Using multiprocessing to speed up Python programs

The video tutorial explains how to use Python's multiprocessing module to enhance performance by utilizing multiple CPUs. It covers the basics of multiprocessing, including creating a pool of interpreters and feeding jobs to them. The tutorial demonstrates a benchmark application that reads a large CSV file and uses multiprocessing to speed up the process. It discusses two main methods of multiprocessing: handling many small jobs or slicing a big job into smaller tasks. The video also highlights the use of the IMAP unordered function for task distribution and addresses the importance of managing overhead and resource contention. Key concepts include setting chunk size and understanding the trade-offs involved in multiprocessing.

How does Python handle contention for resources when using multiprocessing?

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What are some potential risks of using multiprocessing in Python?

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