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Does async method get its own thread [duplicate]

I am new to TPL and I am wondering: How does the asynchronous programming support that is new to C# 5.0 (via the new async and await keywords) relate to the creation of threads?
Specifically, does the use of async/await create a new thread each time that they are used? And if there many nested methods that use async/await, is a new thread created for each of those methods?

In short NO
From Asynchronous Programming with Async and Await : Threads
The async and await keywords don't cause additional threads to be
created. Async methods don't require multithreading because an async
method doesn't run on its own thread. The method runs on the current
synchronization context and uses time on the thread only when the
method is active. You can use Task.Run to move CPU-bound work to a
background thread, but a background thread doesn't help with a process
that's just waiting for results to become available.

According to MSDN : async keyword
An async method runs synchronously until it reaches its first await expression, at which point the method is suspended until the awaited task is complete. In the meantime, control returns to the caller of the method, as the example in the next section shows.
Here is a sample code to check it :
class Program
{
static void Main(string[] args)
{
Program p = new Program();
p.Run();
}
private void Print(string txt)
{
string dateStr = DateTime.Now.ToString("HH:mm:ss.fff");
Console.WriteLine($"{dateStr} Thread #{Thread.CurrentThread.ManagedThreadId}\t{txt}");
}
private void Run()
{
Print("Program Start");
Experiment().Wait();
Print("Program End. Press any key to quit");
Console.Read();
}
private async Task Experiment()
{
Print("Experiment code is synchronous before await");
await Task.Delay(500);
Print("Experiment code is asynchronous after first await");
}
}
And the result :
We see the code of Experiment() method after await executes on another Thread.
But if I replace the Task.Delay by my own code (method SomethingElse) :
class Program
{
static void Main(string[] args)
{
Program p = new Program();
p.Run();
}
private void Print(string txt)
{
string dateStr = DateTime.Now.ToString("HH:mm:ss.fff");
Console.WriteLine($"{dateStr} Thread #{Thread.CurrentThread.ManagedThreadId}\t{txt}");
}
private void Run()
{
Print("Program Start");
Experiment().Wait();
Print("Program End. Press any key to quit");
Console.Read();
}
private async Task Experiment()
{
Print("Experiment code is synchronous before await");
await SomethingElse();
Print("Experiment code is asynchronous after first await");
}
private Task SomethingElse()
{
Print("Experiment code is asynchronous after first await");
Thread.Sleep(500);
return (Task.CompletedTask);
}
}
I notice the thread remains the same !
In conclusion, I'll say async/await code could use another thread, but only if the thread is created by another code, not by async/await.
In this case, I think Task.Delay created the thread, so I can conclude async/await does not create a new Thread like said by #Adriaan Stander.

Sorry for being late to the party.
I am new to TPL and I am wondering: How does the asynchronous
programming support that is new to C# 5.0 (via the new async and await
keywords) relate to the creation of threads?
async/await is not introduced for thread creation, but to utilize the current thread optimally.
Your app might read files, wait for response from another server or even do a computation with high memory access (Simply any IO task). These tasks are not CPU intensive (Any task that will not use 100% of your thread).
Think about the case when you are processing 1000 non CPU intensive tasks. In this case, process of creating 1000s of OS level thread might eat up more CPU and Memory than doing actual work on a single thread (4mb per thread in Windows, 4MB * 1000 = 4GB). At the same time if you run all the tasks sequentially, you might have to wait until the IO tasks gets finished. Which end up in long time to complete the task, while keeping the CPU idle.
Since we require parallelism to complete multiple tasks quickly, at the same time all parallel tasks are not CPU hungry, but creating threads is inefficient.
The compiler will break the execution at any method call to an async method (which gets called with an await) and immediately execute the code outside of the current code branch, once an await is reached, the execution will go inside the previous async. This will be repeated again and again until all the async calls are completed and their awaiters are satisfied.
If any of the async method have heavy CPU load without a call to an async method, then yes, your system will become unresponsive and all the remaining async methods will not get called until the current task is finished.

