Python 界有條不成文的準則： 計算密集型任務適合多進程，IO 密集型任務適合多線程。本篇來作個比較。

通常來說多線程相對于多進程有優勢，因為創建一個進程開銷比較大，然而因為在 python 中有 GIL 這把大鎖的存在，導致執行計算密集型任務時多線程實際只能是單線程。而且由于線程之間切換的開銷導致多線程往往比實際的單線程還要慢，所以在 python 中計算密集型任務通常使用多進程，因為各個進程有各自獨立的 GIL，互不干擾。

而在 IO 密集型任務中，CPU 時常處于等待狀態，操作系統需要頻繁與外界環境進行交互，如讀寫文件，在網絡間通信等。在這期間 GIL 會被釋放，因而就可以使用真正的多線程。

以上是理論，下面做一個簡單的模擬測試： 大量計算用 math.sin() + math.cos() 來代替，IO 密集型用 time.sleep() 來模擬。 在 Python 中有多種方式可以實現多進程和多線程，這里一并納入看看是否有效率差異：

多進程： joblib.multiprocessing, multiprocessing.Pool, multiprocessing.apply_async, concurrent.futures.ProcessPoolExecutor 多線程： joblib.threading, threading.Thread, concurrent.futures.ThreadPoolExecutor

from multiprocessing import Poolfrom threading import Threadfrom concurrent.futures import ThreadPoolExecutor, ProcessPoolExecutorimport time, os, mathfrom joblib import Parallel, delayed, parallel_backenddef f_IO(a): # IO 密集型 time.sleep(5)def f_compute(a): # 計算密集型 for _ in range(int(1e7)): math.sin(40) + math.cos(40) returndef normal(sub_f): for i in range(6): sub_f(i) returndef joblib_process(sub_f): with parallel_backend('multiprocessing', n_jobs=6): res = Parallel()(delayed(sub_f)(j) for j in range(6)) returndef joblib_thread(sub_f): with parallel_backend(’threading’, n_jobs=6): res = Parallel()(delayed(sub_f)(j) for j in range(6)) returndef mp(sub_f): with Pool(processes=6) as p: res = p.map(sub_f, list(range(6))) returndef asy(sub_f): with Pool(processes=6) as p: result = [] for j in range(6): a = p.apply_async(sub_f, args=(j,)) result.append(a) res = [j.get() for j in result]def thread(sub_f): threads = [] for j in range(6): t = Thread(target=sub_f, args=(j,)) threads.append(t) t.start() for t in threads: t.join()def thread_pool(sub_f): with ThreadPoolExecutor(max_workers=6) as executor: res = [executor.submit(sub_f, j) for j in range(6)]def process_pool(sub_f): with ProcessPoolExecutor(max_workers=6) as executor: res = executor.map(sub_f, list(range(6)))def showtime(f, sub_f, name): start_time = time.time() f(sub_f) print('{} time: {:.4f}s'.format(name, time.time() - start_time))def main(sub_f): showtime(normal, sub_f, 'normal') print() print('------ 多進程 ------') showtime(joblib_process, sub_f, 'joblib multiprocess') showtime(mp, sub_f, 'pool') showtime(asy, sub_f, 'async') showtime(process_pool, sub_f, 'process_pool') print() print('----- 多線程 -----') showtime(joblib_thread, sub_f, 'joblib thread') showtime(thread, sub_f, 'thread') showtime(thread_pool, sub_f, 'thread_pool')if __name__ == '__main__': print('----- 計算密集型 -----') sub_f = f_compute main(sub_f) print() print('----- IO 密集型 -----') sub_f = f_IO main(sub_f)

結果：

----- 計算密集型 -----normal time: 15.1212s------ 多進程 ------joblib multiprocess time: 8.2421spool time: 8.5439sasync time: 8.3229sprocess_pool time: 8.1722s----- 多線程 -----joblib thread time: 21.5191sthread time: 21.3865sthread_pool time: 22.5104s----- IO 密集型 -----normal time: 30.0305s------ 多進程 ------joblib multiprocess time: 5.0345spool time: 5.0188sasync time: 5.0256sprocess_pool time: 5.0263s----- 多線程 -----joblib thread time: 5.0142sthread time: 5.0055sthread_pool time: 5.0064s

上面每一方法都統一創建6個進程/線程，結果是計算密集型任務中速度：多進程 > 單進程/線程 > 多線程， IO 密集型任務速度： 多線程 > 多進程 > 單進程/線程。

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