Get Started Guide

  • 2021.2
  • 03/26/2021
  • Public Content

Get Started with
Intel® oneAPI Math Kernel Library

The
Intel® oneAPI Math Kernel Library
(
oneMKL
) helps you achieve maximum performance with a math computing library of highly optimized, extensively parallelized routines for CPU and GPU. The library has C and Fortran interfaces for most routines on CPU, and DPC++ interfaces for some routines on both CPU and GPU. You can find comprehensive support for several math operations in various interfaces including:
For C and Fortran on CPU
  • Linear algebra
  • Fast Fourier Transforms (FFT)
  • Vector math
  • Direct and iterative sparse solvers
  • Random number generators
For DPC++ on CPU and GPU
(Refer to the Intel® oneAPI Math Kernel Library—Data Parallel C++ Developer Reference for more details.)
  • Linear algebra
    • BLAS
    • Selected Sparse BLAS functionality
    • Selected LAPACK functionality
  • Fast Fourier Transforms (FFT)
    • 1D, 2D, and 3D
  • Random number generators
    • Selected functionality
  • Selected Vector Math functionality

Before You Begin

Visit the Release Notes page for the Known Issues and most up-to-date information.
Visit the Intel® oneAPI Math Kernel Library System Requirements page for system requirements.
Visit the Get Started with the Intel® oneAPI DPC++/C++ Compiler for DPC++ Compiler requirements.

Step 1: Install
Intel® oneAPI Math Kernel Library

Download Intel® oneAPI Math Kernel Library from the Intel® oneAPI Base Toolkit.
For Python distributions, note the following limitation:
The oneMKL devel package (mkl-devel) for PIP distribution on Linux* and macOS* does not provide dynamic libraries symlinks (for more information see PIP GitHub issue #5919).
In the case of dynamic or single dynamic library linking with oneMKL devel package (for more information see oneMKL Link Line Advisor ) you must modify link line with oneMKL libraries full names and versions.
oneMKL link line example with the oneAPI Base Toolkit via symlinks:
Linux:
icc app.obj -L${MKLROOT}/lib/intel64 -lmkl_intel_lp64-lmkl_intel_thread -lmkl_core -liomp5 -lpthread -lm -ldl
macOS:
icc app.obj -L${MKLROOT}/lib -Wl,-rpath,${MKLROOT}/lib-lmkl_intel_lp64 -lmkl_intel_thread -lmkl_core -liomp5 -lpthread -lm -ldl
The oneMKL link line example with PIP devel package via libraries full names and versions:
Linux:
icc app.obj ${MKLROOT}/lib/intel64/libmkl_intel_lp64.so.1 ${MKLROOT}/lib/intel64/libmkl_intel_thread.so.1 ${MKLROOT}/lib/intel64/libmkl_core.so.1 -liomp5 -lpthread -lm -ldl
macOS:
icc app.obj -Wl,-rpath,${MKLROOT}/lib${MKLROOT}/lib/intel64/libmkl_intel_lp64.1.dylib ${MKLROOT}/lib/intel64/libmkl_intel_thread.1.dylib ${MKLROOT}/lib/intel64/libmkl_core.1.dylib -liomp5 -lpthread -lm-ldl

Step 2: Select a Function or Routine

Select a function or routine from
oneMKL
that is best suited for your problem. Use these resources:
Resource Link
Contents
The Developer Guide contains detailed information on several topics including:
  • Compiling and linking applications
  • Building custom DLLs
  • Threading
  • Memory Management
The Developer Reference (in C, Fortran, and DPC++ formats) contains detailed descriptions of the functions and interfaces for all library domains.
Use the LAPACK Function Finding Advisor to explore LAPACK routines that are useful for a particular problem. For example, if you specify an operation as:
  • Routine type: Computational
  • Computational problem: Orthogonal factorization
  • Matrix type: General
  • Operation: Perform QR factorization

