Multithreading: What is the point of more threads than cores?_问答_开发者

I thought the point of a multi-core computer is that it could run multiple threads simultaneously. In that case, if you have a quad-core machine, what's the point of having more than 4 threads running at a time? Wouldn't they just be stealing time (CPU Resources) from 开发者_如何转开发each other?

The answer revolves around the purpose of threads, which is parallelism: to run several separate lines of execution at once. In an 'ideal' system, you would have one thread executing per core: no interruption. In reality this isn't the case. Even if you have four cores and four working threads, your process and it threads will constantly be being switched out for other processes and threads. If you are running any modern OS, every process has at least one thread, and many have more. All these processes are running at once. You probably have several hundred threads all running on your machine right now. You won't ever get a situation where a thread runs without having time 'stolen' from it. (Well, you might if it's running real-time, if you're using a realtime OS or, even on Windows, use a real-time thread priority. But it's rare.)

With that as background, the answer: Yes, more than four threads on a true four-core machine may give you a situation where they 'steal time from each other', but only if each individual thread needs 100% CPU. If a thread is not working 100% (as a UI thread might not be, or a thread doing a small amount of work or waiting on something else) then another thread being scheduled is actually a good situation.

It's actually more complicated than that:

What if you have five bits of work that all need to be done at once? It makes more sense to run them all at once, than to run four of them and then run the fifth later.
It's rare for a thread to genuinely need 100% CPU. The moment it uses disk or network I/O, for example, it may be potentially spend time waiting doing nothing useful. This is a very common situation.
If you have work that needs to be run, one common mechanism is to use a threadpool. It might seem to make sense to have the same number of threads as cores, yet the .Net threadpool has up to 250 threads available per processor. I'm not certain why they do this, but my guess is to do with the size of the tasks that are given to run on the threads.

So: stealing time isn't a bad thing (and isn't really theft, either: it's how the system is supposed to work.) Write your multithreaded programs based on the kind of work the threads will do, which may not be CPU-bound. Figure out the number of threads you need based on profiling and measurement. You may find it more useful to think in terms of tasks or jobs, rather than threads: write objects of work and give them to a pool to be run. Finally, unless your program is truly performance-critical, don't worry too much :)

Just because a thread exists doesn't always mean it's actively running. Many applications of threads involve some of the threads going to sleep until it's time for them to do something - for instance, user input triggering threads to wake up, do some processing, and go back to sleep.

Essentially, threads are individual tasks that can operate independently of one another, with no need to be aware of the progress of another task. It's quite possible to have more of these than you have ability to run simultaneously; they're still useful for convenience even if they sometimes have to wait in line behind one another.

The point is that, despite not getting any real speedup when thread count exceeds core count, you can use threads to disentangle pieces of logic that should not have to be interdependent.

In even a moderately complex application, using a single thread try to do everything quickly makes hash of the 'flow' of your code. The single thread spends most of its time polling this, checking on that, conditionally calling routines as needed, and it becomes hard to see anything but a morass of minutiae.

Contrast this with the case where you can dedicate threads to tasks so that, looking at any individual thread, you can see what that thread is doing. For instance, one thread might block waiting on input from a socket, parse the stream into messages, filter messages, and when a valid message comes along, pass it off to some other worker thread. The worker thread can work on inputs from a number of other sources. The code for each of these will exhibit a clean, purposeful flow, without having to make explicit checks that there isn't something else to do.

Partitioning the work this way allows your application to rely on the operating system to schedule what to do next with the cpu, so you don't have to make explicit conditional checks everywhere in your application about what might block and what's ready to process.

If a thread is waiting for a resource (such as loading a value from RAM into a register, disk I/O, network access, launch a new process, query a database, or wait for user input), the processor can work on a different thread, and return to the first thread once the resource is available. This reduces the time the CPU spends idle, as the CPU can perform millions of operations instead of sitting idle.

Consider a thread that needs to read data off a hard drive. In 2014, a typical processor core operates at 2.5 GHz and may be able to execute 4 instructions per cycle. With a cycle time of 0.4 ns, the processor can execute 10 instructions per nanosecond. With typical mechanical hard drive seek times are around 10 milliseconds, the processor is capable of executing 100 million instructions in the time it takes to read a value from the hard drive. There may be significant performance improvements with hard drives with a small cache (4 MB buffer) and hybrid drives with a few GB of storage, as data latency for sequential reads or reads from the hybrid section may be several orders of magnitude faster.

A processor core can switch between threads (cost for pausing and resuming a thread is around 100 clock cycles) while the first thread waits for a high latency input (anything more expensive than registers (1 clock) and RAM (5 nanoseconds)) These include disk I/O, network access (latency of 250ms), reading data off a CD or a slow bus, or a database call. Having more threads than cores means useful work can be done while high-latency tasks are resolved.

