Develop Reference

C# Performance: Few Long Running Tasks vs Many Short Running Tasks

Given a simple program, what would be better from a performance/throughput point of view and why would it be better from a performance point of view?
(I don't care about memory consumption or CPU usage)
Assuming it takes roughly 5 ms to process a given message and there are ~500 messages received every second.
What should give better performance?
50 long running tasks that each receive and process ~10 messages per second
To create and run a new task for each message received
I am wondering with regard to running on a regular ~8 cores PC and not a 100+ core super computer.
Just to clarify; In the 50 long running tasks scenario most of the tasks would most of the time be waiting for a message and once received they would be signaled to wake up while in the creating new tasks scenario there would be ~500 tasks created every second.

It is not possible to predict. Depends a lot on how you are processing messages, how CPU cache comes into play, how CPU-intensive message processing is, what the IO activity is. The only reliable approach is to benchmark your code and see what works better. Keep in mind that the results can be heavily influenced by other processes that run on this computer. Try to emulate your production environment as much as possible.

How can I get started creating a multithreaded load balancer?

I have an interesting exercise to solve from my professor. But I need a little bit of help so it does not become boring during the holidays.
The exercise is to
create a multithreaded load balancer, that reads 1 measuring point from 5 sensors every second. (therefore 5 values every second).
Then do some "complex" calculations with those values.
Printing results of the calculations on the screen. (like max value or average value of sensor 1-5 and so on, of course multithreaded)
As an additional task I also have to ensure that if in the future for example 500 sensors would be read every second the computer doesn't quit the job.(load balancing).
I have a csv textfile with ~400 measuring points from 5 imaginary sensors.
What I think I have to do:
Read the measuring points into an array
Ensure thread safe access to that array
Spawn a new thread for every value that calculates some math stuff
Set a max value for maximum concurrent working threads
I am new to multithreading applications in c# but I think using threadpool is the right way. I am currently working on a queue and maybe starting it inside a task so it wont block the application.
What would you recommend?

There are a couple of environment dependencies here:
What version of .NET are you using?
What UI are you using - desktop (WPF/WinForms) or ASP.NET?
Let's assume that it's .NET 4.0 or higher and a desktop app.
Reading the sensors
In a WPF or WinForms application, I would use a single BackgroundWorker to read data from the sensors. 500 reads per second is trivial - even 500,00 is usually trivial. And the BackgroundWorker type is specifically designed for interacting with desktop apps, for example handing-off results to the UI without worrying about thread interactions.
Processing the calculations
Then you need to process the "complex" calculations. This depends on how long-lived these calculations are. If we assume they're short-lived (say less than 1 second each), then I think using the TaskScheduler and the standard ThreadPool will be fine. So you create a Task for each calculation, and then let the TaskScheduler take care of allocating tasks to threads.
The job of the TaskScheduler is to load-balance the work by queuing lightweight tasks to more heavyweight threads, and managing the ThreadPool to best balance the workload vs the number of cores on the machine. You can even override the default TaskScheduler to schedule tasks in whatever manner you want.
The ThreadPool is a FIFO queue of work items that need to be processed. In .NET 4.0, the ThreadPool has improved performance by making the work queue a thread-safe ConcurrentQueue collection.
Measuring task throughput and efficiency
You can use PerformanceCounter to measure both CPU and memory usage. This will give you a good idea of whether the cores and memory are being used efficiently. The task throughput is simply measured by looking at the rate at which tasks are being processed and supplying results.
Note that I haven't included any code here, as I assume you want to deal with the implementation details for your professor :-)

C# ThreadPool Implementation / Performance Spikes

In an attempt to speed up processing of physics objects in C# I decided to change a linear update algorithm into a parallel algorithm. I believed the best approach was to use the ThreadPool as it is built for completing a queue of jobs.
When I first implemented the parallel algorithm, I queued up a job for every physics object. Keep in mind, a single job completes fairly quickly (updates forces, velocity, position, checks for collision with the old state of any surrounding objects to make it thread safe, etc). I would then wait on all jobs to be finished using a single wait handle, with an interlocked integer that I decremented each time a physics object completed (upon hitting zero, I then set the wait handle). The wait was required as the next task I needed to do involved having the objects all be updated.
The first thing I noticed was that performance was crazy. When averaged, the thread pooling seemed to be going a bit faster, but had massive spikes in performance (on the order of 10 ms per update, with random jumps to 40-60ms). I attempted to profile this using ANTS, however I could not gain any insight into why the spikes were occurring.
My next approach was to still use the ThreadPool, however instead I split all the objects into groups. I initially started with only 8 groups, as that was how any cores my computer had. The performance was great. It far outperformed the single threaded approach, and had no spikes (about 6ms per update).
The only thing I thought about was that, if one job completed before the others, there would be an idle core. Therefore, I increased the number of jobs to about 20, and even up to 500. As I expected, it dropped to 5ms.
So my questions are as follows:
Why would spikes occur when I made the job sizes quick / many?
Is there any insight into how the ThreadPool is implemented that would help me to understand how best to use it?

