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oneAPI Math Kernel Library (oneMKL) Interfaces

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oneAPI Math Kernel Library (oneMKL) Interfaces

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oneMKL Interfaces is 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 cuSOLVER for NVIDIA GPU NVIDIA GPU
NVIDIA cuRAND for NVIDIA GPU NVIDIA GPU
NETLIB LAPACK for x86 CPU x86 CPU
AMD rocBLAS for AMD GPU AMD GPU
AMD rocRAND for AMD GPU AMD GPU

Table of Contents

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 Selecting a Compiler for the choice between dpcpp and clang++ compilers.

Supported Configurations:

Supported domains: BLAS, LAPACK, RNG

Linux*

Domain Backend Library Supported Link Type Supported Compiler
BLAS x86 CPU Intel(R) oneAPI Math Kernel Library Dynamic, Static DPC++, LLVM*, hipSYCL
Intel GPU Dynamic, Static DPC++
NVIDIA GPU NVIDIA cuBLAS Dynamic, Static LLVM*
x86 CPU NETLIB LAPACK Dynamic, Static DPC++, LLVM*, hipSYCL
AMD GPU AMD rocBLAS Dynamic, Static hipSYCL
LAPACK x86 CPU Intel(R) oneAPI Math Kernel Library Dynamic, Static DPC++, LLVM*
Intel GPU Dynamic, Static DPC++
NVIDIA GPU NVIDIA cuSOLVER Dynamic, Static LLVM*
RNG x86 CPU Intel(R) oneAPI Math Kernel Library Dynamic, Static DPC++, LLVM*, hipSYCL
Intel GPU Dynamic, Static DPC++
NVIDIA GPU NVIDIA cuRAND Dynamic, Static LLVM*
AMD GPU AMD rocRAND Dynamic, Static hipSYCL

Windows*

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

* LLVM - Intel project for LLVM* technology with support for NVIDIA CUDA

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.)
    • AMD(R) GPUs see here tested on AMD Vega 20 (gfx906)

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
Ninja (optional)
Conan C++ package manager GNU* FORTRAN Compiler - Sphinx
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
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
AMD GPU hipSYCL with ROCm backend and dependencies 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
hipSYCL later than 2cfa530 No N/A Installed by user BSD-2-Clause License
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
AMD rocBLAS 4.5 No N/A Installed by user AMD License
AMD rocRAND 5.1.0 No N/A Installed by user AMD License
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

Documentation

Contributing

See CONTRIBUTING for more information.

License

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

information.

FAQs

oneMKL

  1. What is the difference between the following oneMKL items?

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

  1. 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.

  2. 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.

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oneAPI Math Kernel Library (oneMKL) Interfaces

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