The CPU has a thread scheduler that assigns priority to each thread, and allows a thread to sleep, then resume after a predetermined time. It is the thread scheduler's job to reduce thrashing, which would occur if each thread executed just 100 instructions before being put to sleep again. The overhead of switching threads would reduce the total useful throughput of the processor core.

For this reason, you may want to break up your problem in to a reasonable number of threads. If you were writing code to perform matrix multiplication, creating one thread per cell in the output matrix might be excessive, whereas one thread per row or per n rows in the output matrix might reduce the overhead cost of creating, pausing, and resuming threads.

This is also why branch prediction is important. If you have an if statement that requires loading a value from RAM but the body of the if and else statements use values already loaded into registers, the processor may execute one or both branches before the condition has been evaluated. Once the condition returns, the processor will apply the result of the corresponding branch and discard the other. Performing potentially useless work here is probably better than switching to a different thread, which could lead to thrashing.

As we have moved away from high clock-speed single-core processors to multi-core processors, chip design has focused on cramming more cores per die, improving on-chip resource sharing between cores, better branch prediction algorithms, better thread switching overhead, and better thread scheduling.

Most of the answers above talk about performance and simultaneous operation. I'm going to approach this from a different angle.

Let's take the case of, say, a simplistic terminal emulation program. You have to do the following things:

watch for incoming characters from the remote system and display them
watch for stuff coming from the keyboard and send them to the remote system

(Real terminal emulators do more, including potentially echoing the stuff you type onto the display as well, but we'll pass over that for now.)

Now the loop for reading from the remote is simple, as per the following pseudocode:

while get-character-from-remote:
    print-to-screen character

The loop for monitoring the keyboard and sending is also simple:

while get-character-from-keyboard:
    send-to-remote character

The problem, though, is that you have to do this simultaneously. The code now has to look more like this if you don't have threading:

loop:
    check-for-remote-character
    if remote-character-is-ready:
        print-to-screen character
    check-for-keyboard-entry
    if keyboard-is-ready:
        send-to-remote character

The logic, even in this deliberately simplified example that doesn't take into account real-world complexity of communications, is quite obfuscated. With threading, however, even on a single core, the two pseudocode loops can exist independently without interlacing their logic. Since both threads will be mostly I/O-bound, they don't put a heavy load on the CPU, even though they are, strictly speaking, more wasteful of CPU resources than the integrated loop would be.

Now of course real-world usage is more complicated than the above. But the complexity of the integrated loop goes up exponentially as you add more concerns to the application. The logic gets ever more fragmented and you have to start using techniques like state machines, coroutines, et al to get things manageable. Manageable, but not readable. Threading keeps the code more readable.

So why would you not use threading?

Well, if your tasks are CPU-bound instead of I/O-bound, threading actually slows your system down. Performance will suffer. A lot, in many cases. ("Thrashing" is a common problem if you drop too many CPU-bound threads. You wind up spending more time changing the active threads than you do running the contents of the threads themselves.) Also, one of the reasons the logic above is so simple is that I've very deliberately chosen a simplistic (and unrealistic) example. If you wanted to echo what was typed to the screen then you've got a new world of hurt as you introduce locking of shared resources. With only one shared resource this isn't so much a problem, but it does start to become a bigger and bigger problem as you have more resources to share.

So in the end, threading is about many things. For example, it's about making I/O-bound processes more responsive (even if less efficient overall) as some have already said. It's also about making logic easier to follow (but only if you minimize shared state). It's about a lot of stuff, and you have to decide if its advantages outweigh its disadvantages on a case by case basis.

I strongly disagree with @kyoryu's assertion that the ideal number is one thread per CPU.

Think about it this way: why do we have multi-processing operating systems? For most of computer history, nearly all computers had one CPU. Yet from the 1960s on, all "real" computers had multi-processing (aka multi-tasking) operating systems.

You run multiple programs so that one can run while others are blocked for things like IO.

lets set aside arguments about whether Windows versions before NT were multi-tasking. Since then, every real OS had multi-tasking. Some don't expose it to users, but its there anyway, doing things like listening to the cellphone radio, talking to the GPS chip, accepting mouse input, etc.

Threads are just tasks that are a bit more efficient. There is no fundamental difference between a task, process, and thread.

A CPU is a terrible thing to waste, so have lots of things ready to use it when you can.

I will agree that with most procedural languages, C, C++, Java etc, writing proper thread safe code is a lot of work. With 6 core CPUs on the market today, and 16 core CPUs not far away, I expect that folks will move away from these old languages, as multi-threading is more and more of a critical requirement.

Disagreement with @kyoryu is just IMHO, the rest is fact.

