oneAPI Math Kernel Library (oneMKL) Interfaces

Contents

• Introduction
• Support and Requirements
• Build Setup
• Building with CMake
• Project Cleanup
• Notice and Disclaimer

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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

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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

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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

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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

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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.

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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

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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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