A Comprehensive Guide to Python Multiprocessing Programming
In compute-intensive tasks, Python’s multiprocessing programming is an important technical means to enhance program execution efficiency. Due to the limitations of the Global Interpreter Lock (GIL), Python’s multithreading cannot fully utilize multi-core CPU resources, while multiprocessing programming can break through this bottleneck by creating independent system processes. The multiprocessing module in the Python standard library provides a complete solution for this.
Basic Concepts of Multiprocessing
A process is the basic unit of resource allocation in an operating system, and each process has its own independent memory space. The multiprocessing module achieves parallel computation by creating child processes, which do not share memory with the main process by default, thus avoiding the common resource contention issues found in multithreading programming. Unlike low-level system calls such as os.fork(), the multiprocessing module abstracts platform differences and provides a cross-platform process management interface.
Core Components and Usage
Process Class
The Process class is the fundamental tool for creating child processes. Developers need to inherit this class and override the run() method, or specify the execution function through the target parameter. The following example demonstrates two ways to create a process:
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from multiprocessing import Process
# Method 1: Function call
def task(name): print(f’Child process executing task: {name}’)
if __name__ == ‘__main__’: p = Process(target=task, args=(‘process1’,)) p.start() p.join() # Method 2: Class inheritance
class MyProcess(Process): def __init__(self, name): super().__init__() self.name = name
def run(self): print(f’Child process executing task: {self.name}’)
if __name__ == ‘__main__’: p = MyProcess(‘process2’) p.start() p.join()
The start() method is used to start the process, and join() will block the main process until the child process ends. The daemon attribute can be used to set the process to daemon mode, where such processes will exit when the main process terminates.
Inter-Process Communication
Due to memory isolation between processes, multiprocessing provides various communication mechanisms:
Queue
The queue is implemented based on pipes and locks to ensure safe data transmission between processes. The following producer-consumer model demonstrates its usage:
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from multiprocessing import Process, Queue
def producer(q): q.put(‘Data1’)
q.put(‘Data2’)
def consumer(q): while not q.empty(): print(‘Consumed:’, q.get())
if __name__ == ‘__main__’: q = Queue() p1 = Process(target=producer, args=(q,)) p2 = Process(target=consumer, args=(q,)) p1.start() p2.start() p1.join() p2.join()
Pipe
Pipes provide a bidirectional communication channel, suitable for direct interaction between two processes:
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from multiprocessing import Pipe
def worker(conn): conn.send(‘Child process message’)
print(‘Received reply from main process:’, conn.recv())
conn.close()
if __name__ == ‘__main__’: parent_conn, child_conn = Pipe() p = Process(target=worker, args=(child_conn,)) p.start() print(‘Received message from child process:’, parent_conn.recv()) parent_conn.send(‘Main process reply’) p.join()
Shared Memory
Value and Array allow the creation of variables that can be shared between processes:
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from multiprocessing import Process, Value, Array
def modify_shared(n, arr): n.value = 3.14
for i in range(len(arr)): arr[i] *= 2
if __name__ == ‘__main__’: num = Value(‘d’, 0.0) arr = Array(‘i’, range(5))
p = Process(target=modify_shared, args=(num, arr)) p.start() p.join()
print(num.value) print(arr[:])
Process Pool
When managing a large number of processes, the Pool class can reuse process resources. The following example demonstrates parallel processing of batch tasks:
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from multiprocessing import Pool
def square(x): return x * x
if __name__ == ‘__main__’: with Pool(4) as pool: # Synchronous execution result = pool.apply(square, (5,)) print(result)
# Asynchronous execution async_result = pool.apply_async(square, (10,)) print(async_result.get())
# Batch processing results = pool.map(square, range(10)) print(results)
map_async() method supports non-blocking batch task submission, while close() and terminate() are used for normal shutdown and forced termination of the process pool, respectively.
Synchronization and Lock Mechanism
When multiple processes access shared resources, locks are needed to ensure data consistency. A typical use case of the Lock class is as follows:
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from multiprocessing import Process, Lock
def write_file(lock, content): with lock: with open(‘log.txt’, ‘a’) as f: f.write(content + ‘\n’)
if __name__ == ‘__main__’: lock = Lock() processes = [] for i in range(3): p = Process(target=write_file, args=(lock, f’Content {i}’)) processes.append(p) p.start() for p in processes: p.join()
Performance Optimization and Considerations
Control of Process Count: The number of processes is usually set to the number of CPU cores or cores + 1; too many processes can lead to increased context switching overhead.
Resource Release: Ensure timely recovery of resources from completed processes to avoid the accumulation of zombie processes.
Platform Differences: Windows systems use the spawn method to create processes, requiring the main logic to be wrapped in if __name__ == ‘__main__’.
Data Serialization: Objects passed through queues or pipes must be serializable; complex objects are recommended to be handled using the pickle module.
Debugging Tips: Use the logging module instead of print output, and use multiprocessing.log_to_stderr() to obtain detailed runtime logs.
While multiprocessing programming can effectively enhance program performance, it also brings higher complexity. Developers need to choose the appropriate solution based on the type of task (CPU-intensive or I/O-intensive) in practical applications, and when necessary, combine multithreading and coroutines to achieve the best results. The Manager component of the multiprocessing module also supports the creation of shared dictionaries, lists, and other data structures, providing more possibilities for process collaboration in complex scenarios.