{"categories":[{"label":"Libraries","url":"https://skillfed.io/packages/category/software-development-libraries/9"}],"enrichment":{"capability":"Provides C and Data Parallel C++ interfaces to Intel's oneAPI Math Kernel Library for optimized mathematical computing on Intel CPUs and GPUs.","skillfed_tags":["numerical-computing","intel-hardware","c-interface"],"use_cases":["Accelerate numerical computations in scientific Python applications by linking to optimized MKL routines from C/C++ code.","Build high-performance machine learning or data science pipelines that require fast mathematical operations on Intel hardware.","Develop GPU-accelerated applications using Data Parallel C++ (DPC++) that target Intel processors and GPUs.","Support research or engineering workflows requiring mathematical operations with Intel CPU/GPU tuning.","Integrate MKL's threaded routines into existing C/C++ codebases for performance-critical sections."],"what_it_does":"mkl-static packages Intel's oneAPI Math Kernel Library as a Python-installable wheel, exposing C and Data Parallel C++ (DPC++) interfaces for high-performance mathematical computing. It is designed for applications requiring optimized mathematical routines on Intel processors and compatible GPUs.\n\nThe package depends on intel-openmp, tbb-devel, and mkl-include at runtime. Installation is straightforward via pip, but the MKLROOT environment variable must be configured manually afterward\u2014it is not set automatically by the PyPI installation. Users typically call MKL routines through C interfaces via ctypes or compiled extensions rather than through direct Python APIs.","worth_installing":"Yes, if you need Intel MKL's optimized math routines in a C/C++ or DPC++ application and are willing to manually configure MKLROOT after installation. The package is actively maintained, has no known vulnerabilities, and receives substantial downloads. The proprietary license and unclear license treatment warrant review before commercial use. Medium install friction is acceptable for the performance gains in numerical computing."},"id":"mkl-static","links":{"html":"https://skillfed.io/packages/mkl-static","md":"https://skillfed.io/packages/mkl-static.md","pypi":"https://pypi.org/project/mkl-static/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-07-01","license_spdx":null,"license_treatment":"unclear","name":"mkl-static","python_support":"unspecified","summary":"Intel\u00ae oneAPI Math Kernel Library"},"popularity":{"monthly_downloads":141050,"position":11255,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"2026.1.0"}