So I've been reading up on the threading model, and Async / Await can certainly lead to new threads being used (not necessarily created - the pool creates them at application start). It's up to the scheduler to determine if a new thread is needed. And as I see it, a call to an awaitable function may have internal details that increase the chances of the scheduler utilizing another thread; simply because more work means more opportunities / reasons for the scheduler to divvy out work.
WinRT async operations automatically happen on the thread pool. And typically you will be calling FROM the thread pool, except for UI thread work .. Xaml/Input/Events.
Async operations started on Xaml/UI threads have their results delivered back to the [calling] UI thread. But asynchronous operation results started from a thread pool thread are delivered wherever the completion happens, which may not be the same thread you were on before. The reason behind this is that code written for the thread pool is likely to be written to be thread safe and it is also for efficiency, Windows doesn't have to negotiate that thread switch.
So again, in answer to the OP, new threads are not necessarily created but your application can and will use multiple threads to complete asynchronous work.
I know this seems to contradict some of the literature regarding async / await, but that's because although the async / await construct is not by itself multithreaded. Awaitables are the, or one of the mechanisms by which the scheduler can divide work and construct calls across threads.
This is at the limit of my knowledge right now regarding async and threading, so I might not have it exactly right, but I do think it's important to see the relationship between awaitables and threading.

Using Async/Await doesn't necessarily cause a new thread to be created. But the use of Async/Await can lead to a new thread to be created because the awaitable function may internally spawn a new thread. And it often does, making the statement 'No, it doesn't spawn threads' almost useless in practice. For example, the following code spawns new threads.
VisualProcessor.Ctor()
{
...
BuildAsync();
}
async void BuildAsync()
{
...
TextureArray dudeTextures = await TextureArray.FromFilesAsync(…);
}
public static async Task<TextureArray> FromFilesAsync(...)
{
Debug.WriteLine("TextureArray.FromFilesAsync() T1 : Thread Id = " + GetCurrentThreadId());
List<StorageFile> files = new List<StorageFile>();
foreach (string path in paths)
{
if (path != null)
files.Add(await Package.Current.InstalledLocation.GetFileAsync(path)); // << new threads
else
files.Add(null);
}
Debug.WriteLine("TextureArray.FromFilesAsync() T2 : Thread Id = " + GetCurrentThreadId());
...
}

In case of Java Spring Framework, a method annotated with #Async runs in a separate thread. Quoting from official guide (https://spring.io/guides/gs/async-method) -
The findUser method is flagged with Spring’s #Async annotation,
indicating that it should run on a separate thread. The method’s
return type is CompletableFuture instead of User, a requirement
for any asynchronous service.
Of course in the backend it uses a Thread Pool and a Queue (where async tasks wait for a thread to be back in the pool).

Is there a way to limit the number of parallel Tasks globally in an ASP.NET Web API application?

I have an ASP.NET 5 Web API application which contains a method that takes objects from a List<T> and makes HTTP requests to a server, 5 at a time, until all requests have completed. This is accomplished using a SemaphoreSlim, a List<Task>(), and awaiting on Task.WhenAll(), similar to the example snippet below:
public async Task<ResponseObj[]> DoStuff(List<Input> inputData)
{
const int maxDegreeOfParallelism = 5;
var tasks = new List<Task<ResponseObj>>();
using var throttler = new SemaphoreSlim(maxDegreeOfParallelism);
foreach (var input in inputData)
{
tasks.Add(ExecHttpRequestAsync(input, throttler));
}
List<ResponseObj> resposnes = await Task.WhenAll(tasks).ConfigureAwait(false);
return responses;
}
private async Task<ResponseObj> ExecHttpRequestAsync(Input input, SemaphoreSlim throttler)
{
await throttler.WaitAsync().ConfigureAwait(false);
try
{
using var request = new HttpRequestMessage(HttpMethod.Post, "https://foo.bar/api");
request.Content = new StringContent(JsonConvert.SerializeObject(input, Encoding.UTF8, "application/json");
var response = await HttpClientWrapper.SendAsync(request).ConfigureAwait(false);
var responseBody = await response.Content.ReadAsStringAsync().ConfigureAwait(false);
var responseObject = JsonConvert.DeserializeObject<ResponseObj>(responseBody);
return responseObject;
}
finally
{
throttler.Release();
}
}
This works well, however I am looking to limit the total number of Tasks that are being executed in parallel globally throughout the application, so as to allow scaling up of this application. For example, if 50 requests to my API came in at the same time, this would start at most 250 tasks running parallel. If I wanted to limit the total number of Tasks that are being executed at any given time to say 100, is it possible to accomplish this? Perhaps via a Queue<T>? Would the framework automatically prevent too many tasks from being executed? Or am I approaching this problem in the wrong way, and would I instead need to Queue the incoming requests to my application?

