Hi, Carlton. This is not an area of expertise of mine, but I took the liberty of Googling the topic and here's the top hit: http://scipy.github.io/old-wiki/pages/ParallelProgramming. The top line is: "This is an archival dump of old wiki content --- see scipy.org for current material," so I clicked on the link to go to the scipy home page and repeated my search there...and the top hit was the same page. :-( Scrolling down in these search results, I see some more-or-less relevant links, but nothing general, so I started reading the "old" page. These are the highlights, IMO:
"numpy/scipy are not perfect in this area, but there are some things you can do."
"The best way to make use of a parallel processing system depend on the task you're doing and on the parallel system you're using." (I regard this as particularly pertinent, absent knowledge of your system, as one thing I do know is that Windows is, or at least was--I stopped using Windoze with the introduction of 8--particularly "protective" of the allocation of processors; I'm less familiar with the "friendliness" of Posix flavors, including Linux and Mac.)
""premature optimization is the root of all evil"...Get your code working first, before even thinking about parallelization. Then ask yourself whether your code actually needs to be any faster. Don't embark on the bug-strewn path of parallelization unless you have to."
Wiser words are rarely spoken. Nevertheless, though billed as old, that document _appears_ to be the most current, general document on parallel programming w/ scipy, so it appears to be the place to start (and it _does_ have specific suggestions on how and when to parallelize scipy code).
I'd hope that more expert voices will chime in, but _sometimes_ that takes a little while, so I wanted to give you something to mull in the meantime.