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This provides a convenient way of passing data to the method, just like with ParameterizedThreadStart. Unlike with Task, QueueUserWorkItem doesn't return an object to help you subsequently manage execution. Also, you must explicitly deal with exceptions in the target code — unhandled exceptions will take down the program.
Asynchronous delegates ThreadPool. Asynchronous delegate invocations asynchronous delegates for short solve this, allowing any number of typed arguments to be passed in both directions.
Asynchronous methods follow a similar protocol outwardly, but they exist to solve a much harder problem, which we describe in Chapter 23 of C 4. BeginInvoke returns immediately to the caller.
You can then perform other activities while the pooled thread is working. Second, it receives the return value as well as any ref or out parameters. Third, it throws any unhandled worker exception back to the calling thread.
In practice, this is open to debate; there are no EndInvoke police to administer punishment to noncompliers!
Optimizing the Thread Pool The thread pool starts out with one thread in its pool. You can set the upper limit of threads that the pool will create by calling ThreadPool.
SetMaxThreads; the defaults are: in Framework 4. The reason there are that many is to ensure progress should some threads be blocked idling while awaiting some condition, such as a response from a remote computer. You can also set a lower limit by calling ThreadPool. Raising the minimum thread count improves concurrency when there are blocked threads see sidebar.
The default lower limit is one thread per processor core — the minimum that allows full CPU utilization.
On server environments, though such ASP. Rather, it instructs the pool manager to create up to x threads the instant they are required. To illustrate, consider a quad-core computer running a client application that enqueues 40 tasks at once. If each task performs a 10 ms calculation, the whole thing will be over in ms, assuming the work is divided among the four cores.
And this is exactly how the thread pool works. Matching the thread count to the core count allows a program to retain a small memory footprint without hurting performance — as long as the threads are efficiently used which in this case they are. But now suppose that instead of working for 10 ms, each task queries the Internet, waiting half a second for a response while the local CPU is idle.
Fortunately, the pool manager has a backup plan. Of course, if needed. When To Go Parallel The entire parallelism concept is nothing more than just a performance play.
That is its key benefit. Even though there are some scenarios where concurrent execution is a clear solution, you usually can't just automatically assume that dividing the workload over several cores will outperform the sequential execution, so a lot of measurement is usually involved.
For this purpose, Stopwatch from the System. Diagnostics namespace usually meets all the requirements needed. With these in mind, you should be able to make the proper and educated decision whether to stay sequential or dive into parallel programming.
Additional overhead: there is always some overhead involved since the TPL engine needs to manage all the features. So if you have just a small amount of work to do, running it concurrently may not outperform the sequential version.
Data coordination: if your pieces of work need to access and alter the same data or resources, you will need to add some kind of coordination. The more of it, the worse parallel performance you will achieve. However, if the pieces are independent and isolated from each other, there is nothing to worry about.
Scaling: the TPL engine will usually take care of all the scaling. Still, there are cases when you need to do it by yourself. Even though there are many options available to play with, these will usually give you uncertain results, so be ready to play the "hit or miss" game. This is due to various hardware designs and their limits. Net framework is not new since it is supported from its very first version 1. We refer to this as a classic threading model.