Parallelization in Python and Machine Learning

1. What is Parallelization?

Parallelization is the process of running multiple tasks simultaneously instead of sequentially, which reduces total computation time.

• Sequential execution: Tasks run one after another. Example: Evaluate 23 hyperparameter combinations one by one.
• Parallel execution: Tasks are distributed across multiple CPU cores and run simultaneously. Example: Evaluate 23 combinations at the same time.

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2. Parallelization in Python

Python provides several ways to run tasks in parallel:

1. multiprocessing module: Creates separate processes that run on different CPU cores, avoiding the Global Interpreter Lock (GIL).
2. concurrent.futures.ProcessPoolExecutor: High-level interface for asynchronous task execution.

Example:

from multiprocessing import Pool

def task(x):
    # Simulate some computation
    return x*x

if __name__ == "__main__":
    inputs = list(range(23))  # 23 tasks
    with Pool(processes=23) as pool:  # 23 parallel processes
        results = pool.map(task, inputs)

    print(results)

Each of the 23 tasks runs in parallel, significantly speeding up the program compared to sequential execution.

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3. Parallelization in IRACE (Machine Learning)

• IRACE evaluates multiple candidate hyperparameter configurations.
• Parallelization allows many candidates to be evaluated simultaneously.
• This is especially important when each evaluation is computationally expensive.
• The number of parallel sessions (23 in this case) often matches the number of CPU cores available to fully utilize the hardware.

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4. Summary

• Parallelization reduces total runtime for computationally heavy tasks.
• Python’s multiprocessing module makes multi-core execution straightforward.
• IRACE supports parallel evaluation for faster hyperparameter tuning.
• Choose the number of parallel processes according to available CPU cores.