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• memory (by distributing a large memory-bound problem over multiple nodes)
• I/O (by allowing multiple network connections between data senders/receivers)

The recommended simplest way of running parallel analysis is to use the "MPIDataSource" pattern.  This allows you to write code as if it was running only on one processor and save small per-event information (numbers and arrays) as well as "end of run" summary data.  This data can optionally be saved to a small HDF5 file, which can be moved, for example, to a laptop computer for analysis with any software that can read HDF5.  This script can be found in /reg/g/psdm/tutorials/examplePython/mpiDataSource.py

from psana import *

dsource = MPIDataSource('exp=xpptut15:run=54:smd')
cspaddet = Detector('cspad')
smldata = dsource.small_data('run54.h5',gather_interval=100)

partial_run_sum = None
for nevt,evt in enumerate(dsource.events()):
   calib = cspaddet.calib(evt)
   if calib is None: continue
   cspad_sum = calib.sum()      # number
   cspad_roi = calib[0][0][3:5] # array
   if partial_run_sum is None:
      partial_run_sum = cspad_roi
   else:
      partial_run_sum += cspad_roi


   # save per-event data
   smldata.event(cspad_sum=cspad_sum,cspad_roi=cspad_roi)


   if nevt>3: break


# get "summary" data
run_sum = smldata.sum(partial_run_sum)
# save HDF5 file, including summary data
smldata.save(run_sum=run_sum)

Run the script with this command:

mpirun -n 2 python mpiDataSource.py

These parallel scripts can be run in real time while the data is being taken and can complete within a few minutes of the end of the run-python supports the use of MPI-parallelization for both offline analysis and real-time analysis.  You can see how to submit MPI psana-python batch jobs in the next "building blocks" topic.Parallelization will become even more critical with the higher data rates of LCLS-IIhere.