Overview

The MWAX correlator was built to be modular and to be run real-time in production at the MRO, or offline using archived voltage subfiles instead of live multicast UDP packets.

Hardware

CUDA GPU

A significant fraction of the xGPU library and the mwax_db2correlate2db process is written in CUDA, so a CUDA-compatible GPU is essential.

Software

CUDA

Visit the Homepage. CUDA is required to be installed prior to PSRDADA, and mwax_xGPU.

CFITSIO library

Visit the Homepage.

$ CFLAGS="-O3" ./configure --prefix=/usr/local --enable-reentrant --enable-sse2 --enable-ssse3 --disable-curl
$ make clean && make
$ make install

PSRDADA library

Visit the Homepage

The MWAX correlator uses, and has tested, this commit (ca505cdb519afbd63ae91c00e4d86af0f3313b68) from the PSRDADA repository.

For Ubuntu 20.04 systems, the following packages also need to be installed in order to compile PSRDADA successfully (this was discovered through trial and error) - other Debian based systems should have similar requirements:

$ sudo apt update
$ sudo apt install automake autoconf csh hwloc libhwloc-dev libhwloc-plugins libtool numactl pkg-config python2.7-dev python

This is how we build it (note: some paths may be different on your system):

$ ./bootstrap
$ ./configure --with-cuda-dir=/usr/local/cuda --with-cuda-lib-dir=/usr/local/cuda/lib64 --with-hwloc-lib-dir=/usr/lib/x86_64-linux-gnu --prefix=/usr/local
$ make
$ make install

If all goes well, you should be able to run commands, such as:

$ dada_db --help

mwax_xGPU library

Visit the github page: https://github.com/MWATelescope/mwax-xGPU

The mwax-xGPU fork must be compiled with very specific flags in order to work. This is for a 128T system:

$ make clean NSTATION=128
$ make CUDA_ARCH={{gpu_sm_arch}} NSTATION=128 NFREQUENCY=6400 NTIME=52 NTIME_PIPE=52
$ make install prefix=/usr/local NSTATION=128 NFREQUENCY=6400

Replace {{gpu_sm_arch}} with the correct SM_ARCH value for your GPU. See CUDA GPU List (wikipedia). Read off the "Compute capability (version)" value, remove the dot and prefix with "sm". E.g. an Nvidia V100 would be sm_70.

For 256T:

$ make clean NSTATION=256
$ make CUDA_ARCH={{gpu_sm_arch}} NSTATION=256 NFREQUENCY=6400 NTIME=56 NTIME_PIPE=28
$ make install prefix=/usr/local NSTATION=256 NFREQUENCY=6400

mwax_db2correlate2db

Visit the github page: https://github.com/MWATelescope/mwax_cbf

mwax_db2fits

Visit the github page: https://github.com/MWATelescope/mwax_db2fits

How to Run

Setup

Prepare input data

• Ensure all the subfiles you wish to process are in one directory e.g. /path/to/subfiles
• For each subfile you will need to modify the following Key/Value pairs to your use case:

See also: MWAX PSRDADA header for more information on the definition of each key within the subfile header.

Create Ring Buffers

# Create input ringbuffer
dada_db -b 32768000 -k 1234 -n 640 -l -p

# create output ringbuffer
dada_db -b 338297856 -k 2345 -n 32 -l -p

Launch Programs

#
# start mwax_db2fits- in offline mode you still need to provide health command line args even if you don't intend to monitor the health packets
#
nohup mwax_db2fits/bin/mwax_db2fits -k 2345 --destination-path=/path/for/output/fits/files/. --health-netiface=eth0 --health-ip=224.0.2.2 --health-port=8005& 2&1> mwax_db2fits.log

#
# start mwax_db2correlate2db correlator
#
nohup mwax_cbf/mwax-fullcorrelator/mwax_db2correlate2db 1234 2345 224.0.2.2 8004 -a 5 -b 160 -d 0 -f 6400 -i 256 -o 1280 -O 2 -r -t -v -v& 2&1> mwax_db2correlate2db.log

Processing

For this you will need a bash script to execute a for/while loop through all subfiles and for each one run:

dada_diskdb -k 1234 -f /path/to/subfiles/obsid_subobs_chNNN.sub

Each time this is executed it will fill the input ringbuffer with the subfile, and that will kickstart mwax_db2correlate2db processing data and filling the output ringbuffer with visibilities and weights, which will then kickstart mwax_db2fits writing out FITS files.

Clean up

# Clean up ringbuffers
dada_db -d -k 1234
dada_db -d -k 2345