MPI is a world-standard for large-scale parallel computing, and is supported by every major academic computer batch system.  It allows for parallelization across multiple nodes, and also provides tools for gathering the results from different CPUs together.  It not only allows you to add more CPU power to a problem, but can also be used to add:

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

psana-python supports the use of MPI-parallelization for both offline analysis and real-time analysis.

Parallelization will become even more critical with the higher data rates of LCLS-II.