[Numpy-discussion] BLAS and LAPACK ABI questions (mostly OSX related)

[Robert McGibbon]

But I can't reproduce #4007 on my machine, so I'm not totally sure.

-Robert


On Sun, Jun 8, 2014 at 9:07 PM, Robert McGibbon <rmcgibbo at gmail.com> wrote:

> #4776 is definitely a separate issue. #4007 could be related similar f2c
> ABI issues, but I don't think so.  The difference between the gfotran and
> g77/f2c ABIs, to my knowledge, is that (1) fortran REAL (single precision
> floating point) return values are actually returned as a c double, not a c
> float. (2) when return values are complex, the function is actually
> implemented with an ABI that looks like a c function with an extra
> pass-by-reference argument at the front of the function where the return
> value is placed.
>
> Despite the differences in the handling of the return values, I don't
> think there is any difference in how float32 parameters are passed *in*, so
> I'm not sure that these issues are related to subroutines like SGEMV which
> is presumably behind #4007.
>
> -Robert
>
>
> On Sun, Jun 8, 2014 at 8:33 PM, Matthew Brett <matthew.brett at gmail.com>
> wrote:
>
>> Hi,
>>
>> On Mon, Jun 9, 2014 at 2:31 AM, Robert McGibbon <rmcgibbo at gmail.com>
>> wrote:
>> > Yeah, that's definitely not the right signature for sdot when linked
>> against
>> > the Accelerate framework.
>> >
>> > But what's the purpose of those wrappers in _umath_linalg? The one for
>> sdot,
>> > for example, doesn't appear to be used internally, and it isn't
>> available
>> > from the python side. While scipy has all of those function pointers
>> > accessible in scipy.linalg.blas (e.g. scipy.linalg.blas.sdot._cpointer),
>> > those function pointers point to the underlying blas function and
>> expose the
>> > ABI differences between platforms, leading to, for example the utter
>> > nightmare of using the scipy blas function pointers from cython in a
>> cross
>> > platform way: https://gist.github.com/anonymous/6659007.
>>
>> Is it possible this is related to:
>>
>> https://github.com/numpy/numpy/issues/4007
>>
>> or even:
>>
>> https://github.com/numpy/numpy/issues/4776
>>
>> ?
>>
>> Best,
>>
>> Matthew
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>>
>
>
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