oneAPI Math Kernel Library (oneMKL) Interfaces

oneMKL interfaces are an open-source implementation of the oneMKL Data Parallel C++ (DPC++) interface according to the oneMKL specification. It works with multiple devices (backends) using device-specific libraries underneath.

oneMKL is part of oneAPI.

User Application	oneMKL Layer	Third-Party Library	Hardware Backend
oneMKL interface	oneMKL selector	Intel(R) oneAPI Math Kernel Library for x86 CPU	x86 CPU
		Intel(R) oneAPI Math Kernel Library for Intel GPU	Intel GPU
		NVIDIA cuBLAS for NVIDIA GPU	NVIDIA GPU
		NVIDIA cuRAND for NVIDIA GPU	NVIDIA GPU
		NETLIB LAPACK for x86 CPU	x86 CPU

Support and Requirements

Supported Usage Models:

There are two oneMKL selector layer implementations:

Run-time dispatching: The application is linked with the oneMKL library and the required backend is loaded at run-time based on device vendor (all libraries should be dynamic).

Example of app.cpp with run-time dispatching:

#include "oneapi/mkl.hpp"

...
cpu_dev = sycl::device(sycl::cpu_selector());
gpu_dev = sycl::device(sycl::gpu_selector());

sycl::queue cpu_queue(cpu_dev);
sycl::queue gpu_queue(gpu_dev);

oneapi::mkl::blas::column_major::gemm(cpu_queue, transA, transB, m, ...);
oneapi::mkl::blas::column_major::gemm(gpu_queue, transA, transB, m, ...);

How to build an application with run-time dispatching:

$> dpcpp -fsycl –I$ONEMKL/include app.cpp
$> dpcpp -fsycl app.o –L$ONEMKL/lib –lonemkl

Compile-time dispatching: The application uses a templated backend selector API where the template parameters specify the required backends and third-party libraries and the application is linked with the required oneMKL backend wrapper libraries (libraries can be static or dynamic).

Example of app.cpp with compile-time dispatching:

#include "oneapi/mkl.hpp"

...
cpu_dev = sycl::device(sycl::cpu_selector());
gpu_dev = sycl::device(sycl::gpu_selector());

sycl::queue cpu_queue(cpu_dev);
sycl::queue gpu_queue(gpu_dev);

oneapi::mkl::backend_selector<oneapi::mkl::backend::mklcpu> cpu_selector(cpu_queue);

oneapi::mkl::blas::column_major::gemm(cpu_selector, transA, transB, m, ...);
oneapi::mkl::blas::column_major::gemm(oneapi::mkl::backend_selector<oneapi::mkl::backend::cublas> {gpu_queue}, transA, transB, m, ...);

How to build an application with compile-time dispatching:

$> clang++ -fsycl –I$ONEMKL/include app.cpp
$> clang++ -fsycl app.o –L$ONEMKL/lib –lonemkl_blas_mklcpu –lonemkl_blas_cublas

Refer to Selection of Compilers for the choice between dpcpp and clang++ compilers.

Supported Configurations:

Supported domains: BLAS, LAPACK, RNG

Linux*

Domain	Backend	Library	Supported Link Type
BLAS	x86 CPU	Intel(R) oneAPI Math Kernel Library	Dynamic, Static
	Intel GPU	Intel(R) oneAPI Math Kernel Library	Dynamic, Static
	NVIDIA GPU	NVIDIA cuBLAS	Dynamic, Static
	x86 CPU	NETLIB LAPACK	Dynamic, Static
LAPACK	x86 CPU	Intel(R) oneAPI Math Kernel Library	Dynamic, Static
LAPACK	Intel GPU	Intel(R) oneAPI Math Kernel Library	Dynamic, Static
RNG	x86 CPU	Intel(R) oneAPI Math Kernel Library	Dynamic, Static
	Intel GPU	Intel(R) oneAPI Math Kernel Library	Dynamic, Static
	NVIDIA GPU	NVIDIA cuRAND	Dynamic, Static

Windows*

Domain	Backend	Library	Supported Link Type
BLAS	x86 CPU	Intel(R) oneAPI Math Kernel Library	Dynamic, Static
	Intel GPU	Intel(R) oneAPI Math Kernel Library	Dynamic, Static
	x86 CPU	NETLIB LAPACK	Dynamic, Static
LAPACK	x86 CPU	Intel(R) oneAPI Math Kernel Library	Dynamic, Static
LAPACK	Intel GPU	Intel(R) oneAPI Math Kernel Library	Dynamic, Static
RNG	x86 CPU	Intel(R) oneAPI Math Kernel Library	Dynamic, Static

Hardware Platform Support

CPU
- Intel Atom(R) Processors
- Intel(R) Core(TM) Processor Family
- Intel(R) Xeon(R) Processor Family
Accelerators
- Intel(R) Processor Graphics GEN9
- NVIDIA(R) TITAN RTX(TM) (Linux* only. cuRAND backend tested also with Quadro and A100 GPUs. Not tested with other NVIDIA GPU families and products.)

