问题描述：

I'm slightly confused about whether multithreading works in Python or not.

I know there has been a lot of questions about this and I've read many of them, but I'm still confused. I know from my own experience and have seen others post their own answers and examples here on StackOverflow that multithreading is indeed possible in Python. So why is it that everyone keep saying that Python is locked by the GIL and that only one thread can run at a time? It clearly does work. Or is there some distinction I'm not getting here?

Many posters/respondents also keep mentioning that threading is limited because it does not make use of multiple cores. But I would say they are still useful because they do work simultaneously and thus get the combined workload done faster. I mean why would there even be a Python thread module otherwise?

Update:

Thanks for all the answers so far. The way I understand it is that multithreading will only run in parallel for some IO tasks, but can only run one at a time for CPU-bound multiple core tasks.

I'm not entirely sure what this means for me in practical terms, so I'll just give an example of the kind of task I'd like to multithread. For instance, let's say I want to loop through a very long list of strings and I want to do some basic string operations on each list item. If I split up the list, send each sublist to be processed by my loop/string code in a new thread, and send the results back in a queue, will these workloads run roughly at the same time? Most importantly will this theoretically speed up the time it takes to run the script?

Another example might be if I can render and save four different pictures using PIL in four different threads, and have this be faster than processing the pictures one by one after each other? I guess this speed-component is what I'm really wondering about rather than what the correct terminology is.

I also know about the multiprocessing module but my main interest right now is for small-to-medium task loads (10-30 secs) and so I think multithreading will be more appropriate because subprocesses can be slow to initiate.

解决方案 1：

The GIL does not prevent threading. All the GIL does is make sure only one thread is executing Python code at a time; control still switches between threads.

What the GIL prevents then, is making use of more than one CPU core or separate CPUs to run threads in parallel.

This only applies to Python code. C extensions can and do release the GIL to allow multiple threads of C code and one Python thread to run across multiple cores. This extends to I/O controlled by the kernel, such as select() calls for socket reads and writes, making Python handle network events reasonably efficiently in a multi-threaded multi-core setup.

What many server deployments then do, is run more than one Python process, to let the OS handle the scheduling between processes to utilize your CPU cores to the max. You can also use the multiprocessing library to handle parallel processing across multiple processes from one codebase and parent process, if that suits your use cases.

Note that the GIL is only applicable to the CPython implementation; Jython and IronPython use a different threading implementation (the native Java VM and .NET common runtime threads respectively).

To address your update directly: Any task that tries to get a speed boost from parallel execution, using pure Python code, will not see a speed-up as threaded Python code is locked to one thread executing at a time. If you mix in C extensions and I/O, however (such as PIL or numpy operations) and any C code can run in parallel with one active Python thread.

Python threading is great for creating a responsive GUI, or for handling multiple short web requests where I/O is the bottleneck more than the Python code. It is not suitable for parallelizing computationally intensive Python code, stick to the multiprocessing module for such tasks or delegate to a dedicated external library.

解决方案 2：

Yes. :)

You have the low level thread module and the higher level threading module. But if you simply want to use multicore machines, the multiprocessing module is the way to go.

Quote from the docs:

In CPython, due to the Global Interpreter Lock, only one thread can
execute Python code at once (even though certain performance-oriented
libraries might overcome this limitation). If you want your
application to make better use of the computational resources of
multi-core machines, you are advised to use multiprocessing. However,
threading is still an appropriate model if you want to run multiple
I/O-bound tasks simultaneously.

解决方案 3：

Threading is Allowed in Python, the only problem is that the GIL will make sure that just one thread is executed at a time (no parallelism).

So basically if you want to multi-thread the code to speed up calculation it won't speed it up as just one thread is executed at a time, but if you use it to interact with a database for example it will.

解决方案 4：

I feel for the poster because the answer is invariably "it depends what you want to do". However parallel speed up in python has always been terrible in my experience even for multiprocessing.

For example check this tutorial out (second to top result in google): https://www.machinelearningplus.com/python/parallel-processing-python/

I put timings around this code and increased the number of processes (2,4,8,16) for the pool map function and got the following bad timings:

serial 70.8921644706279 
parallel 93.49704207479954 tasks 2
parallel 56.02441442012787 tasks 4
parallel 51.026168536394835 tasks 8
parallel 39.18044807203114 tasks 16

code:

increase array size at the start

my compute node has 40 CPUs so I've got plenty to spare here

arr = np.random.randint(0, 10, size=[2000000, 600])
.... more code ....
tasks = [2,4,8,16]

for task in tasks:
    tic = time.perf_counter()
    pool = mp.Pool(task)

    results = pool.map(howmany_within_range_rowonly, [row for row in data])

    pool.close()
    toc = time.perf_counter()
    time1 = toc - tic
    print(f"parallel {time1} tasks {task}")