Download Professional parallel programming with C# : master parallel by Gast?n C. Hillar PDF

By Gast?n C. Hillar

Expert assistance for these programming today’s dual-core processors PCs

As laptop processors explode from one or to now 8 processors, there's an pressing want for programmers to grasp concurrent programming. This publication dives deep into the newest applied sciences on hand to programmers for developing specialist parallel functions utilizing C#, .NET four, and visible Studio 2010. The ebook covers task-based programming, coordination info constructions, PLINQ, thread swimming pools, asynchronous programming version, and extra. It additionally teaches different parallel programming ideas, similar to SIMD and vectorization.

  • Teaches programmers professional-level, task-based, parallel programming with C#, .NET four, and visible Studio 2010
  • Covers concurrent collections, coordinated facts constructions, PLINQ, thread swimming pools, asynchronous programming version, visible Studio 2010 debugging, and parallel trying out and tuning
  • Explores vectorization, SIMD directions, and extra parallel libraries

Master the instruments and expertise you must strengthen thread-safe concurrent purposes for multi-core platforms, with Professional Parallel Programming with C#.

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Additional resources for Professional parallel programming with C# : master parallel extensions with .net 4

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Body — This is the delegate to be invoked, once per iteration and without a predefined execution plan. It can be of the type Action or Action according to the type used in the iteration range definition. For supports neither fl oating-point values nor steps. However, even regular serial for loops over floating-point ranges can be really dangerous because of inexact additions in each round. For works with Int32 and Int64 values, and it runs by adding 1 in each iteration. Because it runs the body in parallel, partitioning the iteration range according to the available hardware resources, there are no guarantees made about the order in which the iterations are executed.

For(1, NUM_AES_KEYS + 1, code snippet is Listing 2_7 The third parameter is the delegate. In this case, the loop doesn’t use the iteration variable. For(1, NUM_AES_KEYS + 1, (int i) => code snippet is Snippet2_5 The previous code was prepared to run alone, perhaps with other methods running in parallel. However, each iteration was not designed to run in parallel with other iterations of the same loop body. For changes the rules. The code has one problem that needs to be solved. The sequential iterations shared the aesM local variable.

However, on this hardware, it requires more time to run many parts of code interleaving concurrency than running a single part of code alone, because the concurrent code is competing for hardware resources, as shown in the previous diagram. You can think of interleaved concurrency as many cars sharing a single lane. This is why interleaved concurrency is also defi ned as a form of virtual parallelism. Concurrency means that different parts of code can run simultaneously, taking advantage of real parallel processing capabilities found in the underlying hardware, as explained in Chapter 1.

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