Supported Operating Systems

Linux*

Operating System	CPU Host/Target	Integrated Graphics from Intel (Intel GPU)	NVIDIA GPU
Ubuntu	18.04.3, 19.04	18.04.3, 19.10	18.04.3, 20.04
SUSE Linux Enterprise Server*	15	Not supported	Not supported
Red Hat Enterprise Linux* (RHEL*)	8	Not supported	Not supported
Linux* kernel	N/A	4.11 or higher	N/A

Windows*

Operating System	CPU Host/Target	Integrated Graphics from Intel (Intel GPU)
Microsoft Windows*	10 (64-bit version only)	10 (64-bit version only)
Microsoft Windows* Server	2016, 2019	Not supported

Software Requirements

What should I download?

General:

Using Conan	Using CMake Directly
	Functional Testing	Build Only	Documentation
Linux* : GNU* GCC 5.1 or higher Windows* : MSVS* 2017 or MSVS* 2019 (version 16.5 or newer)
Python 3.6 or higher	CMake
Python 3.6 or higher	Ninja (optional)
Conan C++ package manager	GNU* FORTRAN Compiler	-	Sphinx
Conan C++ package manager	NETLIB LAPACK	-	-

Hardware and OS Specific:

Operating System	Device	Package	Installed by Conan
Linux/Windows	x86 CPU	Intel(R) oneAPI DPC++ Compiler or Intel project for LLVM* technology	No
	x86 CPU	Intel(R) oneAPI Math Kernel Library	Yes
	Intel GPU	Intel(R) oneAPI DPC++ Compiler	No
		Intel GPU driver	No
		Intel(R) oneAPI Math Kernel Library	Yes
Linux* only	NVIDIA GPU	Intel project for LLVM* technology	No

If Building with Conan, above packages marked as "No" must be installed manually.

If Building with CMake, above packages must be installed manually.

Notice for Use of Conan Package Manager

LEGAL NOTICE: By downloading and using this container or script as applicable (the “Software Package”) and the included software or software made available for download, you agree to the terms and conditions of the software license agreements for the Software Package, which may also include notices, disclaimers, or license terms for third party software (together, the “Agreements”) included in this README file.

If the Software Package is installed through a silent install, your download and use of the Software Package indicates your acceptance of the Agreements.

Product and Version Information:

Product	Supported Version	Installed by Conan	Conan Package Source	Package Install Location on Linux*	License
Python	3.6 or higher	No	N/A	Pre-installed or Installed by user	PSF
Conan C++ Package Manager	1.24 or higher	No	N/A	Installed by user	MIT
CMake	3.13 or higher	Yes (3.15 or higher)	conan-center	~/.conan/data or $CONAN_USER_HOME/.conan/data	The OSI-approved BSD 3-clause License
Ninja	1.10.0	Yes	conan-center	~/.conan/data or $CONAN_USER_HOME/.conan/data	Apache License v2.0
GNU* FORTRAN Compiler	7.4.0 or higher	Yes	apt	/usr/bin	GNU General Public License, version 3
Intel(R) oneAPI DPC++ Compiler	latest	No	N/A	Installed by user	End User License Agreement for the Intel(R) Software Development Products
Intel project for LLVM* technology binary for x86 CPU	Daily builds (experimental) tested with 20200331	No	N/A	Installed by user	Apache License v2
Intel project for LLVM* technology source for NVIDIA GPU	Daily source releases: tested with 20200421	No	N/A	Installed by user	Apache License v2
Intel(R) oneAPI Math Kernel Library	latest	Yes	apt	/opt/intel/inteloneapi/mkl	Intel Simplified Software License
NVIDIA CUDA SDK	10.2	No	N/A	Installed by user	End User License Agreement
NETLIB LAPACK	3.7.1	Yes	conan-community	~/.conan/data or $CONAN_USER_HOME/.conan/data	BSD like license
Sphinx	2.4.4	Yes	pip	~/.local/bin (or similar user local directory)	BSD License

conan-center: https://api.bintray.com/conan/conan/conan-center

conan-community: https://api.bintray.com/conan/conan-community/conan

Selection of Compilers

A compiler needs to be chosen according to the required backend of your application.

