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Decomposition and Challenges in Parallel Programming: Is It Useful for Cloud Computing?

June 23rd, 2010 No comments

A recent article from Dr. Dobb’s introduced Fundamental Concepts of Parallel Programming. Richard Gerber and Andrew Binstock, authors of Programming with Hyper-Threading Technology, discussed three different forms of de-compositions for multi-threading:

  1. Functional decomposition. It’s one of the most common ways to achieve parallel execution. Using this approach, individual tasks are catalogued. If two of them can run concurrently, they are scheduled to do so by the developer.
  2. Producer/Consumer. It’s a form of functional decomposition in which one thread’s output is the input to a second. Can be hard to avoid, but frequently detrimental to performance.
  3. Data decomposition, a.k.a. “data level parallelism.” It breaks down tasks by the data they work on, rather than by nature of the task. Programs that are broken down via data decomposition generally have many threads performing the same work, just on different data items.

To make the three forms easy to understand, the authors used gardening as analogy where the threads map to gardners. For exmaple, the fuctional decomposition in gardening is to have one gardner to move the lawn and the other to weed. I find this analogy very intuitive and easy to follow. Even you don’t know multh-threading, you can guess it out from the gardening analogy.

The challenges while working with multi-threading are: