所以我試圖比較threading是更快還是multiprocessing。理論上，由于GIL，多處理應(yīng)該比多線程更快，因?yàn)橐淮沃贿\(yùn)行一個(gè)線程。但是我得到了相反的結(jié)果，即線程比多處理花費(fèi)的時(shí)間更少，我缺少什么請(qǐng)幫忙。下面是線程的代碼import threadingfrom queue import Queueimport timeprint_lock = threading.Lock()def exampleJob(worker):    time.sleep(10)      with print_lock:        print(threading.current_thread().name,worker)def threader():    while True:        worker = q.get()        exampleJob(worker)        q.task_done()q = Queue()for x in range(4):     t = threading.Thread(target=threader)     print(x)     t.daemon = True     t.start()start = time.time()for worker in range(8):    q.put(worker)q.join()print('Entire job took:',time.time() - start)下面是多處理的代碼import multiprocessing as mpimport timedef exampleJob(print_lock,worker):                 # function simulating some computation    time.sleep(10)    with print_lock:        print(mp.current_process().name,worker)def processor(print_lock,q):                       # function where process pick up the job    while True:        worker = q.get()        if worker is None: # flag to exit the process            break        exampleJob(print_lock,worker)if __name__ == '__main__':    print_lock = mp.Lock()    q = mp.Queue()    processes = [mp.Process(target=processor,args=(print_lock,q)) for _ in range(4)]    for process in processes:        process.start()        start = time.time()    for worker in range(8):        q.put(worker)    for process in processes:        q.put(None) # quit indicator    for process in processes:        process.join()    print('Entire job took:',time.time() - start)