onemkl-sycl-blas
Intel® oneAPI Math Kernel Library
Decision gist · record as of 2026-08-14
Yes, if you are developing C or C++ applications that require high-performance mathematical routines on Intel hardware and are willing to manage the runtime dependencies and MKLROOT configuration. Not recommended for pure Python workflows (use NumPy/SciPy instead) or if you need cross-platform portability beyond Linux and Windows. Verify license compatibility with your project before committing.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- MKLROOT environment variable must be configured manually after installation; dpcpp-cpp-rt, intel-opencl-rt, and mkl runtime dependencies must be installed and available on your system.
- Medium install friction due to three runtime dependencies (dpcpp-cpp-rt, intel-opencl-rt, mkl) that must be present.
- Actively maintained with a recent release.
License · maintenance · safety
(unclear) — Licensed under Intel Simplified Software License (proprietary, not SPDX-identified). License treatment is unclear, so review Intel's terms before integrating into your project to confirm compatibility with your use case.
last release 2026-07-01 (44 days)
0 known vulnerabilities (OSV.dev, 2026-08-14) · 171,820 downloads/mo, #10,353 on PyPI
Alternatives
Verify before relying
# Install
pip install onemkl-sycl-blas
# Import and use (C/DPC++ interfaces available)
import onemkl_sycl_blas
# Call optimized routines via C or DPC++ language bindings- Exact Python version compatibility (requires_python is unspecified in metadata)
- Whether DPC++ compiler and Intel GPU drivers are required for GPU acceleration
- Specific performance gains vs. standard NumPy/SciPy on typical workloads
- Whether MKLROOT setup is documented in the package itself or only in external Intel docs
What it is and what it does
Intel oneAPI Math Kernel Library (oneMKL) is Intel's production-grade library for mathematical computing, providing highly optimized routines for linear algebra, Fourier transforms, and related operations. This package exposes C and Data Parallel C++ (DPC++) language interfaces, allowing you to call these routines directly from C, C++, or any language with C bindings. It targets maximum performance on current and future Intel CPUs and GPUs.
The package is intended for developers and researchers who need to optimize compute-intensive mathematical workloads. It requires three runtime dependencies (dpcpp-cpp-rt, intel-opencl-rt, mkl) and manual configuration of the MKLROOT environment variable after installation. The library is actively maintained and carries a proprietary Intel license.
Use it for
- Optimize matrix multiplication and linear algebra operations in C/C++ applications targeting Intel hardware
- Accelerate scientific computing workloads on Intel GPUs using DPC++ data-parallel programming
- Replace or supplement standard BLAS/LAPACK implementations with Intel's tuned routines for performance-critical code
- Develop high-performance numerical simulations in physics, chemistry, or machine learning on Intel platforms
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you are developing C or C++ applications that require high-performance mathematical routines on Intel hardware and are willing to manage the runtime dependencies and MKLROOT configuration.
Not recommended for pure Python workflows (use NumPy/SciPy instead) or if you need cross-platform portability beyond Linux and Windows. Verify license compatibility with your project before committing.
Install
onemkl-sycl-blas on PyPI
Before you install
Medium install friction due to three runtime dependencies (dpcpp-cpp-rt, intel-opencl-rt, mkl) that must be present. Actively maintained with a recent release. Note that MKLROOT environment variable requires manual setup post-installation.
MKLROOT environment variable must be configured manually after installation; dpcpp-cpp-rt, intel-opencl-rt, and mkl runtime dependencies must be installed and available on your system.
License in practice
Licensed under Intel Simplified Software License (proprietary, not SPDX-identified). License treatment is unclear, so review Intel's terms before integrating into your project to confirm compatibility with your use case.
Quickstart
# Install
pip install onemkl-sycl-blas
# Import and use (C/DPC++ interfaces available)
import onemkl_sycl_blas
# Call optimized routines via C or DPC++ language bindingsVerify before relying
- Exact Python version compatibility (requires_python is unspecified in metadata)
- Whether DPC++ compiler and Intel GPU drivers are required for GPU acceleration
- Specific performance gains vs. standard NumPy/SciPy on typical workloads
- Whether MKLROOT setup is documented in the package itself or only in external Intel docs
Package facts
| License | Not declared unclear |
| Python support | Not specified |
| Install friction | Medium. Platform-specific wheel |
| Runtime dependencies | 3 packagesdpcpp-cpp-rtintel-opencl-rtmkl |
| Maintenance | Actively maintained 44 days since the last release |
| First released | |
| Downloads | 171,820 / month, #10,353 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 5 - Production/StableIntended Audience :: DevelopersIntended Audience :: EducationIntended Audience :: Other AudienceIntended Audience :: Science/ResearchIntended Audience :: System AdministratorsLicense :: Other/Proprietary LicenseOperating System :: POSIX :: LinuxTopic :: Software Development :: Libraries |
Evidence: onemkl_sycl_blas-2026.1.0-py2.py3-none-manylinux_2_28_x86_64.whl; onemkl_sycl_blas-2026.1.0-py2.py3-none-win_amd64.whl
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See also blis · mkl · onemkl-sycl-lapack · onemkl-sycl-sparse · daal · onemkl-sycl-rng · onemkl-sycl-dft · onemkl-license · mkl-include · daal4py