Multiprocessing¶

Multiprocessing in Python allows you to leverage multiple CPU cores to perform computations simultaneously, significantly improving performance for CPU-bound tasks.

Basic Operations¶

Basic Multiprocessing Components

from multiprocessing import Pool, cpu_count

Pool: Creates a pool of worker processes
cpu_count(): Returns the number of CPU cores
Simple Multiprocessing Example

def process_item(item):
    """Function to process a single item"""
    # Perform computation on the item
    return processed_item

def parallel_processing():
    # Determine number of processes
    num_processes = max(1, cpu_count() - 1)

    # Create a process pool
    with Pool(processes=num_processes) as pool:
        # Process items in parallel
        results = pool.map(process_item, data_list)

    return results

Performance Considerations¶

When to Use Multiprocessing¶

CPU-intensive tasks
Large datasets
Independent computations
Data processing and analysis

Common Pitfalls¶

Overhead of creating processes
Increased memory consumption
Serialization of data between processes

Performance Optimization Tips¶

Minimize data transfer between processes
Keep processing functions simple
Use appropriate chunk sizes
Monitor memory usage
Benchmark and profile your code