See my comments interspersed in Ralf's reply. Thanks for the additional
context.
Matti
On 21/4/21 3:10 am, Ralf Gommers wrote:
...
Motivation and Scope
--------------------
Users may wish to override the internal data memory routines with
ones
of their
own. Two such use-cases are to ensure data alignment and to pin
certain
allocations to certain NUMA cores.
It would be great to expand a bit on these two sentences, and add some
links. There's a lot of history here in NumPy development to refer to
as well:
https://numpy-discussion.scipy.narkive.com/MvmMkJcK/numpy-arrays-data-allocation-and-simd-alignement
<https://numpy-discussion.scipy.narkive.com/MvmMkJcK/numpy-arrays-data-allocation-and-simd-alignement>
http://numpy-discussion.10968.n7.nabble.com/Aligned-configurable-memory-allocation-td39712.html
<http://numpy-discussion.10968.n7.nabble.com/Aligned-configurable-memory-allocation-td39712.html>
http://numpy-discussion.10968.n7.nabble.com/Numpy-s-policy-for-releasing-memory-td1533.html
<http://numpy-discussion.10968.n7.nabble.com/Numpy-s-policy-for-releasing-memory-td1533.html>
https://github.com/numpy/numpy/issues/5312
<https://github.com/numpy/numpy/issues/5312>
https://github.com/numpy/numpy/issues/14177
<https://github.com/numpy/numpy/issues/14177>
There must also be a good amount of ideas/discussion elsewhere.
I added more context to this section, trying to focus on the large data
allocations in NumPy.
https://bugs.python.org/issue18835
<https://bugs.python.org/issue18835> discussed an aligned allocator
for Python itself, with fairly detailed discussion about whether/how
NumPy could benefit. With (I think) the conclusion it shouldn't be in
Python, but NumPy/Arrow/others are better off doing their own thing.
I'm wondering if improved memory profiling is a use case as well? Fil
(https://github.com/pythonspeed/filprofiler
<https://github.com/pythonspeed/filprofiler>) for example seems to use
such a strategy:
https://github.com/pythonspeed/filprofiler/blob/master/design/allocator-overrides.md
<https://github.com/pythonspeed/filprofiler/blob/master/design/allocator-overrides.md>
Thanks. I added a sentence about this as well.
Does it interact with our tracemalloc support
(https://numpy.org/doc/stable/release/1.13.0-notes.html#support-for-tracemalloc-in-python-3-6
<https://numpy.org/doc/stable/release/1.13.0-notes.html#support-for-tracemalloc-in-python-3-6>)?
I added a sentence about this. The new C-API wrapper functions preserve
the current status vis-a-vis tracemalloc support. I am not sure that
support is complete. The NEP should not change the situation for better
or worse.
User who wish to change the NumPy data memory management routines
will use
This is design, not motivation or scope. Try to not refer to specific
function names in this section. I suggest moving this content to the
"Detailed design" section (or better, a "high level design" at the
start of that section).
Done.
Cheers,
Ralf
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