I am familar with programming according to the two paradigms, blocking and non-blocking, on the JVM (Java/nio, Scala/Akka).
However, I see a kind of grayzone in between that confuses me.
Look at any non-blocking program of your choice: it is full of blocking statements!
For example, each assignment of a variable is a blocking operation that waits for CPU-registers and memory-reads to succeed.
Moreover, non-blocking programs even contain blocking statements that carry out computations on complex in-memory-collections, without violating the non-blocking paradigm.
In contrast to that, the non-blocking paradigm would clearly be violated if we would call some external web-service in a blocking way to receive its result.
But what is in between these extremes? What about reading/writing a tiny file, a local socket, or making an API-call to an embedded data storage engine (such as SQLite, RocksDb, etc.). Is it ok to do blocking reads/writes to these APIs? They usually give strong timing guarantees in practice (say << 1ms as long as the OS is not stalled), so there is almost no practical difference to pure in-memory-access. As a precise example: is calling RocksDBs get/put within an Akka Actor considered to be an inadvisable blocking I/O?
So, my question is whether there are rules of thumb or precise criteria that help me in deciding whether I may stick to a simple blocking statement in my non-blocking program, or whether I shall wrap such a statement into non-blocking boilerplate (framework-depending, e.g., outsourcing such calls to a separate thread-pool, nesting one step deeper in a Future or Monad, etc.).
for example, each assignment of a variable is a blocking operation that waits for CPU-registers and memory-reads to succeed
That's not really what is considered "blocking". Those operations are constant time, and that constant is very low (a few cycles in general) compared to the latency of any IO operations (anywhere between thousands and billions of cycles) - except for page faults due to swapped memory, but if those happen regularly you have a problem anyway.
And if we want to get all nitpicky, individual instructions do not fully block a CPU thread as modern CPUs can reorder instructions and execute ones that have no data dependencies out of order while waiting for memory/caches or other more expensive instructions to finish.
Moreover, non-blocking programs even contain blocking statements that carry out computations on complex in-memory-collections, without violating the non-blocking paradigm.
Those are not considered as blocking the CPU from doing work. They should not even block user interactivity if they are correctly designed to present the results to the user when they are done without blocking the UI.
Is it ok to do blocking reads/writes to these APIs?
That always depends on why you are using non-blocking approaches in the first place. What problem are you trying to solve? Maybe one API warrants a non-blocking approach while the other does not.
For example most file IO methods are nominally blocking, but writes without fsync can be very cheap, especially if you're not writing to spinning rust so it can be overkill to avoid those methods on your compute threadpool. On the other hand one usually does not want to block a thread in a fixed threadpool while waiting for a multi-second database query
Related
Here are the definitions by Wikipedia:
Asynchrony, in computer programming, refers to the occurrence of events independently of the main program flow and ways to deal with such events. These may be "outside" events such as the arrival of signals, or actions instigated by a program that take place concurrently with program execution, without the program blocking to wait for results.
And:
Concurrent computing is a form of computing in which several computations are executed during overlapping time periods—concurrently—instead of sequentially (one completing before the next starts).
In the context of single-threaded computation, do 'asynchronous', 'non-blocking', and 'concurrent' imply one another?
If not, could you give me a counter-example?
Note that I have excluded the word 'parallel' as it implies multiple threads.
Non-blocking operations are based on two approaches:
by simply returning without data (when no data is available - in such cases the caller has to "come back" by itself and "read" again)
by using callbacks. In that context "blocking" means that you wait for an operation to reach a certain state - whereas "non-blocking" means that you trigger the operation - and when that state is reached, you are notified.
Please note: both options do not imply concurrency or multiple threads on the client side. You absolutely can implement such a system using a single process ( think coroutines or node.js for example ).
In that sense: a non-blocking operation is always asynchronous - as you don't know when it will have results for you - or when it will call you back. Both concepts can be be implemented using concurrency, but there is absolute need for doing it that way.
Non-blocking and concurrent don't really apply to single threaded programs, due to the fact that they refer to ways of managing multiple threads. Non-blocking means that a program doesn't wait for all threads to finish before moving on, and concurrent computation can only happen if you have multiple threads doing the calculation. (Someone please correct me if I'm wrong.)
