EuroPythonとEuroPython2015に関するrokujyouhitomaのブックマーク (11)
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rokujyouhitoma 2015/08/11
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The Python Compiler
The Python compiler Nuitka has evolved from an absurdly compatible Python to C++ translator into a statically optimizing Python compiler. The mere peephole optimization is now accompanied by full function/module level optimization, with more to come, and only increased compatibility. Witness local and module variable value propagation, function in-lining with suitable code, and graceful degradatio
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Get native with Cython
Cython is not only an excellent and widely used tool to speed up computational Python code, it’s also a very comfortable way to talk to native code and libraries. The Cython compiler translates Python code to C or C++ code, and supports static type annotations to allow direct use of C/C++ data types and functions. The tight integration of all three languages makes it possible to freely mix Python
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PyPy and the future of the Python ecosystem
Python has a great versatile ecosystem but the competition is getting better, this talk is about how Python can keep up with these new languages and where PyPy fits into this. Recently we’ve seen the rise of new technologies like Go, Node.js and Julia, those have the ability to build an ecosystem on a clean slate and thus be better than Python in some aspects. What would it take to be as good as t
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The GIL is dead: PyPy-STM
Take a big, non-multithreaded program, and run in on multiple cores! PyPy, the Python implementation written in Python, experimentally supports Software Transactional Memory (STM). It runs without the Global Interpreter Lock (GIL). The strength of STM is not only to remove the GIL, but to also enable a novel use of multithreading, inheritently safe, and more useful in the general case than other a
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Python and PyPy performance (not) for dummies
In this talk we would like to have a short introduction on how Python programs are compiled and executed, with a special attention towards just in time compilation done by PyPy. PyPy is the most advanced Python interpreter around and while it should generally just speed up your programs there is a wide range of performance that you can get out of PyPy, ranging from slightly faster than CPython to
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