If your application requires Intel GPU, use Intel(R) oneAPI DPC++ Compiler dpcpp.
If your application requires NVIDIA GPU, use the latest release of clang++ from Intel project for LLVM* technology.
If neither Intel GPU nor NVIDIA GPU is required, you can use either Intel(R) oneAPI DPC++ Compiler dpcpp or clang++ on Linux and clang-cl on Windows from Intel project for LLVM* technology.

Build Setup

Install Intel(R) oneAPI DPC++ Compiler (select variant as per requirement).
Clone this project to <path to onemkl>, where <path to onemkl> is the root directory of this repository.
You can Build with Conan to automate the process of getting dependencies or you can download and install the required dependencies manually and Build with CMake directly.

Note: Conan package manager automates the process of getting required packages, so that you do not have to go to different web location and follow different instructions to install them.

Building with Conan

** This method currently works on Linux* only **

** Make sure you have completed Build Setup. **

Note: To understand how dependencies are resolved, refer to the Product and Version Information section. For details about Conan package manager, refer to Conan Documentation.

Getting Conan

Conan can be installed from pip:

pip3 install conan

Setting up Conan

Conan Default Directory

Conan stores all files and data in ~/.conan. If you are fine with this behavior, you can skip to Conan Profiles section.

To change this behavior, set the environment variable CONAN_USER_HOME to a path of your choice. A .conan/ directory will be created in this path and future Conan commands will use this directory to find configuration files and download dependent packages. Packages will be downloaded into $CONAN_USER_HOME/data. To change the "/data" part of this directory, refer to the [storage] section of conan.conf file.

To make this setting persistent across terminal sessions, you can add below line to your ~/.bashrc or custom runscript. Refer to Conan Documentation for more details.

export CONAN_USER_HOME=/usr/local/my_workspace/conan_cache

Conan Profiles

Profiles are a way for Conan to determine a basic environment to use for building a project. This project ships with profiles for:

Intel(R) oneAPI DPC++ Compiler for x86 CPU and Intel GPU backend: inteldpcpp_lnx

Open the profile you wish to use from <path to onemkl>/conan/profiles/ and set COMPILER_PREFIX to the path to the root folder of compiler. The root folder is the one that contains the bin and lib directories. For example, Intel(R) oneAPI DPC++ Compiler root folder for default installation on Linux is /opt/intel/inteloneapi/compiler/<version>/linux. User can define custom path for installing the compiler.

COMPILER_PREFIX=<path to Intel(R) oneAPI DPC++ Compiler>

You can customize the [env] section of the profile based on individual requirements.
Install configurations for this project:

# Inside <path to onemkl>
$ conan config install conan/

This command installs all contents of <path to onemkl>/conan/, most importantly profiles, to conan default directory.

Note: If you change the profile, you must re-run the above command before you can use the new profile.

Building

Out-of-source build

# Inside <path to onemkl>
mkdir build && cd build

If you choose to build backends with the Intel(R) oneAPI Math Kernel Library, install the GPG key as mentioned here, https://software.intel.com/en-us/articles/oneapi-repo-instructions#aptpkg
Install dependencies

conan install .. --profile <profile_name> --build missing [-o <option1>=<value1>] [-o <option2>=<value2>]

The conan install command downloads and installs all requirements for the oneMKL DPC++ Interfaces project as defined in <path to onemkl>/conanfile.py based on the options passed. It also creates conanbuildinfo.cmake file that contains information about all dependencies and their directories. This file is used in top-level CMakeLists.txt.

-pr | --profile <profile_name> Defines a profile for Conan to use for building the project.

-b | --build <package_name|missing> Tells Conan to build or re-build a specific package. If missing is passed as a value, all missing packages are built. This option is recommended when you build the project for the first time, because it caches required packages. You can skip this option for later use of this command.

Build Project

conan build .. [--configure] [--build] [--test]  # Default is all

The conan build command executes the build() procedure from <path to onemkl>/conanfile.py. Since this project uses CMake, you can choose to configure, build, test individually or perform all steps by passing no optional arguments.

Optionally, you can also install the package. Similar to cmake --install . --prefix <install_dir>.

conan package .. --build-folder . --install-folder <install_dir>

-bf | --build-folder Tells Conan where to find the built project.

-if | --install-folder Tells Conan where to install the package. It is similar to specifying CMAKE_INSTALL_PREFIX

Note: For a detailed list of commands and options, refer to the Conan Command Reference.

