Get Intel® oneAPI Math Kernel Library

Install with Spack

spack install intel-oneapi-mkl

For more information, refer to Spack documentation.

For the next steps, see the Get Started Guide.

Install with YUM

sudo yum install intel-oneapi-mkl-devel

The following packages are available for installation:

intel-oneapi-mkl-devel: Complete oneMKL package for development
intel-oneapi-mkl: Complete oneMKL package for runtime only
intel-oneapi-mkl-classic-devel: oneMKL development package for C/Fortran functionality and Cluster components
intel-oneapi-mkl-classic-include
intel-oneapi-mkl-classic: oneMKL runtime package for C/Fortran functionality and Cluster components
intel-oneapi-mkl-cluster-devel
intel-oneapi-mkl-cluster
intel-oneapi-mkl-core-devel
intel-oneapi-mkl-core
intel-oneapi-mkl-sycl-devel: oneMKL development package for SYCL functionality and GPU support
intel-oneapi-mkl-sycl-include
intel-oneapi-mkl-sycl: oneMKL runtime package for SYCL functionality and GPU support
intel-oneapi-mkl-sycl-blas: oneMKL runtime package for SYCL functionality and GPU support for BLAS only
intel-oneapi-mkl-sycl-data-fitting: oneMKL runtime package for SYCL functionality and GPU support for Data Fitting only (experimental library)
intel-oneapi-mkl-sycl-dft: oneMKL runtime package for SYCL functionality and GPU support for the Fast Fourier transforms only
intel-oneapi-mkl-sycl-lapack: oneMKL runtime package for SYCL functionality and GPU support for LAPACK only
intel-oneapi-mkl-sycl-rng: oneMKL runtime package for SYCL functionality and GPU support for Random Number Generators only
intel-oneapi-mkl-sycl-sparse: oneMKL runtime package for SYCL functionality and GPU support for Sparse BLAS only
intel-oneapi-mkl-sycl-stats: oneMKL runtime package for SYCL functionality and GPU support for Summary Statistics only
intel-oneapi-mkl-sycl-vm: oneMKL runtime package for SYCL functionality and GPU support for Vector Math only
For distributed SYCL-based applications:
- intel-oneapi-mkl-sycl-distributed-dft: oneMKL runtime package for SYCL functionality and GPU support for distributed Discrete Fourier Transform only (experimental library)
- intel-oneapi-mkl-sycl-distributed-dft-devel: oneMKL development package for SYCL functionality and GPU support for distributed Discrete Fourier Transform only (experimental library)

For the next steps, see the Get Started Guide.

Set Up Your Environment

Install Miniforge.
Create and activate a new conda environment, replacing <your-env-name> with your preferred name for the environment:
```
conda create -n <your-env-name>
conda activate <your-env-name>
```

Install with conda


conda install -c https://software.repos.intel.com/python/conda/ -c conda-forge <package-name>

The following packages are available for installation:

For running applications that require oneMKL:
- mkl includes runtime only
For developing or compiling applications:
- mkl-devel includes libraries, headers, and tools for dynamic linking
- If needed, add mkl-include if your development workflow manages the libraries separately
For static linking:
- mkl-static to statically link oneMKL, creating self-contained binaries
For running Data Parallel C++ (DPC++) applications that require oneMKL:
- mkl-dpcpp provides the runtime support for oneMKL with DPC++
For developing and compiling DPC++ applications:
- mkl-devel-dpcpp includes the development tools and headers for oneMKL with DPC++
For using domain-specific libraries in SYCL-based applications:
- onemkl-sycl-blas provides Basic Linear Algebra Subprograms (BLAS) routines
- onemkl-sycl-lapack provies Linear Algebra Package (LAPACK) routines for more advanced linear algebra computations
- onemkl-sycl-dft provides Discrete Fourier Transform functionality
- onemkl-sycl-sparse provides sparse matrix operations
- onemkl-sycl-vm provides vector math (VM) operations, which optimize common mathematical functions applied to vectors
- onemkl-sycl-datafitting provides functionality for data fitting operations

For the next steps, see the Get Started Guide.

