Concurrent and parallel programming: Parallel programming

What is Parallel Programming?

Parallel programming is a programming model that allows multiple operations to be executed simultaneously by dividing large tasks into smaller subtasks that can be executed in parallel on different cores or processors.

Advantages of Parallel Programming

• Improved Performance: Tasks execute simultaneously, speeding up the overall process.
• Scalability: Parallel applications can take advantage of more CPU cores or more machines for distributed tasks.
• Reduced Execution Time: It allows the total execution time to be significantly reduced by dividing workloads.

Parallel Programming Example in Python with Multiprocessing

This code shows how to use the `multiprocessing` module to run tasks in parallel in different processes:

import multiprocessing

def task(number):
    print(f"Task {number} executed in process {multiprocessing.current_process().name}")

if __name__ == "__main__":
    processes = []
    for i in range(5):
        p = multiprocessing.Process(target=task, args=(i,))
        processes.append(p)
        p.start()

    for p in processes:
        p.join()

In this example, we created multiple processes that execute the same `task` function in parallel, each with a different argument.

Conclusion

Parallel programming is essential for harnessing the power of multi-core systems and improving the efficiency of applications that require high performance when executing concurrent tasks.