• References [Alted]
  • [NumPy]
  • Cython: The best of both worlds




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    Bog'liq
    cython cise

    Further reading
    If you think Cython might help you, then the next
    stop is the Cython Tutorial
    [tutorial]
    .
    [numerics]
    presents optimization strategies and benchmarks
    for computations.
    As
    always,
    the
    online
    documentation
    at
    8
    IEEE Computing in science and Engineering


    http://docs.cython.org
    provides
    the
    most
    up-to-date information.
    If you are ever stuck,
    or
    just
    wondering
    if
    Cython
    will
    be
    able
    to
    solve
    your
    particular
    problem,
    Cython
    has an active and friendly mailing list at
    http://groups.google.com/group/cython-users
    .
    References
    [Alted] F. Alted. Why modern CPUs are starv-
    ing and what can be done about it. CiSE 12,
    68, 2010.
    [comparison] I.
    M.
    Wilbers,
    H.
    P.
    Lang-
    tangen,
    A. Oedegaard,
    Using Cython to
    Speed
    up
    Numerical
    Python
    Programs,
    Proceedings
    of
    MekIT‘09,
    2009.
    URL:
    http://simula.no/research/sc/publications/
    Simula.SC.578
    [fwrap] K.
    W.
    Smith,
    D.
    S.
    Selje-
    botn,
    Fwrap:
    Fortran
    wrappers
    in
    C,
    Cython
    &
    Python.
    URL:
    http://conference.scipy.org/abstract?id=19
    Project homepage:
    http://fwrap.sourceforge.net/
    [numerics] D. S. Seljebotn, Fast numerical com-
    putations with Cython, Proceedings of the 8th
    Python in Science Conference, 2009.
    URL:
    http://conference.scipy.org/proceedings/
    SciPy2009/paper 2
    [NumPy] S. van der Walt, S. C. Colbert, G.
    Varoquaux, The NumPy array: a structure for
    efficient numerical computation, CiSE, present
    issue
    [Pyrex] G. Ewing,
    Pyrex:
    A language for
    writing Python extension modules.
    URL:
    http://www.cosc.canterbury.ac.nz/greg.ewing
    /python/Pyrex/

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