{"repo":"ashvardanian/NumWars","free":true,"listed":false,"github":"https://github.com/ashvardanian/NumWars","clone":"git clone https://github.com/ashvardanian/NumWars.git","description":"Mixed-precision numerics benchmarks in Rust and Python - covering GEMMs, SYRKs, DOTs, and higher-level BLAS and LAPACK-style functionality","language":"Rust","stars":32,"topics":["analytics","benchmark","blas","linear-algebra","matrix-multiplication","simd","simd-instructions"],"license":"Apache-2.0","category":"analytics","readme_excerpt":"NumWars Mixed-Precision Numerics Benchmarks for Rust & Python There are many strong libraries for numerical computing. Most of them are written in C, C++, and Fortran, with excellent Rust wrappers and Python bindings on top. Where Rust is especially convenient is dependency management and reproducible benchmarking, making it a good place to line up apples-to-apples comparisons across native crates and their Python bindings. NumWars exists for the same reason StringWars exists for StringZilla: to compare NumKong against mainstream CPU stacks on the workloads it was built for, including: - ndarray and nalgebra for dense tensor and linear algebra kernels. - faer and matrixmultiply for GEMM-like Rust baselines. - geo for geographic distances. - polars and reduction-heavy analytics workloads. - NumPy , SciPy , and scikit-learn on Python. Of course, the APIs and internal kernels of those projects are different. So this repository focuses on the workload families NumKong was designed for and compares their effective throughput using the native unit for each operation family instead of forcing everything into fake global ops/s . [!IMPORTANT] The numbers below are reference measurements collected on Apple M5 Pro (P-cores) in single-threaded mode. All benchmarks were run single-threaded on an idle system. They will vary with CPU model, compiler flags, BLAS backend, and problem size. Rebuild and rerun on your own hardware before treating them as absolute. Benchmarks at a Glance Packed M","default_branch":null,"files":null,"tree":[],"storefront":"/r/ashvardanian","claimed":false,"request_supported":{"post":"https://gitbuyer.com/r/ashvardanian/NumWars/request-supported","requests":0},"note":"indexed from public GitHub; nothing is for sale on this page. Clone it from GitHub. Paid listings live at /search."}