onemkl-sycl-sparse
Intel® oneAPI Math Kernel Library
Decision gist · record as of 2026-08-14
Yes, if you need sparse linear algebra on Intel hardware and are willing to manage manual MKLROOT configuration and platform constraints. The package is actively maintained, has no known vulnerabilities, and targets a specific high-performance niche. Not suitable for cross-platform portability or if you lack Intel CPU/GPU hardware or the DPC++ toolchain.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- MKLROOT environment variable must be set manually after installation; not configured automatically by PyPI.
- Requires Intel DPC++ compiler toolchain and supported Intel CPU or GPU hardware.
- 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, Windows x86_64 only).
License · maintenance · safety
(unclear) — Licensed under Intel Simplified Software License (proprietary, non-SPDX). 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) · 166,340 downloads/mo, #10,497 on PyPI
Alternatives
Verify before relying
pip install onemkl-sycl-sparse
# Then import and call sparse routines via C/DPC++ interfaces
# Example: link against liboneapi_sycl_sparse.so (Linux) or .dll (Windows)- Whether MKLROOT setup is required for all use cases or only certain sparse operations.
- Specific sparse matrix formats and operations supported by this package version.
- GPU support scope: which Intel GPU architectures are compatible.
- Python version compatibility (requires_python is unspecified in metadata).
What it is and what it does
onemkl-sycl-sparse is Intel's sparse linear algebra library packaged for Python, exposing C and DPC++ interfaces to highly optimized routines for sparse matrix computations. It targets applications requiring maximum performance on Intel hardware—both CPUs and GPUs—and is part of the broader oneAPI Math Kernel Library ecosystem.
The package ships as platform-specific wheels (Linux x86_64 and Windows x86_64) and depends on four runtime libraries: the DPC++ C++ runtime, Intel OpenCL runtime, the base MKL library, and onemkl-sycl-blas. After installation, you must manually configure the MKLROOT environment variable to point to the library installation; this is not done automatically. The library is intended for developers building high-performance numerical applications in C, C++, or languages that can call C interfaces.
Use it for
- Accelerate sparse matrix-vector products and factorizations in scientific computing workflows on Intel CPUs or GPUs.
- Optimize large-scale linear solvers for sparse systems in engineering simulations or machine learning training pipelines.
- Build DPC++ applications that require vendor-optimized sparse BLAS operations without reimplementing algorithms.
- Integrate Intel GPU acceleration into existing C/C++ codebases that perform sparse tensor computations.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you need sparse linear algebra on Intel hardware and are willing to manage manual MKLROOT configuration and platform constraints.
The package is actively maintained, has no known vulnerabilities, and targets a specific high-performance niche. Not suitable for cross-platform portability or if you lack Intel CPU/GPU hardware or the DPC++ toolchain.
Install
onemkl-sycl-sparse 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, Windows x86_64 only). Active maintenance with recent release.
MKLROOT environment variable must be set manually after installation; not configured automatically by PyPI. Requires Intel DPC++ compiler toolchain and supported Intel CPU or GPU hardware.
License in practice
Licensed under Intel Simplified Software License (proprietary, non-SPDX). License treatment is unclear; review Intel's terms before use in commercial or redistributed contexts.
Quickstart
pip install onemkl-sycl-sparse
# Then import and call sparse routines via C/DPC++ interfaces
# Example: link against liboneapi_sycl_sparse.so (Linux) or .dll (Windows)Verify before relying
- Whether MKLROOT setup is required for all use cases or only certain sparse operations.
- Specific sparse matrix formats and operations supported by this package version.
- GPU support scope: which Intel GPU architectures are compatible.
- Python version compatibility (requires_python is unspecified in metadata).
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 | 166,340 / month, #10,497 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_sparse-2026.1.0-py2.py3-none-manylinux_2_28_x86_64.whl; onemkl_sycl_sparse-2026.1.0-py2.py3-none-win_amd64.whl
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See also onemkl-sycl-blas · onemkl-sycl-lapack · mkl · daal · onemkl-sycl-rng · numexpr · onemkl-sycl-dft · qdldl · numkong · blis