Intel® oneAPI Math Kernel Library

The fastest and most-used math library for Intel®-based systems†. Accelerate math processing routines, increase application performance, and reduce development time.

Intel®-Optimized Math Library for Numerical Computing


 

Optimized Library for Scientific Computing
  • Enhanced math routines enable developers and data scientists to create performant science, engineering, or financial applications 
  • Core functions include BLAS, LAPACK, sparse solvers, fast Fourier transforms (FFT), random number generator functions (RNG), summary statistics, data fitting, and vector math 
  • Optimizes applications for current and future generations of Intel® CPUs, GPUs, and other accelerators 
  • Is a seamless upgrade for previous users of the Intel® Math Kernel Library (Intel® MKL) 

 

What's New
  • SYCL* APIs maximize performance and cross-architecture portability
  • Introduces C and Fortran OpenMP offload for Intel® GPU acceleration 

Learn about SYCL

 

What You Need
  • The Intel® oneAPI Math Kernel Library (oneMKL) is available as part of the Intel® oneAPI Base Toolkit.
  • Using oneMKL with Intel® MPI library or Intel® Fortran Compilers requires the Intel® oneAPI HPC Toolkit.

Explore the Intel oneAPI Base Toolkit

Explore the Intel oneAPI HPC Toolkit

Download as Part of the Toolkit

oneMKL is included in the Intel oneAPI Base Toolkit, which is a core set of tools and libraries for developing high-performance, data-centric applications across diverse architectures.

Download the Stand-Alone Version

A stand-alone download of oneMKL is available. You can download binaries from Intel or choose your preferred repository.

Develop in the Free Intel® Cloud

Get what you need to build and optimize your oneAPI projects for free. With an Intel® DevCloud account, you get 120 days of access to the latest Intel® hardware—CPUs, GPUs, FPGAs—and Intel® oneAPI tools and frameworks. No software downloads. No configuration steps. No installations.

Help oneMKL Evolve

oneMKL is part of the oneAPI industry standards initiative. We welcome you to participate.

 

Specification

Open-Source Version (GitHub*)

Features

Linear Algebra

Speed up linear algebra computations with low-level routines that operate on vectors and matrices, and are compatible with these industry-standard BLAS and LAPACK operations:

  • Level 1: Vector-vector operations
  • Level 2: Matrix-vector operations
  • Level 3: Matrix-matrix operations

Sparse Linear Algebra Functions

Perform various operations on sparse matrices with low-level and inspector-executor routines including the following:

  • Multiply sparse matrix with dense vector
  • Multiply sparse matrix with dense matrix
  • Solve linear systems with triangular sparse matrices
  • Solve linear systems with general sparse matrices

Fast Fourier Transforms (FFT)

Transform a signal from its original domain (typically time or space) into a representation in the frequency domain and back. Use FFT functions in one, two, or three dimensions with support for mixed radices. The supported functions include complex-to-complex and real-to-complex transforms of arbitrary length in single-precision and double-precision.

Random Number Generator Functions (RNG)
Use common pseudorandom, quasi-random, and non-deterministic random number engines to solve continuous and discrete distributions.

Data Fitting

Provide spline-based interpolation capabilities that you can use to approximate functions, function derivatives or integrals, and perform cell search operations.

Vector Math
Balance accuracy and performance with vector-based elementary functions. Manipulate values with traditional algebraic and trigonometric functions.

Summary Statistics 
Compute basic statistical estimates (such as raw or central sums and moments) for single- and double-precision multidimensional datasets.

Benchmarks

These benchmarks are offered to help you make informed decisions about which routines to use in your applications, including performance for each major function domain in oneMKL by processor family. Some benchmark charts only include absolute performance measurements for specific problem sizes. Others compare previous versions, popular alternate open-source libraries, and other functions for oneMKL.

Specifications

Processors:

  • Intel Atom® processors
  • Intel® Core™ processors
  • Intel® Xeon® Scalable processors

GPUs:

  • Intel® Processor Graphics Gen9 and above
  • Xe Architecture

Languages:

  • SYCL
  • C and C++
  • Fortran
     

For more information, see the system requirements. 

Operating systems:

  • Windows*
  • Linux*
  • macOS*

Compilers:

  • Intel® oneAPI DPC++/C++ Compiler
  • GNU Compiler Collection (GCC)*
  • Intel® C++ Compiler Classic
  • Intel® Fortran Compiler
  • Intel® Fortran Compiler Classic
  • Other compilers that follow the same standards

Development environments:

  • Windows: Microsoft Visual Studio*
  • Linux: Eclipse* and Eclipse CDT (C/C++ Development Tooling)*

Threading models:

  • Intel® oneAPI Threading Building Blocks
  • OpenMP

 

Get Help

Your success is our success. Access these support resources when you need assistance.

Source

† Data from Evans Data Software Developer survey, 2020

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