This article is written as if shared memory is the only parallel programing paradigm. While it is true, that threads are a very useful construct for writing hig

This article is written as if shared memory is the only parallel programing paradigm. While it is true, that threads are a very useful construct for writing high performance parallel software, distributed memory programming is also a valid approach. If your problem can map to distributed memory techniques, then you have multiple advantages over shared memory programming. Most importantly you can parallelize over multiple machines. Other advantages include decoupling of each parallel task from each other (fewer race conditions and other hard to debug problems). There are several ways to achieve distributed memory parallelism in Python: multiprocessing, zeromq, raw tcp/ip sockets and mpi4py. Which approach makes sense to use will depend on your problem.