Portable mixed-precision BLAS-like vector math library for x86 and ARM
Computing dot-products, similarity measures, and distances between low- and high-dimensional vectors is ubiquitous in Machine Learning, Scientific Computing, Geo-Spatial Analysis, and Information Retrieval. These algorithms generally have linear complexity in time, constant or linear complexity in space, and are data-parallel. In other words, it is easily parallelizable and vectorizable and often available in packages like BLAS (level 1) and LAPACK, as well as higher-level `numpy` and `scipy` Python libraries. Ironically, even with decades of evolution in compilers and numerical computing, [most libraries can be 3-200x slower than hardware potential][benchmarks] even on the most popular hardware, like 64-bit x86 and Arm CPUs. Moreover, most lack mixed-precision support, which is crucial for modern AI! The rare few that support minimal mixed precision, run only on one platform, and are vendor-locked, by companies like Intel and Nvidia. SimSIMD provides an alternative. 1️⃣ SimSIMD functions are practically as fast as `memcpy`. 2️⃣ Unlike BLAS, most kernels are designed for mixed-precision and bit-level operations. 3️⃣ SimSIMD often [ships more binaries than NumPy][compatibility] and has more backends than most BLAS implementations, and more high-level interfaces than most libraries.
acquired package info from repodata DB: repodata/1878cc60a19e65efc93d1a539275c5a6799b0acbd7796ee105712c44cae47ab4-primary.sqlite.bz2