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Слайды и текст этой презентации

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Task Parallel Library Data Parallelism Patterns

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Introduction to Parallel Programming Introduction to Parallel Programming Parallel Loops Parallel Aggregation

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Introduction to Parallel Programming Introduction to Parallel Programming Parallel Loops Parallel Aggregation

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Hardware trends predict more cores instead of faster clock speeds Multicore system features

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Some parallel applications can be written for specific hardware Potential parallelism

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Decomposition Parallel programming patterns aspects

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Tasks are sequential operations that work together to perform a larger operation Decomposition

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Tasks that are independent of one another can run in parallel Coordination

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Tasks often need to share data Scalable sharing of data

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Understand your problem or application and look for potential parallelism across the entire application as a whole Parallel programming design approaches

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Concurrency is a concept related to multitasking and asynchronous input-output (I/O) Concurrency & parallelism

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With parallelism, concurrent threads execute at the same time on multiple cores Concurrency & parallelism

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Amdahl’s law says that no matter how many cores you have, the maximum speedup you can ever achieve is (1 / percent of time spent in sequential processing) The limits of parallelism

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Whenever possible, stay at the highest possible level of abstraction and use constructs or a library that does the parallel work for you Parallel programming tips

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Use patterns Parallel programming tips

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Based on the .NET Framework 4 Code examples of this presentation

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Introduction to Parallel Programming Introduction to Parallel Programming Parallel Loops Parallel Aggregation

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Parallel programming patterns

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Use the Parallel Loop pattern when you need to perform the same independent operation for each element of a collection or for a fixed number of iterations Parallel Loops

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Parallel.For

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Parallel.ForEach

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Almost all LINQ-to-Objects expressions can easily be converted to their parallel counterpart by adding a call to the AsParallel extension method Parallel LINQ (PLINQ)

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Use PLINQ’s ForAll extension method in cases where you want to iterate over the input values but you don’t want to select output values to return PLINQ ForAll

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The .NET implementation of the Parallel Loop pattern ensures that exceptions that are thrown during the execution of a loop body are not lost Exceptions

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Parallel loops Parallel loops variations

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Writing to shared variables Dependencies between loop iterations

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Referencing data types that are not thread safe Dependencies between loop iterations

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Sequential iteration Breaking out of loops early

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Use Break to exit a loop early while ensuring that lower-indexed steps complete Parallel Break

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Calling Break doesn’t stop other steps that might have already started running Parallel Break

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ParallelLoopResult

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Use Stop to exit a loop early when you don’t need all lower-indexed iterations to run before terminating the loop Parallel Stop

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External Loop Cancellation

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Special handling of small loop bodies

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The number of ranges that will be created by a Partitioner object depends on the number of cores in your computer Special handling of small loop bodies

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You usually let the system manage how iterations of a parallel loop are mapped to your computer’s cores, in some cases, you may want additional control Controlling the degree of parallelism

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The PLINQ query in the code example will run with a maximum of eight tasks at any one time Controlling the degree of parallelism

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Sometimes you need to maintain thread-local state during the execution of a parallel loop Task-local state in a loop body

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Random initialization of the large array

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Calling the default Random constructor twice in short succession may use the same random seed Random class in parallel

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You can substitute custom task scheduling logic for the default task scheduler that uses ThreadPool worker threads Using a custom task scheduler

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Step size other than one Anti-Patterns

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Adaptive partitioning Parallel loops design notes

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Introduction to Parallel Programming Introduction to Parallel Programming Parallel Loops Parallel Aggregation

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Parallel programming patterns

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The pattern is more general than calculating a sum The Parallel Aggregation pattern

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Sequential version Calculating a sum

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PLINQ Calculating a sum

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PLINQ is usually the recommended approach Parallel aggregation pattern in .NET

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The PLINQ Aggregate extension method includes an overloaded version that allows a very general application of the parallel aggregation pattern Using PLINQ aggregation with range selection