Set Up Your Environment

Install Miniforge.
Create and activate a new conda environment, replacing <your-env-name> with your preferred name for the environment:
```
conda create -n <your-env-name>
conda activate <your-env-name>
```

Install with conda


conda install -c https://software.repos.intel.com/python/conda/ -c conda-forge <package-name>

The following packages are available for installation:

For running applications that require oneMKL:
- mkl includes runtime only
For developing or compiling applications:
- mkl-devel includes libraries, headers, and tools for dynamic linking
- If needed, add mkl-include if your development workflow manages the libraries separately
For static linking:
- mkl-static to statically link oneMKL, creating self-contained binaries
For single-device SYCL-based applications:
- For running SYCL-based applications that require oneMKL:
  - mkl-dpcpp provides the runtime support for oneMKL with SYCL
- For developing and compiling SYCL-based applications:
  - mkl-devel-dpcpp includes the development tools and headers for oneMKL with SYCL
- For using domain-specific libraries when running SYCL-based applications:
  - onemkl-sycl-blas provides Basic Linear Algebra Subprograms (BLAS) routines
  - onemkl-sycl-lapack provides Linear Algebra Package (LAPACK) routines for more advanced linear algebra computations
  - onemkl-sycl-dft provides Discrete Fourier Transform functionality
  - onemkl-sycl-sparse provides sparse matrix operations
  - onemkl-sycl-vm provides vector math (VM) operations, which optimize common mathematical functions applied to vectors
  - (Experimental feature) onemkl-sycl-datafitting provides functionality for data fitting operations
For distributed SYCL-based applications:
- For using domain-specific libraries when running SYCL-based applications:
  - (Experimental feature) onemkl-sycl-distributed-dft provides runtime support for distributed Discrete Fourier Transform functionality.

For the next steps, see the Get Started Guide.

Install with NuGet

You can install NuGet packages for oneMKL via Microsoft* Visual Studio or command line interface. For more information, refer to the NuGet documentation.

The following packages are available for installation

Development packages contain the development libraries and headers, enabling you to compile and link your applications with the oneMKL libraries: intelmkl.devel.win-x64 intelmkl.devel.win-x86
Static packages contain the static libraries, allowing you to link oneMKL libraries statically into your application. This means your application will not depend on external dynamic libraries at runtime, simplifying distribution: intelmkl.static.win-x64 intelmkl.static.win-x86

Additionally, oneMKL cluster components development and static packages are available: intelmkl.devel.cluster.win-x64 intelmkl.static.cluster.win-x64

For the next steps, see the Get Started Guide.

Set Up Your Environment

Create and activate a virtual environment, replacing <your-env-name> with your preferred name for the environment:


python3.10 -m venv <your-env-name>
source <your-env-name>/bin/activate

Install with pip


sudo pip install <package-name>

The following packages are available for installation:

For running applications that require oneMKL:
- mkl includes runtime only
For developing or compiling applications:
- mkl-devel includes libraries, headers, and tools for dynamic linking
- if needed, add mkl-include if your development workflow manages the libraries separately
For static linking:
- mkl-static to statically link oneMKL, creating self-contained binaries
For running Data Parallel C++ (DPC++) applications that require oneMKL:
- mkl-dpcpp provides the runtime support for oneMKL with DPC++
For developing and compiling DPC++ applications:
- mkl-devel-dpcpp includes the development tools and headers for oneMKL with DPC++
For using domain-specific libraries in SYCL-based applications:
- onemkl-sycl-blas provides Basic Linear Algebra Subprograms (BLAS) routines
- onemkl-sycl-lapack provies Linear Algebra Package (LAPACK) routines for more advanced linear algebra computations
- onemkl-sycl-dft provides Discrete Fourier Transform functionality
- onemkl-sycl-sparse provides sparse matrix operations
- onemkl-sycl-vm provides vector math (VM) operations, which optimize common mathematical functions applied to vectors
- onemkl-sycl-datafitting provides functionality for data fitting operations

For the next steps, see the Get Started Guide.

