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    <p><code>multiprocessing</code> has limited support for <a href="http://docs.python.org/2/library/multiprocessing.html#sharing-state-between-processes" rel="nofollow">implicitly shared objects</a>, which can even share lists and dicts.</p> <p>In general, <code>multiprocessing</code> is <a href="http://docs.python.org/2/library/multiprocessing.html#all-platforms" rel="nofollow">shared-nothing</a> (after the initial fork) and relies on explicit communication between the processes. This adds overhead (how much really depends on the kind of interaction between the processes), but neatly avoids a lot of the pitfalls of multithreaded programming. The high-level building blocks of <code>multiprocessing</code> favor master/slave models (esp. the <a href="http://docs.python.org/2/library/multiprocessing.html#module-multiprocessing.pool" rel="nofollow">Pool class</a>), with masters handing out work items, and slaves operating on them, returning results.</p> <p>Keeping state in sync across several processes may, depending how often they change, incur a prohibitive overhead.</p> <p><strong>TL;DR</strong>: It can be done, but probably shouldn't.</p> <pre><code>import time, multiprocessing class Test(multiprocessing.Process): def __init__(self, manager): super().__init__() self.quit = manager.Event() self.dict = manager.dict() def stop(self): self.quit.set() self.join() def run(self): self.dict['item'] = 0 while not self.quit.is_set(): time.sleep(1) self.dict['item'] += 1 m = multiprocessing.Manager() t = Test(m) t.start() for x in range(10): time.sleep(1.2) print(t.dict) t.stop() </code></pre> <p>The <code>multiprocessing</code> examples show how to create proxies for more <a href="http://docs.python.org/2/library/multiprocessing.html#examples" rel="nofollow">complicated objects</a>, which should allow you to implement the tree structure in your question.</p>
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