  pPython seeks to provide a parallel capability that provides good speed-up
without sacrificing the ease of programming in Python by implementing
partitioned global array semantics (PGAS) on top of a simple file-based
messaging library (PythonMPI) in pure Python. pPython follows a SPMD (single
program multiple data) model of computation. pPython runs on a single-node
(e.g., a laptop) running Windows, Linux, or MacOS operating systems or on any
combination of heterogeneous systems that support Python, including on a
cluster through a Slurm scheduler interface so that pPython can be executed in
a massively parallel computing environment. It is interesting to see what
performance pPython can achieve compared to the traditional socket-based MPI
communication because of its unique file-based messaging implementation. In
this paper, we present the point-to-point and collective communication
performances of pPython and compare them with those obtained by using mpi4py
with OpenMPI. For large messages, pPython demonstrates comparable performance
as compared to mpi4py.
