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This is also why the scipy/numpy crowd doesn't care much about PyPy. In this world Python is the equivalent of ducttape. You don't performance optimize ducttape
by semi-extrinsic 11y ago
This is also why the scipy/numpy crowd doesn't care much about PyPy. In this world Python is the equivalent of ducttape. You don't performance optimize ducttape, you want it to be easy to use and to fix any problem you have.
- camaraj 11y agoPyPy is taking a similar approach. The difference is Numpy is using CPython's CAPI as the duct tape where as PyPy is using cffi as the duct tape. Both approaches end up using the same underlying libraries. Too many people just have the wrong impression, thinking that PyPy plans on re-implementing all the libraries that Numpy and scipy use but that's just completely false. They have re-implemented the Numpy array so that it can take advantage of the JIT and so that parts of an algorithm implemented In Python that uses Numpy can also be optimized. Unlike what occurs when using Numpy under CPython where the Python code does not get optimized unless it is converted to Cython, C Code, or some alternative to Python to have it be optimized.