Examples of running CASA in parallel

The following is a list of typical examples on how to run CASA in parallel. Once CASA is started with mpicasa and the “Multi-MS” is created, there is basically no difference between running CASA in serial and in parallel. You can find an example of a parallelized analysis in the alma-m100-analysis-hpc-regression.py script located in a sub-directory of your CASA distribution. For example, if CASA is untarred in /home/user/casa-release-5.0.0-el6, the alma-m100 script can be found in /home/user/casa-release-5.0.0-el6/lib/python2.7/regressions/

  alma-m100-analysis-hpc-regression.py

Example 1. Run the above regression script in parallel, using 8 cores in parallel and 1 core as the MPI Client.

mpicasa -n 9 /casa --nogui --log2term -c alma-m100-analysis-hpc-regression.py

Example 2. Start CASA as described before for an interactive session, using 5 cores on the local machine.

mpicasa -n 5 /casa

An xterm will be open showing in the tile bar rank0. Rank 0 is where the MPIClient runs. The other 4 cores have been opened and are idle waiting for any activity to be sent to them.

Run importasdm to create a “Multi-MS” and save the online flags to a file. The output will be automatically named uid__A002_X888a.ms, which is an MMS partitioned across spw and scan. The online flags are saved in the file uid__A002_X888a_cmd.txt.

CASA <2>: importasdm('uid__A002_X888a', createmms=True, savecmds=True)

List the contents of the MMS using listobs. In order to see how the MMS is partitioned, use listpartition.

CASA <3>: listobs('uid__A002_X888a.ms', listfile='uid__A002_X888a.listobs')
CASA <4>: listpartition('uid__A002_X888a.ms')

Apply the online flags produced by importasdm, using flagdata in list mode. flagdata is parallelized therefore each engine will work on a separated “Sub-MS” to apply the flags from the uid__A002_X888a_cmd.txt file. You will see messages in the terminal (also saved in the casa-###.log file), containing the strings MPIServer-1, MPIServer-2, etc., for all the cores that process in parallel.

CASA <5>: flagdata('uid__A002_X888a.ms', mode='list', inpfile='uid__A002_X888a_cmd.txt')

Flag auto-correlations and the high Tsys antenna also using list mode for optimization.

CASA <6>: flagdata('uid__A002_X888a.ms', mode='list',
inpfile=["autocorr=True","antenna='DA62'"])

Create all calibration tables in the same way as for a normal MS. Task gaincal is not parallelized, therefore it will work on the MMS as if it was a normal MS.

CASA <7>: gaincal('uid__A002_X888a.ms', caltable='cal-delay_uid__A002_X888a.K',
                  field='*Phase*',spw='1,3,5,7', solint='inf',combine='scan',
                  refant=therefant, gaintable='cal-antpos_uid__A002_X888a',
                  gaintype='K'))

Apply all the calibrations to the MMS. applycal will work in parallel on each “Sub-MS” using the available cores.

CASA <8>: applycal(vis='uid__A002_X888a.ms', field='0', spw='9,11,13,15',
                   gaintable=['uid__A002_X888a.tsys',
                              'uid__A002_X888a.wvr.smooth',
                              'uid__A002_X888a.antpos'],
                   gainfield=['0', '', ''], interp='linear,linear',
                   spwmap=[tsysmap,[],[]], calwt=True, flagbackup=False)

Split out science spectral windows. Task split is also parallelized, therefore it will recognize that the input is an MMS and will process it in parallel, creating also an output MMS.

CASA <9>: split(vis='uid__A002_X888a.ms', outputvis='uid__A002_X888a.ms.split',
datacolumn='corrected', spw='9,11,13,15', keepflags=True)

Run tclean normally to create your images.