My understanding is that the hardware architecture and the operating systems are designed not to block the cpu. When any kind of blocking operation needs to happen, the operating system registers an interruption and moves on to something else, making sure the precious time of the cpu is always effectively used.
It makes me wonder why most programming languages were designed with blocking APIs, but most importantly, since the operating system works in an asynchronous way when it comes to IO, registering interruptions and dealing with results when they are ready later on, I'm really puzzled about how our programming language APIs escape this asynchrony. How does the OS provides synchronous system calls for our programming language using blocking APIs?
Where this synchrony comes from? Certainly not at the hardware level. So, is there an infinite loop somewhere I don't know about spinning and spinning until some interruption is triggered?
My understanding is that the hardware architecture and the operating systems are designed not to block the cpu.
Any rationally designed operating system would have a system service interface that does what you say. However, there are many non-rational operating systems that do not work in this manner at the process level.
Blocking I/O is simpler to program than non-blocking I/O. Let me give you an example from the VMS operating system (Windoze works the same way under the covers). VMS has a system services called SYS$QIO and SYS$QIOW. That is, Queue I/O Request and Queue I/O Request and wait. The system services have identical parameters. One pair of parameters is the address of a completion routine and a parameters to that routine. However, these parameters are rarely used with SYS$QIOW.
If you do a SYS$QIO call, it returns immediately. When the I/O operation completes, the completion routine is called as a software interrupt. You then have to do interrupt programming in your application. We did this all the time. If you want your application to be able to read from 100 input streams simultaneously, this is the way you had to do it. It's just more complicated than doing simple blocking I/O with one device.
If a programming language were to incorporate such a callback system into its I/O statements, it would be mirroring VMS/RSX/Windoze. Ada uses the task concept to implement such systems in a operating-system-independent manner.
In the Eunuchs world, it was traditional to create a separate process for each device. That was simpler until you had to read AND write to each device.
Your observations are correct - the operating system interacts with the underlying hardware asynchronously to perform I/O requests.
The behavior of blocking I/O comes from threads. Typically, the OS provides threads as an abstraction for user-mode programs to use. But sometimes, green/lightweight threads are provided by a user-mode virtual machine like in Go, Erlang, Java (Project Loom), etc. If you aren't familiar with threads as an abstraction, read up on some background theory from any OS textbook.
Each thread has a state consisting of a fixed set of registers, a dynamically growing/shrinking stack (for function arguments, function call registers, and return addresses), and a next instruction pointer. The implementation of blocking I/O is that when a thread calls an I/O function, the underlying platform hosting the thread (Java VM, Linux kernel, etc.) immediately suspends the thread so that it cannot be scheduled for execution, and also submits the I/O request to the platform below. When the platform receives the completion of the I/O request, it puts the result on that thread's stack and puts the thread on the scheduler's execution queue. That's all there is to the magic.
Why are threads popular? Well, I/O requests happen in some sort of context. You don't just read a file or write a file as a standalone operation; you read a file, run a specific algorithm to process the result, and issue further I/O requests. A thread is one way to keep track of your progress. Another way is known as "continuation passing style", where every time you perform an I/O operation (A), you pass a callback or function pointer to explicitly specify what needs to happen after I/O completion (B), but the call (A) returns immediately (non-blocking / asynchronous). This way of programming asynchronous I/O is considered hard to reason about and even harder to debug because now you don't have a meaningful call stack because it gets cleared after every I/O operation. This is discussed at length in the great essay "What color is your function?".
Note that the platform has no obligation to provide a threading abstraction to its users. The OS or language VM can very well expose an asynchronous I/O API to the user code. But the vast majority of platforms (with exceptions like Node.js) choose to provide threads because it's much easier for humans to reason about.
Related
I ask this question because I am looking at multiplexing I/O in Go, which is using epollwait.
When an socket is ready, a goroutine will be waked up and begin to read socket in non-blocking mode. If the read system call still will be blocked during copying data from kernel to user, I assume the kernel thread the gorouine attached to will be put to sleep as well.
