In Python scientific computing, how do I parallelize workloads?

In Python scientific computing, you can parallelize workloads using various libraries and techniques, such as the built-in `multiprocessing` module, `concurrent.futures`, and third-party libraries like `joblib` or `Dask`. These tools help distribute computation across multiple CPU cores or even different machines to speed up processing times.

Here's a simple example of how you might use the `multiprocessing` module to parallelize tasks:

import multiprocessing def square(n): return n * n if __name__ == '__main__': numbers = [1, 2, 3, 4, 5] with multiprocessing.Pool() as pool: results = pool.map(square, numbers) print(results) # Output: [1, 4, 9, 16, 25]

Python parallelization multiprocessing scientific computing concurrent.futures joblib Dask

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