I'm going to assume the code is fixed, i.e., Task.Run is removed and the WaitAsync / Release are adjusted to throttle the HTTP calls instead of List<T>.Add.
I am looking to limit the total number of Tasks that are being executed in parallel globally throughout the application, so as to allow scaling up of this application.
This does not make sense to me. Limiting your tasks limits your scaling up.
For example, if 50 requests to my API came in at the same time, this would start at most 250 tasks running parallel.
Concurrently, sure, but not in parallel. It's important to note that these aren't 250 threads, and that they're not 250 CPU-bound operations waiting for free thread pool threads to run on, either. These are Promise Tasks, not Delegate Tasks, so they don't "run" on a thread at all. It's just 250 objects in memory.
If I wanted to limit the total number of Tasks that are being executed at any given time to say 100, is it possible to accomplish this?
Since (these kinds of) tasks are just in-memory objects, there should be no need to limit them, any more than you would need to limit the number of strings or List<T>s. Apply throttling where you do need it; e.g., number of HTTP calls done simultaneously per request. Or per host.
Would the framework automatically prevent too many tasks from being executed?
The framework has nothing like this built-in.
Perhaps via a Queue? Or am I approaching this problem in the wrong way, and would I instead need to Queue the incoming requests to my application?
There's already a queue of requests. It's handled by IIS (or whatever your host is). If your server gets too busy (or gets busy very suddenly), the requests will queue up without you having to do anything.

If I wanted to limit the total number of Tasks that are being executed at any given time to say 100, is it possible to accomplish this?
What you are looking for is to limit the MaximumConcurrencyLevel of what's called the Task Scheduler. You can create your own task scheduler that regulates the MaximumCongruencyLevel of the tasks it manages. I would recommend implementing a queue-like object that tracks incoming requests and currently working requests and waits for the current requests to finish before consuming more. The below information may still be relevant.
The task scheduler is in charge of how Tasks are prioritized, and in charge of tracking the tasks and ensuring that their work is completed, at least eventually.
The way it does this is actually very similar to what you mentioned, in general the way the Task Scheduler handles tasks is in a FIFO (First in first out) model very similar to how a ConcurrentQueue<T> works (at least starting in .NET 4).
Would the framework automatically prevent too many tasks from being executed?
By default the TaskScheduler that is created with most applications appears to default to a MaximumConcurrencyLevel of int.MaxValue. So theoretically yes.
The fact that there practically is no limit to the amount of tasks(at least with the default TaskScheduler) might not be that big of a deal for your case scenario.
Tasks are separated into two types, at least when it comes to how they are assigned to the available thread pools. They're separated into Local and Global queues.
Without going too far into detail, the way it works is if a task creates other tasks, those new tasks are part of the parent tasks queue (a local queue). Tasks spawned by a parent task are limited to the parent's thread pool.(Unless the task scheduler takes it upon itself to move queues around)
If a task isn't created by another task, it's a top-level task and is placed into the Global Queue. These would normally be assigned their own thread(if available) and if one isn't available it's treated in a FIFO model, as mentioned above, until it's work can be completed.
This is important because although you can limit the amount of concurrency that happens with the TaskScheduler, it may not necessarily be important - if for say you have a top-level task that's marked as long running and is in-charge of processing your incoming requests. This would be helpful since all the tasks spawned by this top-level task will be part of that task's local queue and therefor won't spam all your available threads in your thread pool.

