Hi all, I've posted a couple of times on here before: I maintain a Python extension for GPGPU linear algebra[1], but it uses boost.python. I do most of my scientific computing in Python, but often am forced to use CPython where I would prefer to use PyPy, largely because of the availability of extensions.
I'm looking for an interesting Google Summer of Code project for next year, and would like to continue working on things that help make high-performance computing in Python straight-forward. In particular, I've had my eye on the 'optimising cpyext'[2] project for a while: might work in that area be available? I notice that it is described with difficulty 'hard', and so I'm keen to enquire early so that I can get up to speed before making a potential application in the spring. I would love to work on getting cpyext into a good enough shape that both Cython and Boost.Python extensions are functional with minimal effort on behalf of the user. Does anyone have any advice? Are there particular things I should familiarise myself with? I know there is the module/cpyext tree, but it is quite formidable for someone uninitiated! Of course, I recognise that cpyext is a much trickier proposition in comparison with things like cffi and cppyy. In particular, I'm very excited by cppyy and PyCling, but they seem quite bound up in CERN's ROOT infrastructure, which is a shame. But it's also clear that very many useful extensions currently use the CPython API, and so -- as I have often found -- the apparent relative immaturity of cpyext keeps people away from PyPy, which is also a shame! [1] https://pypi.python.org/pypi/pyviennacl [2] https://bitbucket.org/pypy/pypy/wiki/GSOC%202014 Best, Toby -- Toby St Clere Smithe http://tsmithe.net _______________________________________________ pypy-dev mailing list pypy-dev@python.org https://mail.python.org/mailman/listinfo/pypy-dev