Asynchrony is the only term that applies to single threaded programming, in the form of human input, communication with other programs, etc. Because of this, no, they don't imply each other in the context of single threaded programs.
I've been reading (and working) quite a bit with massively multi-threaded applications, and with IO, and I've found that the term asynchronous has become some sort of catch-all for multiple vague ideas. I'm wondering if I understand it correctly. The way I see it is that there are two main branches of "asynchronicity".
Asynchronous I/O. Such as network read/write. What this really boils down to is efficient parallel processing between multiple CPUs, such as your main CPU and your NIC CPU. The idea is to have multiple processors running in parallel, exchanging data, without blocking waiting for the other to finish and return the results of it's job.
Minimizing context-switching penalties by minimizing use of threads. This seems to be what the .NET framework is focusing on with it's async/await features. Instead of spawning/closing/blocking threads, break parallel jobs into tasks, and use a software task scheduler to keep a pool of threads as busy as possible without resorting to spawning new threads.
These seem like two entirely separate concepts with no similarities that could tie them together, but are both referred to by the same "asynchronous computing" vocabulary.
Am I understanding all of this correctly?
Asynchronous basically means not blocking, i.e. not having to wait for an operation to complete.
Threads are just one way of accomplishing that. There are many ways of doing this, from hardware level, SO level, software level.
Someone with more experience than me can give examples of asyncronicity not related to threads.
What this really boils down to is efficient parallel processing between multiple CPUs, such as your main CPU and your NIC CPU. The idea is to have multiple processors running in parallel...
Asynchronous programming is not all about multi-core CPU's and parallelism: consider a single core CPU, with just one thread creating email messages and sends them. In a synchronous fashion, it would spend a few micro seconds to create the message, and a lot more time to send it through network, and only then create the next message. But in asynchronous program, the thread could create a new message while the previous one is being sent through the network. One implementation for that kind of program can be using .NET async/await feature, where you can have just one thread. But even a blocking IO program could be considered asynchronous: If the main thread creates the messages and queues them in a buffer, which another thread pulls them from and sends them in a blocking IO way. From the main thread's point of view - it's completely async.
.NET async/await just uses the OS api's which are already async - reading /writing a file, send /receive data through network, they are all async anyway - the OS doesn't block on them (the drivers themselves are async).
Asynchronous is a general term, which does not have widely accepted meaning. Different domains have different meanings to it.
For instance, async IO means that instead of blocking on IO call, something else happens. Something else can be really different things, but it usually involves some sort of notification of call completion. Details might differ. For instance, a notification might be built into the call itself - like in MS Completeion Ports (if memory serves). Or, it can be something verify do before you make a call so that the call can not block - this is what poll() and friends do.
Async might also well mean simply parallel execution. For instance, one might say that 'database is updated asynchronously' meaning that there is a dedicated thread which handles database connectivity, and that thread does not slow down the main processing thread.
I searched the web on some technical details about blocking I/O and non blocking I/O and I found several people stating that non-blocking I/O would be faster than blocking I/O. For example in this document.
If I use blocking I/O, then of course the thread that is currently blocked can't do anything else... Because it's blocked. But as soon as a thread starts being blocked, the OS can switch to another thread and not switch back until there is something to do for the blocked thread. So as long as there is another thread on the system that needs CPU and is not blocked, there should not be any more CPU idle time compared to an event based non-blocking approach, is there?
Besides reducing the time the CPU is idle I see one more option to increase the number of tasks a computer can perform in a given time frame: Reduce the overhead introduced by switching threads. But how can this be done? And is the overhead large enough to show measurable effects? Here is an idea on how I can picture it working:
To load the contents of a file, an application delegates this task to an event-based i/o framework, passing a callback function along with a filename
The event framework delegates to the operating system, which programs a DMA controller of the hard disk to write the file directly to memory
The event framework allows further code to run.
Upon completion of the disk-to-memory copy, the DMA controller causes an interrupt.
The operating system's interrupt handler notifies the event-based i/o framework about the file being completely loaded into memory. How does it do that? Using a signal??
