In-depth Comparison and Application Scenarios of Multithreading vs. Multiprocessing in Python

In Python, multithreading and multiprocessing are the two primary methods for achieving concurrent programming. Each has its own characteristics and is suitable for different scenarios. Understanding their differences and applicable conditions is crucial for writing efficient concurrent programs.

1. Basic Concepts

• Process: The basic unit of operating system resource allocation. Each process has its own independent memory space, data segment, and code segment. Processes are isolated from each other; the crash of one process does not directly affect others.
• Thread: The basic unit of CPU scheduling. A thread is an execution unit within a process. Multiple threads within the same process share the process's memory and resources, but each thread has its own independent stack and registers.

2. Special Limitation in Python: GIL (Global Interpreter Lock)

• The GIL is a mutex lock in the CPython interpreter that ensures only one thread executes Python bytecode at a time.
• Due to the GIL, Python multithreading cannot achieve true parallelism in CPU-intensive tasks because even with multiple CPU cores, only one thread runs at any given moment.
• Multiprocessing is not limited by the GIL, as each process has its own independent GIL, enabling true parallel computation.

3. Comparative Analysis of Multithreading and Multiprocessing

3.1 Creation and Destruction Overhead

• Multithreading: Thread creation and destruction have lower overhead because threads share process resources, and context switching is fast.
• Multiprocessing: Process creation and destruction have higher overhead, requiring allocation of independent memory space and incurring higher context-switching costs.

3.2 Memory Sharing and Communication

• Multithreading: Threads share global variables and can directly read/write the same memory space, but locks (e.g., threading.Lock) are necessary to avoid race conditions.
• Multiprocessing: Processes have isolated memory and cannot directly share variables. Communication must use IPC mechanisms such as queues (multiprocessing.Queue), pipes (Pipe), or shared memory (Value, Array).

3.3 Code Example Comparison

• Multithreading Example (I/O-intensive task):

import threading
import time

def task(name):
    print(f"{name} started")
    time.sleep(2)  # Simulate I/O operation
    print(f"{name} finished")

threads = []
for i in range(3):
    t = threading.Thread(target=task, args=(f"Thread-{i}",))
    threads.append(t)
    t.start()

for t in threads:
    t.join()

• Multiprocessing Example (CPU-intensive task):

import multiprocessing
import time

def compute(name):
    print(f"{name} started")
    # Simulate CPU-intensive computation
    result = sum(i*i for i in range(10**6))
    print(f"{name} finished")

processes = []
for i in range(3):
    p = multiprocessing.Process(target=compute, args=(f"Process-{i}",))
    processes.append(p)
    p.start()

for p in processes:
    p.join()

4. Summary of Applicable Scenarios

• Multithreading Applicable Scenarios:
  • I/O-intensive tasks (e.g., network requests, file I/O, database operations), where threads spend most of their time waiting for I/O, minimizing GIL impact.
  • Scenarios requiring lightweight concurrency and data sharing (e.g., GUI event handling).
• Multiprocessing Applicable Scenarios:
  • CPU-intensive tasks (e.g., scientific computing, image processing), leveraging multiple CPU cores for true parallelism.
  • Scenarios requiring process isolation for improved stability (a crash in one process does not affect others).

5. Selection Recommendations

• If the task is primarily I/O-bound, prioritize multithreading (lower overhead).
• If it involves heavy CPU computation, choose multiprocessing (bypasses the GIL).
• Consider data sharing needs: multithreading facilitates sharing but requires careful synchronization; multiprocessing requires explicit communication.
• Note resource limitations: the number of processes is limited by CPU cores, while more threads are possible but must balance context-switching costs.

Through the above comparison, developers can reasonably choose between multithreading and multiprocessing based on specific task types and resource requirements to achieve efficient concurrency.