Optimized SKA-ARL

Introduction

• Algorithm Reference Library (ARL) is a set of algorithms developed for aperture synthesis imaging based on Numpy. ARLO is an optimized version of ARL by converting computational intensive kernels from python to c++ and applying further optimization.

• Applied Computing Lab., ICT, CAS

Installation

1. install required python packages:

   pip3 install -r requirements.txt 

2. compile C++ code to .so files which can be import as a module in Python code.
   • the C++ code is in c-kernel/src
   • there are 4 module should be compiled:
     • gridding: convolution_gridding_mod.so by Makefile.gridding
     • fft: fft_support_mod.so by Makefile.fft
     • cleaners: cleaners_mod.so by Makefile.cleaners
     • parameters: parans_mod.so by Makefile.params
   • use Makefiles to compile: make -f Makefile.xxx
3. move .so files to corresponding directories in arl-python/arl, e.g.

   mv c-kernel/src/fourier_transforms/convolution_gridding_mod.so arl-python/arl/fourier_transforms

   similar for other .so files.

Demo

• Two notebook demos can be run and test: imaging-demo1, imaging-demo2 (modified from the original imaging notebook).
• The intermediate data used by the notebooks are in arl-python/example/arl/results.

Test

c-kernel/test is code to test and verify correctness of the optimized C++ code.

1. Compile code in src first and move the .so libs to corresponding directories to make sure some python modules imported correctly.
2. Run scripts to verify the results of original python code and optimized c++ code.
   • For all tests, run sh test_all.sh.
   • For params, run sh test_params.sh.
   • For gridding, run sh test_gridding.sh.
   • For fft, run sh test_fft.sh.
   • For cleaners, run sh test_cleaners.sh.
3. The data in test code is randomly created.