ARM SVE optimization for L2 distance kernels adapted to new simd_impl structure

[ThatikondV]

Description:

This PR introduces ARM SVE (Scalable Vector Extension) optimization for L2 metric functions. The new implementation extends the existing NEON/SVE SIMD logic with SVE vectorized kernels for distance computations, improving both indexing and search performance.

Benchmarking Setup:

To evaluate the performance impact of the new SVE-enabled L2 distance computations, a dedicated benchmarking script was developed. This script systematically compares the earlier FAISS version and the latest SVE-optimized version using the SIFT1M dataset with 128-dimensional vectors. The benchmarks cover all major index types like FLAT, IVF_FLAT, IVF_PQ, IVF_SQ8, and HNSW.

Results:

Index	OSS QPS	SVE QPS	Improvement
Flat (L2)	888.53	1188.12	33.70%
IVF1024-Flat	33,437.76	45,871.00	37.20%
IVF1024-PQ32x8	33,597.01	40,650.00	21.00%
HNSW (M=16, ef=64)	72,837.67	77,482.00	6.40%
IVF1024-SQ8	17,002.79	20,657.00	21.50%

The above results clearly demonstrate that enabling SVE vectorization yields consistent performance gains across all tested index types, where both indexing and search times improved significantly.

These improvements validate the effectiveness of the SVE implementation in accelerating L2 distance computations on modern ARM platforms.

Co-author: This work was jointly developed with @abhijain1204fujitsu.

[mnorris11]

cc @algoriddle
Thanks @ThatikondV for this contribution, it looks like a big improvement! We are still revamping the SIMD structure of the codebase. We will review the big backlog of SIMD changes hopefully in the next few weeks.

[meta-codesync]

@mnorris11 has imported this pull request. If you are a Meta employee, you can view this in D106149015.