When you have a bunch of items and you want to process them asynchronously and with limited concurrency, the SemaphoreSlim is a great tool for this job. There are two ways that it can be used. One way is to create all the tasks immediately and have each task acquire the semaphore before doing it's main work, and the other is to throttle the creation of the tasks while the source is enumerated. The first technique is eager, and so it consumes more RAM, but it's more maintainable because it is easier to understand and implement. The second technique is lazy, and it's more efficient if you have millions of items to process.
The technique that you have used in your sample code is the second (lazy) one.
Here is an example of using two SemaphoreSlims in order to impose two maximum concurrency policies, one per request and one globally. First the eager approach:
private const int maxConcurrencyGlobal = 100;
private static SemaphoreSlim globalThrottler
= new SemaphoreSlim(maxConcurrencyGlobal, maxConcurrencyGlobal);
public async Task<ResponseObj[]> DoStuffAsync(IEnumerable<Input> inputData)
{
const int maxConcurrencyPerRequest = 5;
var perRequestThrottler
= new SemaphoreSlim(maxConcurrencyPerRequest, maxConcurrencyPerRequest);
Task<ResponseObj>[] tasks = inputData.Select(async input =>
{
await perRequestThrottler.WaitAsync();
try
{
await globalThrottler.WaitAsync();
try
{
return await ExecHttpRequestAsync(input);
}
finally { globalThrottler.Release(); }
}
finally { perRequestThrottler.Release(); }
}).ToArray();
return await Task.WhenAll(tasks);
}
The Select LINQ operator provides an easy and intuitive way to project items to tasks.
And here is the lazy approach for doing exactly the same thing:
private const int maxConcurrencyGlobal = 100;
private static SemaphoreSlim globalThrottler
= new SemaphoreSlim(maxConcurrencyGlobal, maxConcurrencyGlobal);
public async Task<ResponseObj[]> DoStuffAsync(IEnumerable<Input> inputData)
{
const int maxConcurrencyPerRequest = 5;
var perRequestThrottler
= new SemaphoreSlim(maxConcurrencyPerRequest, maxConcurrencyPerRequest);
var tasks = new List<Task<ResponseObj>>();
foreach (var input in inputData)
{
await perRequestThrottler.WaitAsync();
await globalThrottler.WaitAsync();
Task<ResponseObj> task = Run(async () =>
{
try
{
return await ExecHttpRequestAsync(input);
}
finally
{
try { globalThrottler.Release(); }
finally { perRequestThrottler.Release(); }
}
});
tasks.Add(task);
}
return await Task.WhenAll(tasks);
static async Task<T> Run<T>(Func<Task<T>> action) => await action();
}
This implementation assumes that the await globalThrottler.WaitAsync() will never throw, which is a given according to the documentation. This will no longer be the case if you decide later to add support for cancellation, and you pass a CancellationToken to the method. In that case you would need one more try/finally wrapper around the task-creation logic. The first (eager) approach could be enhanced with cancellation support without such considerations. Its existing try/finally infrastructure is
already sufficient.
It is also important that the internal helper Run method is implemented with async/await. Eliding the async/await would be an easy mistake to make, because in that case any exception thrown synchronously by the ExecHttpRequestAsync method would be rethrown immediately, and it would not be encapsulated in a Task<ResponseObj>. Then the task returned by the DoStuffAsync method would fail without releasing the acquired semaphores, and also without awaiting the completion of the already started operations. That's another argument for preferring the eager approach. The lazy approach has too many gotchas to watch for.

Concurrency without multithreading Async/Await

There is a strong emphasis that async/await is unrelated to multi-threading in most tutorials; that a single thread can dispatch multiple I/O operations and then handle the results as they complete without creating new threads. The concept makes sense but I've never seen that actual behavior in practice.
Take the below example:
static void Main(string[] args)
{
// No Delay
// var tasks = new List<int> { 3, 2, 1 }.Select(x => DelayedResult(x, 0));
// Staggered delay
// var tasks = new List<int> { 3, 2, 1 }.Select(x => DelayedResult(x, x));
// Simultaneous Delay
// var tasks = new List<int> { 3, 2, 1 }.Select(x => DelayedResult(x, 1));
var allTasks = Task.WhenAll(tasks);
allTasks.Wait();
Console.ReadLine();
}
static async Task<T> DelayedResult<T>(T result, int seconds = 0)
{
ThreadPrint("Yield:" + result);
await Task.Delay(TimeSpan.FromSeconds(seconds));
ThreadPrint("Continuation:" + result);
return result;
}
static void ThreadPrint(string message)
{
int threadId = Thread.CurrentThread.ManagedThreadId;
Console.WriteLine("Thread:" + threadId + "|" + message);
}
"No Delay" uses only one thread and executes the continuation immediately as though it were synchronous code. Looks good.
Thread:1|Yield:3
Thread:1|Continuation:3
Thread:1|Yield:2
Thread:1|Continuation:2
Thread:1|Yield:1
Thread:1|Continuation:1
"Staggered Delay" uses two threads. We have left the single-threaded world behind and there are absolutely new threads being created in the thread pool. At least the thread used for processing the continuations is reused and processing occurs in the order completed rather than the order invoked.
Thread:1|Yield:3
Thread:1|Yield:2
Thread:1|Yield:1
Thread:4|Continuation:1
Thread:4|Continuation:2
Thread:4|Continuation:3
"Simultaneous Delay" uses...4 threads! This is no better than regular old multi-threading; in fact, its worse since there is an ugly state machine hiding under the covers in the IL.
Thread:1|Yield:3
Thread:1|Yield:2
Thread:1|Yield:1
Thread:4|Continuation:1
Thread:7|Continuation:3
Thread:5|Continuation:2
Please provide a code example for the "Simultaneous Delay" that only uses one thread. I suspect there isn't one...which begs the question of why the async/await pattern is advertised as unrelated to multi-threading when it clearly either a) uses the ThreadPool and dispatches new threads as necessary or b) in a UI or ASP.NET context, simply deadlocks on a single thread unless you await "all the way up" which just means that the magic additional thread is being handled by the framework (not that it does not exist).
IMHO, async/await is an awesome abstraction for using continuations everywhere for high availability without getting mired in callback hell...but let's not pretend we are somehow dodging multi-threading. What am I missing?

