Stefan Krah <[email protected]> added the comment:
Yes, I think it is partly convenience. I want to set ...
ndt_mallocfunc = PyMem_Malloc;
ndt_alignedallocfunc = PyMem_AlignedAlloc;
ndt_callocfunc = PyMem_Calloc;
ndt_reallocfunc = PyMem_Realloc;
ndt_freefunc = PyMem_Free;
... so I can always just call ndt_free(), because there's only one memory
allocator.
But the other part is that datashape allows to specify alignment regardless
of the size of the type. Example:
>>> from ndtypes import *
>>> from xnd import *
>>> t = ndt("{a: int64, b: uint64, align=16}")
>>> xnd(t, {'a': 111, 'b': 222})
<xnd.xnd object at 0x7f82750c2a30>
The xnd object essentially wraps a typed data pointer. In the above case, the
'align' keyword has the same purpose as gcc's __attribute__((aligned(16))).
There are several other cases in datashape where alignment can specified
explicitly.
For the convenience case it would already help if PyMem_AlignedAlloc() did
*not* use the fast allocator, but just delegated to _aligned_malloc() (MSVC)
or aligned_alloc() (C11), ...
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Python tracker <[email protected]>
<https://bugs.python.org/issue18835>
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