Python 中的併發性

併發性通常被誤認為並行性。併發性意味著以系統的方式排程獨立的程式碼來執行。本章重點介紹使用 Python 為作業系統執行併發性。

以下程式有助於為作業系統執行併發性 −

import os
import time
import threading
import multiprocessing

NUM_WORKERS = 4

def only_sleep():
   print("PID: %s, Process Name: %s, Thread Name: %s" % (
      os.getpid(),
      multiprocessing.current_process().name,
      threading.current_thread().name)
   )
   time.sleep(1)

def crunch_numbers():
   print("PID: %s, Process Name: %s, Thread Name: %s" % (
      os.getpid(),
      multiprocessing.current_process().name,
      threading.current_thread().name)
   )
   x = 0
   while x < 10000000:
      x += 1
for _ in range(NUM_WORKERS):
   only_sleep()
end_time = time.time()
print("Serial time=", end_time - start_time)

# Run tasks using threads
start_time = time.time()
threads = [threading.Thread(target=only_sleep) for _ in range(NUM_WORKERS)]
[thread.start() for thread in threads]
[thread.join() for thread in threads]
end_time = time.time()

print("Threads time=", end_time - start_time)

# Run tasks using processes
start_time = time.time()
processes = [multiprocessing.Process(target=only_sleep()) for _ in range(NUM_WORKERS)]
[process.start() for process in processes]
[process.join() for process in processes]
end_time = time.time()

print("Parallel time=", end_time - start_time)

輸出

以上程式生成以下輸出 −

解釋

“多處理”是一個與處理模組類似的包。此包支援本地和遠端併發性。由於此模組，程式設計師可以利用在給定系統上使用多個程序。