Downloaded over 5 Billion times, NumPy is the most popular library for numerical computing in Python. It wraps low-level HPC libraries like BLAS and LAPACK, providing a high-level interface for matrix operations. BLAS is mainly implemented in C, Fortran, or Assembly and is available for most modern chips, not just CPUs. BLAS is fast, but bindings aren’t generally free. So, how much of the BLAS performance is NumPy leaving on the table?| ashvardanian.com
When our Python code is too slow, like most others we switch to C and often get 100x speed boosts, just like when we replaced SciPy distance computations with SimSIMD. But imagine going 100x faster than C code! It sounds crazy, especially for number-crunching tasks that are “data-parallel” and easy for compilers to optimize. In such spots the compiler will typically “unroll” the loop, vectorize the code, and use SIMD instructions to process multiple data elements in parallel.| ashvardanian.com