Conan Build Options

Backend-related Options

The following options are available to pass on conan install when building the oneMKL library:

build_shared_libs=[True | False]. Setting it to True enables the building of dynamic libraries. The default value is True.
target_domains=[<list of values>]. Setting it to blas or any other list of domain(s), enables building of those specific domain(s) only. If not defined, the default value is all supported domains.
enable_mklcpu_backend=[True | False]. Setting it to True enables the building of oneMKL mklcpu backend. The default value is True.
enable_mklgpu_backend=[True | False]. Setting it to True enables the building of oneMKL mklgpu backend. The default value is True.
enable_mklcpu_thread_tbb=[True | False]. Setting it to True enables oneMKL on CPU with TBB threading instead of sequential. The default value is True.

Testing-related Options

build_functional_tests=[True | False]. Setting it to True enables the building of functional tests. The default value is True.

Documentation

build_doc=[True | False]. Setting it to True enables the building of rst files to generate HTML files for updated documentation. The default value is False.

Note: For a mapping between Conan and CMake options, refer to build options under the CMake section.

Example

Build oneMKL as a static library for oneMKL cpu and gpu backend:

# Inside <path to onemkl>
mkdir build && cd build
conan install .. --build missing --profile inteldpcpp_lnx -o build_shared_libs=False
conan build ..

Building with CMake

Make sure you have completed Build Setup.
Build and install all required dependencies.

Building for oneMKL

On Linux*

# Inside <path to onemkl>
mkdir build && cd build
cmake .. [-DCMAKE_CXX_COMPILER=<path_to_dpcpp_compiler>/bin/dpcpp] \  # required only if dpcpp is not found in environment variable PATH
         [-DCMAKE_C_COMPILER=<path_to_icx_compiler>/bin/icx]       \  # required only if icx is not found in environment variable PATH
         [-DMKL_ROOT=<mkl_install_prefix>] \                          # required only if environment variable MKLROOT is not set
         [-DREF_BLAS_ROOT=<reference_blas_install_prefix>] \          # required only for testing
         [-DREF_LAPACK_ROOT=<reference_lapack_install_prefix>]        # required only for testing
cmake --build .
ctest
cmake --install . --prefix <path_to_install_dir>

On Windows*

# Inside <path to onemkl>
md build && cd build
cmake .. -G Ninja [-DCMAKE_CXX_COMPILER=<path_to_dpcpp_compiler>\bin\dpcpp] \  # required only if dpcpp is not found in environment variable PATH
                  [-DCMAKE_C_COMPILER=<path_to_icx_compiler>\bin\icx]       \  # required only if icx is not found in environment variable PATH
                  [-DMKL_ROOT=<mkl_install_prefix>] \                          # required only if environment variable MKLROOT is not set
                  [-DREF_BLAS_ROOT=<reference_blas_install_prefix>] \          # required only for testing
                  [-DREF_LAPACK_ROOT=<reference_lapack_install_prefix>]        # required only for testing
ninja 
ctest
cmake --install . --prefix <path_to_install_dir>

Building for CUDA

On Linux*

With the cuBLAS backend:

# Inside <path to onemkl>
mkdir build && cd build
cmake .. [-DCMAKE_CXX_COMPILER=<path_to_clang++_compiler>/bin/clang++] \  # required only if clang++ is not found in environment variable PATH
         [-DCMAKE_C_COMPILER=<path_to_clang_compiler>/bin/clang]       \  # required only if clang is not found in environment variable PATH
         -DENABLE_CUBLAS_BACKEND=True  \
         -DENABLE_MKLCPU_BACKEND=False \                                  # disable Intel MKL CPU backend
         -DENABLE_MKLGPU_BACKEND=False \                                  # disable Intel MKL GPU backend
         [-DREF_BLAS_ROOT=<reference_blas_install_prefix>] \              # required only for testing
cmake --build .
ctest
cmake --install . --prefix <path_to_install_dir>

To build with the cuRAND backend instead simply replace:

-DENABLE_CUBLAS_BACKEND=True   \

With:

-DENABLE_CURAND_BACKEND=True   \

Build Options

All options specified in the Conan section are available to CMake. You can specify these options using -D<cmake_option>=<value>.

The following table provides a detailed mapping of options between Conan and CMake.

