Over the years, the merits of Intel’s 512-bit Advanced Vector eXtensions (AVX-512) have been extensively debated. Introduced in 2014, CPUs took time to offer robust support. In parallel, Arm Scalable Vector Extensions (SVE), targeting Arm servers, has only gained traction in recent times. Today, the landscape has shifted significantly with Intel’s Sapphire Rapids CPUs on one flank and the AWS Graviton 3 and Ampere Altra chips on the other. Here are compelling reasons to opt for these over the traditional AVX2 and NEON extensions:

1. Masked Loads: Efficient data processing by selectively loading data. 2. Half-Precision Floating Point Math: Accelerated computations with reduced memory footprint.

These features proved invaluable for the latest SimSIMD release. The software now processes vector similarities up to 300x faster using NEON, SVE, AVX2, and AVX-512 extensions across Inner Product, Euclidean, Angular, Hamming, and Jaccard distances. It outstrips commonly used libraries like NumPy and SciPy, famously built on BLAS and LAPACK.

Check out the post for some cool tricks, clarifications on AVX-512 and SVE advantages, and benchmark numbers :)

Here is the repo: https://github.com/ashvardanian/simsimd