71 lines
1.8 KiB
Python
71 lines
1.8 KiB
Python
from multiprocessing import Process
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from random import randint
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from time import time
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# This example compare the speed of CPU-bound operations
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# when using a sequential method and also when using a
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# multi-process method with the 'multiprocessing' Python
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# package
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def calculatePrimeFactors(n):
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prime_factors = []
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d = 2
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while d*d <= n:
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while (n % d) == 0:
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# supposing you want multiple factors repeated
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prime_factors.append(d)
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n //= d
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d += 1
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if n > 1:
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prime_factors.append(n)
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return prime_factors
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# Sequential Example
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def seq_crunch():
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print("Starting sequential number crunching")
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t0 = time()
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for i in range(10000):
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rand = randint(20000, 100000000)
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calculatePrimeFactors(rand)
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t1 = time()
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return f'{t1-t0:.2f}'
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# Multi-processing Example
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def executeProc():
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for i in range(1000):
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rand = randint(20000, 100000000)
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calculatePrimeFactors(rand)
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def multi_proc_crunch():
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print("Starting multi-process number crunching")
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t0 = time()
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processes = []
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# Here we create our processes and kick them off
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for i in range(10):
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proc = Process(target=executeProc, args=())
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processes.append(proc)
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proc.start()
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# Again we use the .join() method in order to wait for
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# execution to finish for all of our processes
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for process in processes:
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process.join()
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t1 = time()
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return f'{t1-t0:.2f}'
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if __name__ == '__main__':
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seq_time = seq_crunch()
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multi_proc_time = multi_proc_crunch()
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print('\n==================================')
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print(f'Crunch prime factors sequentially: {seq_time}s')
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print(f'Crunch prime factors using multi-processing: {multi_proc_time}s')
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print('==================================')
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