Step 3: Link Your Code

Use the oneMKL Link Line Advisor to configure the link command according to your program features.
Some limitations and additional requirements:
Intel® oneAPI Math Kernel Library
for DPC++ only supports using the mkl_intel_ilp64 interface library and sequential or TBB threading.
For DPC++ interfaces with static linking on Linux
dpcpp -fsycl-device-code-split=per_kernel -DMKL_ILP64 <typical user includes and linking flags and other libs> ${MKLROOT}/lib/intel64/libmkl_sycl.a -Wl,--start-group ${MKLROOT}/lib/intel64/libmkl_intel_ilp64.a ${MKLROOT}/lib/intel64/libmkl_<sequential|tbb_thread>.a ${MKLROOT}/lib/intel64/libmkl_core.a -Wl,--end-group -lsycl -lOpenCL -lpthread -ldl -lm
For example, building/statically linking main.cpp with ilp64 interfaces and TBB threading:
dpcpp -fsycl-device-code-split=per_kernel -DMKL_ILP64 -I${MKLROOT}/include main.cpp ${MKLROOT}/lib/intel64/libmkl_sycl.a -Wl,--start-group ${MKLROOT}/lib/intel64/libmkl_intel_ilp64.a ${MKLROOT}/lib/intel64/libmkl_tbb_thread.a ${MKLROOT}/lib/intel64/libmkl_core.a -Wl,--end-group -L${TBBROOT}/lib/intel64/gcc4.8 -ltbb -lsycl -lOpenCL -lpthread -lm -ldl
For DPC++ interfaces with dynamic linking on Linux
dpcpp -DMKL_ILP64 <typical user includes and linking flags and other libs> -L${MKLROOT}/lib/intel64 -lmkl_sycl -lmkl_intel_ilp64 -lmkl_<sequential|tbb_thread> -lmkl_core -lsycl -lOpenCL -lpthread -ldl -lm
For example, building/dynamically linking main.cpp with ilp64 interfaces and TBB threading:
dpcpp -DMKL_ILP64 -I${MKLROOT}/include main.cpp -L${MKLROOT}/lib/intel64 -lmkl_sycl -lmkl_intel_ilp64 -lmkl_tbb_thread -lmkl_core -lsycl -lOpenCL -ltbb -lpthread -ldl -lm
For DPC++ interfaces with static linking on Windows
dpcpp -fsycl-device-code-split=per_kernel -DMKL_ILP64 <typical user includes and linking flags and other libs> "%MKLROOT%"\lib\intel64\mkl_sycl.lib mkl_intel_ilp64.lib mkl_<sequential|tbb_thread>.lib mkl_core_lib sycl.lib OpenCL.lib
For example, building/statically linking main.cpp with ilp64 interfaces and TBB threading:
dpcpp -fsycl-device-code-split=per_kernel -DMKL_ILP64 -I"%MKLROOT%\include" main.cpp"%MKLROOT%"\lib\intel64\mkl_sycl.lib mkl_intel_ilp64.lib mkl_tbb_thread.lib mkl_core.lib sycl.lib OpenCL.lib tbb.lib
For DPC++ interfaces with dynamic linking on Windows
dpcpp -DMKL_ILP64 <typical user includes and linking flags and other libs> "%MKLROOT%"\lib\intel64\mkl_sycl_dll.lib mkl_intel_ilp64_dll.lib mkl_<sequential|tbb_thread>_dll.lib mkl_core_dll.lib tbb.lib sycl.lib OpenCL.lib
For example, building/dynamically linking main.cpp with ilp64 interfaces and TBB threading:
dpcpp -fsycl-device-code-split=per_kernel -DMKL_ILP64 -I"%MKLROOT%\include" main.cpp "%MKLROOT%"\lib\intel64\mkl_sycl_dll.lib mkl_intel_ilp64_dll.lib mkl_tbb_thread_dll.lib mkl_core_dll.lib tbb.lib sycl.lib OpenCL.lib
For C/Fortran Interfaces with OpenMP Offload Support
Use the C/Fotran Intel® oneAPI Math Kernel Library interfaces with OpenMP offload feature to the GPU.
See the C OpenMP Offload Developer Guide for more details about this feature.
Add the following changes to the C/Fortran oneMKL compile/link lines to enable OpenMP offload feature to GPU:
  • Additional compile/link options:
    -fiopenmp -fopenmp-targets=spir64 -mllvm -vpo-paropt-use-raw-dev-ptr -fsycl
  • Additional oneMKL library: oneMKL DPC++ library
For example, building/ dynamically linking main.cpp on Linux with ilp64 interfaces and OpenMP threading:
icx -fiopenmp -fopenmp-targets=spir64 -mllvm -vpo-paropt-use-raw-dev-ptr -fsycl -DMKL_ILP64 -m64 -I$(MKLROOT)/include main.cpp L${MKLROOT}/lib/intel64 -lmkl_sycl -lmkl_intel_ilp64 -lmkl_intel_thread -lmkl_core -liomp5 -lsycl -lOpenCL -lstdc++ -lpthread -lm -ldl
For all other supported configurations, see Intel® oneAPI Math Kernel Library Link Line Advisor.

