How do I slice sets in Python across multiple processes?

Learn how to effectively slice sets in Python across multiple processes. This guide covers how to use Python's multiprocessing module to split sets for parallel processing, enhancing performance for large datasets.

Python, Slicing Sets, Multiprocessing, Parallel Processing, Performance Optimization


# Example Python code to slice sets across multiple processes

import multiprocessing

def worker(sliced_set):
    # Process the sliced set
    return {x * 2 for x in sliced_set}

if __name__ == "__main__":
    # Original set
    original_set = set(range(100))

    # Determine the number of processes
    num_processes = multiprocessing.cpu_count()

    # Split the set into slices
    set_slices = [set(list(original_set)[i::num_processes]) for i in range(num_processes)]

    # Create a pool of workers
    with multiprocessing.Pool(processes=num_processes) as pool:
        results = pool.map(worker, set_slices)

    # Combine results from all processes
    combined_results = set.union(*results)
    
    print(combined_results)
    

Python Slicing Sets Multiprocessing Parallel Processing Performance Optimization