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Sep 23, 2013 · Long-term evidence is showing that PyPy runs certain Python codes slower than CPython and this drawback seems to be rooted very deeply in PyPy. Secondly, the current version of PyPy consumes much more memory than CPython in a rather large set of cases.
PyPy is a very compliant Python interpreter that is a worthy alternative to CPython 2.7, 3.6, and soon 3.7. By installing and running your application with it, you can gain noticeable speed improvements.
Speed: thanks to its Just-in-Time compiler, Python programs often run faster on PyPy. (What is a JIT compiler?) Memory usage: memory-hungry Python programs (several hundreds of MBs or more) might end up taking less space than they do in CPython.
PyPy often runs faster than the standard implementation CPython because PyPy uses a just-in-time compiler. [3] Most Python code runs well on PyPy except for code that depends on CPython extensions, which either does not work or incurs some overhead when run in PyPy.
Plot 2: Speedup compared to cpython, using the inverse of the geometric average of normalized times, out of 45 benchmarks (see paper on why the geometric mean is better for normalized results). Powered by Codespeed, Django and Python. A performance analysis tool for software projects.
3 days ago · CPython extension modules in PyPy are often much slower than in CPython due to the need to emulate refcounting. It is often faster to take out your c-extension and replace it with a pure python or CFFI version that the JIT can optimize.
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According to the PyPy documentation, extension module support is experimental and often runs much slower than in CPython. This means that some third-party libraries that have C extension modules might not work on PyPy — the scientific computing library NumPy is a notable example.
