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 support the use of MPI for offline analysis and real-time analysis.