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    <p>Disclamer: I am a developer of SCOOP.</p> <p>It really depends on your personality. If you prefer getting theoretical information before moving forth, you should read some books or get along with the technologies first. A list of books covering a good part of the subject would be:</p> <ul> <li><em>Parallel Programming for multicore and cluster systems</em> by Thomas Rauber, and Gudula Rünger (Springer-Verlag).</li> <li><em>Principles of Parallel Programming</em> by Calvin Lin and Lawrence Snyder (Addison-Wesley)</li> <li><em>Patterns for Parallel Programming</em> by Timothy G. Mattson and al. (Addison-Wesley)</li> </ul> <p>Data-based technologies you may want to get acquainted with would be the MPI standard (for multi-computers) and OpenMP (for single-computer), as well as the pretty good multiprocessing module which is builtin in Python.</p> <p>If you prefer getting your hands dirty first, you should begin with task-based frameworks which provides a simple and user-friendly usage. Both of these were an utmost focus while creating <a href="http://scoop.googlecode.com" rel="nofollow">SCOOP</a>. You can try it with <code>pip -U scoop</code>. On Windows, you may wish to install PyZMQ first using their <a href="https://github.com/zeromq/pyzmq/downloads" rel="nofollow">executable installers</a>. You can check the provided examples and play with the various parameters to understand what causes performance degradation or increase with ease. I encourage you to compare it to its alternatives such as <a href="http://celeryproject.org/" rel="nofollow">Celery</a> for similar work or <a href="http://www.gevent.org/" rel="nofollow">Gevent</a> for a coroutine framework. If you feel adventurous, don't be shy to test the <a href="http://www.dabeaz.com/coroutines/" rel="nofollow">builtin coroutines functionnalities</a> of Python and plug them with various networking stacks.</p> <p>Using a task-based framework will ease you the burden of theoretical analysis such as load balancing implementation details, serialization and so on which is non-trivial and can take a long time to debug and get working. It provides all the desired level of understanding of distributed systems. Bonus with open source software: Check the code to understand under-the-hood mechanical details.</p>
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    1. POWhere to begin with Distributed Computing / Parallel Processing? (Python / C)
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    1. COVery helpful. I'll definitely check those texts out!
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