Prerequisites for First-Time Users

To add APT repository access, install the prerequisites:
```
sudo apt update
sudo apt install -y gpg-agent wget
```

Set up the repository. To do this, download the key to the system keyring:


wget -O- https://apt.repos.intel.com/intel-gpg-keys/GPG-PUB-KEY-INTEL-SW-PRODUCTS.PUB | gpg --dearmor | sudo tee /usr/share/keyrings/oneapi-archive-keyring.gpg > /dev/null

Add the signed entry to APT sources and configure the APT client to use the Intel repository:


echo "deb [signed-by=/usr/share/keyrings/oneapi-archive-keyring.gpg] https://apt.repos.intel.com/oneapi all main" | sudo tee /etc/apt/sources.list.d/oneAPI.list

Update the packages list and repository index.
```
sudo apt update
```

Install with APT

For running applications that require oneMKL:


sudo apt install intel-oneapi-mkl

For developing and compiling oneMKL applications:


sudo apt install intel-oneapi-mkl-devel

The following packages are available for installation:

intel-oneapi-mkl-devel: Complete oneMKL package for development
intel-oneapi-mkl: Complete oneMKL package for runtime only
intel-oneapi-mkl-classic-devel: oneMKL development package for C/Fortran functionality and Cluster components
intel-oneapi-mkl-classic-include
intel-oneapi-mkl-classic: oneMKL runtime package for C/Fortran functionality and Cluster components
intel-oneapi-mkl-cluster-devel
intel-oneapi-mkl-cluster
intel-oneapi-mkl-core-devel
intel-oneapi-mkl-core
intel-oneapi-mkl-sycl-devel: oneMKL development package for SYCL functionality and GPU support
intel-oneapi-mkl-sycl-include
intel-oneapi-mkl-sycl: oneMKL runtime package for SYCL functionality and GPU support
intel-oneapi-mkl-sycl-blas: oneMKL runtime package for SYCL functionality and GPU support for BLAS only
intel-oneapi-mkl-sycl-data-fitting: oneMKL runtime package for SYCL functionality and GPU support for Data Fitting only (experimental library)
intel-oneapi-mkl-sycl-dft: oneMKL runtime package for SYCL functionality and GPU support for the Fast Fourier transforms only
intel-oneapi-mkl-sycl-lapack: oneMKL runtime package for SYCL functionality and GPU support for LAPACK only
intel-oneapi-mkl-sycl-rng: oneMKL runtime package for SYCL functionality and GPU support for Random Number Generators only
intel-oneapi-mkl-sycl-sparse: oneMKL runtime package for SYCL functionality and GPU support for Sparse BLAS only
intel-oneapi-mkl-sycl-stats: oneMKL runtime package for SYCL functionality and GPU support for Summary Statistics only
intel-oneapi-mkl-sycl-vm: oneMKL runtime package for SYCL functionality and GPU support for Vector Math only
For distributed SYCL-based applications:
- intel-oneapi-mkl-sycl-distributed-dft: oneMKL runtime package for SYCL functionality and GPU support for distributed Discrete Fourier Transform only (experimental library)
- intel-oneapi-mkl-sycl-distributed-dft-devel: oneMKL development package for SYCL functionality and GPU support for distributed Discrete Fourier Transform only (experimental library)

For the next steps, see the Get Started Guide.

Install with Cloudera

In the Cloudera Manager Admin Console, access the Parcels page by clicking the Parcels indicator in the left navigation bar.
At the Parcels page, click the Parcel Repositories & Network Settings button.
In the Remote Parcel Repository URLs list, click the plus symbol to open an additional row. Enter the path to oneMKL Parcel repository: http://parcels.repos.intel.com/mkl/latest. Click the Save & Verify configuration button.
Click the Check for New Parcels button. In the Location selector, click Available Remotely. The latest oneMKL parcel should be available for download.
Click the Download button for the oneMKL parcel. By downloading the package, you agree with the terms and conditions stated in the End-User License Agreement (EULA).
When download is completed, click the Distribute button to distribute the parcel on all cluster nodes.
When distribution is completed, click the Activate button to activate the parcel on all cluster nodes.

For additional information about Clouder parcels, refer to Parcels documentation.

For the next steps, see the Get Started Guide.

Install with DNF

sudo dnf install intel-oneapi-mkl-devel

For the next steps, see the Get Started Guide.

