onemkl-sycl-lapack
Intel® oneAPI Math Kernel Library
Decision gist · record as of 2026-08-14
Yes, if you are developing on Intel hardware (Linux x86_64 or Windows x86_64) and need high-performance linear algebra or DPC++ GPU compute. The medium install friction and manual MKLROOT setup are acceptable trade-offs for Intel-optimized math routines. No known vulnerabilities. License is proprietary and unclear—verify Intel's terms for your use case 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; Linux x86_64 or Windows x86_64 required; Intel runtime libraries (dpcpp-cpp-rt, intel-opencl-rt) must be present.
- Medium install friction due to four runtime dependencies (dpcpp-cpp-rt, intel-opencl-rt, mkl, onemkl-sycl-blas) and platform-specific wheels (Linux x86_64 and Windows only).
- Active maintenance with recent release.
License · maintenance · safety
(unclear) — Licensed under Intel Simplified Software License (proprietary, not SPDX-identified). License treatment is unclear—review Intel's terms before use in commercial or redistributed contexts.
last release 2026-07-01 (44 days)
0 known vulnerabilities (OSV.dev, 2026-08-14) · 171,158 downloads/mo, #10,378 on PyPI
Alternatives
Verify before relying
pip install onemkl-sycl-lapack
# Then import and call C/DPC++ interfaces via ctypes or language bindings
# Requires setting MKLROOT environment variable manually per Intel documentation- Exact Python version compatibility (requires_python not specified in metadata)
- Whether MKLROOT setup is documented in the package or only in external Intel docs
- Performance benchmarks or typical use-case guidance for CPU vs. GPU dispatch
What it is and what it does
onemkl-sycl-lapack is Intel's oneAPI Math Kernel Library packaged for Python, exposing C and Data Parallel C++ (DPC++) interfaces to highly optimized linear algebra and mathematical routines. It targets applications requiring maximum performance on Intel hardware, supporting both current and future generations of Intel CPUs and GPUs through DPC++ parallelism.
The package is a thin wrapper around Intel's native libraries and requires manual environment configuration (MKLROOT) after installation. It is platform-specific, available only for Linux x86_64 and Windows x86_64, and depends on four runtime packages including mkl and onemkl-sycl-blas. Intended for developers, researchers, and system administrators building high-performance numerical applications.
Use it for
- Accelerate linear algebra operations (BLAS, LAPACK) in scientific computing workflows on Intel hardware.
- Build DPC++ applications that dispatch compute to Intel GPUs while maintaining C/C++ compatibility.
- Optimize numerical simulations and data-parallel workloads on Intel CPUs with threaded MKL routines.
- Integrate Intel's math libraries into C/C++ applications via Python bindings for performance-critical sections.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you are developing on Intel hardware (Linux x86_64 or Windows x86_64) and need high-performance linear algebra or DPC++ GPU compute.
The medium install friction and manual MKLROOT setup are acceptable trade-offs for Intel-optimized math routines. No known vulnerabilities. License is proprietary and unclear—verify Intel's terms for your use case before committing.
Install
onemkl-sycl-lapack on PyPI
Before you install
Medium install friction due to four runtime dependencies (dpcpp-cpp-rt, intel-opencl-rt, mkl, onemkl-sycl-blas) and platform-specific wheels (Linux x86_64 and Windows only). Active maintenance with recent release.
MKLROOT environment variable must be configured manually after installation; Linux x86_64 or Windows x86_64 required; Intel runtime libraries (dpcpp-cpp-rt, intel-opencl-rt) must be present.
License in practice
Licensed under Intel Simplified Software License (proprietary, not SPDX-identified). License treatment is unclear—review Intel's terms before use in commercial or redistributed contexts.
Quickstart
pip install onemkl-sycl-lapack
# Then import and call C/DPC++ interfaces via ctypes or language bindings
# Requires setting MKLROOT environment variable manually per Intel documentationVerify before relying
- Exact Python version compatibility (requires_python not specified in metadata)
- Whether MKLROOT setup is documented in the package or only in external Intel docs
- Performance benchmarks or typical use-case guidance for CPU vs. GPU dispatch
Package facts
| License | Not declared unclear |
| Python support | Not specified |
| Install friction | Medium. Platform-specific wheel |
| Runtime dependencies | 4 packagesdpcpp-cpp-rtintel-opencl-rtmklonemkl-sycl-blas |
| Maintenance | Actively maintained 44 days since the last release |
| First released | |
| Downloads | 171,158 / month, #10,378 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_lapack-2026.1.0-py2.py3-none-manylinux_2_28_x86_64.whl; onemkl_sycl_lapack-2026.1.0-py2.py3-none-win_amd64.whl
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See also onemkl-sycl-blas · onemkl-sycl-sparse · onemkl-sycl-dft · onemkl-sycl-rng · mkl · oneccl-devel · mkl-include · onemkl-license · mkl-static · oneccl