Parallize

Parallize is a Python package that provides utilities to parallelize synchronous functions using the concurrent.futures.ProcessPoolExecutor and convert it into async functions to prevent the main thread from blocking. This allows you to execute functions in separate processes, leveraging multiple CPU cores for improved performance.

Features

• Parallelize Synchronous Functions: Execute synchronous functions in parallel using multiple processes.
• Parallelize Asynchronous Functions: Execute asynchronous functions in parallel using multiple processes.
• Customizable Worker Count: Specify the maximum number of worker processes to use, or let the package use the number of available CPU cores by default.

Mini Benchmark

Here are the updated benchmark results comparing serial and parallel execution times using the test_aparallize.py test case:

Benchmark Details

• Concurrent Execution Time: The time taken to execute the CPU-bound task concurrently using a ThreadPoolExecutor.
• Parallel Execution Time: The time taken to execute the same task in parallel using the aparallize decorator.
• Speedup: The ratio of serial execution time to parallel execution time, indicating the performance improvement achieved by parallelizing the task.

Installation

To install Parallize, you can use pip:

pip install parallize

Usage

from parallize import aparallize

def my_function(x, y):
    return x + y

# Call the function as usual
result = await aparallize(my_function)(1, 2)
print(result)  # This will be executed in a separate process

Customizing the Number of Workers

You can specify the maximum number of worker processes to use by passing the max_workers argument to the decorator:

def my_function(x, y):
    return x + y

result = await aparallize(my_function, max_workers=4)(1, 2)

License

This project is licensed under the MIT License. See the LICENSE file for details.

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

Issues

If you encounter any issues or have suggestions for improvements, please open an issue on the GitHub repository.

Acknowledgments

• Thanks to the Python community for providing powerful tools and libraries for parallel processing.

@vikyw89-20240804