Conan Option	CMake Option	Supported Values	Default Value
build_shared_libs	BUILD_SHARED_LIBS	True, False	True
enable_mklcpu_backend	ENABLE_MKLCPU_BACKEND	True, False	True
enable_mklgpu_backend	ENABLE_MKLGPU_BACKEND	True, False	True
Not Supported	ENABLE_CUBLAS_BACKEND	True, False	False
Not Supported	ENABLE_CURAND_BACKEND	True, False	False
Not Supported	ENABLE_NETLIB_BACKEND	True, False	False
enable_mklcpu_thread_tbb	ENABLE_MKLCPU_THREAD_TBB	True, False	True
build_functional_tests	BUILD_FUNCTIONAL_TESTS	True, False	True
build_doc	BUILD_DOC	True, False	False
target_domains (list)	TARGET_DOMAINS (list)	blas, lapack, rng	All domains

Note: build_functional_tests and related CMake option affects all domains at a global scope.

Project Cleanup

Most use-cases involve building the project without the need to cleanup the build directory. However, if you wish to cleanup the build directory, you can delete the build folder and create a new one. If you wish to cleanup the build files but retain the build configuration, following commands will help you do so. They apply to both Conan and CMake methods of building this project.

# If you use "GNU/Unix Makefiles" for building,
make clean

# If you use "Ninja" for building
ninja -t clean

Contributing

See CONTRIBUTING for more information.

License

Distributed under the Apache license 2.0. See [LICENSE](LICENSE) for more

information.

FAQs

oneMKL

What is the difference between the following oneMKL items?
- The oneAPI Specification for oneMKL
- The oneAPI Math Kernel Library (oneMKL) Interfaces Project
- The Intel(R) oneAPI Math Kernel Library (oneMKL) Product

Answer:

The oneAPI Specification for oneMKL defines the DPC++ interfaces for performance math library functions. The oneMKL specification can evolve faster and more frequently than implementations of the specification.
The oneAPI Math Kernel Library (oneMKL) Interfaces Project is an open source implementation of the specification. The project goal is to demonstrate how the DPC++ interfaces documented in the oneMKL specification can be implemented for any math library and work for any target hardware. While the implementation provided here may not yet be the full implementation of the specification, the goal is to build it out over time. We encourage the community to contribute to this project and help to extend support to multiple hardware targets and other math libraries.
The Intel(R) oneAPI Math Kernel Library (oneMKL) product is the Intel product implementation of the specification (with DPC++ interfaces) as well as similar functionality with C and Fortran interfaces, and is provided as part of Intel® oneAPI Base Toolkit. It is highly optimized for Intel CPU and Intel GPU hardware.

Conan

I am behind a proxy. How can Conan download dependencies from external network?
- ~/.conan/conan.conf has a [proxies] section where you can add the list of proxies. For details refer to Conan proxy settings.
I get an error while installing packages via APT through Conan.
```
dpkg: warning: failed to open configuration file '~/.dpkg.cfg' for reading: Permission denied
Setting up intel-oneapi-mkl-devel (2021.1-408.beta07) ...
E: Sub-process /usr/bin/dpkg returned an error code (1)
```
- Although your user session has permissions to install packages via sudo apt, it does not have permissions to update debian package configuration, which throws an error code 1, causing a failure in conan install command.
- The package is most likely installed correctly and can be verified by:
  1. Running the conan install command again.
  2. Checking /opt/intel/inteloneapi for mkl and/or tbb directories.

Name		Name	Last commit message	Last commit date
Latest commit History 105 Commits
.github		.github
cmake		cmake
conan		conan
deps/googletest		deps/googletest
docs		docs
include/oneapi		include/oneapi
scripts		scripts
src		src
tests		tests
third-party-programs		third-party-programs
.gitignore		.gitignore
CMakeLists.txt		CMakeLists.txt
CODE_OF_CONDUCT.md		CODE_OF_CONDUCT.md
CONTRIBUTING.md		CONTRIBUTING.md
LICENSE		LICENSE
README.md		README.md
_clang-format		_clang-format
conanfile.py		conanfile.py
legal_information.md		legal_information.md

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oneAPI Math Kernel Library (oneMKL) Interfaces

Table of Contents

Support and Requirements

Supported Usage Models:

Supported Configurations:

Linux*

Windows*

Hardware Platform Support

Supported Operating Systems

Linux*

Windows*

Software Requirements

General:

Hardware and OS Specific:

Notice for Use of Conan Package Manager

Product and Version Information:

Selection of Compilers

Build Setup

Building with Conan

Getting Conan

Setting up Conan

Conan Default Directory

Conan Profiles

Building

Conan Build Options

Backend-related Options

Testing-related Options

Documentation

Example

Build oneMKL as a static library for oneMKL cpu and gpu backend:

Building with CMake

Building for oneMKL

Building for CUDA

Build Options

Project Cleanup

Contributing

License

FAQs

oneMKL

Conan

Legal information

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