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  • understand the CPU usage of your big-data ("BD") processing loop to make sure the bottleneck isn't "user code".  this can typically be done by running on 1 core.
  • increase the environment variable PS_SMDEB_NODES to be larger than its default of 1.  For many analyses, a number that is 1/16 of the number of big data cores has been good.  This variable, along with the PS_SRV_NODES variable described next determines how many cores are used for which task in psana (see MPITaskStructureToSupportScaling).
  • if you're writing a large amount of hdf5 data increase the environment variable PS_SRV_NODES to have more cores writing hdf5 files.  It is difficult here to provide guidance on the number since it depends on the application
  • set environment variable PS_SMD_N_EVENTS larger to increase the number of events that get sent in a "batch" when transmitting data from SMD0 cores through to BD cores
  • when setting up the smalldata, increase the number of events that get sent in a "batch" when transmitting data from BD cores to SRV cores by setting the batch_size kwarg in the DataSource.smalldata() call.

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