STINNER Victor added the comment:

Antoine Pitrou: "Benchmarks and Intel's recommendation show that aligned 
allocation is actually important for AVX performance, and NumPy depends on 
CPython providing the right allocation APIs (for integration with tracemalloc): 
https://github.com/numpy/numpy/issues/5312";

I don't think that NumPy was ever fully integrated with tracemalloc.

Since Python 3.6, NumPy doesn't have to use Python memory allocators to benefit 
of tracemalloc debugger: I added a new C API to be able to manually 
track/untrack memory blocks which are not directly allocated by Python, see the 
issue #26530. I implemented this feature for NumPy, but since I never got any 
feedback from NumPy, I left the API private.

Moreover, I also added second feature to tracemalloc: it's now possible to 
track memory allocation in different address spaces. The feature was also 
designed for NumPy which can allocate memory in the GPU address space. See the 
issue #26588.

With these new tracemalloc features, I don't think that NumPy can still be used 
to request this feature in CPython core.

--

Raymond: "A principal use case would be PyObject pointers where we want to keep 
all or most of the data fields in the same cache line (i.e. the fields for 
list, tuple, dict, and set objects).

Deques would benefit from having the deque blocks aligned to 64byte boundaries 
and never crossing page boundaries.  Set entries would benefit from 32byte 
alignment."

Victor (me!): "Do you have an idea of performance benefit of memory alignment?"

Since Raymond never provided any evidence that a new aligned memory allocator 
would give a significant speedup, and there issue is inactive for 2 years, I 
close it.

See also the change 6e16b0045cf1, it seems like Raymond doesn't plan to use 
this feature anymore.

--

If someone wants this feature, we need good reasons to implement it.

----------
resolution:  -> rejected
status: open -> closed

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<http://bugs.python.org/issue18835>
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