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By Colin Campbell, Ade Miller
Your CPU meter indicates an issue. One center is working at 100%, yet all of the different cores are idle. Your program is CPU-bound, yet you're utilizing just a fraction of the computing strength of your multicore approach. Is there the way to recover performance?The resolution, in a nutshell, is parallel programming. the place you as soon as may have written the type of sequential code that's frequent to all programmers, you presently locate that this now not meets your functionality pursuits. to take advantage of your system’s CPU assets successfully, you must break up your software into items that could run while. in fact, this can be more straightforward stated than performed. Parallel programming has a name for being the area of specialists and a minefield of sophisticated, hard-to-reproduce software program defects. all people turns out to have a favourite tale a few parallel software that didn't behave as anticipated due to a mysterious bug.These tales may still motivate a fit recognize for the trouble of the issues you are going to face in writing your personal parallel courses. thankfully, support has arrived. The Parallel styles Library (PPL) and the Asynchronous brokers Library introduce a brand new programming version for parallelism that considerably simplifies the activity. behind the curtain are subtle algorithms that dynamically distribute computations on multicore architectures. moreover, Microsoft® visible Studio® 2010 improvement process comprises debugging and research instruments to aid the hot parallel programming model.Proven layout styles are one other resource of aid. This consultant introduces you to crucial and regularly used styles of parallel programming and gives executable code samples for them, utilizing PPL. while brooding about the place to start, a great position to begin is to check the styles during this booklet. See in the event that your challenge has any attributes that fit the six styles offered within the following chapters. If it does, delve extra deeply into the correct development or styles and examine the pattern code.
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Extra info for Parallel Programming with Microsoft Visual C++: Design Patterns for Decomposition and Coordination on Multicore Architectures (Patterns and Practices)
Sample text
The examples you’ve seen so far are simple, but they’re powerful enough to handle many scenarios. For more ways to use tasks, see the section, “Variations,” later in this chapter. An Example An example of task parallelism is an image processing application where images are created with layers. Separate images from different sources are processed independently and then combined with a process known as alpha blending. This process superimposes semitransparent layers to form a single image. The source images that are combined are different, and different image processing operations are performed on each of them.
A good default number of ranges is approximately three to ten times the number of cores. Another approach for handling smaller loop bodies is to use the parallel_for_fixed or parallel_for_each_fixed functions that are provided in the Concurrency Runtime sample pack. By default, the parallel_for and parallel_for_each functions perform dynamic load balancing. When the amount of work for each item is small, the cost of load balancing can become significant. The sample pack’s parallel_for_fixed and parallel_for_each_fixed functions do not perform load balancing so they may outperform parallel_for and parallel_for_each when the loop bodies are small.
Counting the total number of occurrences of each word in a collection of text files. f. Finding the word that occurs most frequently in each file in a collection of text files. 2. Choose a suitable problem from Exercise 1. Code two solutions, using a sequential loop and a parallel loop. 3. codeplex. com. Use command line options to independently vary the number of iterations (the number of accounts) and the amount of work done in the body of each iteration (the number of months in the credit history).