You are forcing the multithreading in the code you posted.
When you await Task.Delay the current thread is freed to acomplish other tasks if the task scheduler decides it must be run asynchronously, in this case after it's released from the three tasks you lock that thread with Task.WhenAll.Wait which is a synchronous function.
Also, when the task scheduler finds the Task.Delay on the tasks it decides the task is going to be long running so it must be executed asynchronously, not synchronously like the No delay case (yes, you also await Task.Delay on the No delay case, but a delay of 0 seconds, the task scheduler is smart enough to distinguish this case).
As all the tasks resume simultaneously the task scheduler finds the first thread occupied so it creates a new thread for the first task resumed, then the next task sees both threads occupied and so on.
Basically you are asking something impossible to the async mechanism, you want the methods to be executed in parallel while being executed in one thread.
Also, async is not announced as unrelated to multithreading, if someone says that then he doesn't understand what async is, in fact, asynchronous implies multithreading but the async mechanism on .net is smart enough to complete some tasks synchronously to ensure the maximum efficiency.
It can be announced as thread efficient as if a thread is waiting for an I/O operation per example, it can be used for other tasks without completely locking that thread doing nothing, take a TcpClient for example which uses a Socket, at the OS level the socket uses completion threads so retaining that thread doing nothing is totally inefficient, or if you want to go more low level, take a disk read/write which uses DMA to transfer data without using the processor, in that case no other thread is needed at all and retaining the thread is a waste of resources.
Just as a fact, take this description from Microsoft when they introduced async:
Visual Studio 2012 introduces a simplified approach, async
programming, that leverages asynchronous support in the .NET Framework
4.5 and the Windows Runtime. The compiler does the difficult work that the developer used to do, and your application retains a logical
structure that resembles synchronous code. As a result, you get all
the advantages of asynchronous programming with a fraction of the
effort.
Also, using async on an UI thread does not lock the thread, that's the benefit, the UI thread will be freed and keep the UI responsive when it's waiting for long tasks, and instead of programming manually the multithreading and synchronization functions the async mechanism takes care of everything for you.

Is there any benefit to using Task.Factory.StartNew<Action>() in this ASP.NET .ashx code snippet?

Here's an .ashx handler that creates a series of Task objects which are then executed in a foreach loop.
List<Task<Action>> list = new List<Task<Action>>();
list.Add(Task.Factory.StartNew<Action>(() => AddHeader(graphics, request, reportParameters)));
list.Add(Task.Factory.StartNew<Action>(() => AddSubmitter (graphics, request, reportParameters)));
list.Add(Task.Factory.StartNew<Action>(() => AddActivity(graphics, request, reportParameters)));
list.Add(Task.Factory.StartNew<Action>(() => AddCharts(graphics, request, reportParameters)));
//Task.WaitAll(list.ToArray()); //AY - This seems unnecessary
foreach (var action in list.Select(t => t.Result))
{
action();
}
Each of the methods that return an Action does a CPU intensive work like the first method below.
private Action AddHeader(XGraphics graphics, HttpRequest request, ReportParameters reportParameters)
{
DataSet ds = new myData().ClientHeader(reportParameters);
// This will be a function waiting to be called.
return () =>
{
// Some CPU intensive operation code here.
};
}
Can someone please help me understand if this code is useful at all
1) from an asynchronous or multiple task perspective?
2) Is this code even creating multiple threads from the ASP.NET threadpool?
When I debug the app, it shows 4 separate tasks on my machine just before it runs the foreach loop. Each of those tasks retrieve the dataset and I think that's because that part of the code in each method runs before the return () Action statement.
So is the whole purpose of this code to enable multiple database calls in separate tasks before executing each CPU intensive action method? Or is the code really doing asynchronous stuff?

