Python Multiprocessing

True parallelism in Python.

Basic Multiprocessing

```python from multiprocessing import Process

def worker(name): print(f"Worker {name} starting...") # CPU-intensive work result = sum([i ** 2 for i in range(1000000)]) print(f"Worker {name} done!")

if __name__ == "__main__": processes = [] for i in range(4): p = Process(target=worker, args=(i,)) p.start() processes.append(p) for p in processes: p.join() ```

Process Pool

```python from multiprocessing import Pool

def square(n): return n ** 2

if __name__ == "__main__": with Pool(4) as pool: results = pool.map(square, range(10)) print(results) # [0, 1, 4, 9, 16, 25, 36, 49, 64, 81] ```

Share Data with Queue

```python from multiprocessing import Process, Queue

def worker(queue, name): queue.put(f"Result from {name}")

if __name__ == "__main__": queue = Queue() processes = [] for i in range(3): p = Process(target=worker, args=(queue, f"Worker{i}")) p.start() processes.append(p) for p in processes: p.join() while not queue.empty(): print(queue.get()) ```

Parallel Processing

```python from multiprocessing import Pool

def process_file(filename): # Process large file with open(filename) as f: return len(f.read())

if __name__ == "__main__": files = ["file1.txt", "file2.txt", "file3.txt"] with Pool() as pool: results = pool.map(process_file, files) print(f"Total size: {sum(results)}") ```

Remember

- Use for CPU-bound tasks - Each process has own memory - Must use if __name__ == "__main__"