[Numpy-discussion] Vectorize or rewrite function to work with array inputs?

John Salvatier jsalvati at u.washington.edu
Tue Feb 1 16:16:48 EST 2011

Good things to know.

On Tue, Feb 1, 2011 at 1:10 PM, Sturla Molden <sturla at molden.no> wrote:

> Den 01.02.2011 20:50, skrev John Salvatier:
> > I am curious: why you recommend against this? Using the C-API through
> > cython seems more attractive than using the Cython-specific numpy
> > features since they need a specific number of dimensions (and I don't
> > think they broadcast) and don't seem that much easier. Are there
> > hidden disadvantages to using the C-API? Have I misunderstood something?
> There is one more thing which should be mentioned:
> If the algorithm needs to pass an ndarray to a function, this might
> cause excessive reference counting and boxing/unboxing with Cython. It
> gets even worse if we want to pass a subarray view of an ndarray, for
> which Cython will create a new array object. Cython will only play
> nicely with NumPy arrays if there are no function calls. If there are
> function calls, we must give up ndarrays and use C pointers directly.
> Users who don't know the internals of Cython might think that using an
> ndarray as a function argument is a good idea, or even use slicing to
> create a view of a subarray. Cython does not forbid this, but it hurts
> performance immensely. In the NumPy C API we can pass a pointer to a
> PyArrayObject, adding no overhead beyond the function call. But
> subarrays are as primitive as in Cython. This makes anything but trivial
> "computational kernels" painful in Cython or the NumPy C API.
> Personally I avoid both Cython and the NumPy C API for this reason.
> There is a very nive language called Fortran 95, for which the compiler
> knows how to work with arrays. For those that don't use Fortran 95,
> there are libraries for C++ such as Blitz++.
> Sturla
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