The code that is currently run within the event i/o framework finishes.
The event-based i/o framework checks its queue and sees the operating system's message from step 5 and executes the callback it got in step 1.
Is that how it works? If it does not, how does it work? That means that the event system can work without ever having the need to explicitly touch the stack (such as a real scheduler that would need to backup the stack and copy the stack of another thread into memory while switching threads)? How much time does this actually save? Is there more to it?
The biggest advantage of nonblocking or asynchronous I/O is that your thread can continue its work in parallel. Of course you can achieve this also using an additional thread. As you stated for best overall (system) performance I guess it would be better to use asynchronous I/O and not multiple threads (so reducing thread switching).
Let's look at possible implementations of a network server program that shall handle 1000 clients connected in parallel:
One thread per connection (can be blocking I/O, but can also be non-blocking I/O).
Each thread requires memory resources (also kernel memory!), that is a disadvantage. And every additional thread means more work for the scheduler.
One thread for all connections.
This takes load from the system because we have fewer threads. But it also prevents you from using the full performance of your machine, because you might end up driving one processor to 100% and letting all other processors idle around.
A few threads where each thread handles some of the connections.
This takes load from the system because there are fewer threads. And it can use all available processors. On Windows this approach is supported by Thread Pool API.
Of course having more threads is not per se a problem. As you might have recognized I chose quite a high number of connections/threads. I doubt that you'll see any difference between the three possible implementations if we are talking about only a dozen threads (this is also what Raymond Chen suggests on the MSDN blog post Does Windows have a limit of 2000 threads per process?).
On Windows using unbuffered file I/O means that writes must be of a size which is a multiple of the page size. I have not tested it, but it sounds like this could also affect write performance positively for buffered synchronous and asynchronous writes.
The steps 1 to 7 you describe give a good idea of how it works. On Windows the operating system will inform you about completion of an asynchronous I/O (WriteFile with OVERLAPPED structure) using an event or a callback. Callback functions will only be called for example when your code calls WaitForMultipleObjectsEx with bAlertable set to true.
Some more reading on the web:
Multiple Threads in the User Interface on MSDN, also shortly handling the cost of creating threads
Section Threads and Thread Pools says "Although threads are relatively easy to create and use, the operating system allocates a significant amount of time and other resources to manage them."
CreateThread documentation on MSDN says "However, your application will have better performance if you create one thread per processor and build queues of requests for which the application maintains the context information.".
Old article Why Too Many Threads Hurts Performance, and What to do About It
I/O includes multiple kind of operations like reading and writing data from hard drives, accessing network resources, calling web services or retrieving data from databases. Depending on the platform and on the kind of operation, asynchronous I/O will usually take advantage of any hardware or low level system support for performing the operation. This means that it will be performed with as little impact as possible on the CPU.
At application level, asynchronous I/O prevents threads from having to wait for I/O operations to complete. As soon as an asynchronous I/O operation is started, it releases the thread on which it was launched and a callback is registered. When the operation completes, the callback is queued for execution on the first available thread.
If the I/O operation is executed synchronously, it keeps its running thread doing nothing until the operation completes. The runtime doesn't know when the I/O operation completes, so it will periodically provide some CPU time to the waiting thread, CPU time that could have otherwise be used by other threads that have actual CPU bound operations to perform.
So, as #user1629468 mentioned, asynchronous I/O does not provide better performance but rather better scalability. This is obvious when running in contexts that have a limited number of threads available, like it is the case with web applications. Web application usually use a thread pool from which they assign threads to each request. If requests are blocked on long running I/O operations there is the risk of depleting the web pool and making the web application freeze or slow to respond.
One thing I have noticed is that asynchronous I/O isn't the best option when dealing with very fast I/O operations. In that case the benefit of not keeping a thread busy while waiting for the I/O operation to complete is not very important and the fact that the operation is started on one thread and it is completed on another adds an overhead to the overall execution.
You can read a more detailed research I have recently made on the topic of asynchronous I/O vs. multithreading here.