Set Up Your Environment

Create and activate a virtual environment, replacing <your-env-name> with your preferred name for the environment:


python3.10 -m venv <your-env-name>
source <your-env-name>/bin/activate

Install with pip


sudo pip install <package-name>

The following packages are available for installation:

For running applications that require oneMKL:
- mkl includes runtime only
For developing or compiling applications:
- mkl-devel includes libraries, headers, and tools for dynamic linking
- If needed, add mkl-include if your development workflow manages the libraries separately
For static linking:
- mkl-static to statically link oneMKL, creating self-contained binaries
For single-device SYCL-based applications:
- For running SYCL-based applications that require oneMKL:
  - mkl-dpcpp provides the runtime support for oneMKL with SYCL
- For developing and compiling SYCL-based applications:
  - mkl-devel-dpcpp includes the development tools and headers for oneMKL with SYCL
- For using domain-specific libraries when running SYCL-based applications:
  - onemkl-sycl-blas provides Basic Linear Algebra Subprograms (BLAS) routines
  - onemkl-sycl-lapack provides Linear Algebra Package (LAPACK) routines for more advanced linear algebra computations
  - onemkl-sycl-dft provides Discrete Fourier Transform functionality
  - onemkl-sycl-sparse provides sparse matrix operations
  - onemkl-sycl-vm provides vector math (VM) operations, which optimize common mathematical functions applied to vectors
  - (Experimental feature) onemkl-sycl-datafitting provides functionality for data fitting operations
For distributed SYCL-based applications:
- For using domain-specific libraries when running SYCL-based applications:
  - (Experimental feature) onemkl-sycl-distributed-dft provides runtime support for distributed Discrete Fourier Transform functionality.

For the next steps, see the Get Started Guide.

Install with Zypper

sudo zypper install intel-oneapi-mkl-devel

For the next steps, see the Get Started Guide.

Command Line Download

Command Line Installation Parameters

wget https://registrationcenter-download.intel.com/akdlm/IRC_NAS/47c7d946-fca1-441a-b0df-b094e3f045ea/intel-onemkl-2025.2.0.629.sh

sudo sh ./intel-onemkl-2025.2.0.629.sh

Command Line Download

Command Line Installation Parameters

wget https://registrationcenter-download.intel.com/akdlm/IRC_NAS/47c7d946-fca1-441a-b0df-b094e3f045ea/intel-onemkl-2025.2.0.629_offline.sh

sudo sh ./intel-onemkl-2025.2.0.629_offline.sh

Prerequisites for First-Time Users

Create the DNF repository file in the /temp directory as a normal user.


tee > /tmp/oneAPI.repo << EOF
[oneAPI]
name=Intel® oneAPI repository
baseurl=https://yum.repos.intel.com/oneapi
enabled=1
gpgcheck=1
repo_gpgcheck=1
gpgkey=https://yum.repos.intel.com/intel-gpg-keys/GPG-PUB-KEY-INTEL-SW-PRODUCTS.PUB
EOF

Move the newly created oneAPI.repo file to the YUM configuration directory.


sudo mv /tmp/oneAPI.repo /etc/yum.repos.d

Prerequisites for First-Time Users

Create the YUM repository file in the /temp directory as a normal user.

tee > /tmp/oneAPI.repo << EOF
[oneAPI]
name=Intel® oneAPI repository
baseurl=https://yum.repos.intel.com/oneapi
enabled=1
gpgcheck=1
repo_gpgcheck=1
gpgkey=https://yum.repos.intel.com/intel-gpg-keys/GPG-PUB-KEY-INTEL-SW-PRODUCTS.PUB
EOF

Move the newly created oneAPI.repo file to the YUM configuration directory /etc/yum.repos.d.
```
sudo mv /tmp/oneAPI.repo /etc/yum.repos.d
```

Prerequisites for First-Time Users

Add the Intel oneAPI repository public key using the following command:

sudo zypper addrepo https://yum.repos.intel.com/oneapi oneAPI

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Get Intel® oneAPI Math Kernel Library (oneMKL)

Install with Spack

Install with YUM

Set Up Your Environment

Install with conda

Set Up Your Environment

Install with conda

Install with NuGet

Set Up Your Environment

Install with pip

Prerequisites for First-Time Users

Install with APT

Install with Cloudera

Install with DNF

Set Up Your Environment

Install with pip

Install with Zypper

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Installation Instructions

Command Line Download

Command Line Download

Installation Instructions

Prerequisites for First-Time Users

Prerequisites for First-Time Users

Prerequisites for First-Time Users

Additional Resources

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