I am not sure of that, hoping someone can help correct me if I am wrong.
I fail to quite parse what you've written.
I'll try to make a sheer guess and conjure you might be overseeing the fact that the write(2) and read(2) syscalls (and those of their ilk such as send(2) and recv(2)) on the sockets put into non-blocking mode are free to consume (and return, respectively) less data than requested.
In other words, a write(2) call on a non-blocking socket told to write 1 megabyte of data will consume just as much data currently fits into the assotiated kernel buffer and return immediately, signalling it consumed only as much data. The next immediate call to write(2) will likely return EWOULDBLOCK.
The same goes for the read(2) call: if you pass it a buffer large enough to hold 1 megabyte of data, and tell it to read that number of bytes, the call will only drain the contents of the kernel buffer and return immediately, signaling how much data it actually copied. The next immediate call to read(2) will likely return EWOULDBLOCK.
So, any attempt to get or put data to the socket succeeds almost immediately: either after the data had been shoveled between the kernel's buffer and the user space or right away—with the EAGAIN return code.
Sure, there's supposedly a possibility for an OS thread to be suspended right in the middle of performing such a syscall, but this does not count as "blocking in a syscall."
Update to the original answer in response to the following comment of the OP:
<…>
This is what I see in book
"UNIX Network Programming" (Volume 1, 3rd), chapter 6.2:
A synchronous I/O operation causes the requesting process
to be blocked until that I/O operation completes. Using these
definitions, the first four I/O models—blocking, nonblocking, I/O
multiplexing, and signal-driven I/O—are all synchronous because the
actual I/O operation (recvfrom) blocks the process.
It uses "blocks" to describe nonblocking I/O operation. That makes me confused.
I still don't understand why the book uses "blocks the process" if the process is actually not blocked.
I can only guess that the book's author intended to highlight that the process is indeed blocked since entering a syscall and until returning from it. Reads from and writes to a non-blocking socket do block to transfer the data, if available, between the kernel and the user space. We colloquially say this does not block because we mean "it does not block waiting and doing nothing for an indeterminate amount of time".
The book's author might contrast this to the so-called asynchronous I/O (called "overlapping" on Windows™)—where you basically give the kernel a buffer with/for data and ask it to do away with it completely in parallel with your code—in the sense the relevant syscall returns right away and the I/O is carried out in background (with regard to your user-space code).
To my knowledge, Go does not use kernel's async I/O facilities on neither platform it supports. You might look there for the developments regarding Linux and its contemporary io_uring subsystem.
Oh, and one more point. The book might (at that point through the narrative at least) be discussing a simplified "classic" scheme where there are no in-process threads, and the sole unit of concurrency is the process (with a single thread of execution). In this scheme, any syscall obviously blocks the whole process. In contrast, Go works only on kernels which support threads, so in a Go program a syscall never blocks the whole process—only the thread it's called on.
Let me take yet another stab at explaining the problem as—I perceive—the OP stated it.
The problem of serving multiple client requests is not new—one of the more visible first statements of it is "The C10k problem".
To quickly recap it, a single threaded server with blocking operations on the sockets it manages is only realistically able to handle a single client at a time.
To solve it, there exist two straightforward approaches:
Fork a copy of the server process to handle each incoming client connection.
On an OS which supports threads, fork a new thread inside the same process to handle each incoming client.
They have their pros and cons but they both suck with regard to resource usage, and—which is more important—they do not play well with the fact most clients have relatively low rate and bandwidth of I/O they perform with regard to the processing resources available on a typical server.
In other words, when serving a typical TCP/IP exchange with a client, the serving thread most of the time sleeps in the write(2) and read(2) calls on the client socket.
This is what most people mean when talking about "blocking operations" on sockets: if a socket is blocking, and operation on it will block until it can actually be carried out, and the originating thread will be put to sleep for an indeterminate amount of time.