To presume a speed improvement due to any form of multi-computing you must presume either that multiple CPU-based tasks are being executed concurrently upon multiple computing resources (generally processor cores) or else that not all of the tasks rely upon the concurrent usage of the same resource -- that is, some tasks may depend on one system subcomponent (disk storage, say) while some tasks depend on another (receiving communication from a peripheral device) and still others may require usage of processor cores.
The first scenario is often referred to as "parallel" programming. The second scenario is often referred to as "concurrent" or "asynchronous" programming, although "concurrent" is sometimes also used to refer to the case of merely allowing an operating system to interleave execution of multiple tasks, regardless of whether such execution must take place serially or if multiple resources can be used to achieve parallel execution. In this latter case, "concurrent" generally refers to the way that execution is written in the program, rather than from the perspective of the actual simultaneity of task execution.
It's very easy to speak about all of this with tacit assumptions. For example, some are quick to make a claim such as "Asynchronous I/O will be faster than multi-threaded I/O." This claim is dubious for several reasons. First, it could be the case that some given asynchronous I/O framework is implemented precisely with multi-threading, in which case they are one in the same and it doesn't make sense to say one concept "is faster than" the other.
Second, even in the case when there is a single-threaded implementation of an asynchronous framework (such as a single-threaded event loop) you must still make an assumption about what that loop is doing. For example, one silly thing you can do with a single-threaded event loop is request for it to asynchronously complete two different purely CPU-bound tasks. If you did this on a machine with only an idealized single processor core (ignoring modern hardware optimizations) then performing this task "asynchronously" wouldn't really perform any differently than performing it with two independently managed threads, or with just one lone process -- the difference might come down to thread context switching or operating system schedule optimizations, but if both tasks are going to the CPU it would be similar in either case.
It is useful to imagine a lot of the unusual or stupid corner cases you might run into.
"Asynchronous" does not have to be concurrent, for example just as above: you "asynchronously" execute two CPU-bound tasks on a machine with exactly one processor core.
Multi-threaded execution doesn't have to be concurrent: you spawn two threads on a machine with a single processor core, or ask two threads to acquire any other kind of scarce resource (imagine, say, a network database that can only establish one connection at a time). The threads' execution might be interleaved however the operating system scheduler sees fit, but their total runtime cannot be reduced (and will be increased from the thread context switching) on a single core (or more generally, if you spawn more threads than there are cores to run them, or have more threads asking for a resource than what the resource can sustain). This same thing goes for multi-processing as well.
So neither asynchronous I/O nor multi-threading have to offer any performance gain in terms of run time. They can even slow things down.
If you define a specific use case, however, like a specific program that both makes a network call to retrieve data from a network-connected resource like a remote database and also does some local CPU-bound computation, then you can start to reason about the performance differences between the two methods given a particular assumption about hardware.
The questions to ask: How many computational steps do I need to perform and how many independent systems of resources are there to perform them? Are there subsets of the computational steps that require usage of independent system subcomponents and can benefit from doing so concurrently? How many processor cores do I have and what is the overhead for using multiple processors or threads to complete tasks on separate cores?
If your tasks largely rely on independent subsystems, then an asynchronous solution might be good. If the number of threads needed to handle it would be large, such that context switching became non-trivial for the operating system, then a single-threaded asynchronous solution might be better.
Whenever the tasks are bound by the same resource (e.g. multiple needs to concurrently access the same network or local resource), then multi-threading will probably introduce unsatisfactory overhead, and while single-threaded asynchrony may introduce less overhead, in such a resource-limited situation it too cannot produce a speed-up. In such a case, the only option (if you want a speed-up) is to make multiple copies of that resource available (e.g. multiple processor cores if the scarce resource is CPU; a better database that supports more concurrent connections if the scarce resource is a connection-limited database, etc.).
Another way to put it is: allowing the operating system to interleave the usage of a single resource for two tasks cannot be faster than merely letting one task use the resource while the other waits, then letting the second task finish serially. Further, the scheduler cost of interleaving means in any real situation it actually creates a slowdown. It doesn't matter if the interleaved usage occurs of the CPU, a network resource, a memory resource, a peripheral device, or any other system resource.