Although you can certainly use threads for speeding up calculations depending on your hardware, one of their main uses is to do more than one thing at a time for user-friendliness reasons.

For example, if you have to do some processing in the background and also remain responsive to UI input, you can use threads. Without threads, the user interface would hang every time you tried to do any heavy processing.

Also see this related question: Practical uses for threads

Imagine a Web server that has to serve an arbitrary number of requests. You have to serve the requests in parallel because otherwise each new request has to wait until all the other requests have been completed (including sending the response over the Internet). In this case, most web servers have way less cores than the number of requests they usually serve.

It also makes it easier for the developer of the server: You only have to write a thread program that serves a request, you don't have to think about storing multiple requests, the order you serve them, and so on.

Many threads will be asleep, waiting for user input, I/O, and other events.

Threads can help with responsiveness in UI applications. Additionally, you can use threads to get more work out of your cores. For instance, on a single core, you can have one thread doing IO and another doing some computation. If it were single threaded, the core could essentially be idle waiting for the IO to complete. That's a pretty high level example, but threads can definitely be used to pound your cpu a bit harder.

A processor, or CPU, is the physical chip that is plugged into the system. A processor can have multiple cores (a core is the part of the chip that is capable of executing instructions). A core can appear to the operating system as multiple virtual processors if it is capable of simultaneously executing multiple threads (a thread is a single sequence of instructions).

A process is another name for an application. Generally, processes are independent of each other. If one process dies, it does not cause another process to also die. It is possible for processes to communicate, or share resources such as memory or I/O.

Each process has a separate address space and stack. A process can contain multiple threads, each able to execute instructions simultaneously. All the threads in a process share the same address space, but each thread will have its own stack.

Hopefully with these definitions and further research using these fundamentals will help your understanding.

The way some API are designed, you have no choice but to run them in a separate thread (anything with blocking operations). An example would be Python's HTTP libraries (AFAIK).

Usually this isn't much of a problem though (if it is a problem, the OS or API should ship with an alternative asynchronous operating mode, ie: select(2)), because it probably means the thread is going to be sleeping during waiting for I/O completion. On the other hand, if something is doing a heavy computation, you have to put it in a separate thread than say, the GUI thread (unless you enjoy manual multiplexing).

The ideal usage of threads is, indeed, one per core.

However, unless you exclusively use asynchronous/non-blocking IO, there's a good chance that you will have threads blocked on IO at some point, which will not use your CPU.

Also, typical programming languages make it somewhat difficult to use 1 thread per CPU. Languages designed around concurrency (such as Erlang) can make it easier to not use extra threads.

In response to your first conjecture: multi-core machines can simultaneously run multiple processes, not just the multiple threads of a single process.

In response to your first question: the point of multiple threads is usually to simultaneously perform multiple tasks within one application. The classic examples on the net are an email program sending and receiving mail, and a web server receiving and sending page requests. (Note that it's essentially impossible to reduce a system like Windows to running only one thread or even only one process. Run the Windows Task Manager and you'll typically see a long list of active processes, many of which will be running multiple threads.)

In response to your second question: most processes/threads are not CPU-bound (ie, not running continuously and uninterrupted), but instead stop and wait frequently for I/O to finish. During that wait, other processes/threads can run without "stealing" from the waiting code (even on a single core machine).

I know this is a super old question with plenty of good answers, but I am here to point out something that is important in current environment:

If you want to design an application for multi-threading, you should not be designing for a specific hardware setting. CPU technology has been advancing quite rapidly for years, and core counts are increasing steadily. If you deliberately design your application such that it uses only 4 threads, then you are potentially restricting yourself in a octa-core system (for example). Now, even 20-core systems are commercially available, so such a design is definitely doing more harm than good.

A thread is an abstraction that enables You to write code as simple as a sequence of operation, blissfully unaware of that the code is executed interlaced with other code, or parked waiting for IO, or (maybe somewhat more aware of) waiting for other thread's events or messages.

The point is that the vast majority of programmers do not understand how to design a state machine. Being able to put everything in its own thread frees the programmer from having to think about how to efficiently represent the state of different in-progress computations so that they can be interrupted and later resumed.

As an example, consider video compression, a very cpu-intensive task. If you're using a gui tool, you probably want the interface to remain responsive (show progress, respond to cancel requests, window resizing, etc.). So you design your encoder software to process a large unit (one or more frames) at a time and run it in its own thread, separate from the UI.

Of course once you realize it would have been nice to be able to save the in-progress encoding state so you can close the program to reboot or play a resource-hungry game, you realize you should have learned how to design state machines from the beginning. Either that, or you decide to engineer a whole new problem of process-hibernation your OS so you can suspend and resume individual apps to disk...