下面是一个实用的Python编程示例，展示了如何使用`multiprocessing`模块创建多个工作进程来处理任务。这个示例包括定义一个工作任务函数，然后使用`multiprocessing.Pool`来并行执行该任务。 ### 示例：使用multiprocessing.Pool并行执行任务 ```python import multiprocessing import time def worker_function(name, task_queue, result_queue): """ 工作进程执行的任务函数。 :param name: 进程名称 :param task_queue: 任务队列 :param result_queue: 结果队列 """ while True: try: task = task_queue.get_nowait() print(f"Process {name} is processing task: {task}") time.sleep(1) # 模拟任务耗时 result_queue.put(f"Task {task} completed by {name}") except Exception as e: # 队列为空，退出循环 print(f"Process {name} has finished all tasks.") break def main(): # 创建任务队列和结果队列 task_queue = multiprocessing.Queue() result_queue = multiprocessing.Queue() # 向任务队列中添加任务 for i in range(10): task_queue.put(i) # 创建工作进程池 num_processes = 4 processes = [] for i in range(num_processes): process_name = f"P{i}" process = multiprocessing.Process(target=worker_function, args=(process_name, task_queue, result_queue)) processes.append(process) process.start() # 等待所有进程完成 for process in processes: process.join() # 输出结果队列中的内容 while not result_queue.empty(): print(result_queue.get()) if __name__ == "__main__": main() ``` #### 代码说明 1. **worker_function**: 这是每个工作进程执行的函数。它从任务队列中获取任务，模拟处理任务（通过`time.sleep(1)`），然后将结果放入结果队列。 2. **task_queue 和 result_queue**: 使用`multiprocessing.Queue`来在进程间传递任务和结果。 3. **multiprocessing.Process**: 创建多个工作进程，并启动它们。 4. **join()**: 主进程等待所有子进程完成后再继续执行。 5. **结果输出**: 最后，主进程从结果队列中取出所有结果并打印。 #### 输出示例 ``` Process P0 is processing task: 0 Process P1 is processing task: 1 Process P2 is processing task: 2 Process P3 is processing task: 3 Process P0 is processing task: 4 Process P1 is processing task: 5 Process P2 is processing task: 6 Process P3 is processing task: 7 Process P0 is processing task: 8 Process P1 is processing task: 9 Process P0 has finished all tasks. Process P1 has finished all tasks. Process P2 has finished all tasks. Process P3 has finished all tasks. Task 0 completed by P0 Task 1 completed by P1 Task 2 completed by P2 Task 3 completed by P3 Task 4 completed by P0 Task 5 completed by P1 Task 6 completed by P2 Task 7 completed by P3 Task 8 completed by P0 Task 9 completed by P1 ``` #### 应用场景 - **并行数据处理**: 适用于需要处理大量独立任务的场景，例如批量文件处理、数据清洗等。 - **分布式任务调度**: 可以扩展为分布式任务调度系统的基础框架。 - **高性能计算**: 在多核CPU上执行计算密集型任务时，可以显著提高效率。 此示例展示了如何利用Python的`multiprocessing`模块进行并行处理任务，适用于需要高效利用多核CPU资源的场景[^4]。 ---