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https://www.praetorian.com/blog/multi-core-and-distributed-programming-in-python
Aug 16, 2012 · The Multi-Core Approach: The multiprocessing package has been available as of Python 2.6, and provides a relatively simple mechanism for creating a sub-process. IMHO, this is much simpler than using threading , which we’ll leave as an exercise for the reader to explore.
https://p16.praetorian.com/blog/multi-core-and-distributed-programming-in-python
The Multi-Core Approach: The multiprocessing package has been available as of Python 2.6, and provides a relatively simple mechanism for creating a sub-process. IMHO, this is much simpler than using threading , which we’ll leave as an exercise for the reader to explore.
https://blog.dominodatalab.com/multicore-data-science-r-python/
May 22, 2017 · In the Multicore Data Science on R and Python video we cover a number of R and Python tools that allow data scientists to leverage large-scale architectures to collect, write, munge, and manipulate data, as well as train and validate models on multicore architectures. You will see sample code, real-world benchmarks, and running of experiments on AWS X1 instances using Domino.
https://github.com/ericsnowcurrently/multi-core-python
Aug 17, 2018 · Python's multi-core story is murky at best. Not only can we be more clear on the matter, we can improve Python's support. The result of any effort must make multi-core (and concurrency) support in Python obvious, unmistakable, and undeniable (and keep it Pythonic).
https://www.quantstart.com/articles/Parallelising-Python-with-Threading-and-Multiprocessing/
Above we alluded to the fact that Python on the CPython interpreter does not support true multi-core execution via multithreading. However, Python DOES have a Threading library. So what is the benefit of using the library if we (supposedly) cannot make use of multiple cores?
http://python-notes.curiousefficiency.org/en/latest/python3/multicore_python.html
Both the Python reference interpreter (CPython), and the alternative interpeter that offers the fastest single-threaded performance for pure Python code (PyPy) use a Global Interpreter Lock to avoid various problems that arise when using threading models that implicitly allowing concurrent access to objects from multiple threads of execution.
https://docs.python.org/2/library/multiprocessing.html
multiprocessing is a package that supports spawning processes using an API similar to the threading module. The multiprocessing package offers both local and remote concurrency, effectively side-stepping the Global Interpreter Lock by using subprocesses instead of threads.
https://blenderartists.org/t/multi-core-cpus-and-python/396091
Apr 23, 2018 · If you really want “true” multi-threading for Python, use Jython or IronPython; the JVM and the CLR do support multi-CPU threads. Of course, be prepared for deadlocks, live-locks, race conditions, and all the other nuisances that come with multi-threaded code.
https://www.reddit.com/r/programming/comments/3crxzm/solving_multicore_python/
It has support for multiple different backends for multi-core support, including using an IPython client to distribute across clusters of computers without you having to worry about it. Essentially the core of the library is that it breaks your large data set into chunks, performs various computations, then returns the result of each chunked computation, often aggregated back into a single array or value.
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