parallel programming with python
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parallel programming is a form of computation where many calculations or processes are carried out simultaneously. this approach is beneficial for tasks that can be divided into smaller sub-tasks that can run concurrently, improving performance, especially on multi-core processors.
in python, there are several libraries for parallel programming, among which the most commonly used are:
1. **threading**: suitable for i/o-bound tasks.
2. **multiprocessing**: suitable for cpu-bound tasks.
3. **concurrent.futures**: a high-level interface for asynchronously executing callables.
4. **dask**: a flexible library for parallel computing in python.
in this tutorial, we will focus on the `multiprocessing` and `concurrent.futures` libraries for parallel programming.
1. using the `multiprocessing` module
the `multiprocessing` module allows you to create multiple processes, each with its own python interpreter, which can take advantage of multiple cpu cores.
example: computing squares of numbers in parallel
explanation:
**def compute_square(n)**: this function computes the square of a number. we simulate a time-consuming task with `time.sleep(1)`.
**if _name_ == "__main__"**: this check prevents the code from being run when the module is imported elsewhere.
**multiprocessing.pool**: we create a pool of worker processes. we can specify the number of processes (in this case, 3).
**pool.map**: this method applies the `compute_square` function to each element in the `numbers` list in parallel.
the results are collected and printed.
2. using the `concurrent.futures` module
the `concurrent.futures` module provides a high-level interface for asynchronously executing callables. it includes both threads and processes.
example: using threadpoolexecutor for i/o-bound tasks
explanation:
**def fetch_data(n)**: this function simulates fetching data with a delay.
**concurrent.futures.threadpoolexecutor**: here, we create a thread pool with a maximum of 3 th ...
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