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oneAPI Math Kernel Library (oneMKL) Interfaces

Contents

Introduction

oneMKL interfaces is an open-source implementation of oneMKL Data Parallel C++ (DPC++) interfaces according to oneMKL specification that can work with multiple devices (backends) using device specific libraries underneath.

User Application oneMKL Layer Third-Party Library Hardware Backend
oneMKL interface oneMKL selector [Intel(R) oneAPI Math Kernel Library](https://software.intel.com/en-us/oneapi/onemkl) for Intel CPU Intel CPU
[Intel(R) oneAPI Math Kernel Library](https://software.intel.com/en-us/oneapi/onemkl) for Intel GPU Intel GPU

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 "onemkl/onemkl.hpp"

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

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

onemkl::blas::gemm(cpu_queue, transA, transB, m, ...);
onemkl::blas::gemm(gpu_queue, transA, transB, m, ...);

How to build an application with run-time dispatching:

$> clang++ -fsycl –I$ONEMKL/include app.cpp
$> clang++ -fsycl app.o –L$ONEMKL/lib –lonemkl
  • Compile-time dispatching: The application uses a templated API where the template parameters specify the required backends and third-party libraries and the application is linked with required onemkl backend wrapper libraries (libraries can be static or dynamic).

Example of app.cpp with compile-time dispatching:

include "onemkl/onemkl.hpp"

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

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

onemkl::blas::gemm<intelcpu,intelmkl>(cpu_queue, transA, transB, m, ...);
onemkl::blas::gemm<intelgpu,intelmkl>(gpu_queue, transA, transB, m, ...);

How to build an application with run-time dispatching:

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

Supported Configurations:

Supported domains: BLAS

Linux*

Backend Library Supported Link Type
Intel CPU Intel(R) oneAPI Math Kernel Library Dynamic, Static
Intel GPU Intel(R) oneAPI Math Kernel Library Dynamic, Static

Support and Requirements

Hardware Platform Support

Linux*

  • CPU
    • Intel Atom(R) Processors
    • Intel(R) Core(TM) Processor Family
    • Intel(R) Xeon(R) Processor Family
  • Accelerators
    • Intel(R) Processor Graphics GEN9

Supported Operating Systems

Linux*

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

Software Requirements

What should I download?

General:

Functional Testing Build Only Documentation
CMake CMake CMake
Ninja (optional) Ninja (optional) Sphinx
GNU* FORTRAN Compiler
NETLIB LAPACK

Hardware and OS Specific:

Operating System Hardware Using CMake
Linux* Any GNU* GCC 5.1 or higher
Intel CPU Intel(R) oneAPI DPC++ Compiler
or
Intel project for LLVM* technology
Intel(R) oneAPI Math Kernel Library
Intel GPU Intel(R) oneAPI DPC++ Compiler
Intel GPU driver
Intel(R) oneAPI Math Kernel Library

Product and Version Information:

Product Supported Version License
Python 3.6 or higher PSF
CMake 3.13 or higher The OSI-approved BSD 3-clause License
Ninja 1.9.0 Apache License v2.0
GNU* FORTRAN Compiler 7.4.0 or higher GNU General Public License v2
Intel(R) oneAPI DPC++ Compiler 2021.1-beta05 End User License Agreement for the Intel(R) Software Development Products
Intel project for LLVM* technology binary for Intel CPU Daily builds (experimental) tested with 20200331 Apache License v2
Intel(R) oneAPI Math Kernel Library 2021.1-beta05 Intel Simplified Software License
NETLIB LAPACK 3.7.1 BSD like license
Sphinx 2.4.4 BSD License

Build Setup

  1. Install Intel(R) oneAPI DPC++ Compiler (select variant as per requirement).

  2. Clone this project to <path to onemkl>, where <path to onemkl> is the root directory of this repository.

  3. Build with CMake.

Building with CMake

  1. Make sure you have completed Build Setup.

  2. Build and install all required dependencies.

Then:

  • On Linux*
# Inside <path to onemkl>
mkdir build && cd build
export CXX=<path_to_dpcpp_compiler>/bin/dpcpp;
cmake .. [-DMKL_ROOT=<mkl_install_prefix>] \               # required only if enviroment variable MKLROOT is not set
         [-DREF_BLAS_ROOT=<reference_blas_install_prefix>] # required only for testing
cmake --build .
ctest
cmake --install . --prefix <path_to_install_dir>

Build Options

You can specify build options using -D<cmake_option>=<value>. The following table provides the list of options supported by CMake.

CMake Option Supported Values Default Value
BUILD_SHARED_LIBS True, False True
ENABLE_MKLCPU_BACKEND True, False True
ENABLE_MKLGPU_BACKEND True, False True
ENABLE_MKLCPU_THREAD_TBB True, False True
BUILD_FUNCTIONAL_TESTS True, False True
BUILD_DOC True, False False

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.

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

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

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