6 Python libraries for parallel processing

Want to distribute that heavy Python workload across multiple CPUs or a compute cluster? These frameworks can make it happen

6 Python libraries for parallel processing
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Python is long on convenience and programmer-friendliness, but it isn’t the fastest programming language around. Some of its 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 the threading module built into Python 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. 

Python does include a native way to run a Python workload across multiple CPUs. The multiprocessing module spins up multiple copies of the Python interpreter, each on a separate core, and provides primitives for splitting tasks across cores. But sometimes even multiprocessing isn’t enough.

Sometimes the job calls for distributing work not only across multiple cores, but also across multiple machines. That’s where these six Python libraries and frameworks come in. All six of the Python toolkits below allow you to take an existing Python application and spread the work across multiple cores, multiple machines, or both.

Ray

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