I encourage one to merge into e.g. {NumPy, SciPy, }; are there PRs?
Though SymPy.physics only yet supports X,Y,Z vectors and doesn't mention e.g. "jaccard"?, FWIW: https://docs.sympy.org/latest/modules/physics/vector/vectors... https://docs.sympy.org/latest/modules/physics/vector/fields.... #cfd
include/simsimd/simsimd.h: https://github.com/ashvardanian/SimSIMD/blob/main/include/si...
conda-forge maintainer docs > Switching BLAS implementation: https://conda-forge.org/docs/maintainer/knowledge_base.html#... :
conda install "libblas=*=*mkl"
conda install "libblas=*=*openblas"
conda install "libblas=*=*blis"
conda install "libblas=*=*accelerate"
conda install "libblas=*=*netlib"
numpy-feedstock: https://github.com/conda-forge/numpy-feedstock/blob/main/rec...
scipy-feedstock: https://github.com/conda-forge/scipy-feedstock/blob/main/rec...
pysimdjson-feedstock: https://github.com/conda-forge/pysimdjson-feedstock/blob/mai...
simdjson-feedstock: https://github.com/conda-forge/simdjson-feedstock/blob/main/...
mkl_random-feedstock: https://github.com/conda-forge/mkl_random-feedstock https://github.com/google/paranoid_crypto/tree/main/paranoid... :
> NumPy-based implementation of random number generation sampling using Intel (R) Math Kernel Library, mirroring numpy.random, but exposing all choices of sampling algorithms available in MKL
blas: https://github.com/conda-forge/blas-feedstock/blob/main/reci...
xtensor-blas-feedstock: https://github.com/conda-forge/xtensor-blas-feedstock
xtensor-fftw (FFT with xtensor (c++)) could probably be AVX-512 and SVE -optimized as well? https://github.com/xtensor-stack/xtensor-fftw
ggml_cpu_has_avx512() https://github.com/search?q=repo%3Aggerganov%2Fggml%20AVX&ty... https://github.com/search?q=repo%3Aggerganov%2Fllama.cpp%20a...
CuPy would also be an impactful place to merge and defend these optimizations; though no GPUs have AVX-512 or SVE? cupyx.scipy.spatial.distance: https://docs.cupy.dev/en/stable/reference/scipy_spatial_dist... https://docs.cupy.dev/en/stable/reference/comparison.html
Hey, thanks for recommendations! Yes, I’m definitely open to contributing there.
Np. Thanks for the optimizations.
From "PostgresML is 8-40x faster than Python HTTP microservices" (2023) https://news.ycombinator.com/item?id=33270638 :
> Apache Ballista and Polars do Apache Arrow and SIMD.
> The Polars homepage links to the "Database-like ops benchmark" of {Polars, data.table, DataFrames.jl, ClickHouse, cuDF, spark, (py)datatable, dplyr, pandas, dask, Arrow, DuckDB, Modin,} but not yet PostgresML? https://h2oai.github.io/db-benchmark/ *
LLM -> Vector database: https://en.wikipedia.org/wiki/Vector_database
/? inurl:awesome site:github.com "vector database" https://www.google.com/search?q=inurl%253Aawesome+site%253Ag... : https://github.com/dangkhoasdc/awesome-vector-database , https://github.com/mileszim/awesome-vector-database , https://github.com/currentslab/awesome-vector-search
/? "vector database" "duckdb" https://www.google.com/search?q=+%22vector+database%22+%22du... ... pgvector
pgvector/pgvector/src/vector.c: vector_spherical_distance https://github.com/pgvector/pgvector/blob/master/src/vector....
postgresml/postgresml: /? distance https://github.com/search?q=repo%3Apostgresml%2Fpostgresml%2...