Another important thing to note is that when the socket becomes ready, the amount of work done is typically miniscule compared to the amount of time slept between the wakeups.
While the tread sleeps, its resources (such as memory) are effectively wasted, as they cannot be used to do any other work.
Enter "polling". It combats the problem of wasted resources by noticing that the points of readiness of networked sockets are relatively rare and far in between, so it makes sense to have lots of such sockets been served by a single thread: it allows to keep the thread almost as busy as theoretically possible, and also allows to scale out when needed: if a single thread is unable to cope with the data flow, add another thread, and so on.
This approach is definitely cool but it has a downside: the code which reads and writes data must be re-written to use callback style instead of the original plain sequential style. Writing with callbacks is hard: you usuaully have to implement intricate buffer management and state machines to deal with this.
The Go runtime solves this problem by adding another layer of scheduling for its execution flow units—goroutines: for goroutines, operations on the sockets are always blocking, but when a goroutine is about to block on a socket, this is transparently handled by suspending only the goroutine itself—until the requested operation will be able to proceed—and using the thread the goroutine was running on to do other work¹.
This allows to have the best of both approaches: the programmer may write classic no-brainer sequential callback-free networking code but the threads used to handle networking requests are fully utilized².
As to the original question of blocking, both the goroutine and the thread it runs on are indeed blocked when the data transfer on a socket is happening, but since what happens is data shoveling between a kernel and a user-space buffer, the delay is most of the time small, and is no different to the classic "polling" case.
Note that performing of syscalls—including I/O on non-pollable descriptors—in Go (at leas up until, and including Go 1.14) does block both the calling goroutine and the thread it runs on, but is handled differently from those of pollable descriptors: when a special monitoring thread notices a goroutine spent in a syscall more that certain amount of time (20 µs, IIRC), the runtime pulls the so-called "processor" (a runtime thing which runs goroutines on OS threads) from under the gorotuine and tries to make it run another goroutine on another OS thread; if there is a goroutine wanting to run but no free OS thread, the Go runtime creates another one.
Hence "normal" blocking I/O is still blocking in Go in both senses: it blocks both goroutines and OS threads, but the Go scheduler makes sure the program as a whole still able to make progress.
This could arguably be a perfect case for using true asynchronous I/O provided by the kernel, but it's not there yet.
¹ See this classic essay for more info.
² The Go runtime is certainly not the first one to pioneer this idea. For instance, look at the State Threads library (and the more recent libtask) which implement the same approach in plain C; the ST library has superb docs which explain the idea.
When a thread does something that may cause it to become blocked locally, for example, waiting for another thread in its process to complete some work, it calls a run-time system procedure. This procedure checks to see if the thread must be put into blocked state. If so, it stores the thread's registers in the thread table, looks in the table for a ready thread to run, and reloads the machine registers with the new thread's saved values. As soon as the stack pointer and program counter have been switched, the new thread comes to life again automatically. If the machine happens to have an instruction to store all the registers and another one to load them all, the entire thread switch can be done in just a handful of instructions. Doing thread switching like this is at least an order of magnitude-maybe more-faster than trapping to the kernel and is a strong argument in favor of user-level threads packages.
Source: Modern Operating Systems (Andrew S. Tanenbaum | Herbert Bos)
The above argument is made in favor of user-level threads. The user-level thread implementation is depicted as kernel managing all the processes, where individual processes can have their own run-time (made available by a library package) that manages all the threads in that process.
Of course, merely calling a function in the run-time than trapping to kernel might have a few less instructions to execute but why the difference is so huge?
For example, if threads are implemented in kernel space, every time a thread has to be created the program is required to make a system call. Yes. But the call only involves adding an entry to the thread table with certain attributes (which is also the case in user space threads). When a thread switch has to happen, kernel can simply do what the run-time (at user-space) would do. The only real difference I can see here is that the kernel is being involved in all this. How can the performance difference be so significant?
Threads implemented as a library package in user space perform significantly better. Why?
