Intel® oneAPI Math Kernel Library (oneMKL)
Accelerate math processing routines, increase application performance, and reduce development time.
High Performance for Numerical Computing on CPUs and GPUs
Why oneMKL?
- Take advantage of the fastest and most-used math library for Intel®-based systems.†
- Create highly performant applications faster.
- Fully use Intel hardware capabilities for AI, HPC, and data science.
- Reach high performance with math solutions like NumPy, SciPy, and MATLAB*.
oneMKL introduces enhanced performance of 2D and 3D real and complex fast Fourier transforms (FFT) targeted for Intel® Data Center GPU Max Series.
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 Intel® Tiber™ Developer Cloud
Build and optimize oneAPI multiarchitecture applications using the latest Intel-optimized oneAPI and AI tools, and test your workloads across Intel® CPUs and GPUs. No hardware installations, software downloads, or configuration necessary.
Help oneMKL Evolve
oneMKL is part of the oneAPI industry standards initiative. We welcome you to participate.
What You Need
- Get started by choosing the best interface for your application:
- oneMKL is available as part of the Intel® oneAPI Base Toolkit.
- Using oneMKL with Intel® MPI library or Intel® Fortran Compilers requires the Intel® HPC Toolkit.
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 nondeterministic 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 alternative open source libraries, and other functions for oneMKL.
Discussion on 4th Gen Intel® Xeon® Scalable Processor Benchmarks
To assess performance in high-performance computing environments, see the oneMKL Benchmarks Suite. It includes:
- Intel® Distribution for LINPACK* Benchmark
- Intel® Distribution for LINPACK* Benchmark supporting MPI for clusters
- Intel® Optimized High Performance Conjugate Gradient Benchmark
Documentation & Code Samples
Documentation
- Get Started Guide
- Release Notes
- System Requirements
- Developer References:
C | Fortran | SYCL - Developer Guides:
Windows* | Linux*
View Current oneMKL Documentation
View Legacy Intel® Math Kernel Library Documentation
Library Linking Guidance
Intel® oneAPI Math Kernel Library Link Line Advisor
This web-based utility identifies which build options for compiler and linker to use with oneMKL, depending on the build environment you use and the feature set you want to enable.
Code Samples
Linear Algebra
- Matrix Multiplication with CPUs and GPUs
Use this sample to examine the oneMKL matrix multiplication functionality. - Block Cholesky Decomposition
Learn how to use oneMKL routines for matrix multiplication, rank-k updates, triangular solves (BLAS), and Cholesky factorization (LAPACK).
Migration to SYCL*
- Fourier Correlation
Learn how to implement the Fourier correlation algorithm using SYCL, oneMKL, and Intel® oneAPI DPC++ Library (oneDPL) functions. - cuBLAS Migration
Use this set of samples to see how cuBLAS routines are transformed to equivalent oneMKL routines after migrating CUDA-based code to SYCL. - Matrix Multiplication cuBLAS Migrated
Learn how to migrate your code to SYCL and use it in a high-performance way, offloading computations to GPU or CPU. See how to optimize the migration steps and improve processing time. - OceanFFT Sample Code cuFFT Migration
The OceanFFT sample simulates an ocean heightfield using oneMKL FFT functionality. Learn how original CUDA source code is migrated to SYCL for portability. - MonteCarloMultiGPU Sample for cuRAND Migration
The MonteCarloMultiGPU sample evaluates fair call price for a given set of European options using the Monte Carlo approach. Learn how to migrate and map cuRAND generator calls to their oneMKL and SYCL API random number generator equivalent.
For Your Industry
- Finance: Monte Carlo European Options
See how to use the oneMKL random number generator (RNG) functionality to compute European option prices. - Finance: Black-Scholes
Learn how to use vector math and the RNG available in oneMKL to calculate the prices of options using the Black-Scholes formula. - Healthcare: Computed Tomography Reconstruction
Learn how to use discrete Fourier transform (DFT) routines to transform raw computed tomography (CT) data into a reconstructed image of the scanned object.
How to work with code samples:
Training Resources
Get Started
- A Quick Overview of oneMKL [5:47]
- Benefits of oneMKL on CPUs and GPUs [4.55]
- oneMKL - an Advanced Version of Intel® MKL
- oneMKL Essentials Learning Path
- How to Use oneMKL
- oneMKL Linking
- oneMKL Verbose Mode: Quick and Easy GPU Library Execution Profiler
Expert Talks
- Why oneMKL? Accelerate Math Computation on the Latest Hardware [59:07]
- Speed Up Math Computations on GPUs with oneMKL [45:22]
Take Advantage of GPUs with SYCL
- How to Move from CUDA Math Library Calls to oneMKL
- Migrate cuBLAS and cuRAND Operations from CUDA to SYCL
- Random Number Generation with cuRAND and oneMKL
- A C++ API with SYCL Support for Data Fitting
Fourier Transform
- Implement the Fourier Correlation Algorithm [47:37]
- Implement the Fourier Correlation Algorithm Using oneAPI
Random Number Generation
Linear Systems Solvers
- OpenMP Offload: Solving Linear Systems Using oneMKL on GPUs [1:04:16]
- Solving Linear Systems Using oneMKL and OpenMP Target Offloading
Case Studies
Specifications
Processors:
- Intel Atom® processors
- Intel® Core™ processors
- Intel® Xeon® Scalable processors
GPUs:
- Intel® UHD Graphics for 11th generation Intel processors or newer
- Intel® Iris® Xe graphics
- Intel® Arc™ graphics
- Intel® Data Center GPU Flex Series
- Intel® Data Center GPU Max Series
Languages:
- SYCL
- C and C++
- Fortran
For more information, see the system requirements.
Operating systems:
- Windows
- Linux
Compilers:
- Intel® oneAPI DPC++/C++ Compiler
- GNU Compiler Collection (GCC)*
- 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.
Stay In the Know on All Things CODE
Sign up to receive the latest tech articles, tutorials, dev tools, training opportunities, product updates, and more, hand-curated to help you optimize your code, no matter where you are in your developer journey. Take a chance and subscribe. You can change your mind at any time.