[Numpy-discussion] Numpy Generalized Ufuncs: Pointer Arithmetic and Segmentation Faults (Debugging?)

[Jaime Fernández del Río]

HI Eleanore,

Thanks for the kind words, you are very welcome!

As for your issues, I think they are coming from the handling of the
strides you are doing in the slow_dtw_dist function.  The strides are the
number of bytes you have to advance your pointer to get to the next item.
In your code, you end up doing something akin to:

dtype *v_i = v0;
...
for (...) {
    ...
    v_i += stride_v;
}

This, rather than increase the v_i pointer by stride_v bytes, increases it
by stride_v * sizeof(dtype), and with the npy_double you seem to be using
as dtype, sends you out of your allocated memory at a rate 8x too fast.

What you increase by stride_v has to be of char* type, so one simple
solution would be to do something like:

char *v_ptr = (char *)v0;
...
for (...) {
    dtype v_val = *(dtype *)v_ptr;
    ...
    v_ptr += stride_v;
}

And use v_val directly wherever you were dereferencing v_i before.

Jaime


On Sun, Oct 25, 2015 at 5:06 AM, <eleanore.young at artorg.unibe.ch> wrote:

> Dear Numpy maintainers and developers,
>
> Thanks for providing such a great numerical library!
>
> I’m currently trying to implement the Dynamic Time Warping metric as a set
> of generalised numpy ufuncs, but unfortunately, I have lasting issues with
> pointer arithmetic and segmentation faults. Is there any way that I can
> use GDB or some such to debug a python/numpy extension? Furthermore: is it
> necessary to use pointer arithmetic to access the function arguments (as
> seen on http://docs.scipy.org/doc/numpy/user/c-info.ufunc-tutorial.html)
> or is element access (operator[]) also permissible?
>
> To break it down quickly, I need to have a fast DTW distance function
> dist_dtw() with two vector inputs (broadcasting should be possible), two
> scalar parameters and one scalar output (signature: (i), (j), (), () -> ())
> usable in python for a 1-Nearest Neighbor classification algorithm. The
> extension also implements two functions compute_envelope() and
> piecewise_mean_reduction() which are used for lower-bounding based on Keogh
> and Ratanamahatana, 2005. The source code is available at
> http://pastebin.com/MunNaP7V and the prominent segmentation fault happens
> somewhere in the chain dist_dtw() —> meta_dtw_dist() —> slow_dtw_dist(),
> but I fail to pin it down.
>
> Aside from my primary questions, I wonder how to approach
> errors/exceptions and unit testing when developing numpy ufuncs. Are there
> any examples apart from the numpy manual that I could use as reference
> implementations of generalised numpy ufuncs?
>
> I would greatly appreciate some insight into properly developing
> generalised ufuncs.
>
> Best,
> Eleanore
>
>
> _______________________________________________
> NumPy-Discussion mailing list
> NumPy-Discussion at scipy.org
> https://mail.scipy.org/mailman/listinfo/numpy-discussion
>
>


-- 
(\__/)
( O.o)
( > <) Este es Conejo. Copia a Conejo en tu firma y ayúdale en sus planes
de dominación mundial.
-------------- next part --------------
An HTML attachment was scrubbed...
URL: <http://mail.python.org/pipermail/numpy-discussion/attachments/20151025/651da0a2/attachment.html>