simdjson-feedstock
numpy-feedstock
simdjson-feedstock | numpy-feedstock | |
---|---|---|
1 | 1 | |
0 | 7 | |
- | - | |
7.3 | 7.3 | |
about 1 month ago | 8 days ago | |
CMake | Shell | |
BSD 3-clause "New" or "Revised" License | BSD 3-clause "New" or "Revised" License |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
simdjson-feedstock
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Show HN: SimSIMD vs. SciPy: How AVX-512 and SVE make SIMD cleaner and ML faster
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?
numpy-feedstock
-
Show HN: SimSIMD vs. SciPy: How AVX-512 and SVE make SIMD cleaner and ML faster
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?
What are some alternatives?
scipy-feedstock - A conda-smithy repository for scipy.
SimSIMD - Up to 200x Faster Inner Products and Vector Similarity — for Python, JavaScript, Rust, and C, supporting f64, f32, f16 real & complex, i8, and binary vectors using SIMD for both x86 AVX2 & AVX-512 and Arm NEON & SVE 📐
xtensor-fftw - FFTW bindings for the xtensor C++14 multi-dimensional array library
mkl_random-feedstock - A conda-smithy repository for mkl_random.
blas-feedstock - A conda-smithy repository for blas.
xtensor-blas-feedstock - A conda-smithy repository for xtensor-blas.
pysimdjson-feedstock - A conda-smithy repository for pysimdjson.