They're not.
The fact is that most task switches are caused by threads blocking (having to wait for IO from disk or network, or from user, or for time to pass, or for some kind of semaphore/mutex shared with a different process, or some kind of pipe/message/packet from a different process) or caused by threads unblocking (because whatever they were waiting for happened); and most reasons to block and unblock involve the kernel in some way (e.g. device drivers, networking stack, ...); so doing task switches in kernel when you're already in the kernel is faster (because it avoids the overhead of switching to user-space and back for no sane reason).
Where user-space task switching "works" is when kernel isn't involved at all. This mostly only happens when someone failed to do threads properly (e.g. they've got thousands of threads and coarse-grained locking and are constantly switching between threads due to lock contention, instead of something sensible like a "worker thread pool"). It also only works when all threads are the same priority - you don't want a situation where very important threads belonging to one process don't get CPU time because very unimportant threads belonging to a different process are hogging the CPU (but that's exactly what happens with user-space threading because one process has no idea about threads belonging to a different process).
Mostly; user-space threading is a silly broken mess. It's not faster or "significantly better"; it's worse.
When a thread does something that may cause it to become blocked locally, for example, waiting for another thread in its process to complete some work, it calls a run-time system procedure. This procedure checks to see if the thread must be put into blocked state. If so, it stores the thread's registers in the thread table, looks in the table for a ready thread to run, and reloads the machine registers with the new thread's saved values. As soon as the stack pointer and program counter have been switched, the new thread comes to life again automatically. If the machine happens to have an instruction to store all the registers and another one to load them all, the entire thread switch can be done in just a handful of instructions. Doing thread switching like this is at least an order of magnitude-maybe more-faster than trapping to the kernel and is a strong argument in favor of user-level threads packages.
This is talking about a situation where the CPU itself does the actual task switch (and either the kernel or a user-space library tells the CPU when to do a task switch to what). This has some relatively interesting history behind it...
In the 1980s Intel designed a CPU ("iAPX" - see https://en.wikipedia.org/wiki/Intel_iAPX_432 ) for "secure object oriented programming"; where each object has its own isolated memory segments and its own privilege level, and can transfer control directly to other objects. The general idea being that you'd have a single-tasking system consisting of global objects using cooperating flow control. This failed for multiple reasons, partly because all the protection checks ruined performance, and partly because the majority of software at the time was designed for "multi-process preemptive time sharing, with procedural programming".
When Intel designed protected mode (80286, 80386) they still had hopes for "single-tasking system consisting of global objects using cooperating flow control". They included hardware task/object switching, local descriptor table (so each task/object can have its own isolated segments), call gates (so tasks/objects can transfer control to each other directly), and modified a few control flow instructions (call far and jmp far) to support the new control flow. Of course this failed for the same reason iAPX failed; and (as far as I know) nobody has ever used these things for the "global objects using cooperative flow control" they were originally designed for. Some people (e.g. very early Linux) did try to use the hardware task switching for more traditional "multi-process preemptive time sharing, with procedural programming" systems; but found that it was slow because the hardware task switch did too many protection checks that could be avoided by software task switching and saved/reloaded too much state that could be avoided by a software task switching;p and didn't do any of the other stuff needed for a task switch (e.g. keeping statistics of CPU time used, saving/restoring debug registers, etc).
Now.. Andrew S. Tanenbaum is a micro-kernel advocate. His ideal system consists of isolated pieces in user-space (processes, services, drivers, ...) communicating via. synchronous messaging. In practice (ignoring superficial differences in terminology) this "isolated pieces in user-space communicating via. synchronous messaging" is almost entirely identical to Intel's twice failed "global objects using cooperative flow control".
Mostly; in theory (if you ignore all the practical problems, like CPU not saving all of the state, and wanting to do extra work on task switches like tracking statistics), for a specific type of OS that Andrew S. Tanenbaum prefers (micro-kernel with synchronous message passing, without any thread priorities), it's plausible that the paragraph quoted above is more than just wishful thinking.