Find More

Resource
Description
Tutorial: Using Intel® oneAPI Math Kernel Library for Matrix Multiplication:
This tutorial demonstrates how you can use oneMKL to multiply matrices, measure the performance of matrix multiplication, and control threading.
The release notes contain information specific to the latest release of oneMKL including new and changed features. The release notes include links to principal online information resources related to the release. You can also find information on:
  • What's new in the release
  • Product contents
  • Obtaining technical support
  • License definitions
The Intel® oneAPI Math Kernel Library (oneMKL) product page. See this page for support and online documentation.
The
Intel® oneAPI Math Kernel Library
contains many routines to help you solve various numerical problems, such as multiplying matrices, solving a system of equations, and performing a Fourier transform.
This document includes an overview, a usage model and testing results of random number generators included in VS.
Performance data obtained using vector statistics (VS) random number generator (RNG) including CPE (clocks per element) unit of measure, basic random number generators (BRNG), generated distribution generators, and length of generated vectors.
Vector Mathematics (VM) computes elementary functions on vector arguments. VM includes a set of highly optimized implementations of computationally expensive core mathematical functions (power, trigonometric, exponential, hyperbolic, and others) that operate on vectors.
Summary Statistics is a subcomponent of the Vector Statistics domain of Intel® oneAPI Math Kernel Library. Summary Statistics provides you with functions for initial statistical analysis, and offers solutions for parallel processing of multi-dimensional datasets.
This document provides code examples for oneMKL LAPACK (Linear Algebra PACKage) routines.

Notices and Disclaimers

Software and workloads used in performance tests may have been optimized for performance only on Intel microprocessors. Performance tests, such as SYSmark and MobileMark, are measured using specific computer systems, components, software, operations and functions. Any change to any of those factors may cause the results to vary. You should consult other information and performance tests to assist you in fully evaluating your contemplated purchases, including the performance of that product when combined with other products. For more complete information visit www.intel.com/benchmarks.
Intel technologies may require enabled hardware, software or service activation.
No product or component can be absolutely secure.
Your costs and results may vary.
© Intel Corporation. Intel, the Intel logo, and other Intel marks are trademarks of Intel Corporation or its subsidiaries. Other names and brands may be claimed as the property of others.
Optimization Notice
Intel's compilers may or may not optimize to the same degree for non-Intel microprocessors for optimizations that are not unique to Intel microprocessors. These optimizations include SSE2, SSE3, and SSSE3 instruction sets and other optimizations. Intel does not guarantee the availability, functionality, or effectiveness of any optimization on microprocessors not manufactured by Intel. Microprocessor-dependent optimizations in this product are intended for use with Intel microprocessors. Certain optimizations not specific to Intel microarchitecture are reserved for Intel microprocessors. Please refer to the applicable product User and Reference Guides for more information regarding the specific instruction sets covered by this notice.
Notice revision #20110804
No license (express or implied, by estoppel or otherwise) to any intellectual property rights is granted by this document.
The products described may contain design defects or errors known as errata which may cause the product to deviate from published specifications. Current characterized errata are available on request.
Intel disclaims all express and implied warranties, including without limitation, the implied warranties of merchantability, fitness for a particular purpose, and non-infringement, as well as any warranty arising from course of performance, course of dealing, or usage in trade.

Product and Performance Information

1

Performance varies by use, configuration and other factors. Learn more at www.Intel.com/PerformanceIndex.