Using threads has a price - you need context switching, you need locking (the job queue is most probably locked when a thread tries to fetch a new job) - it all comes at a price. This price is usually small compared to the actual work your thread is doing, but if the work ends quickly, the price becomes meaningful.
Your solution seems correct. A reasonable rule of thumb is to have twice as many threads as there are cores.

As you probably expect yourself, the spikes are likely caused by the code that manages the thread pools and distributes tasks to them.
For parallel programming, there are more sophisticated approaches than "manually" distributing work across different threads (even if using the threadpool).
See Parallel Programming in the .NET Framework for instance for an overview and different options. In your case, the "solution" may be as simple as this:
Parallel.ForEach(physicObjects, physicObject => Process(physicObject));

Here's my take on your two questions:
I'd like to start with question 2 (how the thread pool works) because it actually holds the key to answering question 1. The thread pool is implemented (without going into details) as a (thread-safe) work queue and a group of worker threads (which may shrink or enlarge as needed). As the user calls QueueUserWorkItem the task is put into the work queue. The workers keep polling the queue and taking work if they are idle. Once they manage to take a task, they execute it and then return to the queue for more work (this is very important!). So the work is done by the workers on-demand: as the workers become idle they take more pieces of work to do.
Having said the above, it's simple to see what is the answer to question 1 (why did you see a performance difference with more fine-grained tasks): it's because with fine-grain you get more load-balancing (a very desirable property), i.e. your workers do more or less the same amount of work and all cores are exploited uniformly. As you said, with a coarse-grain task distribution, there may be longer and shorter tasks, so one or more cores may be lagging behind, slowing down the overall computation, while other do nothing. With small tasks the problem goes away. Each worker thread takes one small task at a time and then goes back for more. If one thread picks up a shorter task it will go to the queue more often, If it takes a longer task it will go to the queue less often, so things are balanced.
Finally, when the jobs are too fine-grained, and considering that the pool may enlarge to over 1K threads, there is very high contention on the queue when all threads go back to take more work (which happens very often), which may account for the spikes you are seeing. If the underlying implementation uses a blocking lock to access the queue, then context switches are very frequent which hurts performance a lot and makes it seem rather random.

answer of question 1:
this is because of Thread switching , thread switching (or context switching in OS concepts) is CPU clocks that takes to switch between each thread , most of times multi-threading increases the speed of programs and process but when it's process is so small and quick size then context switching will take more time than thread's self process so the whole program throughput decreases, you can find more information about this in O.S concepts books .
answer of question 2:
actually i have a overall insight of ThreadPool , and i cant explain what is it's structure exactly.

to learn more about ThreadPool start here ThreadPool Class
each version of .NET Framework adds more and more capabilities utilizing ThreadPool indirectly. such as Parallel.ForEach Method mentioned before added in .NET 4 along with System.Threading.Tasks which makes code more readable and neat. You can learn more on this here Task Schedulers as well.
At very basic level what it does is: it creates let's say 20 threads and puts them into a lits. Each time it receives a delegate to execute async it takes idle thread from the list and executes delegate. if no available threads found it puts it into a queue. every time deletegate execution completes it will check if queue has any item and if so peeks one and executes in the same thread.

Run one method 1000 times in a short period of time

Let's say we are building some public service that grabs the setup of a user (what server, user and pwd he wants to perform the call), logs in into that server and do some processing...
the process takes about 15 seconds to complete
each user has a different setup (server/user/pwd), so the process needs to run against each one
if 1000 users tells the system to run the method at 1:00PM
How can I insure that the method is processed in the next 15 minutes?
What should be the correct approach to this little problem?
I'm thinking that I need to do something Asynchronously, and parallel processing could speed up things, maybe throttling the processes, maybe execute 100 calls per each 30 seconds?
I never did something like this and would love to get your feedback on ideas and future problems just to spend 100 hours of work and realize that I took a wrong road :(
Thank you.
added
The only thing to have in consideration is that this should be a 100% web solution.

If one call to your method does not affect the result of another method call (which seems to be the case here), parallel programming seems to be the way to go.
Consider not processing this in the asp.net application directly, but rather placing such requests on a queue and having another process (windows service may be a good candidate here) pulling items off the queue for processing. The windows service can have multiple threads and can pull as many items off the queue at once as there are processing threads available. With an appropriate queuing mechanism, the windows service can run on separate hardware if needed to reach your performance goals.
You can have the original web page query the result using e.g. Ajax to provide the user feedback if that's a requirement.
UPDATE:
Microsoft has recommended a pattern for long running tasks that can be used in a hosted environment.