I think the answer to this can use a lot of OS and parallel distributive computing knowledge (And I am not sure about the answer but I will try my best)
So if you think about it. The library package will have a greater amount of performance than you write in the kernel itself. In the package thing, interrupt given by this code will be held at once and al the execution will be done. While when you write in kernel different other interrupts can come before. Plus accessing threads again and again is harsh on the kernel since everytime there will be an interrupt. I hope it will be a better view.
it's not correct to say the user-space threads are better that the kernel-space threads since each one has its own pros and cons.
in terms of user-space threads, as the application is responsible for managing thread, its easier to implement such threads and that kind of threads have not much reliance on OS. however, you are not able to use the advantages of multi processing.
In contrary, the kernel space modules are handled by OS, so you need to implement them according to the OS that you use, and it would be a more complicated task. However, you have more control over your threads.
for more comprehensive tutorial, take a look here.
I was wondering when .Net would most probably switch from a thread to another?
I understand we can't predict when this will happen exactly, but is there any intelligence in this? For example, when a thread is executed will it try to wait for a method to returns or a loop to finish before switching?
I'm not an expert on .NET, but in general scheduling is handled by the kernel.
Either your thread's timeslice has expired (threads/processes only get a certain amount of CPU time)
Your thread has blocked for IO.
Some other obscure reason, like waiting for an IPC message, a network packet or something.
Threads can be preempted at any point along their execution path, be it in a loop or returning from a function. This in general isn't handled by the underlying VM (.NET or JVM) but is controlled by the OS.
Of course there is 'intelligence', of a sort:). The set of running threads can only change upon an interrupt, either:
An actual hardware interrupt from a peripheral device, eg. disk, NIC, KB, mouse, timer.
A software interrupt, (ie. a system call), that can change the state of thread/s. This encompasses sleep calls and calls to wait/signal on inter-thread synchro objects, as well as I/O calls that request data that is not immediately available.
If there is no interrupt, the OS cannot change the set of running threads because it is not entered. The OS does not know or care about loops, function/methods calls, (except those that make system calls as above), gotos or any other user-level flow-control mechanisms.
I read your question now, it may not be rellevant anymore, but after reading the above answers, i want to just to make sure:
Threads are managed (or as i know) by the process they belong to. There is nothing to do with the Operation System(and that's is the main reason why working with multithreads is more faster than working with multiprocess, because there are data sharing between threads and the switching between them is occuring faster than the context switch wich occure between process by the Short-Term-Scheduler).
(NOTE: There are two types of threads: USER_MODE' threads and KERNEL_MODE' threadss, and each os can have both of them or just on of them. Anyway a thread that working in a user application environment is considered as a USER_MODE' thread and managed by the process it's belong to.)
Am I Write?
Thanks!!!
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.
Whilst learning the "assembler language" (in linux on a x86 architecture using the GNU as assembler), one of the aha moments was the possibility of using system calls. These system calls come in very handy and are sometimes even necessary as your program runs in user-space.
However system calls are rather expensive in terms of performance as they require an interrupt (and of course a system call) which means that a context switch must be made from your current active program in user-space to the system running in kernel-space.
The point I want to make is this: I'm currently implementing a compiler (for a university project) and one of the extra features I wanted to add is the support for multi-threaded code in order to enhance the performance of the compiled program. Because some of the multi-threaded code will be automatically generated by the compiler itself, this will almost guarantee that there will be really tiny bits of multi-threaded code in it as well. In order to gain a performance win, I must be sure that using threads will make this happen.
My fear however is that, in order to use threading, I must make system calls and the necessary interrupts. The tiny little (auto-generated) threads will therefore be highly affected by the time it takes to make these system calls, which could even lead to a performance loss...
my question is therefore twofold (with an extra bonus question underneath it):
Is it possible to write assembler
code which can run multiple threads
simultaneously on multiple cores at
once, without the need of system
calls?
