![]() Sometimes the job calls for distributing work not only across multiple cores, but also across multiple machines. ![]() But sometimes even multiprocessing isn’t enough. The multiprocessing module spins up multiple copies of the Python interpreter, each on a separate core, and provides primitives for splitting tasks across cores. ![]() Python does include a native way to run a Python workload across multiple CPUs. 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. That is, cPython doesn’t use more than one hardware thread at a time.Īnd while you can use the threading module built into Python to speed things up, threading only gives you concurrency, not parallelism. ![]() Some of its speed limitations are due to its default implementation, cPython, being single-threaded. Python is long on convenience and programmer-friendliness, but it isn’t the fastest programming language around.
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