The main reason to use AIO is for scalability. When viewed in the context of a few threads, the benefits are not obvious. But when the system scales to 1000s of threads, AIO will offer much better performance. The caveat is that AIO library should not introduce further bottlenecks.
One possible implementation of non-blocking I/O is exactly what you said, with a pool of background threads that do blocking I/O and notify the thread of the originator of the I/O via some callback mechanism. In fact, this is how the AIO module in glibc works. Here are some vague details about the implementation.
While this is a good solution that is quite portable (as long as you have threads), the OS is typically able to service non-blocking I/O more efficiently. This Wikipedia article lists possible implementations besides the thread pool.
I am currently in the process of implementing async io on an embedded platform using protothreads. Non blocking io makes the difference between running at 16000fps and 160fps. The biggest benefit of non blocking io is that you can structure your code to do other things while hardware does its thing. Even initialization of devices can be done in parallel.
Martin
In Node, multiple threads are being launched, but it's a layer down in the C++ run-time.
"So Yes NodeJS is single threaded, but this is a half truth, actually it is event-driven and single-threaded with background workers. The main event loop is single-threaded but most of the I/O works run on separate threads, because the I/O APIs in Node.js are asynchronous/non-blocking by design, in order to accommodate the event loop. "
https://codeburst.io/how-node-js-single-thread-mechanism-work-understanding-event-loop-in-nodejs-230f7440b0ea
"Node.js is non-blocking which means that all functions ( callbacks ) are delegated to the event loop and they are ( or can be ) executed by different threads. That is handled by Node.js run-time."
https://itnext.io/multi-threading-and-multi-process-in-node-js-ffa5bb5cde98
The "Node is faster because it's non-blocking..." explanation is a bit of marketing and this is a great question. It's efficient and scaleable, but not exactly single threaded.
The improvement as far as I know is that Asynchronous I/O uses ( I'm talking about MS System, just to clarify ) the so called I/O completion ports. By using the Asynchronous call the framework leverage such architecture automatically, and this is supposed to be much more efficient that standard threading mechanism. As a personal experience I can say that you would sensibly feel your application more reactive if you prefer AsyncCalls instead of blocking threads.
Let me give you a counterexample that asynchronous I/O does not work.
I am writing a proxy similar to below-using boost::asio.
https://github.com/ArashPartow/proxy/blob/master/tcpproxy_server.cpp
However, the scenario of my case is, incoming (from clients side) messages are fast while outgoing (to server side) is slow for one session, to keep up with the incoming speed or to maximize the total proxy throughput, we have to use multiple sessions under one connection.
Thus this async I/O framework does not work anymore. We do need a thread pool to send to the server by assigning each thread a session.
coming from node.js point of view, where all code is non-blocking.
In Go, non-blocking is easily achieved using channels.
if one were writing a node.js type server in go, does it make sense to make it non-blocking? for example, having a database connect() function return a channel, as versus blocking while waiting for the connection to occur.
to me, this seems the correct approach
but ...
Blocking and non-blocking aren't really about performance, they are about an interface.
If you have a single thread of execution then a blocking call prevents your program from doing any useful work while it's waiting.
But if you have multiple threads of execution a blocking call doesn't really matter because you can just leave that thread blocked and do useful work in another.
In Go, a goroutine is swapped out for another one when it blocks on I/O. The Go runtime uses non-blocking I/O syscalls to avoid the operating system blocking the thread so a different goroutine can be run on it while the first is waiting for it's I/O.
Goroutines are really cheap so writing non-blocking style code is not needed.
Write blocking functions. The language allows you to easily turn a synchronous call into an asynchronous one.
If you want to call a function asynchronously, use a go statement. Something like this:
c := make(chan bool)
go func() {
blockingFunction()
c <- true
}()
// do some other stuff here while the blocking function runs
// wait for the blocking function to finish if it hasn't already
<-c
In Go, system calls are implemented in a non-blocking way using the most efficient underlying mechanism that the OS supports (e.g. epoll). If you have no other code to run while you wait for the result of a call, then it blocks the thread (for lack of a better thing to do), but if you have alternate goroutines active, then they will run instead.
