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#.
Read or Download Professional parallel programming with C# : master parallel extensions with .net 4 PDF
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Additional resources for Professional parallel programming with C# : master parallel extensions with .net 4
Body — This is the delegate to be invoked, once per iteration and without a predeﬁned execution plan. It can be of the type Action
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.