NumPy roadmap: https://numpy.org/neps/roadmap.html :
> Improvements to NumPy’s performance are important to many users. We have focused this effort on Universal SIMD (see NEP 38 — Using SIMD optimization instructions for performance) intrinsics which provide nice improvements across various hardware platforms via an abstraction layer. The infrastructure is in place, and we welcome follow-on PRs to add SIMD support across all relevant NumPy functions
"NEP 38 — Using SIMD optimization instructions for performance" (2019) https://numpy.org/neps/nep-0038-SIMD-optimizations.html#nep3...
NumPy docs > CPU/SIMD Optimizations: https://numpy.org/doc/stable/reference/simd/index.html
std::simd: https://doc.rust-lang.org/std/simd/index.html
"Show HN: SimSIMD vs SciPy: How AVX-512 and SVE make SIMD nicer and ML 10x faster" (2023-10) https://news.ycombinator.com/item?id=37808036
"Standard library support for SIMD" (2023-10) https://discuss.python.org/t/standard-library-support-for-si...
Automatic vectorization > Techniques: https://en.wikipedia.org/wiki/Automatic_vectorization#Techni...
SIMD: Single instruction, multiple data: https://en.wikipedia.org/wiki/Single_instruction,_multiple_d...
Category:SIMD computing: https://en.wikipedia.org/wiki/Category:SIMD_computing
Vectorization: Introduction: https://news.ycombinator.com/item?id=36159017 :
> GPGPU > Vectorization, Stream Processing > Compute kernels: https://en.wikipedia.org/wiki/General-purpose_computing_on_g...