(The full set of ParallelExtensionsExtras Tour posts is available here.) In a previous ParallelExtensionsExtras Tour blog post, we talked about implementing a custom partitioner for BlockingCollection<T>. Custom partitioning is an advanced but important feature supported by both Parallel.ForEach and PLINQ, as it allows the developer full control over how data is distributed during parallel processing.
(The full set of ParallelExtensionsExtras Tour posts is available here.) Producer/consumer scenarios could logically be split into two categories: those where the consumers are synchronous, blocking waiting for producers to generate data, and those where the consumers are asynchronous, such that they’re alerted to data being available and only then spin up to process the produced data.
.NET 4 introduces new data structures designed to simplify thread-safe access to shared data, and to increase the performance and scalability of multi-threaded applications. To best take advantage of these data structures, it helps to understand their performance characteristics in different scenarios.
(The full set of ParallelExtensionsExtras Tour posts is available here.) Caches are ubiquitous in computing, serving as a staple of both hardware architecture and software development. In software, caches are often implemented as dictionaries, where some data is retrieved or computed based on a key,
If you’ve played around with PLINQ and Parallel.ForEach loops in .NET 4, you may have noticed that many PLINQ queries can be rewritten as parallel loops, and also many parallel loops can be rewritten as PLINQ queries. However, both parallel loops and PLINQ have distinct advantages in different situations.
Thanks to everyone who attended our three breakout sessions at the Visual Studio 2010 Launch and DevConnections conference this week in Las Vegas. Attached to this blog post are the slide decks that were presented at the talks. The code from the talks is available either as part of our Parallel Programming in .NET 4 samples at https://code.msdn.microsoft.com/ParExtSamples,
(The full set of ParallelExtensionsExtras Tour posts is available here.) Delegates in .NET may have one or more methods in their invocation list. When you invoke a delegate, such as through the Delegate.DynamicInvoke method, the net result is that all of the methods in the invocation list get invoked,
(The full set of ParallelExtensionsExtras Tour posts is available here.) Producer/consumer is a fundamental pattern employed in many parallel applications. With producer/consumer, one or more producer threads generate data that is consumed by one or more consumer threads. These consumers can themselves also be producers of further data,
We’ve spent a lot of time touting improvements to the .NET Framework in .NET 4 around threading, including core enhancements to the performance of the runtime itself. Sometimes data is more powerful than words, however, and it’s useful to be able to see exactly what kind of difference such improvements can make.
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