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<9 Reusable Parallel Data Structures and Algorithms ⁄ >

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This column is less about the mechanics of a common language runtime (CLR) feature and more about how to efficiently use what you've got at your disposal. Selecting the right data structures and algorithms is, of course, one of the most common yet important decisions a programmer must make. The wrong choice can make the difference between success and failure or, as is the case most of the time, good performance and, well, terrible performance. Given that parallel programming is often meant to improve performance and that it is generally more difficult than serial programming, the choices are even more fundamental to your success.
In this column, we'll take a look at nine reusable data structures and algorithms that are common to many parallel programs and that you should be able to adapt with ease to your own .NET software. Each example is accompanied by fully working, though not completely hardened, tested, and tuned, code. The list is by no means exhaustive, but it represents some of the more common patterns. As you'll notice, many of the examples build on each other.
There's something I should mention at the outset. The Microsoft® .NET Framework has several existing concurrency primitives. While I'll show you how to build your own primitives, the existing ones are completely suitable for most situations. I just want to demonstrate that alternatives are sometimes worth considering. Besides, seeing these techniques in action will deepen your understanding of parallel programming in general. I will assume a basic understanding of the existing primitives, though. See Vance Morrison's "What Every Dev Must Know About Multithreaded Apps" in the August 2005 issue of MSDN® Magazine for a comprehensive overview.
Now, let's have a look at the techniques.

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