multithreading - Python: Why is threaded function slower than non thread -

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

I am doing this for the first time non-threaded and then the calculation was divided between 1 to 5000 and 5001 to 10000. I expected it to be very fast using thread, but the output happens in this way:

  ------ - Result -------- Non Threaded duration: 0.012244000000000005 seconds thread thread: 0.012839000000000017 sec  

There is not really any major difference, except that the threaded function is slightly slower.

This is my code:

  Importing Threading Import from Threading Import Math Thread DF Non-Thread (): PrimeNatom (1,10000) DRF Thread (): T1 = Thread (Target = PrimeNet MM, RGS = (1,5000)) T2 = Thread (Target = PrimeNet MM, RGR = (5001,10000)) T1 Start () T2 Start () Join T1 () t2.join () def is_prime (n): If n% 2 == 0 and n> 2: Returns wrong in True category (3, int (math.sqrt (n) ) If (n & gt; m): For print in range (N, M) i ("should be smaller than n meters"): if (is_prime (i)): l. phen (i) if __name__ == '__main__': Import time print ("-------- non-thread count --------") nTstart_time = time.clock () non-thread () non-threaded time = time.clock () - print nTstart_time ("-------- thread count --------") Tstart_time = time.clock () Threaded () Thread Time = time.clock () - Tstart_time Print ( "-------- Result (" Threaded A Print: "Non-Threaded Period:", Non-Thread Time, "Seconds") Print  

From:

In CPython, Global Interpreter Lock, or GIL, is a Mute X by executing Python Bytecodes at once This lock prevents many original threads Mainly required because CPython's memory management is not thread-safe (although, GIL exists , The other attributes guarantee that it applies.)

It means: Since this is CPU-intensive, and not a python thread, it will give you a This process does not allow multiple bitcodes to run at once, therefore, your threads alternate each other, and the switching overhead that you get in the form of additional time.