Callbacks (as you're used to using in js) allow for essentially the same underlying mechanics, but with arguably more mental gymnastics necessary for the programmer.
In Go, your code to run after a function call is specified immediately following the function call rather than defined as a callback. Code that you want to run parallel to an execution path should be wrapped in a goroutine, with communication through channels.
For typical web-server type applications, I would recommend not making everything asynchronous. There are a few reasons.
It's easier to reason about serial blocking code than async code (easier to see bugs)
golang error handling is based on defer(), panic(), and recover(), which probably won't give you what you want with 100% asynchronous code
Goroutines can leak if you're not careful [one discussion]. The more async behavior you have, the harder it becomes to track down these types of problems and the more likely they are to show up.
One strategy is to focus the asynchonicity at a high level and leave everything else blocking. So you might have a "database handler" blob that is logically distinct from the "request handler" blob. They both run in separate goroutines and communicate using channels. But within the "database handler", the calls to establish a database connection and execute each query are blocking.
You don't have to choose 100% asynchronous or 0% asynchronous.
Blocking interfaces are always simpler and better than non-blocking ones. The beauty of Go is that it allows you to write concurrent (and parallel) code in a simple, and easy to reason about, blocking style.
The fashion for non-blocking programming is all due to deficiencies in the languages people are using (specially JavaScript), not because non-blocking programming is intrinsically better.
I believed any kind of asynchronous execution makes a thread in invisible area. But if so,
Async codes does not offer any performance gain than threaded codes.
But I can't understand why so many developers are making many features async form.
Could you explain about difference and cost of them?
The purpose of an asynchronous execution is to prevent the code calling the asynchronous method (the foreground code) from being blocked. This allows your foreground code to go on doing useful work while the asynchronous thread is performing your requested work in the background. Without asynchronous execution, the foreground code must wait until the background task is completed before it can continue executing.
The cost of an asynchronous execution is the same as that of any other task running on a thread.
Typically, an async result object is registered with the foreground code. The async result object can either raise an event when the background task is completed, or the foreground code can periodically check the async result object to see if its completion flag has been set.
Concurrency does not necessarily require threads.
In Linux, for example, you can perform non-blocking syscalls. Using this type of calls, you can for instance start a number of network reads. Your code can keep track of the reads manually (using handles in a list or similar) and periodically ask the OS if new data is available on any of the connections. Internally, the OS also keeps a list of ongoing reads. Using this technique, you can thus achieve concurrency without any (extra) threads, neither in your program nor in the OS.
If you use threads and blocking IO, you would typically start one thread per read. In this scenario, the OS will instead have a list of ongoing threads, which it parks when the tread tries to read data when there is none available. Threads are resumed as data becomes available.
Having the OS switch between threads might involve slightly more overhead in the form of context switching - switching program counter and register content. But the real deal breaker is usually stack allocation per thread. This size is a couple of megabytes by default on Linux. If you have a lot of concurrency in your program, this might push you in the direction of using non-blocking calls to handle more concurrency per thread.
So it is possible to do async programming without threads. If you want to do async programming using only blocking OS-calls you need to dedicate a thread to do the blocking while you continue. But if you use non-blocking calls you can do a lot of concurrent things with just a single thread. Have a look at Node.js, which have great support for many concurrent connections while being single-threaded for most operations.
Also check out Golang, which achieve a similar effect using a sort of green threads called goroutines. Multiple goroutines run concurrently on the same OS thread and they are restrictive in stack memory, pushing the limit much further.
Async codes does not offer any performance gain than threaded codes.
Asynchornous execution is one of the traits of multi-threaded execution, which is becoming more relevant as processors are packing in more cores.
For servers, multi-core only mildly relevant, as they are already written with concurrency in mind and will scale natrually, but multi-core is particularly relevant for desktop apps, which traditionally do only a few things concurrently - often just one foreground task with a background thread. Now, they have to be coded to do many things concurrently if they are to take advantage of the power of the multi-core cpu.
