JNumPy: Writing high-performance C extensions for Python in minutes

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  • jnumpy

    Writing Python C extensions in Julia within 5 minutes.

  • ideas

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  • stl-benchmark

    STL benchmark comparing C++ and Julia ⏱

    • Some of these are just safety-by-default things. For example, IO in Julia is thread-safe by default, which I think is a good idea because safety-first programming is good for say throwing `print` into a threaded loop written in the REPL. Here for example, https://github.com/aaronang/stl-benchmark/pull/3, was a case where Julia saw a performance hit from C++ and I was curious and tracked it down to this locking-by-default behavior. I'm not sure of a better way of handling it: the C/C++ behavior of not locking by default would make doing things correctly simply would be very hard to use (and is very hard in those languages).

    Though I agree parsers haven't gotten much love in Julia. That said, this repo is saying it's for implementing NumPy extensions, and I don't think NumPy has many parsers it's using.

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