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 | ||
| NVIDIA cuFFT for NVIDIA GPU | NVIDIA GPU | ||
| NETLIB LAPACK for x86 CPU | x86 CPU | ||
| AMD rocBLAS for AMD GPU | AMD GPU | ||
| AMD rocSOLVER for AMD GPU | AMD GPU | ||
| AMD rocRAND for AMD GPU | AMD GPU | ||
| AMD rocFFT for AMD GPU | AMD GPU | ||
| portBLAS | x86 CPU, Intel GPU, NVIDIA GPU, AMD GPU | ||
| portFFT | x86 CPU, Intel GPU, NVIDIA GPU, AMD GPU |
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:
if OS is Linux, use icpx compiler. If OS is Windows, use icx compiler. Linux example:
$> icpx -fsycl –I$ONEMKL/include app.cpp
$> icpx -fsycl app.o –L$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_cublasRefer to Selecting a Compiler for the choice between icpx/icx and clang++ compilers.
Header-based and backend-independent Device API can be called within sycl kernel or work from Host code (device-rng-usage-model-example). Currently, the following domains support the Device API:
- RNG. To use RNG Device API functionality it's required to include
oneapi/mkl/rng/device.hppheader file.
Supported domains: BLAS, LAPACK, RNG, DFT, SPARSE_BLAS
| 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*, hipSYCL | |
| x86 CPU | NETLIB LAPACK | Dynamic, Static | DPC++, LLVM*, hipSYCL | |
| AMD GPU | AMD rocBLAS | Dynamic, Static | LLVM*, hipSYCL | |
| x86 CPU, Intel GPU, NVIDIA GPU, AMD GPU | portBLAS | Dynamic, Static | DPC++, LLVM* | |
| 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* | |
| AMD GPU | AMD rocSOLVER | 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*, hipSYCL | |
| AMD GPU | AMD rocRAND | Dynamic, Static | LLVM*, hipSYCL | |
| DFT | Intel GPU | Intel(R) oneAPI Math Kernel Library | Dynamic, Static | DPC++ |
| x86 CPU | Dynamic, Static | DPC++ | ||
| NVIDIA GPU | NVIDIA cuFFT | Dynamic, Static | DPC++ | |
| AMD GPU | AMD rocFFT | Dynamic, Static | DPC++ | |
| x86 CPU, Intel GPU, NVIDIA GPU, AMD GPU | portFFT (limited API support) | Dynamic, Static | DPC++ | |
| SPARSE_BLAS | Intel GPU | Intel(R) oneAPI Math Kernel Library | Dynamic, Static | DPC++ |
| x86 CPU | Dynamic, Static | DPC++ |
| 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* |
| Intel GPU | Dynamic, Static | DPC++ |
* LLVM - Intel project for LLVM* technology with support for NVIDIA CUDA
- CPU
- Intel Atom(R) Processors
- Intel(R) Core(TM) Processor Family
- Intel(R) Xeon(R) Processor Family
- Accelerators
- Intel(R) Arc(TM) A-Series Graphics
- Intel(R) Data Center GPU Max Series
- NVIDIA(R) A100 (Linux* only)
- AMD(R) GPUs see here tested on AMD Vega 20 (gfx906)
| 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 |
| 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 |
What should I download?
| 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 | - | - | |
| 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 or hipSYCL with CUDA backend and dependencies |
No |
| AMD GPU | Intel project for LLVM* technology or 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.
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 | 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 | No | N/A | Installed by user | Apache License v2 |
| Intel project for LLVM* technology source for NVIDIA and AMD GPUs | Daily source releases | 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 |
| AMD rocSOLVER | 5.0.0 | No | N/A | Installed by user | AMD License |
| AMD rocFFT | rocm-5.4.3 | 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 |
| portBLAS | 0.1 | No | N/A | Installed by user | Apache License v2.0 |
| portFFT | 0.1 | No | N/A | Installed by user or automatically by cmake | Apache License v2.0 |
conan-center: https://api.bintray.com/conan/conan/conan-center
conan-community: https://api.bintray.com/conan/conan-community/conan
- Contents
- About
- Get Started
- Developer Reference
- Integrating a Third-Party Library
See CONTRIBUTING for more information.
Distributed under the Apache license 2.0. See [LICENSE](LICENSE) for more
information.
- 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.
- I'm trying to use oneMKL Interfaces in my project using FetchContent, but I keep running into
ONEMKL::SYCL::SYCL target was not foundproblem when I try to build the project. What should I do?
Answer:
Make sure you set the compiler when you configure your project.
E.g. cmake -Bbuild . -DCMAKE_CXX_COMPILER=icpx.
- I'm trying to use oneMKL Interfaces in my project using find_package(oneMKL). I set oneMKL/oneTBB and Compiler environment first, then I built and installed oneMKL Interfaces, and finally I tried to build my project using installed oneMKL Interfaces (e.g. like this
cmake -Bbuild -GNinja -DCMAKE_CXX_COMPILER=icpx -DoneMKL_ROOT=<path_to_installed_oneMKL_interfaces> .) and I noticed that cmake includes installed oneMKL Interfaces headers as a system include which ends up as a lower priority than the installed oneMKL package includes which I set before for building oneMKL Interfaces. As a result, I get conflicts between oneMKL and installed oneMKL Interfaces headers. What should I do?
Answer:
Having installed oneMKL Interfaces headers as -I instead on system includes (as -isystem) helps to resolve this problem. We use INTERFACE_INCLUDE_DIRECTORIES to add paths to installed oneMKL Interfaces headers (check oneMKLTargets.cmake in lib/cmake to find it). It's a known limitation that INTERFACE_INCLUDE_DIRECTORIES puts headers paths as system headers. To avoid that:
- Option 1: Use CMake >=3.25. In this case oneMKL Interfaces will be built with
EXPORT_NO_SYSTEMproperty set totrueand you won't see the issue. - Option 2: If you use CMake < 3.25, set
PROPERTIES NO_SYSTEM_FROM_IMPORTED truefor your target. E.g:set_target_properties(test PROPERTIES NO_SYSTEM_FROM_IMPORTED true).
-
I am behind a proxy. How can Conan download dependencies from external network?
~/.conan/conan.confhas 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 inconan installcommand. - The package is most likely installed correctly and can be verified by:
- Running the
conan installcommand again. - Checking
/opt/intel/inteloneapiformkland/ortbbdirectories.
- Running the
- Although your user session has permissions to install packages via