By Clay Breshears

If you're trying to take complete benefit of multi-core processors with concurrent programming, this sensible publication presents the data and hands-on adventure you wish. The paintings of Concurrency is among the few assets to target enforcing algorithms within the shared-memory version of multi-core processors, instead of simply theoretical versions or distributed-memory architectures. The publication offers targeted reasons and usable samples that will help you remodel algorithms from serial to parallel code, in addition to suggestion and research for heading off error that programmers ordinarily make while first trying those computations. Written through an Intel engineer with over 20 years of parallel and concurrent programming adventure, this publication can assist you:
comprehend parallelism and concurrency discover variations among programming for shared-memory and distributed-memory study guidance for designing multithreaded functions, together with checking out and tuning realize the right way to make most sensible use of alternative threading libraries, together with home windows threads, POSIX threads, OpenMP, and Intel Threading development Blocks discover how you can enforce concurrent algorithms that contain sorting, looking out, graphs, and different functional computations
The artwork of Concurrency indicates you the way to maintain algorithms scalable to use new processors with much more cores. For constructing parallel code algorithms for concurrent programming, this publication is a needs to.

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Extra info for The Art of Concurrency: A Thread Monkey's Guide to Writing Parallel Applications

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You can’t put down shingles until the decking is there, and you can’t nail down the decking unless you have the rafters in place. So, instead of hiring three teams to do these three tasks in parallel, you can hire one roofing crew to do all three in the order required (there is parallelism within each of the roofing steps, plus the act of putting on the roof is independent of installing the electrical wiring, the plumbing, and putting up drywall). To satisfy an execution order constraint, you can schedule tasks that have an order dependency onto the same thread and ensure that the thread executes the tasks in the proper sequence.

Mixing exchange and update computations can complicate the logic of your application, especially to ensure correct data is retrieved. However, the serial application likely had this requirement, too, and the solution to the need for accessing correct data concurrently should simply follow the serial algorithm as much as possible. For example, if you are modeling the distribution of heat from a source through a metal plate, you can simulate the plate by a two-dimensional array of current temperatures at discrete spatial locations.

Several different synchronization mechanisms are available (usually dependent on the threading method you are using) to provide mutual exclusion. Both reading and writing of data must be protected. Multiple threads reading the same data won’t cause any problems. When you have multiple threads writing to the same location, the order of the updates to the memory location will determine the value that is ultimately stored there and the value that will be read out of the location by another thread (recall the airline reservation system that put two passengers in the same seat).

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