecosyste.ms: Awesome
An open API service indexing awesome lists of open source software.
https://github.com/miguelcarcamov/gpuvmem
GPU Framework for Radio Astronomical Image Synthesis
https://github.com/miguelcarcamov/gpuvmem
alma astronomical-algorithms astronomical-images astrophysics complex-systems cuda gpu gpu-acceleration gpu-computing image-synthesis maximum-entropy multi-gpu optimization-methods radio-imaging radio-interferometry radioastronomy ska vla
Last synced: 5 days ago
JSON representation
GPU Framework for Radio Astronomical Image Synthesis
- Host: GitHub
- URL: https://github.com/miguelcarcamov/gpuvmem
- Owner: miguelcarcamov
- License: gpl-3.0
- Created: 2016-03-03T02:58:46.000Z (over 8 years ago)
- Default Branch: master
- Last Pushed: 2024-07-30T18:48:12.000Z (3 months ago)
- Last Synced: 2024-10-18T14:13:20.305Z (18 days ago)
- Topics: alma, astronomical-algorithms, astronomical-images, astrophysics, complex-systems, cuda, gpu, gpu-acceleration, gpu-computing, image-synthesis, maximum-entropy, multi-gpu, optimization-methods, radio-imaging, radio-interferometry, radioastronomy, ska, vla
- Language: Cuda
- Homepage: https://doi.org/10.1016/j.ascom.2017.11.003
- Size: 503 MB
- Stars: 28
- Watchers: 6
- Forks: 3
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE.txt
Awesome Lists containing this project
README
# Papers and documentation
- Paper:
- Wiki:
# Citing
If you use GPUVMEM for your research please do not forget to cite Cárcamo et al.
```tex
@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 ( - 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
```bash
docker pull ghcr.io/miguelcarcamov/gpuvmem:latest
```
# Compiling
```bash
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]`
```text
-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:
```bash
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 - [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
# CONTRIBUTION AND BUG REPORTS
**Describe the bug**
A clear and concise description of what the bug is.
**To Reproduce**
Steps to reproduce the behavior:
1\. Go to '...'
2\. Click on '....'
3\. Scroll down to '....'
4\. See error
**Expected behavior**
A clear and concise description of what you expected to happen.
**Screenshots**
If applicable, add screenshots to help explain your problem.
**Desktop (please complete the following information):**
- OS: [e.g. Ubuntu 16.04]
- CUDA version [e.g. 9]
- gpuvmem Version [e.g. 22]
**Additional context**
Add any other context about the problem here.
# FEATURE REQUEST
**Is your feature request related to a problem? Please describe.**
A clear and concise description of what the problem is. Ex. I'm always frustrated when [...]
**Describe the solution you'd like**
A clear and concise description of what you want to happen.
**Describe alternatives you've considered**
A clear and concise description of any alternative solutions or features you've considered.
**Additional context**
Add any other context or screenshots about the feature request here.