python-parallelization

To parallelize Python code, you provide a sequential function and the strategy automates code transformation into a concurrent implementation, validating correctness and speedup while applying safe patterns to improve performance.

Use asyncio for IO-bound workloads and multiprocessing for CPU-bound tasks. The strategy classifies your workload type to select the appropriate concurrency approach, applying safe patterns and avoiding common pitfalls.

For data-parallel workloads, vectorization is suggested alongside multiprocessing or asyncio. The strategy selects the best fit by classifying workload type, performing code transformation, and validating speedup across data processing pipelines.

Yes, you can automatically transform sequential Python scripts into concurrent implementations. The strategy identifies parallelization opportunities, selects an approach like multiprocessing or asyncio, and performs the code transformation directly.

Vectorization works with multiprocessing for CPU-bound numeric simulations. The strategy identifies parallelization opportunities, selects vectorization or multiprocessing based on workload type, and validates correctness and speedup.

Common pitfalls when applying parallelization involve unsafe concurrent patterns. The strategy provides guidance on safe patterns and common pitfalls, ensuring correctness and speedup are validated after code transformation.