Some of Python's speed limitations are due to its default implementation, CPython, being single-threaded. That is, CPython doesn't use more than one hardware thread at a time.
And while you can use Python's built-in threading module to speed things up, threading only gives you concurrency, not parallelism. It's good for running multiple tasks that aren't CPU-dependent, but does nothing to speed up multiple tasks that each require a full CPU.
Here are seven frameworks you can use to spread an existing Python application and its workload across multiple cores, multiple machines, or both.
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