Will I get a performance gain if I have really tiny threads (tiny as in the total execution time of the thread), performance loss, or isn't it worth the effort at all?
My guess is that multithreaded assembler code is not possible without system calls. Even if this is the case, do you have a suggestion (or even better: some real code) for implementing threads as efficient as possible?
The short answer is that you can't. When you write assembly code it runs sequentially (or with branches) on one and only one logical (i.e. hardware) thread. If you want some of the code to execute on another logical thread (whether on the same core, on a different core on the same CPU or even on a different CPU), you need to have the OS set up the other thread's instruction pointer (CS:EIP) to point to the code you want to run. This implies using system calls to get the OS to do what you want.
User threads won't give you the threading support that you want, because they all run on the same hardware thread.
Edit: Incorporating Ira Baxter's answer with Parlanse. If you ensure that your program has a thread running in each logical thread to begin with, then you can build your own scheduler without relying on the OS. Either way, you need a scheduler to handle hopping from one thread to another. Between calls to the scheduler, there are no special assembly instructions to handle multi-threading. The scheduler itself can't rely on any special assembly, but rather on conventions between parts of the scheduler in each thread.
Either way, whether or not you use the OS, you still have to rely on some scheduler to handle cross-thread execution.
"Doctor, doctor, it hurts when I do this". Doctor: "Don't do that".
The short answer is you can do multithreaded programming without
calling expensive OS task management primitives. Simply ignore the OS for thread
scheduling operations. This means you have to write your own thread
scheduler, and simply never pass control back to the OS.
(And you have to be cleverer somehow about your thread overhead
than the pretty smart OS guys).
We chose this approach precisely because windows process/thread/
fiber calls were all too expensive to support computation
grains of a few hundred instructions.
Our PARLANSE programming langauge is a parallel programming language:
See http://www.semdesigns.com/Products/Parlanse/index.html
PARLANSE runs under Windows, offers parallel "grains" as the abstract parallelism
construct, and schedules such grains by a combination of a highly
tuned hand-written scheduler and scheduling code generated by the
PARLANSE compiler that takes into account the context of grain
to minimimze scheduling overhead. For instance, the compiler
ensures that the registers of a grain contain no information at the point
where scheduling (e.g., "wait") might be required, and thus
the scheduler code only has to save the PC and SP. In fact,
quite often the scheduler code doesnt get control at all;
a forked grain simply stores the forking PC and SP,
switches to compiler-preallocated stack and jumps to the grain
code. Completion of the grain will restart the forker.
Normally there's an interlock to synchronize grains, implemented
by the compiler using native LOCK DEC instructions that implement
what amounts to counting semaphores. Applications
can fork logically millions of grains; the scheduler limits
parent grains from generating more work if the work queues
are long enough so more work won't be helpful. The scheduler
implements work-stealing to allow work-starved CPUs to grab
ready grains form neighboring CPU work queues. This has
been implemented to handle up to 32 CPUs; but we're a bit worried
that the x86 vendors may actually swamp use with more than
that in the next few years!
PARLANSE is a mature langauge; we've been using it since 1997,
and have implemented a several-million line parallel application in it.
Implement user-mode threading.
Historically, threading models are generalised as N:M, which is to say N user-mode threads running on M kernel-model threads. Modern useage is 1:1, but it wasn't always like that and it doesn't have to be like that.
You are free to maintain in a single kernel thread an arbitrary number of user-mode threads. It's just that it's your responsibility to switch between them sufficiently often that it all looks concurrent. Your threads are of course co-operative rather than pre-emptive; you basically scatted yield() calls throughout your own code to ensure regular switching occurs.
If you want to gain performance, you'll have to leverage kernel threads. Only the kernel can help you get code running simultaneously on more than one CPU core. Unless your program is I/O bound (or performing other blocking operations), performing user-mode cooperative multithreading (also known as fibers) is not going to gain you any performance. You'll just be performing extra context switches, but the one CPU that your real thread is running will still be running at 100% either way.
