Thanks, this seems like it would improve performance on aarch64. Would
the routines also work with the Apple silicon arm64?
If these are new routines, it would be better to implement them in terms
of the numpy universal intrinsics rather than adding a new submodule.
Matti
On 8/11/22 13:30, Chris Sidebottom wrote:
Hello,
Here at Arm, we've been investigating how we can improve performance
on AArch64. One way in which we can improve performance is by
integrating some existing optimized routines
(https://github.com/ARM-software/optimized-routines), similar to the
SVML methods for AVX512 that are currently included as a git
submodule. Our intent is to include the optimized routines repository
as an additional submodule which we can then use to provide routines
on AArch64 for ASIMD, SVE and beyond.
Currently, we're targeting 4-ULP as this aligns with libmvec
(https://sourceware.org/glibc/wiki/libmvec) and the SVML integration
(https://github.com/numpy/numpy/pull/19478). This is alongside adding
sufficient error handling to pass the Numpy test suite, meeting the
test requirements highlighted in the SVML integration
(https://github.com/numpy/numpy/pull/19478#issuecomment-893001722).
We've already started curating the necessary functions, let us know if
you have any feedback.
Cheers,
Chris
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