Multiprocessing in python python tutorial day 98
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# python multiprocessing tutorial
introduction
the `multiprocessing` module in python is a powerful library that allows you to create multiple processes, enabling parallel execution of code. this is especially useful for cpu-bound tasks where you want to take full advantage of multiple cpu cores. unlike threads, which are limited by python's global interpreter lock (gil), processes can run truly concurrently on separate cpu cores.
key concepts
1. **process**: an instance of a running program. each process has its own memory space.
2. **queue**: a thread- and process-safe way to pass messages between processes.
3. **pool**: a convenient way to manage a pool of worker processes.
why use multiprocessing?
- to speed up cpu-bound operations (like numerical computations).
- to perform i/o-bound tasks (like web scraping) concurrently.
- to utilize multiple cores of a machine.
basic usage
here’s a simple tutorial on how to use the `multiprocessing` module.
step 1: import the required libraries
step 2: define a function to run in parallel
let’s create a simple function that simulates a time-consuming operation:
step 3: create processes
you can create processes using the `process` class. here’s how to spawn multiple processes:
step 4: using a pool of workers
using a `pool` can simplify the management of multiple processes:
step 5: using queues for inter-process communication
you can use `queue` to send and receive data between processes:
conclusion
the `multiprocessing` module in python is a powerful tool for achieving parallelism in your programs. by creating multiple processes, you can effectively utilize your cpu cores to speed up computations and handle i/o-bound tasks concurrently. whether you use individual processes or pools for managing workers, the `multiprocessing` module provides a robust framework for concurrent execution.
feel free to experiment with the examples provided and adapt them to your spec ...
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