README
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= basic usage (for reference purposes, only) =

* linear-evaluation.py
  demonstrates running a function (sub) on a dataset in linear order

= using the threading module =

* threading-without-sync.py
  solve the "too much milk" problem as independent threads (ends up in
  having too much milk in the fridge, unfortunately)

* threading-with-sync.py
  solve the "too much milk" problem as cooperative threads using global
  variables (ends up in having the right amount of milk in the fridge)

* threading-with-lock.py
  solve the "too much milk" problem as independent threads using a lock
  (ends up in having the right amount of milk in the fridge)

= using the multiprocessing module =

  reference: https://docs.python.org/3/library/multiprocessing.html

* multiprocessing-sub.py
  how to run two functions (subs) in parallel

* multiprocessing-pool.py
  how to run a pool of agents (subs) in parallel doing a calculation
  on a dataset

* multiprocessing-pool-arbitrary.py
  how to run a pool of agents (subs) in parallel doing a calculation
  on a dataset with output in arbitrary order

* multiprocessing-threadpool.py
  how to run a pool of threads in parallel doing a calculation on a 
  dataset

= using the subprocess module =

* diskfree.py
  show the available disk space using an external call

* diskfree-grab-output.py
  show the available disk space using an external call, grab both the
  return value, and the output

= using the threading module = 

* threading-example.py
  how to run a pool of threads in parallel doing a calculation on a 
  dataset

* threading-queue.py
  how to invoke two threads, and store the result in a queue