Python's multiprocessing module allows developers to take advantage of multiple CPU cores, but moving data between processes can be slow, which reduces the performance benefits of using worker processes.
Processes in Python do not share the same memory space, which means data needs to be copied between them, resulting in computation overhead. This overhead can be reduced by minimizing the amount of data being passed between processes, or by using libraries such as NumPy or Polars, which are designed for parallelism.
Passing data via multiprocessing.shared_memory or writing data to disk with modules such as Parquet can also help. However, using the "fork" context in Linux can cause deadlocks and data corruption, making it an unreliable option. Developers should measure their software's performance and identify bottlenecks before deciding on a solution.
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