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python-parallelization

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Transform sequential Python code into parallel/concurrent implementations. Use when asked to parallelize Python code, improve code performance through concurrency, convert loops to parallel execution, or identify parallelization opportunities. Handles CPU-bound (multiprocessing), I/O-bound (asyncio, threading), and data-parallel (vectorization) scenarios.

When to use

• parallelize Python code
• improve code performance through concurrency
• convert loops to parallel execution
• identify parallelization opportunities
• handle CPU-bound multiprocessing
• handle I/O-bound asyncio and threading
• handle data-parallel vectorization

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