It is not the best way to utilize tasks that represent thread pool execution units:
Because each such task will block one thread from the thread pool, and ASP.NET uses the same thread pool as the tasks, you will reduce the amount of concurrent requests by a factor of five.
Utilizing thread pool threads for database(I/O) bound code is ineffective, because thread pool threads will just sit idle, so use async code for database calls.
But it is not the last problem - foreach (var action in list.Select(t => t.Result)) will block the execution till it gets the Result from each task sequentially. It means that returned actions will not be executed as soon as they are ready to execute, they will be executed in the same order as they were added to the list negating any potential advantage that could have been gained had we executed them as soon as they are ready.
So, as a conclusion:
Such code just starts 4 database calls in the least resource-effective way, and then squanders 4 misused thread pool threads executing their "continuation" on the original thread in an order that doesn't utilize the possibility that 4 task may execute in arbitrary order.
P.S.: And even when you really need to do CPU-intensive processing do not use Factory.StartNew - use Task.Run. It is not the actual error, because they basically do the same thing, but rather a good recommendation.

multithreading in regards toTask, async, and await

I have the following code and just want to make sure I have the concept of multithreading down on a high level.
public async Task<List<Category>> GetProjectsByCategoryIDAsync(Int16 categoryid)
{
try
{
using (YeagerTechEntities DbContext = new YeagerTechEntities())
{
DbContext.Configuration.ProxyCreationEnabled = false;
DbContext.Database.Connection.Open();
var category = await DbContext.Categories.Include("Projects").Where(p => p.CategoryID == categoryid).ToListAsync();
return category;
}
}
catch (Exception)
{
throw;
}
}
It is my understanding of the following:
async - declares a method to run asynchounously instead of
synchrounously.
Task - declares a method to run as a task on a single thread
await - the task waits for the operation to complete.
Where I am a little fuzzy about is the await keyword. Obviously, the benefit of asynchrounous programming is that the method supposedly doesn't have to wait for the task to complete before another request comes in right behind it. But with the await keyword, the task waits until the operation is finished.
With synchrounous programming, everything is processed in a sequential pattern.
How does this methodology allow for requests to come in simultaneously and be executed in a much faster fashion than synchronous programming??
I just need a high level explanation to get the concept down.
Thanks so much in advance.

Consider the following code:
public async Task DoSomething()
{
Console.WriteLine("Begin");
int i = await DoSomethingElse();
Console.WriteLine("End " + i);
}
public Task<int> DoSomethingElse()
{
return new Task<int>(() =>
{
// do heavy work
Thread.Sleep(1000);
return 1;
});
}
With synchrounous programming, everything is processed in a sequential
pattern.
The code above is asynchronous, but is still sequential. The difference between that code and its synchronous version (e.g., public int DoSomethingElse) is that when you await DoSomethingElse, the main thread will be freed to do other work, instead of blocking waiting for DoSomethingElse to complete.
What actually happens is: your async DoSomething method will run on thread A and be broken in two.
the first part will print "Begin" and make an async call, and then return.
the second part will print "End"
After the first part of the method executes, Thread A will be free to do other work.
Meanwhile, Thread B will be executing the lambda expression that does some heavy work.
Whenever Thread B completes, the second part of your method will be scheduled to run on Thread A, and "End" will be printed.
Notice that, while Thread B was executing the heavy work, Thread A was free to do other stuff.
How does this methodology allow for requests to come in simultaneously
and be executed in a much faster fashion than synchronous
programming??
In frameworks such as ASP.NET MVC, your application has a finite number of threads available to handle incoming requests (lets call these "request threads"). By delegating heavy work to other threads and awaiting, your request threads will be free to handle more incoming requests while heavy work is being done.
This diagram, although complex, illustrates the execution/suspension flow of threads executing asynchronous work:
Notice how at step 6 the thread was yielded, and then step 7 resumed the execution of the method.
As you can see, the await keyword effectively breaks the method in two.