동시성 — multiprocessing & concurrent.futures

Python code

import time
import math
from concurrent.futures import ProcessPoolExecutor, ThreadPoolExecutor, as_completed

def is_prime(n):
    """CPU 바운드 작업: 소수 판별"""
    if n < 2:
        return False
    for i in range(2, int(math.sqrt(n)) + 1):
        if n % i == 0:
            return False
    return True

def count_primes(start, end):
    """범위 내 소수 개수 계산"""
    return sum(1 for n in range(start, end) if is_prime(n))

def benchmark(executor_class, name, ranges):
    start = time.perf_counter()
    results = []
    with executor_class(max_workers=4) as executor:
        futures = {
            executor.submit(count_primes, s, e): (s, e)
            for s, e in ranges
        }
        for future in as_completed(futures):
            s, e = futures[future]
            count = future.result()
            results.append(count)
    elapsed = time.perf_counter() - start
    total = sum(results)
    print(f"  {name}: {total}개 소수, {elapsed:.3f}초")
    return elapsed

if __name__ == '__main__':
    RANGE = 500_000
    CHUNKS = 4
    chunk_size = RANGE // CHUNKS
    ranges = [(i * chunk_size, (i+1) * chunk_size) for i in range(CHUNKS)]

    print(f"0~{RANGE:,} 범위 소수 계산 (4 workers):")

    # 순차 실행
    start = time.perf_counter()
    total = count_primes(0, RANGE)
    seq_time = time.perf_counter() - start
    print(f"  순차 실행: {total}개 소수, {seq_time:.3f}초")

    # 멀티프로세스 (CPU 바운드에 효과적)
    proc_time = benchmark(ProcessPoolExecutor, "프로세스풀", ranges)

    # 멀티스레드 (GIL로 인해 CPU 바운드에 비효율)
    thread_time = benchmark(ThreadPoolExecutor, "스레드풀  ", ranges)

    print(f"\n속도 향상: 프로세스풀 {seq_time/proc_time:.1f}x")