Python: Why is threaded function slower than non thread

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别跟我提以往
别跟我提以往 2020-12-11 19:09

Hello I\'m trying to calculate the first 10000 prime numbers.

I\'m doing this first non threaded and then splitting the calculation in 1 to 5000 and 5001 to 10000. I

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  • 2020-12-11 19:43

    You can use the multiprocessing module, which gives results like below:

    ('Non threaded Duration: ', 0.016599999999999997, 'seconds')
    ('Threaded Duration: ', 0.007172000000000005, 'seconds')
    

    ...after making just these changes to your code (changing 'Thread' to 'Process'):

    import math
    #from threading import Thread
    from multiprocessing import Process
    
    def nonThreaded():
        primeNtoM(1,10000)
    
    
    def threaded():
        #t1 = Thread(target=primeNtoM, args=(1,5000))
        #t2 = Thread(target=primeNtoM, args=(5001,10000))
        t1 = Process(target=primeNtoM, args=(1,5000))
        t2 = Process(target=primeNtoM, args=(5001,10000))
        t1.start()
        t2.start()
        t1.join()
        t2.join()
    

    By spawning actual OS processes instead of using in-process threading, you eliminate the GIL issues discussed in @Luis Masuelli's answer.

    multiprocessing is a package that supports spawning processes using an API similar to the threading module. The multiprocessing package offers both local and remote concurrency, effectively side-stepping the Global Interpreter Lock by using subprocesses instead of threads. Due to this, the multiprocessing module allows the programmer to fully leverage multiple processors on a given machine. It runs on both Unix and Windows.

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  • 2020-12-11 19:50

    from: https://wiki.python.org/moin/GlobalInterpreterLock

    In CPython, the global interpreter lock, or GIL, is a mutex that prevents multiple native threads from executing Python bytecodes at once. This lock is necessary mainly because CPython's memory management is not thread-safe. (However, since the GIL exists, other features have grown to depend on the guarantees that it enforces.)

    This means: since this is CPU-intensive, and python is not threadsafe, it does not allow you to run multiple bytecodes at once in the same process. So, your threads alternate each other, and the switching overhead is what you get as extra time.

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