Well, 1000 * 15 seconds is more than 4 hours, so you can only complete the entire task within the 15 minute time frame if you parallelize the batch.
I would set up a queue and have a sufficient number of threads or processes pull from that queue.
You can define an in-process queue with Queue<T> or out-of-process either with a database table or MSMQ.
If you don't want to write multithreaded code, you can just have a bunch of different processes running on different machines, all pulling from the same queue.
A console application can do this, but a Windows Service is definitely also an alternative.

Threading cost - minimum execution time when threads would add speed

I am working on a C# application that works with an array. It walks through it (meaning that at one time only a narrow part of the array is used). I am considering adding threads in it to make it perform faster (it runs on a dualcore computer). The problem is that I do not know if it would actually help, because threads cost something and this cost could easily be more than the parallel gain... So how do I determine if threading would help?

Try writing some benchmarks that mimic, as closely as possible, the real-world conditions in which your software will actually be used.
Test and time the single-threaded version. Test and time the multi-threaded version. Compare the two sets of results.

If your application is CPU bound (i.e. it isn't spending time trying to read files or waiting for data from a device) and there is little to no sharing of live data (data being altered, if its read only its fine) between the threads then you can pretty much increase the speed by 50->75% by adding another thread (as long as it still remains CPU bound of course).
The main overhead in multithreading comes from 2 places.
Creation & initialization of the thread. Creating a thread requires quite a few resources to be allocated and involves swaps between kernel and user mode, this is expensive though a once off per thread so you can pretty much ignore it if the thread is running for any reasonable amount of time. The best way to mitigate this problem is to use a thread pool as it will keep the thread on hand and not need to be recreated.
Handling synchronization of data. If one thread is reading from data that another is writing, bad things will generally happen (worse if both are changing it). This requires you to lock your data before altering it so that no thread reads a half written value. These locks are generally quite slow as well. To mitigate this problem, you need to design your data layout so that the threads don't need to read or write to the same data as much as possible. If you do need a lot of these locks it can then become slower than the single thread option.
In short, if you are doing something that requires the CPU's to share a lot of data, then multi-threading it will be slower and if the program isn't CPU bound there will be little or no difference (could be a lot slower depending on what it is bound to, e.g. a cd/hard drive). If your program matches these conditions, then it will PROBABLY be worthwhile to add another thread (though the only way to be certain would be profiling).
One more little note, you should only create as many CPU bound threads as you have physical cores (threads that idle most of the time, such as a GUI message pump thread, can be ignored for this condition).
P.S. You can reduce the cost of locking data by using a methodology called "lock-free programming", though this something that should really only be attempted by people with a lot of experience with multi-threading and a clear understanding of their target architecture (including how the cache is treated and the memory bus).

I agree with Luke's answer. Benchmark it, it's the only way to be sure.
I can also give a prediction of the results - the fastest version will be when the number of threads matches the number of cores, EXCEPT if the array is very small and each thread would have to process just a few items, the setup/teardown times might get larger than the processing itself. How few - that depends on what you do. Again - benchmark.
I'd advise to find out a "minimum number of items for a thread to be useful". Then, when you are deciding how many threads to spawn (or take from a pool), check how many cores the computer has and how many items there are. Spawn as many threads as possible, but no more than the computer has cores, and not so many that each thread would have less than the minimum number of items to process.
For example if the minimum number of items is, say, 1000; and the computer has 4 cores; and your list contains 2500 items, you would spawn just 2 threads, because more threads would be inefficient (each would process less than 1000 items).

Making a step by step list for Luke's idea:
Make a single threaded test app
Download Sysinternals Process Monitor and run it
Run your test app and find it on the process list (remember to run it as a release build outside of Visual Studio)
Double click the process and select the Performance Graph tab
Observe the CPU time used by your process
If the CPU time is sittling flat 50% for more than a few seconds, you can probably speed your overall process up using threads (assuming the bunch of stuff Mr Peters refered to holds true)
(However, the best you can do on a duel core machine is to halve the time it takes to run. If your process only take 4 seconds, it might not be worth getting it to run in 2 seconds)
Using the task parallel library / Rx provides a friendlier interface than System.Threading.ThreadPool, which might make your world a bit easier.

You miss imho one item, which is that it is not always about execution time. There is:
The problem to koop a UI operational during an operation. Even if the UI is "dormant", a nonresponsive message pump makes a worse impression.
The possibility to use a thread pool to actually not ahve to start / stop threads all the time. I use thread pools very extensively, and various parts of the applications keep them busy.
Anyhow, ignoring my point 1 - where you may go multi threaded without speeding things up in order to keep your UI responsive - I would say it is always then faster when you can actually either split up work (so you can keep more than one core busy) or offload it for othe reasons.