66 lines
1.5 KiB
Python
66 lines
1.5 KiB
Python
#!/usr/bin/env python3
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"""
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threads.py: shows that Python threads are slower than
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sequential code for CPU-intensive work.
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"""
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import os
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import sys
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from queue import SimpleQueue
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from time import perf_counter
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from typing import NamedTuple
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from threading import Thread
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from primes import is_prime, NUMBERS
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class PrimeResult(NamedTuple): # <3>
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n: int
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prime: bool
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elapsed: float
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JobQueue = SimpleQueue[int]
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ResultQueue = SimpleQueue[PrimeResult]
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def check(n: int) -> PrimeResult:
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t0 = perf_counter()
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res = is_prime(n)
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return PrimeResult(n, res, perf_counter() - t0)
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def worker(jobs: JobQueue, results: ResultQueue) -> None:
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while n := jobs.get():
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results.put(check(n))
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def main() -> None:
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if len(sys.argv) < 2: # <1>
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workers = os.cpu_count() or 1 # make mypy happy
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else:
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workers = int(sys.argv[1])
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print(f'Checking {len(NUMBERS)} numbers with {workers} threads:')
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jobs: JobQueue = SimpleQueue() # <2>
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results: ResultQueue = SimpleQueue()
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t0 = perf_counter()
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for n in NUMBERS: # <3>
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jobs.put(n)
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for _ in range(workers):
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proc = Thread(target=worker, args=(jobs, results)) # <4>
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proc.start() # <5>
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jobs.put(0) # <6>
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while True:
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n, prime, elapsed = results.get() # <7>
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label = 'P' if prime else ' '
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print(f'{n:16} {label} {elapsed:9.6f}s')
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if jobs.empty(): # <8>
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break
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elapsed = perf_counter() - t0
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print(f'Total time: {elapsed:.2f}s')
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if __name__ == '__main__':
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main()
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