System calls have gotten faster. Modern CPUs have support for the sysenter instruction, which is significantly faster than the old int instruction. See also this article for how Linux does system calls in the fastest way possible.
Make sure that the automatically-generated multithreading has the threads run for long enough that you gain performance. Don't try to parallelize short pieces of code, you'll just waste time spawning and joining threads. Also be wary of memory effects (although these are harder to measure and predict) -- if multiple threads are accessing independent data sets, they will run much faster than if they were accessing the same data repeatedly due to the cache coherency problem.
Quite a bit late now, but I was interested in this kind of topic myself.
In fact, there's nothing all that special about threads that specifically requires the kernel to intervene EXCEPT for parallelization/performance.
Obligatory BLUF:
Q1: No. At least initial system calls are necessary to create multiple kernel threads across the various CPU cores/hyper-threads.
Q2: It depends. If you create/destroy threads that perform tiny operations then you're wasting resources (the thread creation process would greatly exceed the time used by the tread before it exits). If you create N threads (where N is ~# of cores/hyper-threads on the system) and re-task them then the answer COULD be yes depending on your implementation.
Q3: You COULD optimize operation if you KNEW ahead of time a precise method of ordering operations. Specifically, you could create what amounts to a ROP-chain (or a forward call chain, but this may actually end up being more complex to implement). This ROP-chain (as executed by a thread) would continuously execute 'ret' instructions (to its own stack) where that stack is continuously prepended (or appended in the case where it rolls over to the beginning). In such a (weird!) model the scheduler keeps a pointer to each thread's 'ROP-chain end' and writes new values to it whereby the code circles through memory executing function code that ultimately results in a ret instruction. Again, this is a weird model, but is intriguing nonetheless.
Onto my 2-cents worth of content.
I recently created what effectively operate as threads in pure assembly by managing various stack regions (created via mmap) and maintaining a dedicated area to store the control/individualization information for the "threads". It is possible, although I didn't design it this way, to create a single large block of memory via mmap that I subdivide into each thread's 'private' area. Thus only a single syscall would be required (although guard pages between would be smart these would require additional syscalls).
This implementation uses only the base kernel thread created when the process spawns and there is only a single usermode thread throughout the entire execution of the program. The program updates its own state and schedules itself via an internal control structure. I/O and such are handled via blocking options when possible (to reduce complexity), but this isn't strictly required. Of course I made use of mutexes and semaphores.
To implement this system (entirely in userspace and also via non-root access if desired) the following were required:
A notion of what threads boil down to:
A stack for stack operations (kinda self explaining and obvious)
A set of instructions to execute (also obvious)
A small block of memory to hold individual register contents
What a scheduler boils down to:
A manager for a series of threads (note that processes never actually execute, just their thread(s) do) in a scheduler-specified ordered list (usually priority).
A thread context switcher:
A MACRO injected into various parts of code (I usually put these at the end of heavy-duty functions) that equates roughly to 'thread yield', which saves the thread's state and loads another thread's state.
So, it is indeed possible to (entirely in assembly and without system calls other than initial mmap and mprotect) to create usermode thread-like constructs in a non-root process.
I only added this answer because you specifically mention x86 assembly and this answer was entirely derived via a self-contained program written entirely in x86 assembly that achieves the goals (minus multi-core capabilities) of minimizing system calls and also minimizes system-side thread overhead.
System calls are not that slow now, with syscall or sysenter instead of int. Still, there will only be an overhead when you create or destroy the threads. Once they are running, there are no system calls. User mode threads will not really help you, since they only run on one core.
First you should learn how to use threads in C (pthreads, POSIX theads). On GNU/Linux you will probably want to use POSIX threads or GLib threads.
Then you can simply call the C from assembly code.
Here are some pointers:
Posix threads: link text
A tutorial where you will learn how to call C functions from assembly: link text
Butenhof's book on POSIX threads link text