Parallelism in .NET – Part 10, Cancellation in PLINQ and the Parallel class
Many routines are parallelized because they are long running processes. When writing an algorithm that will run for a long period of time, its typically a good practice to allow that routine to be cancelled. I previously discussed terminating a parallel loop from within, but have not demonstrated how a routine can be cancelled from the caller’s perspective. Cancellation in PLINQ and the Task Parallel Library is handled through a new, unified cooperative cancellation model introduced with .NET 4.0.
Parallelism in .NET – Part 9, Configuration in PLINQ and TPL
Parallel LINQ and the Task Parallel Library contain many options for configuration. Although the default configuration options are often ideal, there are times when customizing the behavior is desirable. Both frameworks provide full configuration support.
Parallelism in .NET – Part 8, PLINQ’s ForAll Method
Parallel LINQ extends LINQ to Objects, and is typically very similar. However, as I previously discussed, there are some differences. Although the standard way to handle simple Data Parellelism is via Parallel.ForEach, it’s possible to do the same thing via PLINQ.
PLINQ adds a new method unavailable in standard LINQ which provides new functionality…
Parallelism in .NET – Part 7, Some Differences between PLINQ and LINQ to Objects
In my previous post on Declarative Data Parallelism, I mentioned that PLINQ extends LINQ to Objects to support parallel operations. Although nearly all of the same operations are supported, there are some differences between PLINQ and LINQ to Objects. By introducing Parallelism to our declarative model, we add some extra complexity. This, in turn, adds some extra requirements that must be addressed.
In order to illustrate the main differences, and why they exist, let’s begin by discussing some differences in how the two technologies operate, and look at the underlying types involved in LINQ to Objects and PLINQ .
Parallelism in .NET – Part 6, Declarative Data Parallelism
When working with a problem that can be decomposed by data, we have a collection, and some operation being performed upon the collection. I’ve demonstrated how this can be parallelized using the Task Parallel Library and imperative programming using imperative data parallelism via the Parallel class. While this provides a huge step forward in terms of power and capabilities, in many cases, special care must still be given for relative common scenarios.
C# 3.0 and Visual Basic 9.0 introduced a new, declarative programming model to .NET via the LINQ Project. When working with collections, we can now write software that describes what we want to occur without having to explicitly state how the program should accomplish the task. By taking advantage of LINQ, many operations become much shorter, more elegant, and easier to understand and maintain. Version 4.0 of the .NET framework extends this concept into the parallel computation space by introducing Parallel LINQ.
