{"categories":[{"label":"Artificial Intelligence","url":"https://skillfed.io/packages/category/scientific-engineering-artificial-intelligence/5"},{"label":"Information Analysis","url":"https://skillfed.io/packages/category/scientific-engineering-information-analysis/2"},{"label":"Mathematics","url":"https://skillfed.io/packages/category/scientific-engineering-mathematics/2"},{"label":"Bio-Informatics","url":"https://skillfed.io/packages/category/scientific-engineering-bio-informatics"},{"label":"Chemistry","url":"https://skillfed.io/packages/category/scientific-engineering-chemistry"}],"enrichment":{"capability":"NumKong provides mixed-precision linear algebra and distance kernels with automatic accumulator widening, GIL-free batched operations, and low-precision dtype support (BFloat16, Float8, Float6, packed bits) across x86, ARM, RISC-V, and other architectures.","skillfed_tags":["mixed-precision","simd-kernels","linear-algebra"],"use_cases":["Computing dot products and distances with automatic precision widening when input data is in low-precision formats like Float16 or BFloat16.","Batch similarity search or all-pairs distance computation where packed matrix reuse or symmetric kernel optimization reduces redundant work.","Mixed-precision inference pipelines needing fine-grained control over accumulator dtype without explicit boilerplate.","Geometric or spatial computations (mesh alignment, angular distances) where specialized kernels are needed.","High-throughput numerical workloads where GIL-free batched operations and runtime SIMD dispatch improve throughput on heterogeneous CPU hardware."],"what_it_does":"NumKong is a high-level Python SDK for numerical computing that bridges buffer protocol interoperability with native mixed-precision kernels and backend-specific optimizations. It targets operations where precision control matters: dot products, distances (squared Euclidean, Euclidean, angular), and element-wise reductions. Unlike standard libraries, it lets you work with low-precision dtypes (BFloat16, Float8, Float6, packed bits) while automatically widening accumulators to prevent precision loss\u2014and it releases the GIL around native work in batched, packed, and symmetric kernels.\n\nThe API provides shape-aware outputs and familiar scalar, batched, and all-pairs entrypoints with explicit control over accumulator dtype and output allocation. It supports dense, packed, and symmetric matrix operations, sparse helpers, and geometric mesh alignment across x86, ARM, RISC-V, LoongArch, Power, and WebAssembly. Pre-built wheels cover Python 3.9\u20133.14 on Linux, macOS, and Windows; source builds require platform-specific compiler support.","worth_installing":"Yes, if you need mixed-precision linear algebra with automatic accumulator widening and GIL-free kernels. The library is actively maintained with no known vulnerabilities and supports Python 3.9\u20133.14 across major platforms. Install friction is moderate due to compiled wheels; pre-built binaries are available for common setups. Not necessary if you are satisfied with standard precision models or do not use low-precision dtypes."},"id":"numkong","links":{"html":"https://skillfed.io/packages/numkong","md":"https://skillfed.io/packages/numkong.md","pypi":"https://pypi.org/project/numkong/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-08-07","license_spdx":null,"license_treatment":"permissive","name":"numkong","python_support":"supports_current","summary":"Portable mixed-precision math, linear-algebra, & retrieval library with 2000+ SIMD kernels for x86, Arm, RISC-V, LoongArch, Power, & WebAssembly"},"popularity":{"monthly_downloads":496013,"position":6339,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"7.8.0"}