As to the performance - on single-core - the asynchornous tasks slow down the system as much as they would if run sequentially (this a simplication, but true for the most part.) So, running task A, which takes 10s and task B which takes 5s on a single core, the total time needed will be 15s, if B is run asynchronously or not. The reason is, is that as B runs, it takes away cpu resources from A - A and B compete for the same cpu.
With a multi-core machine, additional tasks run on otherwise unused cores, and so the situation is different - the additional tasks don't really consume any time - or more correctly, they don't take away time from the core running task A. So, runing tasks A and B asynchronously on multi-core will conume just 10s - not 15s as with single core. B's execution runs at the same time as A, and on a separate core, so A's execution time is unaffected.
As the number of tasks and cores increase, then the potential improvements in performance also increase. In parallel computing, exploiting parallelism to produce an improvement in performance is known as speedup.
we are already seeing 64-core cpus, and it's esimated that we will have 1024 cores commonplace in a few years. That's a potential speedup of 1024 times, compared to the single-threaded synchronous case. So, to answer your question, there clearly is a performance gain to be had by using asynchronous execution.
I believed any kind of asynchronous execution makes a thread in invisible area.
This is your problem - this actually isn't true.
The thing is, your whole computer is actually massively asynchronous - requests to RAM, communication via a network card, accessing a HDD... those are all inherently asynchronous operations.
Modern OSes are actually built around asynchronous I/O. Even when you do a synchronous file request, for example (e.g. File.ReadAllText), the OS sends an asynchronous request. However, instead of giving control back to your code, it blocks while it waits for the response to the asynchronous request. And this is where proper asynchronous code comes in - instead of waiting for the response, you give the request a callback - a function to execute when the response comes back.
For the duration of the asynchronous request, there is no thread. The whole thing happens on a completely different level - say, the request is sent to the firmware on your NIC, and given a DMA address to fill the response. When the NIC finishes your request, it fills the memory, and signals an interrupt to the processor. The OS kernel handles the interrupt by signalling the owner application (usually an IOCP "channel") the request is done. This is still all done with no thread whatsoever - only for a short time right at the end, a thread is borrowed (in .NET this is from the IOCP thread pool) to execute the callback.
So, imagine a simple scenario. You need to send 100 simultaneous requests to a database engine. With multi-threading, you would spin up a new thread for each of those requests. That means a hundred threads, a hundread thread stacks, the cost of starting a new thread itself (starting a new thread is cheap - starting a hundred at the same time, not so much), quite a bit of resources. And those threads would just... block. Do nothing. When the response comes, the threads are awakened, one after another, and eventually disposed.
On the other hand, with asynchronous I/O, you can simply post all the requests from a single thread - and register a callback when each of those is finished. A hundred simultaneous requests will cost you just your original thread (which is free for other work as soon as the requests are posted), and a short time with threads from the thread pool when the requests are finished - in "worst" case scenario, about as many threads as you have CPU cores. Provided you don't use blocking code in the callback, of course :)
This doesn't necessarily mean that asynchronous code is automatically more efficient. If you only need a single request, and you can't do anything until you get a response, there's little point in making the request asynchronous. But most of the time, that's not your actual scenario - for example, you need to maintain a GUI in the meantime, or you need to make simultaneous requests, or your whole code is callback-based, rather than being written synchronously (a typical .NET Windows Forms application is mostly event-based).
The real benefit from asynchronous code comes from exactly that - simplified non-blocking UI code (no more "(Not Responding)" warnings from the window manager), and massively improved parallelism. If you have a web server that handles a thousand requests simultaneously, you don't want to waste 1 GiB of address space just for the completely unnecessary thread stacks (especially on a 32-bit system) - you only use threads when you have something to do.
So, in the end, asynchronous code makes UI and server code much simpler. In some cases, mostly with servers, it can also make it much more efficient. The efficiency improvements come precisely from the fact that there is no thread during the execution of the asynchronous request.
Your comment only applies to one specific kind of asynchronous code - multi-threaded parallelism. In that case, you really are wasting a thread while executing a request. However, that's not what people mean when saying "my library offers an asynchronous API" - after all, that's a 100% worthless API; you could have just called await Task.Run(TheirAPIMethod) and gotten the exact same thing.